公开(公告)号：US11868417B2

公开(公告)日：2024-01-09

申请号：US17774894

申请日：2019-11-06

Applicant: Google LLC

Inventor： Yew Jin Lim ,  David Adam Faden ,  Mario Tanev ,  Lauren Ashley Koepnick ,  Sagar Gandhi ,  William Ming Zhang

IPC: G06F16/9535 ,  G06F16/9536 ,  G06F16/9538

CPC classification number: G06F16/9535 ,  G06F16/9536 ,  G06F16/9538

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that identify and issue search queries expected to be issued in the future. A set of search queries that have been issued by multiple user devices can be obtained. For each query instance, contextual data can be obtained. A first query and its contextual data can be input to a model that outputs the query's likelihood of being issued in the future. The model can be trained using contextual data for training queries and a corresponding labels for the training queries. The learning model outputs the first query's likelihood of being issued in future, and this query is stored as a repeatable query if the likelihood satisfying a repeatability threshold. Subsequently, a stored repeatable query is issued upon a selection of a user selectable interface component and the search engine provides search results for the query.

公开(公告)号：US11574339B1

公开(公告)日：2023-02-07

申请号：US16705919

申请日：2019-12-06

Applicant: Google LLC

Inventor： Yew Jin Lim ,  James Kunz ,  Randolph Gregory Brown ,  Beidou Wang ,  David Hou ,  Kyle Zaragoza ,  Yi Li ,  Nikita Kirnosov ,  Tao Feng

IPC: G06Q30/00 ,  G06Q30/02 ,  G06N20/00 ,  G06Q50/00

Abstract: Processor(s) of a client device can: analyze one or more features of an electronic resource that is under consideration for solicitation to a user; determine a notification likelihood that the user will access the electronic resource in response to an unsolicited notification of the electronic resource being output to the user; determine a baseline likelihood that the user will access the electronic resource without being solicited; compare the notification likelihood with the baseline likelihood; and cause, based on the comparing, the unsolicited notification to be output to the user. In some implementations, determining the notification likelihood and/or the baseline likelihood is based on applying data associated with the electronic resource as input across a machine learning model to generate output indicative of the notification likelihood and/or the baseline likelihood. In other implementations, determining the notification likelihood and/or the baseline likelihood is based on past behavior or preference(s) of the user.

公开(公告)号：US20220391459A1

公开(公告)日：2022-12-08

申请号：US17774894

申请日：2019-11-06

Applicant: Google LLC

Inventor： Yew Jin Lim ,  David Adam Faden ,  Mario Tanev ,  Lauren Ashley Koepnick ,  Sagar Gandhi ,  William Ming Zhang

IPC: G06F16/9535 ,  G06F16/9536 ,  G06F16/9538

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that identify and issue search queries expected to be issued in the future. A set of search queries that have been issued by multiple user devices can be obtained. For each query instance, contextual data can be obtained. A first query and its contextual data can be input to a model that outputs the query's likelihood of being issued in the future. The model can be trained using contextual data for training queries and a corresponding labels for the training queries. The learning model outputs the first query's likelihood of being issued in future, and this query is stored as a repeatable query if the likelihood satisfying a repeatability threshold. Subsequently, a stored repeatable query is issued upon a selection of a user selectable interface component and the search engine provides search results for the query.

公开(公告)号：US11461342B2

公开(公告)日：2022-10-04

申请号：US17118346

申请日：2020-12-10

Applicant: Google LLC

Inventor： Yew Jin Lim ,  Joseph Linn ,  Yuling Liang ,  Carsten Steinebach ,  Wei Lwun Lu ,  Dong Hyun Kim ,  James Kunz ,  Lauren Koepnick ,  Min Yang

IPC: G06F16/30 ,  G06F16/2457 ,  G06N20/00 ,  G06F16/335 ,  G06F16/9535 ,  G06N5/04

Abstract: A computing system is described that determines, based on user-initiated actions performed by a group of computing devices, an intent of a search using a particular search query received from a computing device. The computing system adjusts, based on the intent, at least a particular portion of search results obtained from the search using the search query by emphasizing information that satisfies the intent. The computing system sends, to the computing device, an indication of the adjusted search results.

公开(公告)号：US20210089548A1

公开(公告)日：2021-03-25

申请号：US17118346

申请日：2020-12-10

Applicant: Google LLC

Inventor： Yew Jin Lim ,  Joseph Linn ,  Yuling Liang ,  Carsten Steinebach ,  Wei Lwun Lu ,  Dong Hyun Kim ,  James Kunz ,  Lauren Koepnick ,  Min Yang

IPC: G06F16/2457 ,  G06N20/00 ,  G06F16/335 ,  G06F16/9535 ,  G06N5/04

Abstract: A computing system is described that determines, based on user-initiated actions performed by a group of computing devices, an intent of a search using a particular search query received from a computing device. The computing system adjusts, based on the intent, at least a particular portion of search results obtained from the search using the search query by emphasizing information that satisfies the intent. The computing system sends, to the computing device, an indication of the adjusted search results.

公开(公告)号：US20190355361A1

公开(公告)日：2019-11-21

申请号：US16530660

申请日：2019-08-02

Applicant: Google LLC

Inventor： Vikram Aggarwal ,  Yew Jin Lim ,  Adam Goldstein

IPC: G10L15/22 ,  G06F17/28 ,  G06F17/27 ,  G10L15/30

Abstract: Methods, apparatus, and computer readable media related to soliciting feedback from a user regarding one or more content parameters of a suggestion or other content provided by the automated assistant. The user's feedback may be used to influence future suggestions and/or other content subsequently provided, by the automated assistant in future dialog sessions, to the user and/or to other users. In some implementations, content is provided to a user by an automated assistant in a dialog session between the user and the automated assistant—and the automated assistant provides a prompt that solicits user feedback related to the provided content in a future dialog session between the user and the automated assistant. In some of those implementations, the prompt is provided following input from the user and/or output from the automated assistant, in the future dialog session, that is unrelated to the content provided in the previous dialog session.