US20100008255A1

US20100008255A1 - Mesh network services for devices supporting dynamic direction information

Info

Publication number: US20100008255A1
Application number: US12/476,406
Authority: US
Inventors: Moe Khosravy; Lev Novik
Original assignee: Microsoft Corp
Current assignee: Microsoft Technology Licensing LLC
Priority date: 2008-06-20
Filing date: 2009-06-02
Publication date: 2010-01-14
Also published as: US20130265223A1; US20140347268A1; US8868374B2; US20100009662A1; US10509477B2; US20090319181A1; US8467991B2; US9703385B2; US20170269703A1

Abstract

With the addition of directional information in the environment, a variety of service(s) can be provided on top of user identification or interaction with specific object(s) of interest. For instance, a user can opt into a mesh network and leverage services available via the mesh network. The user can also contribute to knowledge within the mesh network by allowing for information related to the user to be aggregated and employed by others, e.g., targeted provisioning of services. The interaction with the mesh network can occur passively (e.g., as a background application not generally visible to the user), or actively where for example the user can initiate collecting or logging of information from/to the mesh network.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser. No. 61/074,415, filed on Jun. 20, 2008, entitled “MOBILE COMPUTING SERVICES BASED ON DEVICES WITH DYNAMIC DIRECTION INFORMATION,” and U.S. Provisional Application Ser. No. 61/074,590, filed on Jun. 20, 2008, entitled “MOBILE COMPUTING SERVICES BASED ON DEVICES WITH DYNAMIC DIRECTION INFORMATION”, the entireties of which are incorporated herein by reference.

TECHNICAL FIELD

The subject disclosure relates to provision of direction-based services based on direction information and/or other information, such as location information, of a device, and to opting into mesh network(s) and related services that leverage information gathered from multiple devices supporting the direction-based services.

BACKGROUND

By way of background concerning some conventional systems, mobile devices, such as portable laptops, PDAs, mobile phones, navigation devices, and the like have been equipped with location based services, such as global positioning system (GPS) systems, WiFi, cell tower triangulation, etc. that can determine and record a position of mobile devices. For instance, GPS systems use triangulation of signals received from various satellites placed in orbit around Earth to determine device position. A variety of map-based services have emerged from inclusion of such location based systems that help users of these devices to be found on a map, and to facilitate point to point navigation in real-time and search for locations near a point on a map.
However, such navigation and search scenarios are currently limited to displaying relatively static information about endpoints and navigation routes. While some of these devices with location based navigation or search capabilities allow update of the bulk data representing endpoint information via a network, e.g., when connected to a networked portable computer (PC) or laptop, such data again becomes fixed in time. Accordingly, it would be desirable to provide a set of richer experiences for users than conventional experiences predicated on location and conventional processing of static bulk data representing potential endpoints of interest.
The above-described deficiencies of today's location based systems and devices are merely intended to provide an overview of some of the problems of conventional systems, and are not intended to be exhaustive. Other problems with the state of the art and corresponding benefits of some of the various non-limiting embodiments may become further apparent upon review of the following detailed description.

SUMMARY

A simplified summary is provided herein to help enable a basic or general understanding of various aspects of exemplary, non-limiting embodiments that follow in the more detailed description and the accompanying drawings. This summary is not intended, however, as an extensive or exhaustive overview. Instead, the sole purpose of this summary is to present some concepts related to some exemplary non-limiting embodiments in a simplified form as a prelude to the more detailed description of the various embodiments that follow.
Direction based pointing services are provided for portable devices or mobile endpoints. Mobile endpoints can include a positional component for receiving positional information as a function of location of the portable electronic device, a directional component that outputs direction information as a function of an orientation of the portable electronic device and a processing engine that processes the positional information and the direction information to determine a subset of points of interest relative to the portable electronic device as a function of the positional information and/or the direction information.
Devices or endpoints can include compass(es), e.g., magnetic or gyroscopic, to determine a direction, and location based systems for determining location, e.g., GPS. To supplement the positional information and/or the direction information, devices or endpoints can also include component(s) for determining speed and/or acceleration information for processing by the engine, e.g., to aid in determination of gestures made with the device.
With the addition of directional information in the environment, a variety of service(s) can be provided on top of user identification or interaction with specific object(s) of interest. For instance, a user can opt into a mesh network and leverage services available via the mesh network. The user can also contribute to knowledge within the mesh network by allowing for information related to the user to be aggregated and employed by others, e.g., targeted provisioning of services. The interaction with the mesh network can occur passively (e.g., as a background application not generally visible to the user), or actively where for example the user can initiate collecting or logging of information from/to the mesh network.
An aspect of the claimed subject matter relates to optimizing the mesh network and interaction therewith so as to facilitate provisioning of services. User and/or device data can be collected and mined in connection with improving services provisioned to users/devices. For example, demographic information can be collected and correlated with actions taken as well as extrinsic evidence (e.g., weather, time of day, user or device context, state of user or device, holidays, weekend versus business day, path of navigation, . . . ) to map preferences among like users or devices. Accordingly, such mapping can be leveraged to provision targeted services to users or devices within the mesh network. Suggestions regarding products or services based on the data aggregation and/or mappings can be improved. Automated actions can be taken based on a utility-based analysis, e.g., a cost benefit analysis where the benefit associated with a particular automated action with a given confidence level outweighs the cost of taking an incorrect or undesired action.
These and other embodiments are described in more detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

Various non-limiting embodiments are further described with reference to the accompanying drawings in which:

FIGS. 1 and 2 are block diagrams of non-limiting architectures for mesh network interaction systems in accordance with one or more embodiments;

FIG. 3 is a flow diagram of a non-limiting process for data collection in connection with a mesh network;

FIG. 4 is a flow diagram of a non-limiting process for data processing in connection with a mesh network;

FIGS. 5 and 6 are flow diagrams of non-limiting processes for service(s) provisioning in connection with a mesh network;

FIG. 7 is a flow diagram of a non-limiting process for targeted advertisement provisioning in connection with a mesh network;

FIG. 8 is a flow diagram of a non-limiting process for advertisement pricing in connection with a mesh network;

FIG. 9 is a flow diagram of a non-limiting process for modification of services provisioning in connection with a mesh network;

FIG. 10 is a flow diagram of a non-limiting process for data collection and filtering across multiple devices in connection with a mesh network;

FIG. 11 is a flow diagram of a non-limiting process for matching users with services in connection with a mesh network;

FIGS. 12 and 13 are flow diagrams of non-limiting processes for services provisioning in connection with a mesh network;

FIG. 14 is a flow diagram of a non-limiting process for marking points of interest for later interaction;

FIG. 15 is a flow diagram of a non-limiting process for designating points of interest for later interaction;

FIG. 16 is a block diagram of a device illustrating a specific apparatus for marking points of interest for delayed interaction in connection with direction based services;

FIG. 17 is a flow diagram of a non-limiting process for designating points of interest associated with a given place for later interaction;

FIG. 18 is a block diagram of an exemplary device architecture for device that supports direction based services in accordance with one or more embodiments;

FIG. 19 illustrates an exemplary sequence for delayed interaction with POIs at a location as a future advertising source;

FIG. 20 is a block diagram illustrating a non-limiting implementation in which POIs are scanned based on encoding information;

FIG. 21 is a block diagram illustrating market players in an overall advertising model established based on interest in satisfying the delayed interactions defined according to one or more embodiments herein;

FIG. 22 is a block diagram illustrating a variety of ways in which user intent can be inferred or implied with respect to points of interest;

FIGS. 23 and 24 illustrate representative scenarios where a delayed interaction facilitates a better timing for a transaction regarding a given item of interest;

FIG. 25 is a block diagram illustrating the formation of motion vectors for use in connection with location based services;

FIG. 26, FIG. 27 and FIG. 28 illustrate aspects of algorithms for determining intersection endpoints with a pointing direction of a device;

FIG. 29 represents a generic user interface for a mobile device for representing points of interest based on pointing information;

FIG. 30 represents some exemplary, non-limiting alternatives for user interfaces for representing point of interest information;

FIG. 31 represents some exemplary, non-limiting fields or user interface windows for displaying static and dynamic information about a given point of interest;

FIG. 32 illustrates a sample overlay user interface for overlaying point of interest information over a camera view of a mobile device;

FIG. 33 illustrates a process for predicting points of interest and aging out old points of interest in a region-based algorithm;

FIG. 34 illustrates a first process for a device upon receiving a location and direction event;

FIG. 35 illustrates a second process for a device upon receiving a location and direction event;

FIG. 36 is a block diagram representing an exemplary non-limiting networked environment in which embodiment(s) may be implemented; and

FIG. 37 is a block diagram representing an exemplary non-limiting computing system or operating environment in which aspects of embodiment(s) may be implemented.

DETAILED DESCRIPTION

Overview

As discussed in the background, among other things, current location services systems and services, e.g., GPS, cell triangulation, P2P location service, such as Bluetooth, WiFi, etc., tend to be based on location of the device only, and tend to provide static experiences that are not tailored to a user because the data about endpoints of interest is relatively static, or fixed in time. Another problem is that experiences of a single user are by definition limited, and thus services predicated on information from the single user are limited to scope of that information. In such a system, a user may interact with a number of different points of interest (POIs), and such user data about the interactions can be stored in a cloud, but as mentioned, not much can be inferred from a single user alone.
An aspect of the claimed subject matter provides a way for users to opt into a mesh network, which enables collective data to be integrated into the application experience. For instance, a single user path based on directional vector information recorded from a direction based services enabled device does not show much about human behavior, but aggregate user paths charted over time can indicate a lot about traffic in a roadway, for instance, or peak times to avoid getting coffee at a certain coffee shop. In this respect, most any POI can have statistical data about how users interact with it, and applications can be built on that statistical data to the benefit of mesh users. A variety of real-time scenarios are explored where mesh data is used for particular user beneficial applications.
At least partly in consideration of these deficiencies of conventional location based services, various embodiments of a portable device are provided that collect or provide information from/to the user or device in connection with employing services associated with the mesh network. A dynamic data aggregation scheme can be employed that builds a knowledge base of the mesh network. Information regarding user or device interaction with the mesh network can be collected, aggregated, analyzed, conditioned, filtered, mined, and the like to improve accuracy of a data model that facilitated provisioning of services associated with the mesh network. As noted herein, interaction with the mesh network can occur passively (e.g., as a background application not generally visible to the user), or actively where for example the user can initiate collecting or logging of information from/to the mesh network. Various incentives can be employed to facilitate gathering of such user or device data. For example, enhanced services, free goods or services, discounts, tertiary products/services (e.g., free media) can be offered in exchange for a user or device providing valuable personal data. Likewise, a point, reward, sharing threshold system or the like can be employed to encourage contributing to the mesh network knowledge base.
As a result of building a robust and accurate mesh network services model, targeted services can be provided that facilitate provisioning of services to users or devices within the mesh network. For example, observing user or device interaction within a particular geographic region, or at certain times of the day can result in learning that users of a particular demographic profile (e.g., particular age, income level, . . . ) traverse from point A to point B with population density F during time range H and have a J probability of responding to a targeted advertisement for coffee with a confidence level of M where (A, B, F, H, J, and M are variables). Additionally, enhanced filtering schemes can be employed that prevent nuisance data interruptions from reaching a user or device. It is to be appreciated that information desirable to one individual may be a nuisance to another. The above-noted knowledge base can facilitate provisioning of services so that it enhances a user or device experience so that data and type thereof is provided when desired and useful, and undesired or non-useful information is filtered with high confidence level.
An aspect of the claimed subject matter relates to optimizing the mesh network and interaction therewith so as to facilitate provisioning of services. User and/or device data can be collected and mined in connection with improving services provisioned to users/devices. For example, demographic information can be collected and correlated with actions taken as well as extrinsic evidence (e.g., weather, time of day, user or device context, state of user or device, holidays, weekend versus business day, path of navigation, . . . ) to map preferences among like users or devices. Accordingly, such mapping can be leveraged to provision targeted services to users or devices within the mesh network. Suggestions regarding products or services based on the data aggregation and/or mappings can be improved. Automated actions can be taken based on a utility-based analysis, e.g., a cost benefit analysis where the benefit associated with a particular automated action with a given confidence level outweighs the cost of taking an incorrect or undesired action.
While each of the various embodiments herein are presented independently, e.g., as part of the sequence of respective Figures, one can appreciate that a portable device and/or associated network services, as described, can incorporate or combine two or more of any of the embodiments. Given that each of the various embodiments improve the overall services ecosystem in which users wish to operate, together a synergy results from combining different benefits. Accordingly, the combination of different embodiments described below shall be considered herein to represent a host of further alternate embodiments.
A non-limiting device provisioned for direction based services includes an engine for analyzing location information (e.g., GPS), direction information such as compass information (e.g., North, West, South, East), and optionally movement information (e.g., accelerometer information) to allow a platform for pointing to and thereby finding objects of interest in a user's environment. A variety of scenarios are contemplated based on a user finding information of interest about objects of interests, such as restaurants, or other items around an individual, or persons, places or events of interest nearby a user and tailoring information to that user (e.g., coupons, advertisements), however at a time later than the finding.
Details of various other exemplary, non-limiting embodiments are provided below.

