Invention Grant
- Patent Title: Adaptive optimization of a content item using continuously trained machine learning models
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Application No.: US16831627Application Date: 2020-03-26
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Publication No.: US11436634B2Publication Date: 2022-09-06
- Inventor: Yuri Khidekel , Mikhail Zaleshin , Vladimir Bashmakov
- Applicant: Adxcel Inc.
- Applicant Address: US CA Pleasanton
- Assignee: Adxcel Inc.
- Current Assignee: Adxcel Inc.
- Current Assignee Address: US CA Pleasanton
- Agency: Lowenstein Sandler LLP
- Main IPC: G06Q30/02
- IPC: G06Q30/02 ; G06N20/20

Abstract:
A processor receives requests for content items and identifies a first subset of machine learning (ML) models that satisfy a reliability criterion and a second subset of ML models that fail to satisfy the reliability criterion, wherein each ML model is associated with a respective content template and is trained to output a probability that a target associated with an input set of characteristics would perform a target action responsive to being presented with a content item generated based on the respective associated content template. For each request in a first group, the processor inputs the respective set of characteristics associated with the request into each ML model of the first subset, selects a content template, and generates a content item based on the selected content template. For each request in the second group, the processor generates a content item based on a content template associated with the second subset.
Public/Granted literature
- US20210209641A1 ADAPTIVE OPTIMIZATION OF A CONTENT ITEM USING CONTINUOUSLY TRAINED MACHINE LEARNING MODELS Public/Granted day:2021-07-08
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