Sharad Agarwal - Seattle WA, US Weili Liu - Redmond WA, US
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06F 15/173
US Classification:
709224, 709218, 709223, 709225, 370252, 370253
Abstract:
Embodiments calculate an estimated latency between computing devices. A latency service aggregates latency records defining latency measurements and corresponding latency factors from a plurality of computing devices. From the aggregated latency records, the latency service defines relationships between the latency measurements and the corresponding latency factors. Responsive to a request for an estimated latency from a mobile computing device, the latency service applies the defined relationships to estimate the latency based on the latency factors associated with the received request. In some embodiments, the estimated latency includes three portions: a first latency value representing the latency from the mobile computing device to a cell site, a second latency value representing the latency from the cell site to an access point, and a third latency value representing the latency from the access point to a destination computing device.
User-Selected Tags For Annotating Geographic Domains Containing Points-Of-Interest
A location-based service is provided that allows a user of a mobile device to tag and track the places he or she visits and share these places with social networking members. To implement this service a system receives location information from the user's location-aware mobile device over a wireless network. The location information identifies various locations or other points-of-interest that the user has visited. Based on this information the system determines a geographical domain that encompasses at least some of the locations the user has visited. Once a geographic domain has been determined it is presented to the user via the mobile device. The user is prompted to tag the geographical domain with an annotation that describes the geographic domain. The system stores the geographical domain and the annotation associated therewith so that it can be searched and accessed or retrieved by the user or members of the user's social network.
Clustering Crowd-Sourced Data For Determining Beacon Positions
Sindhura Bandhakavi - Redmond WA, US Weili Liu - Redmond WA, US
Assignee:
MICROSOFT CORPORATION - Redmond WA
International Classification:
H04W 64/00
US Classification:
370328
Abstract:
Embodiments analyze crowd-sourced data to identify a moved or moving beacon. The crowd-sourced data for the beacon is grouped 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 beacon.
Clustering Crowd-Sourced Data To Identify Event Beacons
Weili Liu - Redmond WA, US Sindhura Bandhakavi - Redmond WA, US
Assignee:
MICROSOFT CORPORATION - Redmond WA
International Classification:
G06F 17/30
US Classification:
707709, 707737, 707E17089, 707E17108
Abstract:
Embodiments for identifying event beacons are provided. Position observations for a beacon are grouped into a plurality of clusters based at least on spatial distance. A location of each cluster is compared to event locations corresponding to events. Based on the comparison, the beacon is associated with the event, and the location of the beacon is set to the location of the event. In some embodiments, location requests are analyzed to identify event beacons, and the event information for the event beacons is used to identify event locations in response to the location requests.
Comparison Of Modeling And Inference Methods At Multiple Spatial Resolutions
Gursharan Singh Sidhu - Seattle WA, US Sindhura Bandhakavi - Redmond WA, US Weili Liu - Redmond WA, US
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06F 15/18
US Classification:
706 12
Abstract:
Embodiments provide a position service experimentation system to enable comparison of modeling and inference methods as well as characterization of input datasets for correspondence to output analytics. Crowd-sourced positioned observations are divided into a training dataset and a test dataset. A beacons model is generated based on the training dataset, while device position estimations are calculated for the test dataset based on the beacons model. The device position estimations are compared to the known position of the computing devices generating the positioned observations to produce accuracy values. The accuracy values are assigned to particular geographic areas based on the position of the observing computing device and aggregated to enable a systematic analysis of the accuracy values based on geographic area and/or positioned observations characteristics.
Embodiments calculate an estimated latency between computing devices. A latency service aggregates latency records defining latency measurements and corresponding latency factors from a plurality of computing devices. From the aggregated latency records, the latency service defines relationships between the latency measurements and the corresponding latency factors. Responsive to a request for an estimated latency from a mobile computing device, the latency service applies the defined relationships to estimate the latency based on the latency factors associated with the received request. In some embodiments, the estimated latency includes three portions: a first latency value representing the latency from the mobile computing device to a cell site, a second latency value representing the latency from the cell site to an access point, and a third latency value representing the latency from the access point to a destination computing device.