Invention Grant
- Patent Title: Utilizing machine-learning models to create target audiences with customized auto-tunable reach and accuracy
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Application No.: US17820346Application Date: 2022-08-17
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Publication No.: US11620683B2Publication Date: 2023-04-04
- Inventor: Lei Liu , Hunter North
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Agency: Keller Preece PLLC
- Main IPC: G06Q30/02
- IPC: G06Q30/02 ; G06Q30/0251 ; G06Q30/0204 ; G06N20/00 ; G06F18/214 ; G06F18/243

Abstract:
This disclosure describes one or more implementations of a model segmentation system that generates accurate audience segments for client devices/individuals utilizing multi-class decision tree machine-learning models. For example, in various implementations, the model segmentation system generates a customized loss penalty matrix from multiple loss penalty matrices. In particular, the model segmentation system can generate regression mappings of model evaluation metrics for a plurality of decision tree models and combine loss penalty matrices based on the regression mappings to generate a customized loss penalty matrix that best fits an administrator's customized needs of segment accuracy and reach. The model segmentation system then utilizes the customized loss penalty matrix to train a multi-class decision tree machine-learning model to classify client devices into non-overlapping audience segments. Further, in one or more implementations, the model segmentation system refines the multi-class decision tree machine-learning model based on adjusting the tree depth.
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