Invention Grant
- Patent Title: Deep learning model for learning program embeddings
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Application No.: US17423103Application Date: 2019-10-01
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Publication No.: US11900250B2Publication Date: 2024-02-13
- Inventor: Ke Wang
- Applicant: Visa International Service Association
- Applicant Address: US CA San Francisco
- Assignee: Visa International Service Association
- Current Assignee: Visa International Service Association
- Current Assignee Address: US CA San Francisco
- Agency: Kilpatrick Townsend & Stockton LLP
- International Application: PCT/US2019/054075 2019.10.01
- International Announcement: WO2020/149897A 2020.07.23
- Date entered country: 2021-07-14
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06F8/41 ; G06F8/36 ; G06N20/00 ; G06N3/044 ; G06N3/045 ; G06F11/36 ; G06F11/34

Abstract:
A system and method for using a deep learning model to learn program semantics is disclosed. The method includes receiving a plurality of execution traces of a program, each execution trace comprising a plurality of variable values. The plurality of variable values are encoded by a first recurrent neural network to generate a plurality of program states for each execution trace. A bi-directional recurrent neural network can then determine a reduced set of program states for each execution trace from the plurality of program states. The reduced set of program states are then encoded by a second recurrent neural network to generate a plurality of executions for the program. The method then includes pooling the plurality of executions to generate a program embedding and predicting semantics of the program using the program embedding.
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