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.

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.

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

IPC: H04L29/06 ,  G06N20/00 ,  G06N5/04 ,  G06Q30/02

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.

公开(公告)号：US20240232525A9

公开(公告)日：2024-07-11

申请号：US18048900

申请日：2022-10-24

Applicant: ADOBE INC.

Inventor： Rajiv Bhawanji Jain ,  Michelle Yuan ,  Vlad Ion Morariu ,  Ani Nenkova Nenkova ,  Smitha Bangalore Naresh ,  Nikolaos Barmpalios ,  Ruchi Deshpande ,  Ruiyi Zhang ,  Jiuxiang Gu ,  Varun Manjunatha ,  Nedim Lipka ,  Andrew Marc Greene

IPC: G06F40/20 ,  G06F40/169 ,  G06N3/08

CPC classification number: G06F40/20 ,  G06F40/169 ,  G06N3/08

Abstract: Systems and methods for document classification are described. Embodiments of the present disclosure generate classification data for a plurality of samples using a neural network trained to identify a plurality of known classes; select a set of samples for annotation from the plurality of samples using an open-set metric based on the classification data, wherein the annotation includes an unknown class; and train the neural network to identify the unknown class based on the annotation of the set of samples.

公开(公告)号：US20240135096A1

公开(公告)日：2024-04-25

申请号：US18048900

申请日：2022-10-23

Applicant: ADOBE INC.

Inventor： Rajiv Bhawanji Jain ,  Michelle Yuan ,  Vlad Ion Morariu ,  Ani Nenkova Nenkova ,  Smitha Bangalore Naresh ,  Nikolaos Barmpalios ,  Ruchi Deshpande ,  Ruiyi Zhang ,  Jiuxiang Gu ,  Varun Manjunatha ,  Nedim Lipka ,  Andrew Marc Greene

IPC: G06F40/20 ,  G06F40/169 ,  G06N3/08

CPC classification number: G06F40/20 ,  G06F40/169 ,  G06N3/08

Abstract: Systems and methods for document classification are described. Embodiments of the present disclosure generate classification data for a plurality of samples using a neural network trained to identify a plurality of known classes; select a set of samples for annotation from the plurality of samples using an open-set metric based on the classification data, wherein the annotation includes an unknown class; and train the neural network to identify the unknown class based on the annotation of the set of samples.