Mesh Network Services for Devices Supporting Dynamic Direction Information

Referring now to the drawings, with reference initially to FIG. 1, computer-implemented system 100 can aggregate information regarding a set of devices or users interacting with services, devices, individuals, groups, or the like. Generally, system 100 can include provisioning component 102 that can interface with network-accessible data storage service 104, and facilitate provisioning of targeted services to respective users or devices. Data storage service 104 can facilitate archival of data associated with customers, clients, devices (e.g., user 106) of data storage service 104 as well as retrieval of any such archived data 108 by user 106. Moreover, as described in greater detail infra data storage service 104 can store a knowledge base or data model which can facilitate provisioning of services in accordance with embodiments described herein. The knowledge base or data model can be stored at a single location, in a distributed framework, or a hybrid (e.g., individual knowledge bases or data models can dynamically integrate or interact in a mesh network like fashion). It is to be appreciated that the term “user” can apply to an individual, group, device(s), services or the like. Both archival or retrieval of archived/aggregated data 108 are labeled and referred to herein as transactions 110, which can generalize other transactions associated with archived/aggregated data 108 as well, such as on-demand conversion or migration requested by user 106 and so forth. Moreover, it should be understood that data storage service 104 can be constructed to operate according to very particular networking configurations or topologies, which are discussed infra in connection with FIG. 2. Appreciably, provisioning component 102 (as well as other components described herein) can be configured in a distinct or specific manner based upon the configuration of data storage service 104.
While still referring to FIG. 1, but turning now also to FIG. 2, diagram 200 depicts an example topology to which data storage service 104 conforms. In particular, in one or more aspects of the claimed subject matter, data storage service 104 can be configured as a distributed network and/or a mesh network. Mesh networking is a way to route data, voice and instructions between nodes (also referred to as “peers”). It allows for continuous connections and reconfiguration around broken or blocked paths by “hopping” from node to node until a destination is reached. A mesh network whose nodes are all connected to each other is a fully connected network. Mesh networks differ from other networks in that the component parts can all connect to each other via multiple hops. As employed herein mesh networks are type(s) of ad hoc network, and the term is intended to include mobile ad hoc networks (MANET) which provides for mobility of the nodes.
Mesh networks can be self-healing: the network can still operate even when a node breaks down or a connection goes bad. As a result, such mesh network is very reliable. This concept is applicable to wireless networks, wired networks, and software interaction.
More specifically, the mesh or distributed network can include multiple peers, denoted here as peers 202 ₁-202 _N, where N can be substantially any integer. Each peer 202 can be connected to other peers 202, by way of any suitable connectivity scheme or architecture such as a wide area network (WAN) or a local area network (LAN), labeled here as WAN/LAN 204. The mesh network can be a full mesh network in which every peer 202 is connected to all other peers 202. Alternatively, the mesh network can be configured according to a partial mesh topology in which one peer 202 can be connected to all other peers 202, while a second peer 202 is connected only to a subset of all other peers 202, typically only the peers 202 in which the second peer 202 communicates with the most or exchanges the most data.
Each peer 202 can represent a node in the mesh network (or another distributed network) and can be physically implemented by a server, workstation, personal computer, laptop, game console, phone, personal digital assistant (PDA), or substantially any other suitable device. Moreover, regardless of the type of device that constitutes a given peer 202, that peer 202 can store all or a portion of archived/aggregated data 108 as well as post-aggregated, processed data. In other words, multiple peers 202 can each store a respective portion of data 108. In some cases, each portion of data (labeled data 108 ₁-108 _N) can be mutually exclusive (e.g., no file included in data 108 exists on more than one peer 202), while in other cases each portion can be unique (e.g., the set of files included in each portion of data 108 is unique even though multiple peers 202 can include the same file), while in still other cases substantially identical file sets can be included on multiple peers 202 or devices associated therewith. Alternatively, the data can persist and be managed at a central storage 206, and a subset thereof selectively streamed to respective users 106 on an as needed or desired basis.
Furthermore, regardless of the networking topology, it should be appreciated and understood that any portion of data 108 hosted by peer 202 can reside not only on the peer 202 proper, but also on devices attached to or inserted in that peer 202. For example, consider a CD-ROM drive (e.g., attached device) associated the user's laptop (e.g., peer 202).
Regardless of whether or not data storage service 104 is implemented according to a distributed networking topology, data storage service 104 can, according to one or more aspects, maintain a network-accessible data repository that stores or hosts at least a portion of data 108. Thus, one example device that embodies a peer 202 can be device 206 illustrated as an example for peer 202 ₁. Likewise, one or more peers 202 can be embodied by a device 208, which can be, e.g., a home computer or laptop associated with user 106, as illustrated in connection with peer 202 ₂. Moreover, other devices 210 that are not directly associated with user 106 or data storage service 104 can embody a peer (e.g., peer 202 _N) as well. For example, peers 202 can be hosted by contacts of user 106 or members of a community associated with user 106 or data storage service 104.
It should be appreciated that the examples depicted for each peer 202 ₁-202 _Nare intended to be exemplary in nature, and need not necessarily be representative of ordering or a constituent composition of peers 202. Moreover, data storage service 104 need not in all aspects maintain any data repository at all. For example, peers 202 need not include data storage service 104, and data 108 regardless of apportionment can be stored by other devices or data stores, even if at least transactions 110 are managed by data storage service 104.
Continuing the discussion of FIG. 1, system 100 can further include monitor component 112 that can perform a variety of examination and monitoring tasks. For example, in accordance with one or more aspects of the claimed subject matter, monitor component 112 can monitor user 106 action, context, state, etc., in connection with data collection features discussed above. An aggregation component 114 can process, structure, filter, and/or aggregate the data to facilitate utilizing the aggregated data in connection with provisioning of services to respective users 106. The aggregated, processed, and/or conditioned data can be employed to populate a knowledge base or data model residing in storage 104. System 100 can also include analysis component 120 that can determine a respective cost-benefit 122 for each of a plurality of data monitoring, collection, aggregation, and/or provisioning scenarios.
Accordingly, system 100 facilitates a dynamic data aggregation scheme that can be employed to build a knowledge base of the mesh network. Information regarding user or device interaction with the mesh network can be collected, aggregated, analyzed, conditioned, filtered, mined, or the like to improve accuracy of a data model that facilitates provisioning of services associated with the mesh network. As noted supra, interaction with the mesh network can occur passively (e.g., as a background application not generally visible to the user), or actively where for example the user can initiate collecting or logging of information from/to the mesh network. Various incentives can be employed to facilitate gathering of such user or device data. As a result of building a robust and accurate mesh network services model, targeted services can be provided that facilitate provisioning of services to users or devices within the mesh network. An aspect of the claimed subject matter relates to optimizing the mesh network and interaction therewith so as to facilitate provisioning of services. User and/or device data can be collected and mined in connection with improving services provisioned to users/devices. Suggestions regarding products or services based on the data aggregation and/or mappings can be improved. Automated actions can be taken based on a utility-based analysis, e.g., a cost benefit analysis where the benefit associated with a particular automated action with a given confidence level outweighs the cost of taking an incorrect or undesired action
FIGS. 3-11 illustrate various exemplary methodologies associated with aggregating, analyzing, data mining, and provisioning of services in connection with a mesh network. It is to be appreciated that the acts are described in connection with functional modules or blocks. Moreover, the order in which the acts are described is intended to be non-limiting, and any suitable arrangement of modules or blocks, or subset thereof can be implemented in connection with carrying out aspects described and claimed herein.
Turning now to FIG. 3, a methodology in connection with collecting data from a user or device to facilitate populating a knowledge database or model of a mesh network is described. At 300 identification, inference or determination is made of a user or device entering into the mesh network. This act can be accomplished through any of a variety of suitable manners. For example, a user device logging onto an access point within the mesh network. It is to be appreciated that authentication schemes can be employed to facilitate identifying the user as well as determine level of access rights and privileges. At 310, a manner of collecting data from the user or device is determined. For example, the data can be collected passively, e.g., as a background application or service, or actively (e.g., direct interaction with the user). At 320, level and type of data collection is determined. The volume of data as well as type of data to be collected is determined. It is appreciated that as device capabilities increase over time the amount and type of data that can be collected from respective devices can be voluminous. Accordingly, selective data collection schemes can be employed to facilitate gathering information relevant to building the mesh network data model or knowledge base. For example, various types of contextual information or the like can be employed to facilitate selective data collection. If a user is pointing a device at a certain item, data collection can be focused on the POI and data not deemed relevant to the POI or user goal is not collected. At 330, data collection is initiated.
FIG. 4 illustrates a methodology relating to aggregating collected data to facilitate building the mesh network data model or knowledge base. At 400 collected data is conditioned or schematized to facilitate working therewith or analyzing the data. At 410, the conditioned data is processed or analyzed. For example, the data can be learned such that mappings, correlations, dependencies, etc, are identified. At 420 a data model is generated, or for existing data models it is refined based on newly collected data as well as aged out data. The data aggregation can facilitate provisioning of services to respective users in a tailored manner. For example, based on aggregated and learned data, the data model can infer or determine that other individuals similar to the current user (e.g., in demographics, interest group, genre, current goal, or the like) can provide recommendations, automated action, etc.
FIG. 5 illustrates a high-level methodology in connection with provisioning of services within a mesh network. At 500, identification, inference, or determination is made that a user or device has entered into the mesh network. At 510, the user or device is analyzed. For example, user or device location, state, speed, direction, intent is inferred or determined. It is to be appreciated that artificial intelligence based techniques can be employed in connection with making such inferences or determinations. Statistical, probabilistic, linear, non-linear, deterministic, non-deterministic, and other suitable mathematical techniques can be utilized in connection with gleaning relevant context-based information regarding the user or device. Additionally, extrinsic evidence (e.g., time of day, weather, business conditions, third party information, historical data, user preferences, etc.) can be considered. At 520, a mapping is performed in connection with mapping services to be provisioned to the user from the mesh network. Given a certain set of evidence (e.g., collected, stored, received . . . ) most suitable services given user/device state, desires, expectations, etc. are identified for potential provisioning to the user/device. A utility-based analysis can be employed that factors the cost of provisioning an undesired service to the user versus the benefit of provisioning a service that is desired by the user or device. At 530 a determination is made to provision or not provision various subsets of available services.
FIG. 6 illustrates an example provisioning scenario. At 600, user or device intent or goals are inferred or determined. The inference or determination can be effected via numerous manners such as, for example, employing state information, contextual information, historical data, preference information, extrinsic evidence, etc. As a function of the inference or determination, at 610 a subset of available services are identified for provisioning as well as a subset of available services are identified for not provisioning (e.g., blocking or filtering). At 620 a utility based analysis is performed in connection with provisioning various services. The cost benefit analysis can identify for example which services to provision, order of provisioning, reordering of provisioning, when, where, and how to provision respective services. The above-noted data model or knowledge base is employed in connection with such utility-based analyses. Demographic information, state information, preferences, what like people have done, historical data, available resources, user or device context, value of information, authentication level, privacy concerns, cost of interruption, user cognitive load, temporal value of information, staleness of data, priority of information, etc. are examples of data types, metrics, or information that can be factored in connection with provisioning or not provisioning of services within the mesh network.
FIG. 7 illustrates an advertising methodology in connection with aspects of the claimed subject matter. At 700, user or device intent or goals are determined. At 710 as a function of the inferred or determined goals, targeted advertisements are identified that have high probability of success with the targeted user or device. Accordingly, factors such as user or device state, location, demographics, direction of movement, speed, context, preferences, and other metrics or data types described herein can be considered in connection with provisioning of targeted advertisements. At 720, any one or more of ordering, display, mode of delivery, when, where, or how to deliver the targeted advertisement(s) is determined. At 730 the subset of targeted advertisements are provisioned. It is to be appreciated that pricing of targeted advertisements or impressions can be a function of confidence level of click through rate. For example, a mesh network host service could vary pricing of impressions to merchants as a function of likelihood of success of the targeted advertisement that is provisioned as a function of the knowledge data base or data model. The richness of the data model or knowledge base increases probability of provisioning of services or advertisements with high success rate. Accordingly, pricing can vary as a function of the historical or expected success rate. At 740, an optional act of feeding back results of targeted advertisement provisioning can be performed in connection with training the data model or refining the knowledge base. Use of feedback facilitates improving accuracy of the model as well as adapting it to changing trends, preferences, conditions, etc.
