Determining targeting information based on a predictive targeting model
First Claim
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1. A method for use by at least one data processing device, the method comprising:
- receiving, from a client device, targeting criteria that comprises at least a targeted behavior and an identification of a geographic region;
segmenting the geographic region identified by the targeting criteria into a three-dimensional grid that comprises a plurality of cells based on latitude data, longitude data, and altitude data, the plurality of cells including a cell that encompasses a location within the region, the cell comprising a cell identifier;
receiving behavioral information associated with at least a user, wherein the behavioral information includes location data that comprises at least device altitude data, and temporal data, the location data identifying the location encompassed by the cell;
calculating a behavior match metric for the cell based on the behavioral information projected onto the geographic region, the behavior match metric indicating a number of visits by the user to the location represented by the cell;
assigning the behavioral match metric to a key that comprises a tuple that comprises the cell identifier of the cell and a user identifier of the user;
retrieving feature data for the cell, the feature data indicating attributes of the location encompassed by the cell;
generating a labeled feature vector table based on the key and the attributes of the location encompassed by the cell;
training a model for predicting a conversion rate of the cell based on the labeled feature vector table, wherein the conversion rate provides a probability of the user performing the targeted behavior within the cell;
applying the model to the feature data to predict the conversion rate of the cell; and
presenting targeting information based on the conversion rate of the cell to the client device.
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Abstract
A targeting system based on a predictive targeting model based on observed behavioral data including visit data, user profile and/or survey data, and geographic features associated with a geographic region. The predictive targeting model analyzes the observed behavioral data and the geographic features data to predict conversion rates for every cell in a square grid of predefined size on the geographic region. The conversion rate of a cell indicates a likelihood that any random user in that cell will perform a targeted behavior.
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Citations
20 Claims
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1. A method for use by at least one data processing device, the method comprising:
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receiving, from a client device, targeting criteria that comprises at least a targeted behavior and an identification of a geographic region; segmenting the geographic region identified by the targeting criteria into a three-dimensional grid that comprises a plurality of cells based on latitude data, longitude data, and altitude data, the plurality of cells including a cell that encompasses a location within the region, the cell comprising a cell identifier; receiving behavioral information associated with at least a user, wherein the behavioral information includes location data that comprises at least device altitude data, and temporal data, the location data identifying the location encompassed by the cell; calculating a behavior match metric for the cell based on the behavioral information projected onto the geographic region, the behavior match metric indicating a number of visits by the user to the location represented by the cell; assigning the behavioral match metric to a key that comprises a tuple that comprises the cell identifier of the cell and a user identifier of the user; retrieving feature data for the cell, the feature data indicating attributes of the location encompassed by the cell; generating a labeled feature vector table based on the key and the attributes of the location encompassed by the cell; training a model for predicting a conversion rate of the cell based on the labeled feature vector table, wherein the conversion rate provides a probability of the user performing the targeted behavior within the cell; applying the model to the feature data to predict the conversion rate of the cell; and presenting targeting information based on the conversion rate of the cell to the client device. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A system comprising:
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memory; at least one processor in communication with the memory and configured to execute a plurality of instructions stored in the memory to; receive, from a client device, targeting criteria that comprises at least a targeted behavior and an identification of a geographic region; segment the geographic region identified by the targeting criteria into a three-dimensional grid that comprises a plurality of cells based on latitude data, longitude data, and altitude data, the plurality of cells including a cell that encompasses a location within the region, the cell comprising a cell identifier; receive behavioral information associated with at least a user, wherein the behavioral information includes location data that comprises at least device altitude data, and temporal data, the location data identifying the location encompassed by the cell; calculate a behavior match metric for the cell based on the behavioral information projected onto the geographic region, the behavior match metric indicating a number of visits by the user to the location represented by the cell; assign the behavioral match metric to a key that comprises a tuple that comprises the cell identifier of the cell and a user identifier of the user; retrieve feature data for the cell, the feature data indicating attributes of the location encompassed by the cell; generate a labeled feature vector table based on the key and the attributes of the location encompassed by the cell; train a model for predicting a conversion rate of the cell based on the labeled feature vector table, the conversion rate providing a probability of the user performing the targeted behavior within the cell; apply the model to the feature data to predict the conversion rate of the cell; and present targeting information based on the conversion rate of the cell to the client device. - View Dependent Claims (18)
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19. A non-transitory computer-readable medium storing computer-executable instructions that cause a machine to perform operations comprising:
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receiving, from a client device, targeting criteria that comprises at least a targeted behavior and an identification of a geographic region; segmenting the geographic region identified by the targeting criteria into a three-dimensional grid that comprises a plurality of cells based on latitude data, longitude data, and altitude data, the plurality of cells including a cell that encompasses a location within the region, the cell comprising a cell identifier; receiving behavioral information associated with at least a user, wherein the behavioral information includes location data that comprises at least device altitude data, and temporal data, the location data identifying the location encompassed by the cell; calculating a behavior match metric for the cell based on the behavioral information projected onto the geographic region, the behavior match metric indicating a number of visits by the user to the location represented by the cell; assigning the behavioral match metric to a key that comprises a tuple that comprises the cell identifier of the cell and a user identifier of the user; retrieving feature data for the cell, the feature data indicating attributes of the location encompassed by the cell; generating a labeled feature vector table based on the key and the attributes of the location encompassed by the cell; training a model for predicting a conversion rate of the cell based on the labeled feature vector table, wherein the conversion rate provides a probability of the user performing the targeted behavior within the cell; applying the model to the feature data to predict the conversion rate of the cell; and presenting targeting information based on the conversion rate of the cell to the client device. - View Dependent Claims (20)
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Specification