Abstract: Embodiments infer position information using captured content items. A computing device such as a mobile computing or a cloud service analyzes one or more content items to generate information describing the content items. For content items that include photographs, for example, the photographs are processed to identify faces, objects, places, text, etc. The generated information is compared to reference content items (e.g., image comparison), user data (e.g., calendar data, contact information, etc.), and/or public data (e.g., address information, event information, etc.) to identify at least one match. Location information associated with the matched reference content items, user data, and/or public data is obtained. A position is determined for the content items based on the location information obtained from the matched reference content item, user data, and/or public data.

Abstract: A system and method for calculating a position in response to a position request. Observed beacon data associated with the request is used to select a calculation method based on available data for a venue and device capabilities. If sufficient venue data based on previously verified beacon positions is available, a position calculation can resolve floor and venue information. If insufficient previously observed data is available for a venue, the position is calculated using 2D data based on GPS observations. Following the choice a calculation model, the calculation position is returned in response to the position request.

Abstract: A system and method determining an error radius reflecting the accuracy of a calculated position of a processing device is provided. A data structure includes an error radius mapped to a scaled geographic area or “tile” comprising an area in which a calculated position may be determined. The data structure may include a plurality of first fields identifying a scaled geographic area based on a global projection reference system, and a plurality of second fields identifying, for each of the first fields, a position error radius associated with a scaled geographic area and level. For any calculation of an inferred position based on beacon observations, a rapid lookup of the corresponding scaled area including the new inferred position in the data structure returns an error radius for the new inferred position.

Abstract: Embodiments analyze crowd-sourced data to identify a moved or moving beacon. The crowd-sourced data involving a particular beacon is filtered based on a cluster start time associated with the beacon. A clustering analysis groups the filtered crowd-sourced data for the beacon into a plurality of clusters based on spatial distance. Timestamps associated with the crowd-sourced data in the clusters are compared to select one of the clusters. The crowd-sourced data associated with the selected cluster is used to determine position information for the moved beacon. The cluster start time for the beacon is adjusted based on the earliest timestamp associated with the positioned observations corresponding to the selected cluster. Adjusting the cluster start time removes from a subsequent analysis the positioned observations associated with one or more prior positions of the beacon.

Abstract: The description relates to receiver gain offset. One example can obtain data sensed by a mobile device at a position. The example can evaluate the sensed data and survey data to identify a venue proximate to the position. The survey data of the venue can be organized into regions and then individual regions can be organized into sub-regions. The example can compare signal strengths of the sensed data to signal strengths of the survey data to identify the position relative to an individual region. The comparison can utilize a receiver gain offset estimation between the mobile device and another device that acquired the survey data. The example can refine the receiver gain offset estimation and attempt to identify the position relative to an individual sub-region within the individual region utilizing the refined receiver gain offset estimation.

Abstract: Embodiments infer position information using captured content items. A computing device such as a mobile computing or a cloud service analyzes one or more content items to generate information describing the content items. For content items that include photographs, for example, the photographs are processed to identify faces, objects, places, text, etc. The generated information is compared to reference content items (e.g., image comparison), user data (e.g., calendar data, contact information, etc.), and/or public data (e.g., address information, event information, etc.) to identify at least one match. Location information associated with the matched reference content items, user data, and/or public data is obtained. A position is determined for the content items based on the location information obtained from the matched reference content item, user data, and/or public data.

Abstract: The description relates to mobile device location. One example can access stored survey data relating to a venue. The stored survey data can include internal data from inside the venue and external data from outside the venue. The example can obtain current sensing data at a location proximate to the venue. The example can determine whether the location is inside the venue or outside the venue by comparing the stored survey data and the current sensing data.

Abstract: A method and apparatus is described for rendering web pages by a wireless device in order to reduce an amount of data transmitted over-the-air to the wireless device. In one embodiment, a method is performed by a server, comprising receiving a markup language document from a web server via a network interface, the markup language document corresponding to a web page. Next, the markup language document is parsed into a number of blocks by a processor, each of the blocks comprising code representing original content of the web page. Then, the processor creates one or more replacement blocks, each of the replacement blocks corresponding to a respective one of the blocks, each of the replacement blocks comprising code representing substituted content of the web page. Some of the blocks and at least one of the replacement blocks are then sent to a wireless device for rendering.

Abstract: An RF fingerprinting methodology is generalized to include non-RF related factors. For each fingerprinted tile, there is an associated distance function between two fingerprints (the training fingerprint and the test fingerprint) from within that tile which may be a linear or non-linear combination of the deltas between multiple factors of the two fingerprints. The distance function for each tile is derived from a training dataset corresponding to that specific tile, and optimized to minimize the total difference between real distances and predicted distances. Upon receipt of an inference request, a result is derived from a combination of the fingerprints from the training dataset having the least distance per application of the distance function. Likely error for the tile is also determined to ascertain whether to rely on other location methods.

Abstract: Dynamically evaluating candidate connections as alternatives to an active connection between a first computing device and a second computing device. The first computing device transitions to one of the candidate connections to replace the active connection based on the evaluation. The evaluation and transition occurs based on time intervals, events, or conditions. Maintaining the candidate connections enables mobile devices, for example, to be resilient to and tolerant of topology changes affecting the active connection.

