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公开(公告)号:US11689507B2
公开(公告)日:2023-06-27
申请号:US16695636
申请日:2019-11-26
Applicant: Adobe Inc.
Inventor: Nikolaos Barmpalios , Ruchi Rajiv Deshpande , Randy Lee Swineford , Nargol Rezvani , Andrew Marc Greene , Shawn Alan Gaither , Michael Kraley
IPC: H04L9/40 , G06Q30/0202 , G06N5/04 , G06N20/00
CPC classification number: H04L63/04 , G06N5/04 , G06N20/00 , G06Q30/0202
Abstract: Systems and techniques for privacy preserving document analysis are described that derive insights pertaining to a digital document without communication of the content of the digital document. To do so, the privacy preserving document analysis techniques described herein capture visual or contextual features of the digital document and creates a stamp representation that represents these features without included the content of the digital document. The stamp representation is projected into a stamp embedding space based on a stamp encoding model generated through machine learning techniques capturing feature patterns and interaction in the stamp representations. The stamp encoding model exploits these feature interactions to define similarity of source documents based on location within the stamp embedding space. Accordingly, the techniques described herein can determine a similarity of documents without having access to the documents themselves.
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公开(公告)号:US20230336532A1
公开(公告)日:2023-10-19
申请号:US18317338
申请日:2023-05-15
Applicant: Adobe Inc.
Inventor: Nikolaos Barmpalios , Ruchi Rajiv Deshpande , Randy Lee Swineford , Nargol Rezvani , Andrew Marc Greene , Shawn Alan Gaither , Michael Kraley
IPC: H04L9/40 , G06Q30/0202 , G06N5/04 , G06N20/00
CPC classification number: H04L63/04 , G06Q30/0202 , G06N5/04 , G06N20/00
Abstract: Systems and techniques for privacy preserving document analysis are described that derive insights pertaining to a digital document without communication of the content of the digital document. To do so, the privacy preserving document analysis techniques described herein capture visual or contextual features of the digital document and creates a stamp representation that represents these features without included the content of the digital document. The stamp representation is projected into a stamp embedding space based on a stamp encoding model generated through machine learning techniques capturing feature patterns and interaction in the stamp representations. The stamp encoding model exploits these feature interactions to define similarity of source documents based on location within the stamp embedding space. Accordingly, the techniques described herein can determine a similarity of documents without having access to the documents themselves.
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公开(公告)号:US20210160221A1
公开(公告)日:2021-05-27
申请号:US16695636
申请日:2019-11-26
Applicant: Adobe Inc.
Inventor: Nikolaos Barmpalios , Ruchi Rajiv Deshpande , Randy Lee Swineford , Nargol Rezvani , Andrew Marc Greene , Shawn Alan Gaither , Michael Kraley
Abstract: Systems and techniques for privacy preserving document analysis are described that derive insights pertaining to a digital document without communication of the content of the digital document. To do so, the privacy preserving document analysis techniques described herein capture visual or contextual features of the digital document and creates a stamp representation that represents these features without included the content of the digital document. The stamp representation is projected into a stamp embedding space based on a stamp encoding model generated through machine learning techniques capturing feature patterns and interaction in the stamp representations. The stamp encoding model exploits these feature interactions to define similarity of source documents based on location within the stamp embedding space. Accordingly, the techniques described herein can determine a similarity of documents without having access to the documents themselves.
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