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公开(公告)号：US20230146053A1
公开(公告)日：2023-05-11
申请号：US18076662
申请日：2022-12-07
Applicant: Google LLC
Inventor： Cong Li , Jay Adams , Manas Joglekar , Pranav Khaitan , Quoc V. Le , Mei Chen
IPC: G06F16/9035 , G06F40/242 , G06F11/34 , G06N20/00 , G06N3/08
CPC classification number: G06F16/9035 , G06F40/242 , G06F11/3466 , G06N20/00 , G06N3/08
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining, for each of one or more categorical features, a respective vocabulary of categorical feature values of the categorical feature that should be active during processing of inputs by a machine learning model. In one aspect, a method comprises: generating a batch of output sequences, each output sequence in the batch specifying, for each of the categorical features, a respective vocabulary of categorical feature values of the categorical feature that should be active; for each output sequence in the batch, determining a performance metric of the machine learning model on a machine learning task after the machine learning model has been trained to perform the machine learning task with only the respective vocabulary of categorical feature values of each categorical feature specified by the output sequence being active.
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公开(公告)号：US11537664B2
公开(公告)日：2022-12-27
申请号：US16878912
申请日：2020-05-20
Applicant: Google LLC
Inventor： Cong Li , Jay Adams , Manas Joglekar , Pranav Khaitan , Quoc V. Le , Mei Chen
IPC: G06F16/9035 , G06F40/242 , G06F11/34 , G06N20/00 , G06N3/08
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining, for each of one or more categorical features, a respective vocabulary of categorical feature values of the categorical feature that should be active during processing of inputs by a machine learning model. In one aspect, a method comprises: generating a batch of output sequences, each output sequence in the batch specifying, for each of the categorical features, a respective vocabulary of categorical feature values of the categorical feature that should be active; for each output sequence in the batch, determining a performance metric of the machine learning model on a machine learning task after the machine learning model has been trained to perform the machine learning task with only the respective vocabulary of categorical feature values of each categorical feature specified by the output sequence being active.
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公开(公告)号：US11714857B2
公开(公告)日：2023-08-01
申请号：US18076662
申请日：2022-12-07
Applicant: Google LLC
Inventor： Cong Li , Jay Adams , Manas Joglekar , Pranav Khaitan , Quoc V. Le , Mei Chen
IPC: G06F16/9035 , G06F40/242 , G06F11/34 , G06N20/00 , G06N3/08
CPC classification number: G06F16/9035 , G06F11/3466 , G06F40/242 , G06N3/08 , G06N20/00
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining, for each of one or more categorical features, a respective vocabulary of categorical feature values of the categorical feature that should be active during processing of inputs by a machine learning model. In one aspect, a method comprises: generating a batch of output sequences, each output sequence in the batch specifying, for each of the categorical features, a respective vocabulary of categorical feature values of the categorical feature that should be active; for each output sequence in the batch, determining a performance metric of the machine learning model on a machine learning task after the machine learning model has been trained to perform the machine learning task with only the respective vocabulary of categorical feature values of each categorical feature specified by the output sequence being active.
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公开(公告)号：US11418471B2
公开(公告)日：2022-08-16
申请号：US16692821
申请日：2019-11-22
Applicant: Google LLC
Inventor： Timothy Seeds Milligan , Hila Shemer , Dennis Kiilerich , Gang Ji , Ori Gershony , Sergey Nazarov , Pranav Khaitan , Sushant Prakash , Anton Volkov , Ricardo Escutia , David Citron
IPC: H04L51/00 , H04L51/02 , H04L67/53 , H04L51/063 , H04L51/046
Abstract: A system and method for identifying an entity from a message exchange thread and generating a suggestion that is directed to the entity, such as a suggestion for a user to take an action on the entity. A suggestion application receives at least one electronic message from a message exchange thread, identifies an entity that can be actionable from the electronic message, determines contextual indicators of the entity, determines whether the entity is actionable based on the contextual indicators, and responsive to the entity being actionable, provides a suggestion that is directed to the entity to a participant(s) of the message exchange thread.
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公开(公告)号：US11551159B2
公开(公告)日：2023-01-10
申请号：US16724604
申请日：2019-12-23
Applicant: Google LLC
Inventor： Abhinav Kumar Rastogi , Raghav Gupta , Xiaoxue Zang , Srinivas Kumar Sunkara , Pranav Khaitan
Abstract: Generally, the present disclosure is directed to systems and methods for performing task-oriented response generation that can provide advantages for artificial intelligence systems or other computing systems that include natural language processing for interpreting user input. Example implementations can process natural language descriptions of various services that can be accessed by the system. In response to a natural language input, systems can identify relevant values for executing one of the service(s), based in part on comparing embedded representations of the natural language input and the natural language description using a machine learned model.
