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US 2024/0177191 A1Published application
Cross-Platform Resource Optimization
AssigneeOracle International Corporation
InventorsJason Canney, Richard Martin Berger, Megan Margraff, Frank Appiah, Hilary Joy Traut, Andrew John Grabowski
Life of this patent
Estimating the term…
Abstract
Techniques for determining recommended allocations of resources among different platforms that sell a common type of inventory. Determining the allocations can include obtaining parameters of a campaign from a client. Determining the allocations can include combining current campaign parameters and scoring with historical campaign performance data to create recommendations for dividing resources among different media platforms.
Claims24
A non-transitory computer readable medium comprising instructions which, when executed by one or more hardware processors, causes performance of operations comprising:
- obtaining a set of historical data points, associated with a target demographic for a plurality of advertising campaigns, based on historical platform impressions generated for a plurality of tenants across a plurality of media platforms, individual data points of the set of data points comprising:
- a first portion of a particular resource used for generating platform impressions on a first media platform of the plurality of media platforms;
- a first quantity of impressions generated on the first media platform based on the first portion of the particular resource;
- a second portion of the particular resource used for generating impressions on a second media platform of the plurality of media platforms;
- and
- a second quantity of impressions generated on the second media platform based on the second portion of the particular resource;
- determining historical weighted values of the set of historical data points, wherein determining the weighted values comprises:
- applying a first weight for the first media platform to the first quantity of impressions to generate a first weighted value;
- applying a second weight for the second media platform to the second quantity of impressions to generate a second weighted value;
- obtaining historical effectiveness information corresponding to the plurality of advertising campaigns;
- identifying a plurality of sets of training data points using the historical weighted values of the set of the set of historical data points and historical effectiveness information;
- training a machine leaning model based on the plurality of sets of training data points to compute effectiveness values for advertising campaigns;
- obtaining a candidate set of data points corresponding to a plurality of candidate advertising campaigns;
- computing a particular set of weighted values based on the candidate set of data points;
- computing, by the machine leaning model, effectiveness values for the individual data points of the candidate set of data points based on the particular set of weighted values;
- determining a maximum of the effectiveness values;
- selecting a target data point of the candidate set of data points associated with the maximum of the effectiveness values;
- communicating recommended divisions of a second resource for allocation among the plurality of media platforms based on the target data point associated with the maximum of the effectiveness values;
- and
- updating the machine learning model based on feedback corresponding to the effectiveness values computed for at least one of the target set of data points.
17 more elements
A method comprising:
- obtaining a set of historical data points, associated with a target demographic for a plurality of advertising campaigns, based on historical platform impressions generated for a plurality of tenants across a plurality of media platforms, individual data points of the set of data points comprising:
- a first portion of a particular resource used for generating platform impressions on a first media platform of the plurality of media platforms;
- a first quantity of impressions generated on the first media platform based on the first portion of the particular resource;
- a second portion of the particular resource used for generating impressions on a second media platform of the plurality of media platforms;
- and
- a second quantity of impressions generated on the second media platform based on the second portion of the particular resource;
- determining historical weighted values of the set of historical data points, wherein determining the weighted values comprises:
- applying a first weight for the first media platform to the first quantity of impressions to generate a first weighted value;
- applying a second weight for the second media platform to the second quantity of impressions to generate a second weighted value;
- obtaining historical effectiveness information corresponding to the plurality of advertising campaigns;
- identifying a plurality of sets of training data points using the historical weighted values of the set of the set of historical data points and historical effectiveness information;
- training a machine leaning model based on the plurality of sets of training data points to compute effectiveness values for advertising campaigns;
- obtaining a candidate set of data points corresponding to a plurality of candidate advertising campaigns;
- computing a particular set of weighted values based on the candidate set of data points;
- computing, by the machine leaning model, effectiveness values for the individual data points of the candidate set of data points based on the particular set of weighted values;
- determining a maximum of the effectiveness values;
- selecting a target data point of the candidate set of data points associated with the maximum of the effectiveness values;
- communicating recommended divisions of a second resource for allocation among the plurality of media platforms based on the target data point associated with the maximum of the effectiveness values;
- and
- updating the machine learning model based on feedback corresponding to the effectiveness values computed for at least one of the target set of data points.
17 more elements
A system comprising a processor and a computer-readable data storage device storing program instructions that, when executed by the processor, control the system to perform operations comprising:
- obtaining a set of historical data points, associated with a target demographic for a plurality of advertising campaigns, based on historical platform impressions generated for a plurality of tenants across a plurality of media platforms, individual data points of the set of data points comprising:
- a first portion of a particular resource used for generating platform impressions on a first media platform of the plurality of media platforms;
- a first quantity of impressions generated on the first media platform based on the first portion of the particular resource;
- a second portion of the particular resource used for generating impressions on a second media platform of the plurality of media platforms;
- and
- a second quantity of impressions generated on the second media platform based on the second portion of the particular resource;
- determining historical weighted values of the set of historical data points, wherein determining the weighted values comprises:
- applying a first weight for the first media platform to the first quantity of impressions to generate a first weighted value;
- applying a second weight for the second media platform to the second quantity of impressions to generate a second weighted value;
- obtaining historical effectiveness information corresponding to the plurality of advertising campaigns;
- identifying a plurality of sets of training data points using the historical weighted values of the set of the set of historical data points and historical effectiveness information;
- training a machine leaning model based on the plurality of sets of training data points to compute effectiveness values for advertising campaigns;
- obtaining a candidate set of data points corresponding to a plurality of candidate advertising campaigns;
- computing a particular set of weighted values based on the candidate set of data points;
- computing, by the machine leaning model, effectiveness values for the individual data points of the candidate set of data points based on the particular set of weighted values;
- determining a maximum of the effectiveness values;
- selecting a target data point of the candidate set of data points associated with the maximum of the effectiveness values;
- communicating recommended divisions of a second resource for allocation among the plurality of media platforms based on the target data point associated with the maximum of the effectiveness values;
- and
- updating the machine learning model based on feedback corresponding to the effectiveness values computed for at least one of the target set of data points.
17 more elements
Dependencies are inferred from the claim text.