Ninthdecimal, Inc.

Veronica Milenkiy

Mark Christopher Dixon

Kevin Toshio Ching

Paolo D'Alberto

Elena Rose

Ahmad Fairiz Azizi

Keith Kilpatrick

Yury Zhyshko

Andrew Shapiro

Todd A Rose

Systems and methods including mobile devices determining their locations using location determination units, such as GPS receivers. A computing device generates a statistical measurement of location pattern changes in relation to a predetermined region for a predetermined period of time. The measurement is generated from a difference between responses of exposed mobile devices and responses of non-exposed mobile devices, which devices are selected based on a cluster analysis to match the distributions of the exposed mobile devices and the non-exposed mobile devices in an attribute space. Each response of a mobile device can be computed based on applying, at a time instance, an anti-symmetric weight function having an exponential distribution to a time series of visitation measurements to generate a response corresponding to the time instance, and summing the responses corresponding to time instances falling within the predetermined period of time as the response of the mobile device.

Systems and methods of tracking locations visited by mobile devices to quantify a change computed based on matching populations used in change measurement

Systems and methods including mobile devices determining their locations using a location determination system, such as a global positioning system. A set of locations, including locations of one or more mobile devices, are identified by their coordinates on the surface of the Earth. The set of locations are efficiently organized into a graph of locations connecting to neighboring locations with edges representing distances to their neighboring locations. For each respective location, a computing device combines coordinates of the respective location into an identifier of a cell that contains the respective location without floating point computations, and stores cell-location data associating respective cells with respective locations. For each respective location, the computing device identifies neighboring cells of the cell that contains the respective location, looks up locations associated with the identifiers of the cell and its neighboring cells, as neighboring locations or candidates for neighboring locations.

Systems and methods to track locations visited by mobile devices and determine neighbors of and distances among locations

Systems and methods including mobile devices determining their location using a location determination system, such as a global positioning system. A database stores the identifiers of cells representative of predefined regions in a hierarchical grid system. The location coordinates as function of time are converted to visitation patterns of the mobile devices to the predefined regions, based on converting the coordinates to cell identifiers and searching the database to find matches between the cell identifiers representing the regions and the cell identifiers representing the mobile device locations. For example, mobile devices having similar visitation patterns can be identified and divided into two groups; and information relevant to a region is transmitted to one of the group but not another. The visitation patterns of the two group after the transmission is identified and compared to measure a difference.

Systems and methods to track regions visited by mobile devices and detect changes in location patterns

Systems and methods including mobile devices determining their locations using location determination units, such as GPS receivers. A computing device generates a statistical measurement of location pattern changes in relation to a predetermined region for a predetermined period of time. The measurement is generated from a difference between the average response of exposed mobile devices and the average response of non-exposed mobile devices, where each of the exposed/non-exposed mobile devices is selected for having a home location or a visitation pattern within an activity area associated with the predetermined region, and each response of a mobile device is based on determining whether locations of the mobile device is within the predetermined region and counts of visitations to the predetermined region by the mobile device in different time intervals.

Systems and methods of tracking locations visited by mobile devices residing within a predetermined activity area to quantify a change

Systems and methods including a database storing the identifiers of cells representing residential parcels of real estate properties. Mobile devices determine the coordinates of their locations during a period of time using a location determination system, such as a global positioning system. The coordinates are converted to cell identifiers to look up corresponding residential parcels that have been visited by the mobile devices. A server generates a visitation data set for each residential parcel visited by each mobile device, including different types of frequencies of the mobile device visiting the residential parcel (e.g., night, weekend). A server filters the residential parcels based on visitation frequencies to identify home candidates and then further filters the home candidates based on the count of mobile devices having the home candidates. A home parcel, and thus its address, is identified from the filtered home candidate(s) for each mobile device.

Systems and methods to identify home addresses of mobile devices

Systems and methods including mobile devices determining their locations using location determination units, such as GPS receivers. A computing device generates a statistical measurement of location pattern changes in relation to a predetermined region for a predetermined period of time. The measurement is generated from a difference between the average response of exposed mobile devices and the average response of non-exposed mobile devices, where each response of a mobile device is computed by: determining a sequence of time instances of the mobile devices being at locations within the region, generating a time series of visitation measurements from the time sequence, applying at a time instance an anti-symmetric weight function having an exponential distribution to the time series to generate the response corresponding to the time instance, and summing the responses corresponding to time instances falling within the predetermined period of time as the response of the mobile device.

Systems and methods of tracking locations visited by mobile devices to quantify a change from a time series of responses

Systems and methods including computing patterns of visitation to predefined regions by converting coordinates of mobile devices to cell identifiers and searching a database to find matches between cell identifiers representing regions and the cell identifiers of the device locations. Devices having similar visitation patterns are divided into two groups, based on status data of whether or not information about a region has been transmitted to respective users. The visitation patterns after the transmission are compared by a system between the two groups to measure a difference. Identifiers of devices, users, or households are initially provided to the system without the status data to obtain attributes associated individually with the identifiers. In return, the system is provided with the status data associated with the attributes individually for the devices, users, or households but without sufficient information to map the status data individually to the identifiers of the devices, users, or households.

Systems and methods to track regions visited by mobile devices and detect changes in location patterns based on integration of data from different sources

Systems and methods including mobile devices determining their locations using location determination units, such as GPS receivers. Locations of the mobile devices and predefined locations/regions that have been visited by the mobile devices during a period of time are efficiently organized into a location graph that identifies the neighbors of predefined locations, distances to neighbors, and visitation counts of mobile devices to the predefined locations during the period of time. Keyword values for keywords representing user attributes are assigned to mobile devices and the predefined locations. Based on the location graph, the keyword values are propagated via the location graph, from neighbors to predefined locations, from mobile devices to predefined locations, and from predefined locations to mobile devices, to incrementally improve the accuracy of the keyword values over a number of time periods.

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Burlingame, CA, United States of America

Veronica Milenkiy