FIG. 8 illustrates a methodology for effecting pricing of advertisements or services within a mesh network. At 800 user, device, or service provider goals are inferred or determined. It is to be appreciated that at any given time, a matrix of price points, user, device, customer, service provides goals or interest are in variance. The subject methodology at 810 performs optimization analyses in connection with dynamic data sets to set pricing points in a manner to facilitate maximizing utility of service provisioning. For example, based on user state, provider inventory, available resources an optimal price can be determined that increases likelihood of sale or a service or good. At 820 a selective set of services are provisioned as a function of the analyses. For example, given a certain set of evidence a same set of services may be provisioned to user 1 at a higher price point than user 2. Likewise, cost for an advertising impression can also be dynamically varied across potential advertisers. A static pricing model can also optionally be employed as well as a temporal model where pricing changes over time but not necessarily each target individual.
FIG. 9 illustrates a methodology for dynamically modifying services to be provisioned, At 900 activities associated with a set of users/devices within the mesh network are modified. For example, population densities, traffic pattern, resource utilization, service throughput, etc. At 910 the activity data is analyzed in connection with modifying provisioning of services to another set of users/devices. It is to be appreciated that there can be commonality between various subsets of users/devices across the respective sets. For example, if there is a high density of traffic at a coffee shop, and the frequency of users/devices entering the shop is greater than those exiting an inference can be made that there may be a long wait time. Moreover, based on the analyzed data an expected wait time can be calculated within a reasonable standard deviation of error. Based on the analyses, at 930 services to be provisioned can be tailored or modified. For example, a user could be advised to not patronize the coffee shop he/she regularly goes to because of traffic, long wait time, or the like, and to go to another coffee shop that overall will provide for the user to make an important meeting on time. Accordingly, shopping can be substantially optimized to meet current user goals and timeliness.
FIG. 10 illustrates a methodology relating to data collection. At 1000, data related to user or device context is collected from one or more devices. It is to be appreciated that a user often is in proximity to and/or interacting with multiple device concurrently. For example, a user may be in an automobile, using a cell phone as well as a navigation system, and on-board display as well as audio. The number of devices, types, when, where, and how can provide rich context and state information about the user and/or device of interest. At 1010, the available data is filtered as a function of relevancy in connection with aggregating the data, and processing it to populate the knowledge base or data model. At 1020, the filtered data is aggregated and populated into a data store.
FIG. 11 illustrates a methodology for matching a user or device with a set of services. At 1100, user activity is determined or identified. As discussed above, intrinsic as well as extrinsic information can be employed to make such determination. One goal is to identify user state, preferences, desires, etc. so as to optimize provisioning of services to the user in connection with maximizing user satisfaction as well as optimizing return on investment to service providers. At 1110, a set of available services are identified that may be suitable for provisioning to a particular user. Demographics, social networks, preferences, user state, device state, device capabilities, historical information, available resources, traffic, price, weather, urgency, priorities, user goals, etc. are some factors that can be considered in connection with identifying suitable services for provisioning. At 1120, a matching function is performed to match most suitable services to provision given a set of evidence. Optionally, a utility based analysis can be performed that effects a cost benefit analysis in connection with services provisioning. At 1130, a set of services deemed most appropriate are provisioned to the user or target device.
FIGS. 12 and 13 illustrate various methodologies in connection with employment of pointing based services where a user can point a device in a particular direction or at an item of interest. The act of pointing a device provides for focusing on a particular category of interest, and allows for filtering out data that is not relevant. FIG. 12 is a flow diagram of an exemplary non-limiting process for using a device and services as described herein. At 1200, a place where a portable device is located is determined based on position information. At 1210, the device interacts with an item of interest in the place via direction based services. At 1220, a desired characteristic for the item of interest is indicated. At 1230, results regarding the item of interest are received later when the characteristic is satisfied. At 1240, updated content about the item of interest can be provided.
FIG. 13 is a flow diagram of a non-limiting scenario for the process of FIG. 12. At 1300, a place where the portable device is located is determined based on position information. At 1310, the device interacts with an item of interest in the place via direction based location services as described elsewhere herein. At 1320, the user identifies a target price for the item of interest. At 1330, results respecting the item of interest are delivered later when the price drops, e.g., 2 months later. At 1340, the content about the item of interest can be updated on an ongoing basis or at the time of delivery of the delayed content (e.g., new or updated advertisement, recent user reviews, etc.).
It is to be appreciated that switching among channels can be effected for any of missing infrastructure to engage in pointing based services. users can engage pointing based services using a first network, but then one or more aspects of connectivity are lost, connectivity can be regained from a second network, e.g., local Bluetooth network, other user pointing devices, etc. Users who have opted in for mesh device cooperation can share processing resources, information missing from other devices within the cooperative network, for instance, if one device has the missing information in its local cache, etc.
The possibilities for mesh networking based on movable endpoints in a pointing based device system are limitless. For instance, one mesh network scenario would be to determine actual traffic patterns by uploading user path data from millions of users over the country for one year. What would emerge from such a large mesh network of cooperating users is data about roads that statistically represents traffic for various conditions on those roads, e.g., at night, when raining, time of day, etc. Thus, a driving or direction service predicated on such information and the actual paths that drivers take down the roads including all of their turns can be more effective than the mathematical assumptions made by navigation systems today.
User data, user paths, user transactions, and interactions can thus be uploaded to a network service that tracks business intelligence about people or groups of people. Business intelligence can be created from these types of social scenarios and user interactions. Users can opt into one or more cooperative mesh network scenarios, e.g., “use my car data to build road data.” Data from each user can collected and aggregated over time and, the aggregate data examined for macro trends and patterns. As a result of the key trends and patterns discovered, services can be built on key trends and patterns, such as the improved directions based on actual user path data. The trends and patterns can be organized into additional reports and subscription data in which various marketing or other organizations may be interested.
In addition, customized scenarios can be designed for different types of devices, or for different types of placement of those devices. For instance, a set of services for a biker is different than the set of services that a car driver wants. Thus, that different kinds of moving objects inherently have different requirements, e.g., a biker does not need gas.
In this regard, leveraging digital compasses and location services to provide direction and location information enables a next-generation of direction or pointer based location search services, scan services, discoverability services, etc., where the digital compass and GPS can be used to point at objects of interest, thus defining the entry point for one or more data transactions between the device and one or more third party devices providing service(s) for the objects of interest at which the device is pointed.
As reflected in various embodiments, a device is provided that can hone in on, interact with, or otherwise transact with, a specific object or specific objects of interest by way of location and direction of the device, creating a new advertising model not previously known. As an example, when a user interacts with a particular product on a shelf at a retail store in connection with a direction based service, this creates an opportunity for anyone having an interest in the particular product to engage the user, e.g., communicate some information to that user. Any context that can be discerned from the user's actions and interactions can also be taken into account when acting on the opportunity. In this regard, users can interact with the endpoints in a host of context sensitive ways to provide or update information associated with endpoints of interest, or to receive beneficial information or instruments (e.g., coupons, offers, etc.) from entities associated with the endpoints of interest, and any of such actions can be joined to form a mesh network. With location services, it can be determined that a user's device is physically inside an actual store, or near a window display of a store. Coupling that to the user's interacting with an object of interest with direction information to enable direction-based services results in a new opportunity to take action based on the interaction with specific items.
In one embodiment, a portable electronic device is provided having a positional component for receiving position information as a function of a location of the portable electronic device; and at least one processor configured to process the position information to determine identifier(s) of points of interest associated with the location of the portable electronic device and based on intent information determined for the portable electronic device and the identifier(s), a set of delay criterion are set for later interaction with the given points of interest represented by the identifier(s).
As mentioned, a device can include a directional component that outputs direction information as a function of an orientation of the portable electronic device and that facilitates determining an intent of the device. The directional component can optionally be a digital compass that outputs the direction information. The device can determine a subset of items of interest relative to candidate items of interest within a 3-D space as a function of the positional information or the direction information.
The device can request delayed content based on a selection of an item of interest and the identifier(s). The request for the delayed content can be based on a scan of an encoding associated with an item of interest and the identifier(s). The request for delayed content can be based on a keyword received as input by the device and the identifier(s). The request for delayed content can be based on the intent information and the identifier(s) from at least one network service. The request for delayed content can also be automatic, or made by other explicit or implicit request by the user.
At a later time, and potentially from a different device, a content package can be received based on the request for the delayed content from the network service. The device can optionally include a display or sound devices, such as speakers, to display or render some or all of the graphical (e.g., text, icon, image data, video data, etc.) and/or audio content of the content package.
An initial interacting can include orientating the device toward some item(s) of interest and determining direction information associated with the orientation of the device from which a subset of the item(s) of interest are identified. For instance, interacting can include pointing the device in a direction defining a pointing line generally towards items of interest in the place(s) and determining a set of candidate items of interest as a subset of items of interest that substantially intersect with the pointing line, and enabling the selection of one or more items from the set of candidate items.
In one embodiment, a method for a device provisioned for direction based services comprises determining direction information associated with a pointed to direction relative to a pre-defined orientation of the device and identifying POIs within an area defined as a function of the pointed to direction including determining which of a set of POIs intersect with the area. Next, information corresponding to the POIs identified within the area is displayed, e.g., on a map or list, and POIs identified within the area can be designated for a delayed interaction at a later time. In one embodiment, IDs associated with the designated POIs are transmitted to a network service enabling information about the designated POIs to be exposed at the later time, e.g., from a different form factor device.
The designation of POIs for later interaction can include explicit input with respect to the designated one or more POIs, such as one or more of a gesture input, a keyword input, an audio input, a video camera input or a touchscreen input with respect to the one or more POIs. The designation of POIs for later interaction can include implicit input with respect to the designated one or more POIs including making inferences about the delayed interaction at the later time based on a context of present interaction.
The displaying of POI information can be made on a topographical map visually representing at least the area defined as a function of a pointed to direction and graphical indications of the POIs can be displayed within the area at corresponding locations on the topographical map view. The POIs can also be represented in a filtered list view, e.g., filtered by restaurants in the area. The designating of POIs can include designating pre-defined criteria explicitly or implicitly defining the delay of the delayed interaction. The designating can include marking one or more POIs with touchscreen input relating to the one or more designated POIs, tagging the one or more POIs with tag information defining the delayed interaction, or other ways to designate POIs for later interaction.
In another embodiment, a portable electronic device includes a positional component for receiving position information as a function of a location of the portable electronic device and a directional component that outputs direction information as a function of an orientation of the portable electronic device. In addition, the device includes a processor configured to process the position information and the direction information to determine identifiers or IDs of POIs within a pre-defined geographical area of the device, interact with a selected ID, receive information about the POI corresponding to the selected identifier, and receive input regarding the selected ID defining a future interaction.