Abstract: Embodiments enhance the functionality of a vehicle, a user device, or both by the selection and sharing of data. Upon detection of each other, the vehicle device and the user device obtain and share data. The data may be associated with the user, the user computing device, and/or the vehicle and may be stored in cloud-based services. Functionality of the vehicle and/or user device is customized to the user based on the shared data. For example, the user device may provide assisted global positioning system (GPS) data to the vehicle to reduce a time-to-fix (TTF) when determining a location of the vehicle. In other examples, settings of the vehicle are personalized to the user, and location-relevant content is downloaded to the user device.

Abstract: Defining subscriptions to location information for a computing device (e.g., a mobile computing device). Application programs, services, and/or peer devices define the subscriptions based on constraints associated with requests for the location information. A location request scheduler coordinates the subscriptions temporally and spatially to reduce the quantity of the requests from the computing device. In some embodiments, the subscriptions are automatically defined based on an observed mobility pattern of the computing device.

Abstract: Example apparatus and methods concern rigorous survey-plan based sensor data collection where physical survey locations are correlated to logical locations rather than being tightly coupled to physical map locations. An embodiment includes accessing a venue map and a survey plan associated with the venue map. A survey plan includes a survey path defined by one or more logical survey points. A logical survey point includes a unique co-ordinate free identifier, a description of a recognizable location in the venue, and a co-ordinate configured to register the logical survey point to the corresponding venue map. A surveyor surveys the venue using the survey plan. Surveying the venue includes following the survey plan and acquiring sensor fingerprints at sensor reading points along the survey path. A fingerprint observation data store is populated with survey points that are registered to the survey plan. Survey points include sensor fingerprint data and correlation data.

Abstract: Example apparatus and methods concern rigorous survey-plan based sensor data collection where physical survey locations are correlated to logical locations rather than being tightly coupled to physical map locations. An embodiment includes accessing a venue map and a survey plan associated with the venue map. A survey plan includes a survey path defined by one or more logical survey points. A logical survey point includes a unique co-ordinate free identifier, a description of a recognizable location in the venue, and a co-ordinate configured to register the logical survey point to the corresponding venue map. A surveyor surveys the venue using the survey plan. Surveying the venue includes following the survey plan and acquiring sensor fingerprints at sensor reading points along the survey path. A fingerprint observation data store is populated with survey points that are registered to the survey plan. Survey points include sensor fingerprint data and correlation data.

Abstract: A system for recommending social events includes a front-end interface for establishing communication over a communications network and a user database module for accessing a plurality of user profiles. An activities database module is provided for accessing a plurality of activities that users may attend. The system also includes a recommendation engine operable in at least a first user-selectable mode. The first user-selectable mode provides at least one recommendation. Each of the recommendations includes a plurality of invitees and at least one activity to be attended by the plurality of invitees. The invitees are selected from a set of invitees accessible through the user database module and the activity is selected from a set of activities accessible through the activities database module. In one mode of operation, the invitees and the activity are both selected by the system and are not preselected by the user.

Abstract: Selecting devices from which to receive data for adjusting the performance of a positioning system. The positioning system infers the location of the devices based on beacons observed by the devices. The performance of the positioning system is compared to performance targets. One or more of the devices are selected based on the comparison. Data collection from the devices is adjusted to affect performance of the positioning system (e.g., improved or reduced). For example, if the positioning system predicts positions poorly for a particular area, data collection from selected devices within the particular area may be increased.

Abstract: Defining subscriptions to location information for a computing device (e.g., a mobile computing device). Application programs, services, and/or peer devices define the subscriptions based on constraints associated with requests for the location information. A location request scheduler coordinates the subscriptions temporally and spatially to reduce the quantity of the requests from the computing device. In some embodiments, the subscriptions are automatically defined based on an observed mobility pattern of the computing device.

Abstract: Embodiments order observed beacons based on relative signal strength to create a correspondence between beacon sets and positions. A computing device such as a mobile device provides a positioned observation including a plurality of observed beacons and a position of the mobile device during observation. The observed beacons are ordered based on quality indicators such as signal strength relative to each other. A set of the beacons are selected based on the ordering (e.g., the beacons with the strongest signal strength are selected in order). The position of the observing mobile device is associated with the beacon set to enable location inference for other devices providing observations including the same beacon set.

Abstract: Embodiments analyze crowd-sourced data to identify a moved or moving beacon. The crowd-sourced data involving a particular beacon is filtered based on a cluster start time associated with the beacon. A clustering analysis groups the filtered crowd-sourced data for the beacon into a plurality of clusters based on spatial distance. Timestamps associated with the crowd-sourced data in the clusters are compared to select one of the clusters. The crowd-sourced data associated with the selected cluster is used to determine position information for the moved beacon. The cluster start time for the beacon is adjusted based on the earliest timestamp associated with the positioned observations corresponding to the selected cluster. Adjusting the cluster start time removes from a subsequent analysis the positioned observations associated with one or more prior positions of the beacon.

Abstract: Determining geospatial patterns from device data collected from a plurality of computing devices. The devices represent, for example, a plurality of sources providing the device data. The device data describes the computing devices and/or environments thereof. Some embodiments present the determined patterns to users for editing, update maps with the edited patterns, and distribute the maps to the users. The maps are stored to create a searchable map library.