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公开(公告)号:US11886828B1
公开(公告)日:2024-01-30
申请号:US18236760
申请日:2023-08-22
Applicant: GOOGLE LLC
Inventor: Matthew K. Gray , John Blitzer , Corinn Herrick , Srinivasan Venkatachary , Jayant Madhavan , Sam Oates , Phiroze Parakh , Aditya Shah , Mahsan Rofouei , Ibrahim Badr
IPC: G06F40/40 , G06F16/332
CPC classification number: G06F40/40 , G06F16/3328
Abstract: At least selectively utilizing a large language model (LLM) in generating a natural language (NL) based summary to be rendered in response to a query. In some implementations, in generating the NL based summary additional content is processed using the LLM. The additional content is in addition to query content of the query itself and, in generating the NL based summary, can be processed using the LLM and along with the query content—or even independent of the query content. Processing the additional content can, for example, mitigate occurrences of the NL based summary including inaccuracies and/or can mitigate occurrences of the NL based summary being over-specified and/or under-specified.
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公开(公告)号:US10180964B1
公开(公告)日:2019-01-15
申请号:US14824654
申请日:2015-08-12
Applicant: Google LLC
Inventor: Steven D. Baker , Srinivasan Venkatachary , Robert Andrew Brennan , Per Bjornsson , Yi Liu , Nitin Gupta , Diego Federici , Lingkun Chu
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating candidate answer passages. In one aspect, a method includes receiving a query determined to be a question query data identifying resources determined to be responsive to the query; for each resource in a top-ranked subset of the resources: identifying a plurality of passage units in the resource; applying a set of passage unit selection criterion to the passage units, each passage unit selection criterion specifying a condition for inclusion of a passage unit in a candidate answer passage, wherein a first subset of passage unit selection criteria applies to structured content and a second subset of passage unit selection criteria applies to unstructured content; and generating, from passage units that satisfy the set of passage unit selection criterion, a set of candidate answer passages.
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公开(公告)号:US20250005303A1
公开(公告)日:2025-01-02
申请号:US18829990
申请日:2024-09-10
Applicant: GOOGLE LLC
Inventor: Matthew K. Gray , John Blitzer , Corinn Herrick , Srinivasan Venkatachary , Jayant Madhavan , Sam Oates , Phiroze Parakh , Aditya Shah , Mahsan Rofouei , Ibrahim Badr
IPC: G06F40/40 , G06F16/332
Abstract: At least selectively utilizing a large language model (LLM) in generating a natural language (NL) based summary to be rendered in response to a query. In some implementations, in generating the NL based summary additional content is processed using the LLM. The additional content is in addition to query content of the query itself and, in generating the NL based summary, can be processed using the LLM and along with the query content—or even independent of the query content. Processing the additional content can, for example, mitigate occurrences of the NL based summary including inaccuracies and/or can mitigate occurrences of the NL based summary being over-specified and/or under-specified.
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公开(公告)号:US12118325B2
公开(公告)日:2024-10-15
申请号:US18232144
申请日:2023-08-09
Applicant: GOOGLE LLC
Inventor: Matthew K. Gray , John Blitzer , Corinn Herrick , Srinivasan Venkatachary , Jayant Madhavan , Sam Oates , Phiroze Parakh , Aditya Shah , Mahsan Rofouei , Ibrahim Badr
IPC: G06F40/40 , G06F16/332
CPC classification number: G06F40/40 , G06F16/3328
Abstract: At least selectively utilizing a large language model (LLM) in generating a natural language (NL) based summary to be rendered in response to a query. In some implementations, in generating the NL based summary additional content is processed using the LLM. The additional content is in addition to query content of the query itself and, in generating the NL based summary, can be processed using the LLM and along with the query content—or even independent of the query content. Processing the additional content can, for example, mitigate occurrences of the NL based summary including inaccuracies and/or can mitigate occurrences of the NL based summary being over-specified and/or under-specified.
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公开(公告)号:US20240220735A1
公开(公告)日:2024-07-04
申请号:US18232144
申请日:2023-08-09
Applicant: GOOGLE LLC
Inventor: Matthew K. Gray , John Blitzer , Corinn Herrick , Srinivasan Venkatachary , Jayant Madhavan , Sam Oates , Phiroze Parakh , Aditya Shah , Mahsan Rofouei , Ibrahim Badr
IPC: G06F40/40 , G06F16/332
CPC classification number: G06F40/40 , G06F16/3328
Abstract: At least selectively utilizing a large language model (LLM) in generating a natural language (NL) based summary to be rendered in response to a query. In some implementations, in generating the NL based summary additional content is processed using the LLM. The additional content is in addition to query content of the query itself and, in generating the NL based summary, can be processed using the LLM and along with the query content—or even independent of the query content. Processing the additional content can, for example, mitigate occurrences of the NL based summary including inaccuracies and/or can mitigate occurrences of the NL based summary being over-specified and/or under-specified.