Information about the selected ID defining the future interaction is transmitted along with the point of interest to a network service. In one embodiment, a pointer structure is provided on the device that visually indicates the orientation of the portable electronic device based upon which the directional component outputs the direction information. For example, this could be a triangular structure that comes to a point to show a primary orientation of the device. This could also be indicated on the display of the device during provision of direction based services.
In one embodiment, the position information and the direction information determine a pointing line and a set of candidate points of interest are determined as a subset of points of interest that substantially intersect with a function based on the pointing line. An intersection test for determining subsets of points of interest can include defining an arc based on an angle with respect to a pointing line, defining a cone based on an angle with respect to the pointing line, or a line function defining a rectangular space oriented along the pointing line (2-D or 3-D depending on the application). A speaker can render audio content if a condition upon which the future interaction is predicated occurs. The directional component can be a digital compass that outputs the direction information.
In another embodiment, a method comprises determining a place in which a portable device is located based on location information determined for the device and identifying a subset of items of interest in the place including determining an orientation of the device based on direction information of the device and determining the subset of items of interest in the place as a function of the orientation. Next, input with respect to an item of the subset of items is received as well as input defining a delayed interaction with the item or place.
The delayed interaction can include receiving a notification when a characteristic of the item meets a pre-defined condition, such as when a price of the item meets a target price condition, thereby initiating the delayed interaction. One way of identifying and designating an item for delayed interaction can be with a scan input from scanning or imaging a bar code associated with the item of interest.
FIG. 14 is a block diagram illustrating some concepts of one or more embodiments for enabling delayed interaction with endpoints in a direction based location system. In this regard, two main things are specified by the device participating in direction based services 1420. First, the device provides POI information 1400 from pointing with the device, i.e., the device identifies nearby POIs of particular interest to a user. This can be done explicitly 1402 or implicitly 1404. Also, the device associates criteria for delay 1410 with the POIs. This too can be done explicitly 1412 or implicitly 1414. Together, the POI information 1400 and delay information 1410 are transmitted to direction based services 1420, which enables a whole host of scenarios 1430 for later interaction with the POIs 1400 according to the criteria 1410 being satisfied.
FIG. 15 is an exemplary non-limiting diagram of an architecture for achieving one or more embodiments described herein. At the device layer Layer1 for specifying delayed POI information, location information 1500, direction information 1502 and user intent information 1504 can be input to a Layer2 with various services 1510, including web services 1512, cloud services 1514, other data services 1516, etc. Any of services 1510 can have input to a set of brick and mortar store databases in Layer3, such as data store(s) 1520, 1522, 1524, etc. or set of online or electronic retailer databases in Layer4, such as data store(s) 1530, 1532, 1534, etc. In this regard, user intent 1504 coupled with a place of the device can be utilized by one or more services 1510 to retrieve and deliver custom content 1540 to the device based on the intent and place of the device, but at a later time according to a set of explicit or implicit criteria.
FIG. 16 is a flow diagram of a scenario where a user delays interaction with a point of interest. At 1600, a user points a pointer device in one or more directions to define scope of endpoints. At 1610, the user receives an indication of one or more endpoints within scope in response from a network service. At 1620, the user marks endpoint(s) for later interaction or viewing. At 1630, when the user reconnects to the service, e.g., from a PC, the user can receive reminders about marked endpoints. Other ways for triggering the interaction can be employed at 1630 as described elsewhere herein. The user can follow through with interaction/viewing at 1640, as desired.
FIG. 17 is a flow diagram illustrating an exemplary process for designating POIs for delayed interaction in connection with direction based services. At 1700, direction information is determined from a pointed to direction relative to a pre-defined orientation. At 1710, POIs are identified within an area defined as a function of the pointed to direction and it is determined which POIs intersect the area and are within scope of a given user experience. At 1720, information corresponding to the POIs identified within the area is displayed, e.g., on a map or in a list. At 1730, the POI(s) identified within the area are designated for a delayed interaction at a later time. At 1740, the information (e.g., ID information) associated with the POIs designated for later interaction is transmitted to a network service enabling later interactions.
FIG. 18 illustrates an exemplary non-limiting device 1800 including processor(s) 1810 having a position engine or subsystem 1820 for determining a location of the device 1800 and a direction engine or subsystem 1830 for determining a direction or orientation of the device 1800. Then, by interacting with local application(s) 1840 and/or service(s) 1870, content can be delivered to the device, which is tailored to device intent and a place in which the device is present. The tailored content can be rendered by graphic subsystem or display/UI 1850 or audio subsystem 1860. In one non-limiting embodiment, point structure 1890 is included, e.g., a triangular piece that points along an orientation line 1895 upon which directional calculations are based. Similarly, the orientation line 1895 can be indicated by graphics subsystem display/UI 1850 with or without point structure 1890. In this regard, various embodiments herein enable delayed POI ID information 1880 to and from services 1870 so that the delayed interactions can occur in the future as assisted by services 1870.
FIG. 19 is a flow diagram illustrating a process for delaying interaction with respect to a specific item within an identified place. At 1900, a place at which a portable device is located is determined based on location information of the device. At 1910, an orientation of the device is determined based on direction information of the device and a subset of items of interest are identified in the place as a function of the orientation. At 1920, an item of the subset of items can be selected and a delayed interaction with the item (or place) is thereby enabled by direction based services.
FIG. 20 illustrates a mobile computing device 100 according to an embodiment. In this regard, a set of services 2060 that support delayed interaction with points of interest can be built based on location information 2022 and direction information 2032 collected by a mobile device, such as a phone. For instance, location information 2022 can be recorded by a location subsystem 2020 such as a GPS subsystem communicating with GPS satellites 2040. Direction or pointing information 2032 can be collected by a direction subsystem 2030, such as a compass, e.g., gyroscopic, magnetic, digital compass, etc. In addition, optionally, movement information 2012 can be gathered by the device 2000, e.g., via tower triangulation algorithms, and/or acceleration of the device 2000 can be measured as well, e.g., with an accelerometer. The collective information 2050 can be used to gain a sense of not only where the device 2000 is located in relation to other potential points of interest tracked or known by the overall set of services 2060, but also what direction the user is pointing the device 2000, so that the services 2060 can appreciate at whom or what the user is pointing the device 2000.
In addition, a gesture subsystem 2070 can optionally be included, which can be predicated on any one or more of the motion information 2012, location information 2022 or direction information 2032. In this regard, not only can direction information 2032 and location information 2022 be used to define a set of unique gestures, but also motion information 2012 (such as speed and acceleration) can be used to define a more sophisticated set of gestures.
FIG. 20 thus illustrates a gesture subsystem 2070 can optionally be included in a device 2000. In this regard, one can appreciate that a variety of algorithms could be adopted for a gesture subsystem 2070. For instance, a simple click-event when in the “pointing mode” for the device 2000 can result in determining a set of points of interest for the user. Another gesture on top of that pointing gesture with respect to a POI can result in delaying interaction with that POI.
In this regard, a device can include a variety of spatial and map components and intelligence engines to determine intersections for directional arcs (or cones in 3-D). For instance, objects of interest could be represented with exact boundaries, approximated with spheres, subshells (stores in a mall) of a greater shell (mall), hierarchically arranged, etc. Dynamically generated bounding boxes can also be implemented work, i.e., any technique can be used to obtain boundary information for use in an intersection algorithm. Thus, such boundaries can be implicitly or explicitly defined for the POIs.
Thus, a device can include an intersection component that interprets pointing information relative to a set of potential points of interest. The engine can perform such intersections knowing what the resolution of the measuring instruments are on the device, such as a given resolution of a GPS system. Such techniques can include taking into account how far a user is from a plane of objects of interest, such as items on a shelf or wall, the size of the item of interest and how that is defined, as well as the resolution of location instrumentation, such as the GPS system. The device can also optionally include an altimeter, or any other device that gives altitude information, such as measuring radar or sonar bounce from the floor. The altitude information can supplement existing location information for certain specialized services where points of interest vary significantly at different altitudes. It is noted that GPS itself has some information about altitude in its encoding.
FIG. 21 is a block diagram illustrating an exemplary non-limiting exchange between a device 2100 and service 2110. After start 2102, an example request for illustrative purpose is made by a device 2100 to a service 2110, which includes data related to the location of the device, a given POI, and information about delaying the interaction. For instance, then, at 2115, the service 2110 gets all offers for the POI 2115 and/or at 2120, gets the offers for the given location. At 2125, or earlier, it is determined if the delay criteria have been met for the given POI, if so, the service 2110 may get updated content associated with the location 2130 along with an optional branded user interface at 2135. At 2140, assuming the delay criteria have been met, an updated content package is created and delivered to the device 2100 at 2145. The device can undergo a check for the device at 2150. The delayed or future interaction can take place at 2155. Optionally, based on an advertising model, the content providers or owners can be billed at 2160.
FIG. 22 illustrates a general block diagram for an optional encoding technique for POI information. In this regard, various pieces of static and dynamic information 2202, 2204, 2206, 2208, 2210, etc. for a POI, which are normally represented in UI 2200 of the device, can also be encoded as an image or a bar code 2220, or some other device readable compact encoding. Then, a user can scan an item of interest, and coupled with presence in a physical store, a request 2215 can be made to a service 2240 with a key representing the scanned item and information representing the place, whereby the service 2240 determines content 2225 to return to the device 2200 based on the scanned item and the place. In addition, delay criterion 2225 specify how to delay the interaction with the scanned item.
For instance, in an optional Quick Response (QR) support embodiment, decompression allows users to take pictures of a QR code and process its contents where information has been encoded into a sticker/printout for display outside of a business (e.g., in the form of a copyrighted URL). The code need not be a QR code, but could be any code that can be read or scanned or processed to determine its underlying content. For instance, with a visual representation, a picture can be taken and processed, or with the bar code, the device can scan it. RF identification technology could also be used. For the avoidance of doubt, any encoded image format can be used, like a bar code, only one example of which is a QR code.
In effect, this enables a query for POI information via a QR code or other encoding. The user scans or images the code with a device 2230, and then transmits the code to the service, which translates the code into static and dynamically updated user information for display as a UI 2200 (or other user interface representation) so that the user can query about a POI merely by pointing at it. A URL for the POI can also be encoded in a format such as a QR code. In one non-limiting embodiment, the user can point the device at a QR code, and decode a given image with the QR code.
FIG. 23 illustrates at a high level, via a block diagram, a beneficial advertising model enabled by a direction/location based services with delayed interaction with endpoints as described in one or more embodiments herein. For instance, scanned items 2300 for delay, or pointed to items 2300 for delay, or any other action taken with respect to items 2300 for delay can be sent as information 2305 to a service 2310 that brokers interested third parties 2320 or 2330 who wish to advertise given the place and particular items 2300. Accordingly, such third parties 2320 or 2330 (third parties may be misleading because third parties can include parties related to a retail establishment where the device is currently located) can provide content as part of interactions 2325 or 2335 as part of a push or pull experience from the user perspective. This opportunity to provide content in a delayed manner enables services 2310 to notify 2315 parties 2320 interested in the delayed interaction. This beneficial information provided to third parties 2320 or 2330 about potential delayed interactions with particular POIs and places is therefore an opportunity to monetize the transaction back to those who benefit from a resulting transaction or advertising opportunity.
FIG. 24 is a block diagram illustrating a vast wealth of actions and interactions that can help define intent/context 2420 to delay for a given POI and location at which the device is present. For instance, text 2400 may be received by the device, a product search query 2402 local to the store, bar code scan 2404, image scan 2406, explicit designation of a product (e.g., by pointing at a product, or taking an image of the product and performing image recognition) 2408, price compare request 2410, gesture input 2412, other interaction 2414, etc. can all be taken into account in discerning intent of the device at a given place, along with direction information 2450. This combined with location information 2440 for discerning the place in which the device is in results in advertising opportunities 2430 for a whole host of third party advertising transactions for delayed delivery to the device.