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Patent Agency Ranking
公开(公告)号：US20210192397A1
公开(公告)日：2021-06-24
申请号：US16724604
申请日：2019-12-23
Applicant: Google LLC
Inventor： Abhinav Kumar Rastogi , Raghav Gupta , Xiaoxue Zang , Srinivas Kumar Sunkara , Pranav Khaitan
Abstract: Generally, the present disclosure is directed to systems and methods for performing task-oriented response generation that can provide advantages for artificial intelligence systems or other computing systems that include natural language processing for interpreting user input. Example implementations can process natural language descriptions of various services that can be accessed by the system. In response to a natural language input, systems can identify relevant values for executing one of the service(s), based in part on comparing embedded representations of the natural language input and the natural language description using a machine learned model.
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公开(公告)号：US20230162098A1
公开(公告)日：2023-05-25
申请号：US18152553
申请日：2023-01-10
Applicant: Google LLC
Inventor： Abhinav Kumar Rastogi , Raghav Gupta , Xiaoxue Zang , Srinivas Kumar Sunkara , Pranav Khaitan
CPC classification number: G06Q10/02 , G06N20/00 , G06F40/20 , G06Q50/30 , G06F9/54 , G06F16/243 , G06F16/211
Abstract: Generally, the present disclosure is directed to systems and methods for performing task-oriented response generation that can provide advantages for artificial intelligence systems or other computing systems that include natural language processing for interpreting user input. Example implementations can process natural language descriptions of various services that can be accessed by the system. In response to a natural language input, systems can identify relevant values for executing one of the service(s), based in part on comparing embedded representations of the natural language input and the natural language description using a machine learned model.
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公开(公告)号：US20200372076A1
公开(公告)日：2020-11-26
申请号：US16878912
申请日：2020-05-20
Applicant: Google LLC
Inventor： Cong Li , Jay Adams , Manas Joglekar , Pranav Khaitan , Quoc V. Le , Mei Chen
IPC: G06F16/9035 , G06F40/242 , G06N3/08 , G06N20/00 , G06F11/34
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining, for each of one or more categorical features, a respective vocabulary of categorical feature values of the categorical feature that should be active during processing of inputs by a machine learning model. In one aspect, a method comprises: generating a batch of output sequences, each output sequence in the batch specifying, for each of the categorical features, a respective vocabulary of categorical feature values of the categorical feature that should be active; for each output sequence in the batch, determining a performance metric of the machine learning model on a machine learning task after the machine learning model has been trained to perform the machine learning task with only the respective vocabulary of categorical feature values of each categorical feature specified by the output sequence being active.
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公开(公告)号：US10530723B2
公开(公告)日：2020-01-07
申请号：US15386162
申请日：2016-12-21
Applicant: Google LLC
Inventor： Timothy Seeds Milligan , Hila Shemer , Dennis Kiilerich , Gang Ji , Ori Gershony , Sergey Nazarov , Pranav Khaitan , Sushant Prakash , Anton Volkov , Ricardo Escutia , David Citron
Abstract: A system and method for identifying an entity from a message exchange thread and generating a suggestion that is directed to the entity, such as a suggestion for a user to take an action on the entity. A suggestion application receives at least one electronic message from a message exchange thread, identifies an entity that can be actionable from the electronic message, determines contextual indicators of the entity, determines whether the entity is actionable based on the contextual indicators, and responsive to the entity being actionable, provides a suggestion that is directed to the entity to a participant(s) of the message exchange thread.
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公开(公告)号：US10482139B2
公开(公告)日：2019-11-19
申请号：US14071867
申请日：2013-11-05
Applicant: Google LLC
Inventor： Pranav Khaitan , Shobha Diwakar
IPC: G06F16/00 , G06F16/9535
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving user-specific content, the user-specific content being associated with a user of one or more computer-implemented services, processing the user-specific content using one or more parsers to identify one or more entities and one or more relationships between entities, a parser being specific to a schema, and the one or more entities and the one or more relationships between entities being identified based on the schema, providing one or more user-specific knowledge graphs, a user-specific knowledge graph being specific to the user and including nodes and edges between nodes to define relationships between entities based on the schema, and storing the one or more user-specific knowledge graphs.
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