Invention Application
- Patent Title: ARTIFICIAL INTELLIGENCE APPROACHES FOR PREDICTING CONVERSION ACTIVITY PROBABILITY SCORES AND KEY PERSONAS FOR TARGET ENTITIES
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Application No.: US17339700Application Date: 2021-06-04
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Publication No.: US20220394337A1Publication Date: 2022-12-08
- Inventor: Liana Vagharshakian , Atanu R. Sinha , Camille Girabawe , Gautam Choudhary , Omar Rahman , Scott Trafton , Vivek Sinha
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Main IPC: H04N21/466
- IPC: H04N21/466 ; H04N21/45 ; H04N21/258

Abstract:
The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and efficiently predicting conversion probability scores and key personas for target entities utilizing an artificial intelligence approach. For example, the disclosed systems utilize a conversion activity score neural network to predict conversion activity probability scores for target entities and utilize a persona prediction machine learning model to predict key personas associated with target entities. In particular, the disclosed systems utilize the conversion activity score neural network to generate a predicted conversion activity probability score for a target entity from input data including client device interactions of digital profiles belonging to the target entity as well as an entity feature vector representing characteristics of the target entity. The disclosed systems also (or alternatively) utilize a persona prediction machine learning model to determine a set of key personas for the target entity from the entity feature vector.
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