Supplemental Context Regarding Pointing Devices, Architectures and Services

The following description contains supplemental context regarding potential non-limiting pointing devices, architectures and associated services to further aid in understanding one or more of the above embodiments. Any one or more of any additional features described in this section can be accommodated in any one or more of the embodiments described above with respect to delayed direction based services at a particular location for given POI(s). While such combinations of embodiments or features are possible, for the avoidance of doubt, no embodiments set forth in the subject disclosure should be considered limiting on any other embodiments described herein.
As mentioned, a broad range of scenarios can be enabled by a device that can take location and direction information about the device and build a service on top of that information. For example, by using an accelerometer in coordination with an on board digital compass, an application running on a mobile device updates what each endpoint is “looking at” or pointed towards, attempting hit detection on potential points of interest to either produce real-time information for the device or to allow the user to select a range, or using the GPS, a location on a map, and set information such as “Starbucks—10% off cappuccinos today” or “The Alamo—site of . . . ” for others to discover. One or more accelerometers can also be used to perform the function of determining direction information for each endpoint as well. As described herein, these techniques can become more granular to particular items within a Starbucks, such as “blueberry cheesecake” on display in the counter, enabling a new type of sale opportunity.
Accordingly, a general device for accomplishing this includes a processing engine to resolve a line of sight vector sent from a mobile endpoint and a system to aggregate that data as a platform, enabling a host of new scenarios predicated on the pointing information known for the device. The act of pointing with a device, such as the user's mobile phone, thus becomes a powerful vehicle for users to discover and interact with points of interest around the individual in a way that is tailored for the individual. Synchronization of data can also be performed to facilitate roaming and sharing of POV data and contacts among different users of the same service.
In a variety of embodiments described herein, 2-dimensional (2D), 3-dimensional (3D) or N-dimensional directional-based search, discovery, and interactivity services are enabled for endpoints in the system of potential interest to the user.
The pointing information and corresponding algorithms depend upon the assets available in a device for producing the pointing or directional information. The pointing information, however produced according to an underlying set of measurement components, and interpreted by a processing engine, can be one or more vectors. A vector or set of vectors can have a “width” or “arc” associated with the vector for any margin of error associated with the pointing of the device. A panning angle can be defined by a user with at least two pointing actions to encompass a set of points of interest, e.g., those that span a certain angle defined by a panning gesture by the user.
In one non-limiting embodiment, a portable electronic device includes a positional component for receiving positional information as a function of a location of the portable electronic device, a directional component that outputs direction information as a function of an orientation of the portable electronic device and a location based engine that processes the positional information and the direction information to determine a subset of points of interest relative to the portable electronic device as a function of at least the positional information and the direction information.
The positional component can be a positional GPS component for receiving GPS data as the positional information. The directional component can be a magnetic compass and/or a gyroscopic compass that outputs the direction information. The device can include acceleration component(s), such as accelerometer(s), that outputs acceleration information associated with movement of the portable electronic device. The use of a separate sensor can also be used to further compensate for tilt and altitude adjustment calculations.
In one embodiment, the device includes a cache memory for dynamically storing a subset of endpoints of interest that are relevant to the portable electronic device and at least one interface to a network service for transmitting the positional information and the direction information to the network service. In return, based on real-time changes to the positional information and direction/pointing information, the device dynamically receives in the cache memory an updated subset of endpoints that are potentially relevant to the portable electronic device.
For instance, the subset of endpoints can be updated as a function of endpoints of interest within a pre-defined distance substantially along a vector defined by the orientation of the portable electronic device. Alternatively or in addition, the subset of endpoints can be updated as a function of endpoints of interest relevant to a current context of the portable electronic device. In this regard, the device can include a set of Representational State Transfer (REST)-based application programming interfaces (APIs), or other stateless set of APIs, so that the device can communicate with the service over different networks, e.g., Wi-Fi, a GPRS network, etc. or communicate with other users of the service, e.g., Bluetooth. For the avoidance of doubt, the embodiments are in no way limited to a REST based implementation, but rather any other state or stateful protocol could be used to obtain information from the service to the devices.
The directional component outputs direction information including compass information based on calibrated and compensated heading/directionality information. The directional component can also include direction information indicating upward or downward tilt information associated with a current upward or downward tilt of the portable electronic device, so that the services can detect when a user is pointing upwards or downwards with the device in addition to a certain direction. The height of the vectors itself can also be taken into account to distinguish between an event of pointing with a device from the top of a building (likely pointing to other buildings, bridges, landmarks, etc.) and the same event from the bottom of the building (likely pointing to a shop at ground level), or towards a ceiling or floor to differentiate among shelves in a supermarket. A 3-axis magnetic field sensor can also be used to implement a compass to obtain tilt readings.
Secondary sensors, such as altimeters or pressure readers, can also be included in a mobile device and used to detect a height of the device, e.g., what floor a device is on in a parking lot or floor of a department store (changing the associated map/floorplan data). Where a device includes a compass with a planar view of the world (e.g., 2-axis compass), the inclusion of one or more accelerometers in the device can be used to supplement the motion vector measured for a device as a virtual third component of the motion vector, e.g., to provide measurements regarding a third degree of freedom. This option may be deployed where the provision of a 3-axis compass is too expensive, or otherwise unavailable.
In this respect, a gesturing component can also be included in the device to determine a current gesture of a user of the portable electronic device from a set of pre-defined gestures. For example, gestures can include zoom in, zoom out, panning to define an arc, all to help filter over potential subsets of points of interest for the user.
For instance, web services can effectively resolve vector coordinates sent from mobile endpoints into <x,y,z> or other coordinates using location data, such as GPS data, as well as configurable, synchronized POV information similar to that found in a GPS system in an automobile. In this regard, any of the embodiments can be applied similarly in any motor vehicle device. One non-limiting use is also facilitation of endpoint discovery for synchronization of data of interest to or from the user from or to the endpoint.
Among other algorithms for interpreting position/motion/direction information, as shown in FIG. 25, a device 2500 employing the direction based location based services 2502 as described herein in a variety of embodiments herein include a way to discern between near objects, such as POI 2514 and far objects, such as POI 2516. Depending on the context of usage, the time, the user's past, the device state, the speed of the device, the nature of the POIs, etc., the service can determine a general distance associated with a motion vector. Thus, a motion vector 2506 will implicate POI 2514, but not POI 2516, and the opposite would be true for motion vector 2508.
In addition, a device 2500 includes an algorithm for discerning items substantially along a direction at which the device is pointing, and those not substantially along a direction at which the device is pointing. In this respect, while motion vector 2504 might implicate POI 2512, without a specific panning gesture that encompassed more directions/vectors, POIs 2514 and 2516 would likely not be within the scope of points of interest defined by motion vector 2504. The distance or reach of a vector can also be tuned by a user, e.g., via a slider control or other control, to quickly expand or contract the scope of endpoints encompassed by a given “pointing” interaction with the device.
In one non-limiting embodiment, the determination of at what or whom the user is pointing is performed by calculating an absolute “Look” vector, within a suitable margin of error, by a reading from an accelerometer's tilt and a reading from the magnetic compass. Then, an intersection of endpoints determines an initial scope, which can be further refined depending on the particular service employed, i.e., any additional filter. For instance, for an apartment search service, endpoints falling within the look vector that are not apartments ready for lease, can be pre-filtered.
In addition to the look vector determination, the engine can also compensate for, or begin the look vector, where the user is by establish positioning (˜15 feet) through an A-GPS stack (or other location based or GPS subsystem including those with assistance strategies) and also compensate for any significant movement/acceleration of the device, where such information is available.
As mentioned, in another aspect, a device can include a client side cache of potentially relevant points of interest, which, based on the user's movement history can be dynamically updated. The context, such as geography, speed, etc. of the user can be factored in when updating. For instance, if a user's velocity is 2 miles an hour, the user may be walking and interested in updates at a city block by city block level, or at a lower level granularity if they are walking in the countryside. Similarly, if a user is moving on a highway at 60 miles per hour, the block-by-block updates of information are no longer desirable, but rather a granularity can be provided and predictively cached on the device that makes sense for the speed of the vehicle.
In an automobile context, the location becomes the road on which the automobile is travelling, and the particular items are the places and things that are passed on the roadside much like products in a particular retail store on a shelf or in a display. The pointing based services thus creates a virtual “billboard” opportunity for items of interest generally along a user's automobile path. Proximity to location can lead to an impulse buy, e.g., a user might stop by a museum they are passing and pointing at with their device, if offered a discount on admission.
In various alternative embodiments, gyroscopic or magnetic compasses can provide directional information. A REST based architecture enables data communications to occur over different networks, such as Wi-Fi and GPRS architectures. REST based APIs can be used, though any stateless messaging can be used that does not require a long keep alive for communicated data/messages. This way, since networks can go down with GPRS antennae, seamless switching can occur to Wi-Fi or Bluetooth networks to continue according to the pointing based services enabled by the embodiments described herein.
A device as provided herein according to one or more embodiments can include a file system to interact with a local cache, store updates for synchronization to the service, exchange information by Bluetooth with other users of the service, etc. Accordingly, operating from a local cache, at least the data in the local cache is still relevant at a time of disconnection, and thus, the user can still interact with the data. Finally, the device can synchronize according to any updates made at a time of re-connection to a network, or to another device that has more up to date GPS data, POI data, etc. In this regard, a switching architecture can be adopted for the device to perform a quick transition from connectivity from one networked system (e.g., cell phone towers) to another computer network (e.g., Wi-Fi) to a local network (e.g., mesh network of Bluetooth connected devices).
With respect to user input, a set of soft keys, touch keys, etc. can be provided to facilitate in the directional-based pointing services provided herein. A device can include a windowing stack in order to overlay different windows, or provide different windows of information regarding a point of interest (e.g., hours and phone number window versus interactive customer feedback window). Audio can be rendered or handled as input by the device. For instance, voice input can be handled by the service to explicitly point without the need for a physical movement of the device. For instance, a user could say into a device “what is this product right in front of me? No, not that one, the one above it” and have the device transmit current direction/movement information to a service, which in turn intelligently, or iteratively, determines what particular item of interest the user is pointing at, and returns a host of relevant information about the item.
One non-limiting way for determining a set of points of interest is illustrated in FIG. 26. In FIG. 26, a device 2600 is pointed (e.g., point and click) in a direction D1, which according to the device or service parameters, implicitly defines an area within arc 2610 and distance 2620 that encompasses POI 2630, but does not encompass POI 2632. Such an algorithm will also need to determine any edge case POIs, i.e., whether POIs such as POI 2634 are within the scope of pointing in direction D1, where the POI only partially falls within the area defined by arc 2610 and distance 2620.
Other gestures that can be of interest in for a gesturing subsystem include recognizing a user's gesture for zoom in or zoom out. Zoom in/zoom out can be done in terms of distance like FIG. 27. In FIG. 27, a device 2700 pointed in direction D1 may include zoomed in view which includes points of interest within distance 2720 and arc 2710, or a medium zoomed view representing points of interest between distance 2720 and 2722, or a zoomed out view representing points of interest beyond distance 2722. These zoom zones correspond to POIs 2730, 2732 and 2734, respectively. More or less zones can be considered depending upon a variety of factors, the service, user preference, etc.
For another non-limiting example, with location information and direction information, a user can input a first direction via a click, and then a second direction after moving the device via a second click, which in effect defines an arc 2810 for objects of interest in the system as illustrated in FIG. 28. For instance, via first pointing act by the user at time t1 in direction D1 and a second pointing act at time t2 by the user in direction D2, an arc 2810 is implicitly defined. The area of interest implicitly includes a search of points of object within a distance 2820, which can be zoomed in and out, or selected by the service based on a known granularity of interest, selected by the user, etc. This can be accomplished with a variety of forms of input to define the two directions. For instance, the first direction can be defined upon a click-and-hold button event, or other engage-and-hold user interface element, and the second direction can be defined upon release of the button. Similarly, two consecutive clicks corresponding to the two different directions D1 and D2 can also be implemented.
Also, instead of focusing on real distance, zooming in or out could also represent a change in terms of granularity, or size, or hierarchy of objects. For example, a first pointing gesture with the device may result in a shopping mall appearing, but with another gesture, a user could carry out a recognizable gesture to gain or lose a level of hierarchical granularity with the points of interest on display. For instance, after such gesture, the points of interest could be zoomed in to the level of the stores at the shopping mall and what they are currently offering.
