Invention Grant
- Patent Title: De-conflicting data labeling in real time deep learning systems
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Application No.: US16210584Application Date: 2018-12-05
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Publication No.: US11610110B2Publication Date: 2023-03-21
- Inventor: Eren Kursun , William David Kahn
- Applicant: BANK OF AMERICA CORPORATION
- Applicant Address: US NC Charlotte
- Assignee: BANK OF AMERICA CORPORATION
- Current Assignee: BANK OF AMERICA CORPORATION
- Current Assignee Address: US NC Charlotte
- Agency: Moore & Van Allen PLLC
- Agent Anup Shrinivasan Iyer
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N3/04 ; G06N3/042

Abstract:
Systems, computer program products, and methods are described herein for de-conflicting data labeling in real-time deep learning systems. The present invention is configured to retrieve one or more dynamically generated expert profiles; and determine an optimal expert mix of experts to classify the transaction into a transaction types, wherein the expert profiles comprises: (i) shared information metrics, (ii) divergence metrics, (iii) characteristics associated with the one or more experts, (iv) a predictive accuracy of the one or more experts, (v) an exposure score associated with the one or more experts, and (vi) information associated with the transaction, wherein the optimal expert mix comprises: (i) a best expert for classifying the transaction, (ii) a combination score from at least the portion of the one or more experts evaluating the transaction simultaneously, and (iii) a sequence of at least the portion of the one or more experts analyzing the transaction.
Public/Granted literature
- US20200184326A1 DE-CONFLICTING DATA LABELING IN REAL TIME DEEP LEARNING SYSTEMS Public/Granted day:2020-06-11
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