公开(公告)号：US20230409824A1

公开(公告)日：2023-12-21

申请号：US17836816

申请日：2022-06-09

Applicant: Google LLC

Inventor： Nishir Shelat ,  Tim Sears ,  Tanuj Sharma ,  Srivatsan Narayanan ,  Shruti Jain ,  Luiz Franca Pereira Filho ,  Kashish Bansal ,  Julian Rajeshwar ,  Chris Terefinko ,  Asim Fazal ,  Archit Gupta

IPC: G06F40/197 ,  G06F40/166 ,  G06F40/12 ,  G06F16/93 ,  G06Q10/10

CPC classification number: G06F40/197 ,  G06F40/166 ,  G06Q10/101 ,  G06F16/93 ,  G06F40/12

Abstract: Techniques are described herein for using operational transforms to perform operations on parallel copies of a document model. A method includes: determining that a first operation is to be performed on a second parallel copy; and in response: determining that a revision of a first parallel copy matches a revision of the second parallel copy; and in response: performing the first operation on the second parallel copy to obtain a calculation result including a first list of commands; applying the first list of commands to the second parallel copy; performing an operational transform on at least one command in the first list of commands based on queued user edits to the first parallel copy, the queued user edits including a second list of commands, to obtain a transformed list of commands; and applying the transformed list of commands to the first parallel copy.

公开(公告)号：US20230162091A1

公开(公告)日：2023-05-25

申请号：US18070195

申请日：2022-11-28

Applicant: GOOGLE LLC

Inventor： Archit Gupta ,  Hariharan Chandrasekaran ,  Harish Chandran

IPC: G06N20/00 ,  G06F16/783 ,  G06F16/33 ,  G06F16/9536 ,  G06F16/583 ,  G06N7/01 ,  G06N5/02

CPC classification number: G06N20/00 ,  G06F16/783 ,  G06F16/334 ,  G06F16/9536 ,  G06F16/583 ,  G06N7/01 ,  G06N5/02

Abstract: Training and/or utilizing a machine learning model to generate request agnostic predicted interaction scores for electronic communications, and to utilization of request agnostic predicted interaction scores in determining whether, and/or how, to provide corresponding electronic communications to a client device in response to a request. A request agnostic predicted interaction score for an electronic communication provides an indication of quality of the communication, and is generated independent of corresponding request(s) for which it is utilized. In many implementations, a request agnostic predicted interaction score for an electronic communication is generated “offline” relative to corresponding request(s) for which it is utilized, and is pre-indexed with (or otherwise assigned to) the electronic communication. This enables fast and efficient retrieval, and utilization, of the request agnostic interaction score by computing device(s), when the electronic communication is responsive to a request.

公开(公告)号：US11514353B2

公开(公告)日：2022-11-29

申请号：US15795204

申请日：2017-10-26

Applicant: Google LLC

Inventor： Archit Gupta ,  Hariharan Chandrasekaran ,  Harish Chandran

IPC: G06N5/02 ,  G06N7/00 ,  G06N20/00 ,  G06F16/783 ,  G06F16/33 ,  G06F16/9536 ,  G06F16/583

Abstract: Training and/or utilizing a machine learning model to generate request agnostic predicted interaction scores for electronic communications, and to utilization of request agnostic predicted interaction scores in determining whether, and/or how, to provide corresponding electronic communications to a client device in response to a request. A request agnostic predicted interaction score for an electronic communication provides an indication of quality of the communication, and is generated independent of corresponding request(s) for which it is utilized. In many implementations, a request agnostic predicted interaction score for an electronic communication is generated “offline” relative to corresponding request(s) for which it is utilized, and is pre-indexed with (or otherwise assigned to) the electronic communication. This enables fast and efficient retrieval, and utilization, of the request agnostic interaction score by computing device(s), when the electronic communication is responsive to a request.

公开(公告)号：US20190130304A1

公开(公告)日：2019-05-02

申请号：US15795204

申请日：2017-10-26

Applicant: Google LLC

Inventor： Archit Gupta ,  Hariharan Chandrasekaran ,  Harish Chandran

IPC: G06N99/00 ,  G06N5/02 ,  G06N7/00

Abstract: Training and/or utilizing a machine learning model to generate request agnostic predicted interaction scores for electronic communications, and to utilization of request agnostic predicted interaction scores in determining whether, and/or how, to provide corresponding electronic communications to a client device in response to a request. A request agnostic predicted interaction score for an electronic communication provides an indication of quality of the communication, and is generated independent of corresponding request(s) for which it is utilized. In many implementations, a request agnostic predicted interaction score for an electronic communication is generated “offline” relative to corresponding request(s) for which it is utilized, and is pre-indexed with (or otherwise assigned to) the electronic communication. This enables fast and efficient retrieval, and utilization, of the request agnostic interaction score by computing device(s), when the electronic communication is responsive to a request.

公开(公告)号：US11941355B2

公开(公告)日：2024-03-26

申请号：US17836816

申请日：2022-06-09

Applicant: Google LLC

Inventor： Nishir Shelat ,  Tim Sears ,  Tanuj Sharma ,  Srivatsan Narayanan ,  Shruti Jain ,  Luiz Franca Pereira Filho ,  Kashish Bansal ,  Julian Rajeshwar ,  Chris Terefinko ,  Asim Fazal ,  Archit Gupta

IPC: G06F17/00 ,  G06F16/93 ,  G06F40/12 ,  G06F40/166 ,  G06F40/197 ,  G06Q10/101

CPC classification number: G06F40/197 ,  G06F16/93 ,  G06F40/12 ,  G06F40/166 ,  G06Q10/101

Abstract: Techniques are described herein for using operational transforms to perform operations on parallel copies of a document model. A method includes: determining that a first operation is to be performed on a second parallel copy; and in response: determining that a revision of a first parallel copy matches a revision of the second parallel copy; and in response: performing the first operation on the second parallel copy to obtain a calculation result including a first list of commands; applying the first list of commands to the second parallel copy; performing an operational transform on at least one command in the first list of commands based on queued user edits to the first parallel copy, the queued user edits including a second list of commands, to obtain a transformed list of commands; and applying the transformed list of commands to the first parallel copy.