In addition, a variety of even richer behaviors and gestures can be recognized when acceleration of the device in various axes can be discerned. Panning, arm extension/retraction, swirling of the device, backhand tennis swings, breaststroke arm action, golf swing motions could all signify something unique in terms of the behavior of the pointing device, and this is to just name a few motions that could be implemented in practice. Thus, any of the embodiments herein can define a set of gestures that serve to help the user interact with a set of services built on the pointing platform, to help users easily gain information about points of information in their environment.
Furthermore, with relatively accurate upward and downward tilt of the device, in addition to directional information such as calibrated and compensated heading/directional information, other services can be enabled. Typically, if a device is ground level, the user is outside, and the device is “pointed” up towards the top of buildings, the granularity of information about points of interest sought by the user (building level) is different than if the user was pointing at the first floor shops of the building (shops level), even where the same compass direction is implicated. Similarly, where a user is at the top of a landmark such as the Empire State building, a downward tilt at the street level (street level granularity) would implicate information about different points of interest that if the user of the device pointed with relatively no tilt at the Statue of Liberty (landmark/building level of granularity).
Also, when a device is moving in a car, it may appear that direction is changing as the user maintains a pointing action on a single location, but the user is still pointing at the same thing due to displacement. Thus, thus time varying location can be factored into the mathematics and engine of resolving at what the user is pointing with the device to compensate for the user experience based upon which all items are relative.
Accordingly, armed with the device's position, one or more web or cloud services can analyze the vector information to determine at what or whom the user is looking/pointing. The service can then provide additional information such as ads, specials, updates, menus, happy hour choices, etc., depending on the endpoint selected, the context of the service, the location (urban or rural), the time (night or day), etc. As a result, instead of a blank contextless Internet search, a form of real-time visual search for users in real 3-D environments is provided.
In one non-limiting embodiment, the direction based pointing services are implemented in connection with a pair of glasses, headband, etc. having a corresponding display means that acts in concert with the user's looking to highlight or overlay features of interest around the user.
As shown in FIG. 29, once a set of objects is determined from the pointing information according to a variety of contexts of a variety of services, a mobile device 2900 can display the objects via representation 2902 according to a variety of user experiences tailored to the service at issue. For instance, a virtual camera experience can be provided, where POI graphics or information can be positioned relative to one another to simulate an imaging experience. A variety of other user interface experiences can be provided based on the pointing direction as well.
For instance, a set of different choices are shown in FIG. 30. UI 3000 and 3002 illustrate navigation of hierarchical POI information. For instance, level1 categories may include category1, category2, category3, category4 and category5, but if a user selects around the categories with a thumb-wheel, up-down control, or the like, and chooses one such as category2. Then, subcategory1, subcategory2, subcategory3 and subcategory4 are displayed as subcategories of category2. Then, if the user selects, for instance, subcategory4, perhaps few enough POIs, such as buildings 3000 and 3010 are found in the subcategory in order to display on a 2D map UI 3004 along the pointing direction, or alternatively as a 3D virtual map view 3006 along the pointing direction.
Once a single POI is implicated or selected, then a full screen view for the single POI can be displayed, such as the exemplary UI 3100. UI 3100 can have one or more of any of the following representative areas. UI 3100 can include a static POI image 3102 such as a trademark of a store, or a picture of a person. UI 3100 can also include other media, and a static POI information portion 3104 for information that tends not to change such as restaurant hours, menu, contact information, etc. In addition, UI 3100 can include an information section for dynamic information to be pushed to the user for the POI, e.g., coupons, advertisements, offers, sales, etc. In addition, a dynamic interactive information are 3108 can be included where the user can fill out a survey, provide feedback to the POI owner, request the POI to contact the user, make a reservation, buy tickets, etc. UI 3100 also can include a representation of the direction information output by the compass for reference purposes. Further, UI 3100 can include other third party static or dynamic content in area 3112.
When things change from the perspective of either the service or the client, a synchronization process can bring either the client or service, respectively, up to date. In this way, an ecosystem is enabled where a user can point at an object or point of interest, gain information about it that is likely to be relevant to the user, interact with the information concerning the point of interest, and add value to services ecosystem where the user interacts. The system thus advantageously supports both static and dynamic content.
Other user interfaces can be considered such as left-right, or up-down arrangements for navigating categories or a special set of soft-keys can be adaptively provided.
Where a device includes a camera, in one embodiment shown in FIG. 32, a representative non-limiting overlay UI 3200 is shown having 3 POIs POI1, POI2 and POI3. The POIs are overlaid over actual image data being real time viewed on the device via an LCD screen or like display. The actual image data can be of products on a shelf or other display or exhibit in a store. Thus, as the user aims the camera around his or her environment, the lens becomes the pointer, and the POI information can be overlaid intelligently for discovery of endpoints of interest. Moreover, a similar embodiment can be imagined even without a camera, such as a UI in which 3-D objects are virtually represented based on real geometries known for the objects relative to the user.
Thus, the device UI can be implemented consistent with a camera, or a virtual camera, view for intuitive use of such devices. The pointer mechanism of the device could also switch based on whether the user was currently in live view mode for the camera or not. Moreover, assuming sufficient processing power and storage, real time image processing could discern an object of interest and based on image signatures, overlay POI information over such image in a similar manner to the above embodiments. In this regard, with the device provided herein, a variety of gestures can be employed to zoom in zoom out, perform tilt detection for looking down or up, or panning across a field of view to obtain a range of POIs associated with the panning scope.
With respect to a representative set of user settings, a number or maximum number of desired endpoints delivered as results can be configured. How to filter can also be configured, e.g., 5 most likely, 5 closest, 5 closest to 100 feet away, 5 within category or sub-category, alphabetical order, etc. In each case, based on a pointing direction, implicitly a cone or other cross section across physical space is defined as a scope of possible points of interest. In this regard, the width or deepness of this cone or cross section can be configurable by the user to control the accuracy of the pointing, e.g., narrow or wide radius of points and how far out to search.
To support processing of vector information and aggregating POI databases from third parties, a variety of storage techniques, such as relational storage techniques can be used. For instance, Virtual Earth data can be used for mapping and aggregation of POI data can occur from third parties such as Tele Atlas, NavTeq, etc. In this regard, businesses not in the POI database will want to be discovered and thus, the service provides a similar, but far superior from a spatial relevance standpoint, Yellow Pages experiences where businesses will desire to have their additional information, such as menus, price sheets, coupons, pictures, virtual tours, etc. accessible via the system.
In addition, a synchronization platform or framework can keep the roaming caches in sync, thereby capturing what users are looking at and efficiently processing changes. Or, where a user goes offline, local changes can be recorded, and when the user goes back online, such local changes can be synchronized to the network or service store. Also, since the users are in effect pulling information they care about in the here and in the now through the act of pointing with the device, the system generates high cost per thousand impression (CPM) rates as compared to other forms of demographic targeting. Moreover, the system drives impulse buys, since the user may not be physically present in a store, but the user may be near the object, and by being nearby and pointing at the store, information about a sale concerning the object can be sent to the user.
As mentioned, different location subsystems, such as tower triangulation, GPS, A-GPS, E-GPS, etc. have different tolerances. For instance, with GPS, tolerances can be achieved to about 10 meters. With A-GPS, tolerances can be tightened to about 12 feet. In turn, with E-GPS, tolerance may be a different error margin still. Compensating for the different tolerances is part of the interpretation engine for determining intersection of a pointing vector and a set of points of interest. In addition, as shown in FIGS. 4-6, a distance to project out the pointing vector can be explicit, configurable, contextual, etc.
In this regard, the various embodiments described herein can employ any algorithm for distinguishing among boundaries of the endpoints, such as boundary boxes, or rectangles, triangles, circles, etc. As a default radius, e.g., 150 feet could be selected, and such value can be configured or be context sensitive to the service provided. On-line real estate sites can be leveraged for existing POI information. Since different POI databases may track different information at different granularities, a way of normalizing the POI data according to one convention or standard can also be implemented so that the residential real estate location data of Zillow can be integrated with GPS information from Starbucks of all the Starbucks by country.
In addition, similar techniques can be implemented in a moving vehicle client that includes GPS, compass, accelerometer, etc. By filtering based on scenarios (e.g., I need gas), different subsets of points of interest (e.g., gas stations) can be determined for the user based not only on distance, but actual time it may take to get to the point of interest. In this regard, while a gas station may be 100 yards to the right off the highway, the car may have already passed the corresponding exit, and thus more useful information to provide is what gas station will take the least amount of time to drive from a current location based on direction/location so as to provide predictive points of interest that are up ahead on the road, and not already aged points of interest that would require turning around from one's destination in order to get to them.
For existing motor vehicle navigation devices, or other conventional portable GPS navigation devices, where a device does not natively include directional means such as a compass, the device can have an extension slot that accommodates direction information from an external directional device, such as a compass. Similarly, for laptops or other portable electronic devices, such devices can be outfitted with a card or board with a slot for a compass. While any of the services described herein can make web service calls as part of the pointing and retrieval of endpoint process, as mentioned, one advantageous feature of a user's locality in real space is that it is inherently more limited than a general Internet search for information. As a result, a limited amount of data can be predictively maintained on a user's device in cache memory and properly aged out as data becomes stale.
While there are a variety of implementations, and ways to sub-divide regions, whether overlapping or not, predictive caching and aging 3300 is conceptually illustrated by FIG. 33 in which a user's present location 3302 is discerned. At this point, the local cache still includes age out candidate location 3310, but as the velocity of the user indicates the user will be at predicted locations 3304 and 3306 in the future, these regions of POIs are downloaded to the mobile device. Accordingly, as the user travels to predicted location 3306, it starts to be clear that the user no longer needs the data from the age out candidate location 3310, which can then be removed, or flagged for removal when storage is challenged.
Accordingly, using the regional data cache, callbacks and an update mechanism that is updated dynamically based on movement, new point of interest can be added by a service or by a user. Update is thus performed continuously or substantially continuously based on updated travel, velocity, speed, etc. In this regard, a user can add a new point of interest in the region, add info to a local cache, and then upload to the zone. To appreciate the problem, the number of worldwide POIs is practically limitless, however only a small number of POIs will be relevant to a user at a given time. Thus, predictively, a cube of data can be taken to the device, the user can go offline such that when the user reconnects, the device is intelligent to figure out what has changed, been weighted, etc., so that the device can synchronize with the network services and expose the user's changes for other people.
The predictive algorithms again depend on what the user is interested in finding, what service the user may be using, the context of the user, etc. They can also be based on velocity, direction, time, etc. For instance, if it is nighttime, assumptions based on demographics or preferences may lead the device to return information about nightclubs or all night diners. Or, instead of giving directions as driving directions that calculate distances as absolute distances, i.e., as the crow flies, a device can take road curves into account since instantaneous pointing information on roads can be collected and handled by a corresponding service when giving driving directions. Or, as another alternative, the direction one is heading on a road, such as a highway with a concrete divider, is relevant to the directions that a navigation system should give. Where a U-turn is unavailable and user passes an exit with a point of interest, for instance, directions should take this into account and consider the heading of the vehicle.
Any device can include the embodiments described herein, including MP3 players, such as a Zune device, GPS navigation devices, bike computers, sunglass/visor systems, motor vehicles, mobile phones, laptops, PDA, etc.
One way to obtain the service applications, assuming the underlying measuring instruments to participate in the real-time gathering of directional information, is to message to a service to obtain the application, e.g., by text messaging to service, or getting a client download link. Another vehicle for enabling the service is to provide it natively in the operating system or applications of a mobile devices. Since a hardware abstraction layer accommodates different methods for collecting position, direction, acceleration information, the same platform can be used on any device regardless of the precise underlying hardware.
In another aspect of any of the embodiments described herein, because stateless messaging is employed, if communications drop with one network, the device can begin further communicating via another network. For instance, a device has two channels, and a user gets on a bus, but no longer have GPRS or GPS activity. Nonetheless the user is able to get the information the device needs from some other channel. Just because a tower, or satellites are down, does not mean that the device cannot connect through an alternative channel, e.g., the bus's GPS location information via Bluetooth.
With respect to exemplary mobile client architectures, a representative device can include, as described variously herein, client Side Storage for housing and providing fast access to cached POI data in the current region including associated dynamically updated or static information, such as annotations, coupons from businesses, etc. This includes usage data tracking and storage. In addition, regional data can be a cached subset of the larger service data, always updated based on the region in which the client is roaming. For instance, POI data could include as a non-limiting example, the following information:


