Ninthdecimal, Inc.

Inmarket Media LLC

Mark Christopher Dixon

Kevin Toshio Ching

Veronica Milenkiy

Elena Rose

Yury Zhyshko

Keith Kilpatrick

John Raymond Klein

Paolo D'Alberto

Ahmad Fairiz Azizi

Andrew Shapiro

Todd A Rose

Kevin McKenzie

Grigory Sokol

Leonid Blyukher

Robert Abusaidi

Jeff Pittelkau

Luke Gain

David Staas

Partha Roy Chowdhury

Clark Alan Burdick

Art Haedike

Some implementations disclosed herein enable matching identifiers across multiple sources. This may involve adding a unique attribute (e.g., anonymous unique homomorphic identifiers) and/or using randomization to enable comparing data from multiple sources, while also maintaining data privacy. In one example, matches across multiple sources are identified, for example, identifying that there are 100 user identifiers that are in private data sets of three different sources. Such matching may be used to enable private, multi-touch attribution. In another example, techniques are used to determine that data maintained by one source is not also within other sources (e.g., identifying that there are 200 user identifiers that are in data from a first source but not in data from a second source and not in data from a third source. Such determinations may be used to generate control group data that does not match data from other sources.

Multi-touch attribution and control group creation using private commutative encrypted match service

Location graph-based derivation of user attributes. Location data associated with a user, such as a current and/or past location at which the user has been, is received. A user attribute data associated with the location data is determined and used to update a user profile associated with the user.

Location graph based derivation of attributes

Systems and methods to identify a region in which a mobile device is located, by: applying device attributes of the mobile device and region attributes of coordinates of the mobile device to a predictive model to generate an accuracy indicator, identifying a plurality of locations from the coordinates of the mobile device that represent one point and the accuracy indicator, converting coordinates of the locations to cell identifiers of a grid reference system, determining region(s) containing the locations by finding matching cell identifiers that are pre-associated with the region(s), and determining a confidence level of a region that contains at least a portion of the locations based on the weights of locations in the portion. The region is identified in response to a determination that the confidence level is above a threshold.

Systems and methods to evaluate accuracy of locations of mobile devices

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 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

Location graph-based derivation of user attributes is disclosed. In various embodiments, location data associated with a user, such as a current and/or past location at which the user has been, is received. A user attribute data associated with the location data is determined and used to update a user profile associated with the user.

Growing community of inventors

Belmont, CA, United States of America

Mark Christopher Dixon