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公开(公告)号:US11409748B1
公开(公告)日:2022-08-09
申请号:US15923385
申请日:2018-03-16
Applicant: Google LLC
Inventor: Nitin Gupta , Srinivasan Venkatachary , Lingkun Chu , Steven D. Baker
IPC: G06F16/2457
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for context scoring adjustments for candidate answer passages. In one aspect, a method includes scoring candidate answer passages. For each candidate answer passage, the system determines a heading vector that describes a path in the heading hierarchy from the root heading to the respective heading to which the candidate answer passage is subordinate; determines a context score based, at least in part, on the heading vector; and adjusts answer score of the candidate answer passage at least in part by the context score to form an adjusted answer score. The system then selects an answer passage from the candidate answer passages based on the adjusted answer scores.
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公开(公告)号:US09959315B1
公开(公告)日:2018-05-01
申请号:US14169960
申请日:2014-01-31
Applicant: Google LLC
Inventor: Nitin Gupta , Srinivasan Venkatachary , Lingkun Chu , Steven D. Baker
IPC: G06F17/30
CPC classification number: G06F17/30528 , G06F17/30675
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for context scoring adjustments for candidate answer passages. In one aspect, a method includes scoring candidate answer passages. For each candidate answer passage, the system determines a heading vector that describes a path in the heading hierarchy from the root heading to the respective heading to which the candidate answer passage is subordinate; determines a context score based, at least in part, on the heading vector; and adjusts answer score of the candidate answer passage at least in part by the context score to form an adjusted answer score. The system then selects an answer passage from the candidate answer passages based on the adjusted answer scores.
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公开(公告)号:US11900068B1
公开(公告)日:2024-02-13
申请号:US18232112
申请日:2023-08-09
Applicant: GOOGLE LLC
Inventor: Matthew K. Gray , John Blitzer , Corinn Herrick , Srinivasan Venkatachary , Jayant Madhavan , Sam Oates , Phiroze Parakh , Aditya Shah , Mahsan Rofouei , Ibrahim Badr
IPC: G06F40/40 , G06F16/332
CPC classification number: G06F40/40 , G06F16/3328
Abstract: At least selectively utilizing a large language model (LLM) in generating a natural language (NL) based summary to be rendered in response to a query. In some implementations, in generating the NL based summary additional content is processed using the LLM. The additional content is in addition to query content of the query itself and, in generating the NL based summary, can be processed using the LLM and along with the query content—or even independent of the query content. Processing the additional content can, for example, mitigate occurrences of the NL based summary including inaccuracies and/or can mitigate occurrences of the NL based summary being over-specified and/or under-specified.
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公开(公告)号:US11769017B1
公开(公告)日:2023-09-26
申请号:US18123861
申请日:2023-03-20
Applicant: GOOGLE LLC
Inventor: Matthew K. Gray , John Blitzer , Corinn Herrick , Srinivasan Venkatachary , Jayant Madhavan , Sam Oates , Phiroze Parakh , Aditya Shah , Mahsan Rofouei , Ibrahim Badr
IPC: G06F40/40 , G06F16/332
CPC classification number: G06F40/40 , G06F16/3328
Abstract: At least selectively utilizing a large language model (LLM) in generating a natural language (NL) based summary to be rendered in response to a query. In some implementations, in generating the NL based summary additional content is processed using the LLM. The additional content is in addition to query content of the query itself and, in generating the NL based summary, can be processed using the LLM and along with the query content—or even independent of the query content. Processing the additional content can, for example, mitigate occurrences of the NL based summary including inaccuracies and/or can mitigate occurrences of the NL based summary being over-specified and/or under-specified.
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公开(公告)号:US10783156B1
公开(公告)日:2020-09-22
申请号:US15902659
申请日:2018-02-22
Applicant: Google LLC
Inventor: Steven D. Baker , Srinivasan Venkatachary , Robert Andrew Brennan , Per Bjornsson , Yi Liu , Hadar Shemtov , Massimiliano Ciaramita , Ioannis Tsochantaridis
IPC: G06F16/00 , G06F16/2457 , G06F16/2452 , G06F16/24
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for scoring candidate answer passages. In one aspect, a method includes receiving a query determined to be a question query that seeks an answer response and data identifying resources determined to be responsive to the query; for a subset of the resources: receiving candidate answer passages; determining, for each candidate answer passage, a query term match score that is a measure of similarity of the query terms to the candidate answer passage; determining, for each candidate answer passage, an answer term match score that is a measure of similarity of answer terms to the candidate answer passage; determining, for each candidate answer passage, a query dependent score based on the query term match score and the answer term match score; and generating an answer score that is a based on the query dependent score.
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