Invention Grant
- Patent Title: Learning apparatus, inferring apparatus, computer program, computer system, inferring method, and learning method
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Application No.: US17247063Application Date: 2020-11-25
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Publication No.: US11769221B2Publication Date: 2023-09-26
- Inventor: Shuji Okuno
- Applicant: AXELL CORPORATION
- Applicant Address: JP Tokyo
- Assignee: AXELL CORPORATION
- Current Assignee: AXELL CORPORATION
- Current Assignee Address: JP Tokyo
- Agency: MASUVALLEY & PARTNERS
- Agent Peter R. Martinez
- Priority: JP 20042563 2020.03.12 JP 20106917 2020.06.22
- Main IPC: G06T1/20
- IPC: G06T1/20 ; G06N3/08 ; G06T3/40 ; G06V10/82 ; G06V10/26 ; G06V20/70 ; G06N5/04 ; G06F18/214

Abstract:
To provide a learning apparatus and an inferring apparatus that can prevent problems such that a computation amount increases, efficiency of a learning process decreases, and an inferring result does not have high accuracy.
A learning apparatus and an inferring apparatus include a predictable area determining unit that determines whether target data has a predictable area in which an inferring result of an inferring process can be easily predicted. In the learning apparatus and the inferring apparatus, predetermined data processing is performed on a data area that is determined to be a predictable area, data required to infer an area that is not a predictable area is output to a machine learning model, and data processing is performed in an average time shorter than that of the inferring process.
A learning apparatus and an inferring apparatus include a predictable area determining unit that determines whether target data has a predictable area in which an inferring result of an inferring process can be easily predicted. In the learning apparatus and the inferring apparatus, predetermined data processing is performed on a data area that is determined to be a predictable area, data required to infer an area that is not a predictable area is output to a machine learning model, and data processing is performed in an average time shorter than that of the inferring process.
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