POI coordinates and data	//{−70.26322, 43.65412,
“STARBUCK'S”}
Localized annotations	//Menu, prices, hours of operation, etc
Coupons and ads	//Classes of coupons (new user,
returning, etc)

Support for different kinds of information (e.g., blob v structured information (blob for storage and media; structured for tags, annotations, etc.)
A device can also include usage data and preferences to hold settings as well as usage data such as coupons “activated,” waypoints, businesses encountered per day, other users encountered, etc. to be analyzed by the cloud services for business intelligence analysis and reporting.
A device can also include a continuous update mechanism, which is a service that maintains the client's cached copy of a current region updated with the latest. Among other ways, this can be achieved with a ping-to-pull model that pre-fetches and swaps out the client's cached region using travel direction and speed to facilitate roaming among different regions. This is effectively a paging mechanism for upcoming POIs. This also includes sending a new or modified POI for the region (with annotations+coupons), sending a new or modified annotation for the POIs (with coupons), or sending a new or modified coupon for the POI.
A device can also include a Hardware Abstraction Layer (HAL) having components responsible for abstracting the way the client communicates with the measuring instruments, e.g., the GPS driver for positioning and LOS accuracy (e.g., open eGPS), magnetic compass for heading and rotational information (e.g., gyroscopic), one or more accelerometers for gestured input and tilt (achieves 3D positional algorithms, assuming gyroscopic compass).
As described earlier, a device can also include methods/interfaces to make REST calls via GPRS/Wi-Fi and a file system and storage for storing and retrieving the application data and settings.
A device can also include user input and methods to map input to the virtual keys. For instance, one non-limiting way to accomplish user input is to have softkeys as follows, though it is to be understood a great variety of user inputs can be used to achieve interaction with the user interfaces of the pointing based services.


SK up/down:	//Up and down on choices
SK right, SK ok/confirm:	//Choose an option or drill down/next
page
SK left, SK cancel/back,	//Go back to a previous window, cancel
Exit / Incoming Call events	//Exit the app or minimize

In addition, a representative device can include a graphics and windowing stack to render the client side UI, as well as an audio stack to play sounds/alerts.
As mentioned, such a device may also include spatial and math computational components including a set of APIs to perform 3D collision testing between subdivided surfaces such as spherical shells (e.g., a simple hit testing model to adopt and boundary definitions for POs), rotate points, and cull as appropriate from conic sections.
As described in various embodiments herein, FIGS. 34 and 35 illustrate two processes for a device when location (e.g., GPS) and direction (e.g., compass) events occur. In FIG. 34, upon the occurrence of a location or direction event, at 3400, it is determined whether predictive caching should be initiated for a next region to which a user is travelling. At 3410, if so, then the next region of data can be pre-fetched. At 3420, old regional data no longer of relevance can be aged out. At 3430, any usage data can be uploaded to the service framework for business intelligence, input to an advertisement engine, etc.
FIG. 35 represents another process for filtering potential POIs after a pointing event. Upon the detection of a location and direction event, at 3500, for POIs in the device's local cache, a group of POIs are determined that pass an intersection algorithm for the direction of pointing of the device. At 3510, POIs in the group can be represented in some fashion on a UI, e.g., full view if only 1 POI, categorized view, 2-D map view, 3-D perspective view, or user images if other users, etc. The possibilities for representation are limitless; the embodiments described herein are intuitive based on the general notion of pointing based direction services.
At 3520, upon selection of a POI, static content is determined and any dynamic content is acquired via synchronization. When new data becomes available, it is downloaded to stay up to date. At 3530, POI information is filtered further by user specific information (e.g., if it is the user's first time at the store, returning customer, loyalty program member, live baseball game offer for team clothing discounts, etc.). At 3540, static and dynamic content that is up to date is rendered for the POI. In addition, updates and/or interaction with POI information is allowed which can be synced back to the service.

Exemplary Networked and Distributed Environments

One of ordinary skill in the art can appreciate that the various embodiments of methods and devices for pointing based services and related embodiments described herein can be implemented in connection with any computer or other client or server device, which can be deployed as part of a computer network or in a distributed computing environment, and can be connected to any kind of data store. In this regard, the various embodiments described herein can be implemented in any computer system or environment having any number of memory or storage units, and any number of applications and processes occurring across any number of storage units. This includes, but is not limited to, an environment with server computers and client computers deployed in a network environment or a distributed computing environment, having remote or local storage.
FIG. 36 provides a non-limiting schematic diagram of an exemplary networked or distributed computing environment. The distributed computing environment comprises computing objects 3610, 3612, etc. and computing objects or devices 3620, 3622, 3624, 3626, 3628, etc., which may include programs, methods, data stores, programmable logic, etc., as represented by applications 3630, 3632, 3634, 3636, 3638. It can be appreciated that objects 3610, 3612, etc. and computing objects or devices 3620, 3622, 3624, 3626, 3628, etc. may comprise different devices, such as PDAs, audio/video devices, mobile phones, MP3 players, laptops, etc.
Each object 3610, 3612, etc. and computing objects or devices 3620, 3622, 3624, 3626, 3628, etc. can communicate with one or more other objects 3610, 3612, etc. and computing objects or devices 3620, 3622, 3624, 3626, 3628, etc. by way of the communications network 3640, either directly or indirectly. Even though illustrated as a single element in FIG. 36, network 3640 may comprise other computing objects and computing devices that provide services to the system of FIG. 36, and/or may represent multiple interconnected networks, which are not shown. Each object 3610, 3612, etc. or 3620, 3622, 3624, 3626, 3628, etc. can also contain an application, such as applications 3630, 3632, 3634, 3636, 3638, that might make use of an API, or other object, software, firmware and/or hardware, suitable for communication with or implementation of the delayed interaction model as provided in accordance with various embodiments.
There are a variety of systems, components, and network configurations that support distributed computing environments. For example, computing systems can be connected together by wired or wireless systems, by local networks or widely distributed networks. Currently, many networks are coupled to the Internet, which provides an infrastructure for widely distributed computing and encompasses many different networks, though any network infrastructure can be used for exemplary communications made incident to the techniques as described in various embodiments.
Thus, a host of network topologies and network infrastructures, such as client/server, peer-to-peer, or hybrid architectures, can be utilized. In a client/server architecture, particularly a networked system, a client is usually a computer that accesses shared network resources provided by another computer, e.g., a server. In the illustration of FIG. 36, as a non-limiting example, computers 3620, 3622, 3624, 3626, 3628, etc. can be thought of as clients and computers 3610, 3612, etc. can be thought of as servers where servers 3610, 3612, etc. provide data services, such as receiving data from client computers 3620, 3622, 3624, 3626, 3628, etc., storing of data, processing of data, transmitting data to client computers 3620, 3622, 3624, 3626, 3628, etc., although any computer can be considered a client, a server, or both, depending on the circumstances. Any of these computing devices may be processing data, or requesting services or tasks that may implicate the delayed interaction model and related techniques as described herein for one or more embodiments.
A server is typically a remote computer system accessible over a remote or local network, such as the Internet or wireless network infrastructures. The client process may be active in a first computer system, and the server process may be active in a second computer system, communicating with one another over a communications medium, thus providing distributed functionality and allowing multiple clients to take advantage of the information-gathering capabilities of the server. Any software objects utilized pursuant to the direction based services can be provided standalone, or distributed across multiple computing devices or objects.
In a network environment in which the communications network/bus 3640 is the Internet, for example, the servers 3610, 3612, etc. can be Web servers with which the clients 3620, 3622, 3624, 3626, 3628, etc. communicate via any of a number of known protocols, such as the hypertext transfer protocol (HTTP). Servers 3610, 3612, etc. may also serve as clients 3620, 3622, 3624, 3626, 3628, etc., as may be characteristic of a distributed computing environment.

