US 2026/0260262 A1Published application

Using comparative statistical tests to rank content attribute values for a target audience

AssigneeAdobe Inc.

InventorsBiswanath HALDER, Kunal Kumar JAIN

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Abstract

Methods and systems are provided for using comparative statistical tests to rank content attribute values for a target audience. In embodiments described herein, historical content and corresponding historical performance metrics are accessed. Attribute values of content attributes are determined from the historical content. A ranked set of content attributes is determined based on a computed impact of the attribute values of a particular content attribute on the corresponding historical performance metric. A ranked set of attribute values is determined by scoring each attribute value using a comparative statistical test to compare the historical performance metric of each content asset with the attribute value to the historical performance metric of each remaining content asset without the attribute value. The ranked set of content attributes and the ranked set of attribute values are displayed or utilized by a generative artificial intelligence model to generate content.

Claims20

Dependent claims

A computing system comprising:

a processor;
and
a non-transitory computer-readable medium having stored thereon instructions that when executed by the processor, cause the processor to perform operations including:
accessing, by a historical content accessing engine, historical content and a corresponding historical performance metric of each content asset of the historical content;
determining, by a content attribute value extraction engine, corresponding attribute values of content attributes from each content asset of the historical content by:
training a machine learning model to extract at least one of attribute values of text-based content attributes from text-based content, image-based content attributes from image-based content, or video-based content attributes from video-based content;
and
detecting the corresponding attribute values of the content attributes from each content asset of the historical content using the machine learning model;
determining, by a content attribute ranking engine, a ranked set of content attributes by scoring each content attribute based on a computed impact of the corresponding attribute values of a particular content attribute of the content attributes on the corresponding historical performance metric using a weighted sum of an absolute gain score, a Kullback-Leibler (KL) divergence score, and a comparative statistical test score;
determining, by an attribute value ranking engine, a ranked set of attribute values by scoring each attribute value using a comparative statistical test that compares the corresponding historical performance metric of each content asset with the attribute value to the corresponding historical performance metric of each remaining content asset without the attribute value;
and
causing display of the ranked set of content attributes and the ranked set of attribute values.

9 more elements

A non-transitory computer-readable medium storing executable instructions, which when executed by a processing device, cause the processing device to perform operations comprising:

accessing, by a historical content accessing engine, historical content and a corresponding historical performance metric of each content asset of the historical content;
determining, by a content attribute value extraction engine, corresponding attribute values of content attributes from each content asset of the historical content;
determining, by a content attribute ranking engine, a ranked set of content attributes by scoring each content attribute based on a computed impact of the corresponding attribute values of a particular content attribute of the content attributes on the corresponding historical performance metric using a weighted sum of an absolute gain score, a Kullback-Leibler (KL) divergence score, and a comparative statistical test score;
determining, by an attribute value ranking engine, a ranked set of attribute values by scoring each attribute value using a comparative statistical test that compares the corresponding historical performance metric of each content asset with the attribute value to the corresponding historical performance metric of each remaining content asset without the attribute value;
and
causing a generative artificial intelligence model to generate new content by applying content and instructions to generate the new content based on the content, the ranked set of content attributes and the ranked set of attribute values in a prompt to the generative artificial intelligence model.

3 more elements

A computer-implemented method comprising:

accessing, by a historical content accessing engine, historical content and a corresponding historical performance metric of each content asset of the historical content;
determining, by a content attribute value extraction engine, corresponding attribute values of content attributes from each content asset of the historical content;
determining, by a content attribute ranking engine, a ranked set of content attributes by scoring each content attribute based on a computed impact of the corresponding attribute values of a particular content attribute of the content attributes on the corresponding historical performance metric based on a weighted sum of an absolute gain score, a Kullback-Leibler (KL) divergence score, and a comparative statistical test score;
determining, by an attribute value ranking engine, a ranked set of attribute values by scoring each attribute value using a comparative statistical test that compares the corresponding historical performance metric of each content asset with the attribute value to the corresponding historical performance metric of each remaining content asset without the attribute value;
and
causing display of the ranked set of content attributes and the ranked set of attribute values.

3 more elements

Dependencies are inferred from the claim text.