发明申请
- 专利标题: MACHINE LEARNING BASED EXTRACTION OF PARTITION OBJECTS FROM ELECTRONIC DOCUMENTS
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申请号: US16790945申请日: 2020-02-14
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公开(公告)号: US20200327373A1公开(公告)日: 2020-10-15
- 发明人: Dan G. TECUCI , Ravi Kiran Reddy PALLA , Hamid Reza Motahari NEZHAD , Vincent POON , Nigel Paul DUFFY , Joseph NIPKO
- 申请人: Ernst & Young U.S. LLP
- 申请人地址: US NY New York
- 专利权人: Ernst & Young U.S. LLP
- 当前专利权人: Ernst & Young U.S. LLP
- 当前专利权人地址: US NY New York
- 主分类号: G06K9/62
- IPC分类号: G06K9/62 ; G06K9/00 ; G06N3/02 ; G06N20/20 ; G06F3/0482
摘要:
An object-extraction method includes generating multiple partition objects based on an electronic document, and receiving a first user selection of a data element via a user interface of a compute device. In response to the first user selection, and using a machine learning model, a first subset of partition objects from the multiple partition objects is detected and displayed via the user interface. A user interaction, via the user interface, with one of the partition objects is detected, and in response, a weight of the machine learning model is modified, to produce a modified machine learning model. A second user selection of the data element is received via the user interface, and in response and using the modified machine learning model, a second subset of partition objects from the multiple partition objects is detected and displayed via the user interface, the second subset different from the first subset.
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