Exemplary Computing Device

As mentioned, various embodiments described herein apply to any device wherein it may be desirable to perform pointing based services, and delay interactions with points of interest. It should be understood, therefore, that handheld, portable and other computing devices and computing objects of all kinds are contemplated for use in connection with the various embodiments described herein, i.e., anywhere that a device may request pointing based services. Accordingly, the below general purpose remote computer described below in FIG. 26 is but one example, and the embodiments of the subject disclosure may be implemented with any client having network/bus interoperability and interaction.
Although not required, any of the embodiments can partly be implemented via an operating system, for use by a developer of services for a device or object, and/or included within application software that operates in connection with the operable component(s). Software may be described in the general context of computer-executable instructions, such as program modules, being executed by one or more computers, such as client workstations, servers or other devices. Those skilled in the art will appreciate that network interactions may be practiced with a variety of computer system configurations and protocols.
FIG. 37 thus illustrates an example of a suitable computing system environment 3700 in which one or more of the embodiments may be implemented, although as made clear above, the computing system environment 3700 is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of any of the embodiments. Neither should the computing environment 3700 be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment 3700.
With reference to FIG. 37, an exemplary remote device for implementing one or more embodiments herein can include a general purpose computing device in the form of a handheld computer 3710. Components of handheld computer 3710 may include, but are not limited to, a processing unit 3720, a system memory 3730, and a system bus 3721 that couples various system components including the system memory to the processing unit 3720.
Computer 3710 typically includes a variety of computer readable media and can be any available media that can be accessed by computer 3710. The system memory 3730 may include computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM) and/or random access memory (RAM). By way of example, and not limitation, memory 3730 may also include an operating system, application programs, other program modules, and program data.
A user may enter commands and information into the computer 3710 through input devices 3740 A monitor or other type of display device is also connected to the system bus 3721 via an interface, such as output interface 3750. In addition to a monitor, computers may also include other peripheral output devices such as speakers and a printer, which may be connected through output interface 3750.
The computer 3710 may operate in a networked or distributed environment using logical connections to one or more other remote computers, such as remote computer 3770. The remote computer 3770 may be a personal computer, a server, a router, a network PC, a peer device or other common network node, or any other remote media consumption or transmission device, and may include any or all of the elements described above relative to the computer 3710. The logical connections depicted in FIG. 37 include a network 3771, such local area network (LAN) or a wide area network (WAN), but may also include other networks/buses. Such networking environments are commonplace in homes, offices, enterprise-wide computer networks, intranets and the Internet.
As mentioned above, while exemplary embodiments have been described in connection with various computing devices, networks and advertising architectures, the underlying concepts may be applied to any network system and any computing device or system in which it is desirable to derive information about surrounding points of interest.
There are multiple ways of implementing one or more of the embodiments described herein, e.g., an appropriate API, tool kit, driver code, operating system, control, standalone or downloadable software object, etc. which enables applications and services to use the pointing based services. Embodiments may be contemplated from the standpoint of an API (or other software object), as well as from a software or hardware object that provides pointing platform services in accordance with one or more of the described embodiments. Various implementations and embodiments described herein may have aspects that are wholly in hardware, partly in hardware and partly in software, as well as in software.
The word “exemplary” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, for the avoidance of doubt, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.
As mentioned, the various techniques described herein may be implemented in connection with hardware or software or, where appropriate, with a combination of both. As used herein, the terms “component,” “system” and the like are likewise intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on computer and the computer can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers.
The aforementioned systems have been described with respect to interaction between several components. It can be appreciated that such systems and components can include those components or specified sub-components, some of the specified components or sub-components, and/or additional components, and according to various permutations and combinations of the foregoing. Sub-components can also be implemented as components communicatively coupled to other components rather than included within parent components (hierarchical). Additionally, it should be noted that one or more components may be combined into a single component providing aggregate functionality or divided into several separate sub-components, and any one or more middle layers, such as a management layer, may be provided to communicatively couple to such sub-components in order to provide integrated functionality. Any components described herein may also interact with one or more other components not specifically described herein but generally known by those of skill in the art.
Furthermore, the claimed subject matter may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter. The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier, or media. For example, computer readable media can include but are not limited to magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips . . . ), optical disks (e.g., compact disk (CD), digital versatile disk (DVD) . . . ), smart cards, and flash memory devices (e.g., card, stick, key drive . . . ). Additionally it should be appreciated that a carrier wave can be employed to carry computer-readable electronic data such as those used in transmitting and receiving electronic mail or in accessing a network such as the Internet or a local area network (LAN). Of course, those skilled in the art will recognize many modifications may be made to this configuration without departing from the scope or spirit of the claimed subject matter.
As used herein, the terms “infer” or “inference” generally refer to the process of reasoning about or inferring states of the system, environment, and/or user from a set of observations as captured via events and/or data. Inference can be employed to identify a specific context or action, or can generate a probability distribution over states, for example. The inference can be probabilistic—that is, the computation of a probability distribution over states of interest based on a consideration of data and events. Inference can also refer to techniques employed for composing higher-level events from a set of events and/or data. Such inference results in the construction of new events or actions from a set of observed events and/or stored event data, whether or not the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources.
In view of the exemplary systems described supra, methodologies that may be implemented in accordance with the disclosed subject matter will be better appreciated with reference to the flowcharts of the various figures. While for purposes of simplicity of explanation, the methodologies are shown and described as a series of blocks, it is to be understood and appreciated that the claimed subject matter is not limited by the order of the blocks, as some blocks may occur in different orders and/or concurrently with other blocks from what is depicted and described herein. Where non-sequential, or branched, flow is illustrated via flowchart, it can be appreciated that various other branches, flow paths, and orders of the blocks, may be implemented which achieve the same or a similar result. Moreover, not all illustrated blocks may be required to implement the methodologies described hereinafter.
While the various embodiments have been described in connection with the preferred embodiments of the various figures, it is to be understood that other similar embodiments may be used or modifications and additions may be made to the described embodiment for performing the same function without deviating therefrom. Still further, one or more aspects of the above described embodiments may be implemented in or across a plurality of processing chips or devices, and storage may similarly be effected across a plurality of devices. Therefore, the present invention should not be limited to any single embodiment, but rather should be construed in breadth and scope in accordance with the appended claims.

Claims

1. A portable electronic device, comprising:

a memory that stores at least information related to at least direction information representing information pertaining to orientation of the device, position information representing information pertaining to location of the device and point of interest information representing information pertaining to points of interest substantially within interactive range of the device;

a positional component for receiving the position information as a function of a location of the portable electronic device;

a directional component that measures the direction information as a function of an orientation of the portable electronic device; and

a transmission component that transmits device data regarding interaction with points of interest by the portable electronic device to at least one remote device for aggregation of data regarding interaction with at least some of the points of interest from a set of peer devices of a mesh network.

2. The device of claim 1, wherein the device is provisioned with a set of direction based services tailored to the device based on an analysis of the aggregated data by the at least one remote device.

3. The system of claim 1, wherein the transmission component transmits device data including any one or more of user activity, state, or context within the mesh network.

4. The device of claim 1, further comprising:

at least one processor configured to process the position information and the direction information to determine at least one identifier of at least one point of interest substantially within interactive proximity of the device, to receive information corresponding to the at least one identifier of the at least one point of interest, and to display information if the device interacts with the at least one point of interest with the device.

5. A method for provisioning direction based services, comprising:

inferring or determining a device has entered into a mesh network of cooperating devices;

inferring or determining goals of the device;

mapping the goals to a set of direction based services, which are based at least partly on device orientation, available within the mesh network; and

provisioning a subset of the direction based services to the device.

6. The method of claim 5, wherein the mapping act comprises mapping based on at least one of: user demographics, user state, user context, user preferences, or available resources.

7. The method of claim 5 further comprising:

identifying a set of direction based services that are not provisioned to the device.

8. The method of claim 5, further comprising:

refining a knowledge base or data model utilized in connection with direction based services provisioning based on results relating to the provisioning of direction based services.

9. The method of claim 5, wherein the provisioning includes provisioning the device with at least one targeted advertisement.

10. The method of claim 5, further comprising:

analyzing activity regarding a plurality of devices within the mesh network, and based on the analyzing, tailoring the provisioning of direction based services in real-time or future.

11. The method of claim 5, further comprising:

analyzing activity regarding a plurality of devices within the mesh network, and based on the analyzing, determining at least one of: user traffic, wait time for service, resource availability, or user density.

12. A mesh-network based host computer system for provisioning services, comprising:

at least one storage device that stores a data model that facilitates provisioning of services within a mesh network, the data model employs aggregated and processed data relating to prior user or device interactions within the mesh network;

a monitor component that receives data regarding user or device interaction within the mesh network;

an analysis component that analyzes the received data, and infers or determines a user or device goal; and

a provisioning component that employs the data model and results of the data analyses to identify and provision a set of pointing based services to the user or device within the mesh network.

13. The system of claim 12, the analysis component factors as part of the analyses at least one of: user demographics, user state, user context, user preferences, or available resources.

14. The system of claim 13, wherein the provisioning component provisions targeted advertisements to the user or device.

15. The system of claim 13, wherein as part of the analyses the analysis component infers or determines level of user traffic at a store, and wait time to completion of a commercial transaction, and the provisioning component recommends another store as a function of the analyses.

16. The system of claim 15, wherein the analysis component factors user goals, and time constraints as part of the analysis.

17. The system of claim 13, wherein the provisioning component orders items to be provisioned to the user or device based on a pre-defined ordering algorithm.

18. The system of claim 17, wherein the provisioning component employs a utility-based analysis in connection with ordering and provisioning of pointing based services.

19. The system of claim 13, wherein results of provisioning of pointing based services is employed to train the data model.

20. The system of claim 13, wherein the data model can adaptively interact with other data models.