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：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：US20240086479A1

公开(公告)日：2024-03-14

申请号：US18517509

申请日：2023-11-22

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.