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公开(公告)号：US12242811B2
公开(公告)日：2025-03-04
申请号：US17671034
申请日：2022-02-14
Applicant: Google LLC
Inventor： Joseph Lange
IPC: G06F40/35 , G06F40/237
Abstract: Aspects of the disclosure provide for a system for navigating a conversation graph using a language model trained to generate Application Programming Interface (API) calls in response to natural language input from a user computing device. A conversational agent implementing a state handler and a language model (LM) communicates with a user computing device through a user frontend. Rather than communicating directly with a user with output in natural language, the agent uses a (LM) trained as described herein to navigate a conversation graph. The state handler receives API calls generated by the LM and updates the state of a conversation with a user as indicated in the graph. After the update, the state handler can perform one or more predetermined actions associated with a node indicating the current state of the conversation.
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公开(公告)号：US11775254B2
公开(公告)日：2023-10-03
申请号：US17251468
申请日：2020-01-31
Applicant: Google LLC
Inventor： Joseph Lange , Asier Aguirre , Olivier Siegenthaler , Michal Pryt
CPC classification number: G06F3/167 , G06T7/70 , G06V20/00 , G10L15/26 , G06T2200/24
Abstract: Implementations are described herein for analyzing existing graphical user interfaces (“GUIs”) to facilitate automatic interaction with those GUIs, e.g., by automated assistants or via other user interfaces, with minimal effort from the hosts of those GUIs. For example, in various implementations, a user intent to interact with a particular GUI may be determined based at least in part on a free-form natural language input. Based on the user intent, a target visual cue to be located in the GUI may be identified, and object recognition processing may be performed on a screenshot of the GUI to determine a location of a detected instance of the target visual cue in the screenshot. Based on the location of the detected instance of the target visual cue, an interactive element of the GUI may be identified and automatically populate with data determined from the user intent.
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3.发明申请公开(公告)号：US20210295841A1
公开(公告)日：2021-09-23
申请号：US17339114
申请日：2021-06-04
Applicant: Google LLC
Inventor： Mugurel Ionut Andreica , Vladimir Vuskovic , Joseph Lange , Sharon Stovezky , Marcin Nowak-Przygodzki
Abstract: Implementations are set forth herein for creating an order of execution for actions that were requested by a user, via a spoken utterance to an automated assistant. The order of execution for the requested actions can be based on how each requested action can, or is predicted to, affect other requested actions. In some implementations, an order of execution for a series of actions can be determined based on an output of a machine learning model, such as a model that has been trained according to supervised learning. A particular order of execution can be selected to mitigate waste of processing, memory, and network resources—at least relative to other possible orders of execution. Using interaction data that characterizes past performances of automated assistants, certain orders of execution can be adapted over time, thereby allowing the automated assistant to learn from past interactions with one or more users.
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4.发明授权公开(公告)号：US11031007B2
公开(公告)日：2021-06-08
申请号：US16343285
申请日：2019-02-07
Applicant: Google LLC
Inventor： Mugurel Ionut Andreica , Vladimir Vuskovic , Joseph Lange , Sharon Stovezky , Marcin Nowak-Przygodzki
Abstract: Implementations are set forth herein for creating an order of execution for actions that were requested by a user, via a spoken utterance to an automated assistant. The order of execution for the requested actions can be based on how each requested action can, or is predicted to, affect other requested actions. In some implementations, an order of execution for a series of actions can be determined based on an output of a machine learning model, such as a model that has been trained according to supervised learning. A particular order of execution can be selected to mitigate waste of processing, memory, and network resources—at least relative to other possible orders of execution. Using interaction data that characterizes past performances of automated assistants, certain orders of execution can be adapted over time, thereby allowing the automated assistant to learn from past interactions with one or more users.
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公开(公告)号：US10885077B2
公开(公告)日：2021-01-05
申请号：US16135205
申请日：2018-09-19
Applicant: Google LLC
Inventor： Vladimir Vuskovic , Joseph Lange , Behshad Behzadi , Marcin M. Nowak-Przygodzki
IPC: G06F16/332 , G06F16/33
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating subqueries from a query. In one aspect, a method includes obtaining a query, generating a set of two subqueries from the query, where the set includes a first subquery and a second subquery, determining a quality score for the set of two subqueries, determining whether the quality score for the set of two subqueries satisfies a quality threshold, and in response to determining that the quality score for the set of two subqueries satisfies the quality threshold, providing a first response to the first subquery that is responsive to a first operation that receives the first subquery as input and providing a second response to the second subquery that is responsive to a second operation that receives the second subquery as input.
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Patent Agency Ranking
6.发明申请公开(公告)号：US20200302924A1
公开(公告)日：2020-09-24
申请号：US16343285
申请日：2019-02-07
Applicant: Google LLC
Inventor： Mugurel Ionut Andreica , Vladimir Vuskovic , Joseph Lange , Sharon Stovezky , Marcin Nowak-Przygodzki
Abstract: Implementations are set forth herein for creating an order of execution for actions that were requested by a user, via a spoken utterance to an automated assistant. The order of execution for the requested actions can be based on how each requested action can, or is predicted to, affect other requested actions. In some implementations, an order of execution for a series of actions can be determined based on an output of a machine learning model, such as a model that has been trained according to supervised learning. A particular order of execution can be selected to mitigate waste of processing, memory, and network resources—at least relative to other possible orders of execution. Using interaction data that characterizes past performances of automated assistants, certain orders of execution can be adapted over time, thereby allowing the automated assistant to learn from past interactions with one or more users.
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公开(公告)号：US12147732B2
公开(公告)日：2024-11-19
申请号：US18234760
申请日：2023-08-16
Applicant: GOOGLE LLC
Inventor： Joseph Lange , Asier Aguirre , Olivier Siegenthaler , Michal Pryt
Abstract: Implementations are described herein for analyzing existing graphical user interfaces (“GUIs”) to facilitate automatic interaction with those GUIs, e.g., by automated assistants or via other user interfaces, with minimal effort from the hosts of those GUIs. For example, in various implementations, a user intent to interact with a particular GUI may be determined based at least in part on a free-form natural language input. Based on the user intent, a target visual cue to be located in the GUI may be identified, and object recognition processing may be performed on a screenshot of the GUI to determine a location of a detected instance of the target visual cue in the screenshot. Based on the location of the detected instance of the target visual cue, an interactive element of the GUI may be identified and automatically populate with data determined from the user intent.
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8.发明授权公开(公告)号：US12118998B2
公开(公告)日：2024-10-15
申请号：US18231112
申请日：2023-08-07
Applicant: GOOGLE LLC
Inventor： Mugurel Ionut Andreica , Vladimir Vuskovic , Joseph Lange , Sharon Stovezky , Marcin Nowak-Przygodzki
CPC classification number: G10L15/22 , G06N3/08 , G10L15/02 , G10L2015/223
Abstract: Implementations are set forth herein for creating an order of execution for actions that were requested by a user, via a spoken utterance to an automated assistant. The order of execution for the requested actions can be based on how each requested action can, or is predicted to, affect other requested actions. In some implementations, an order of execution for a series of actions can be determined based on an output of a machine learning model, such as a model that has been trained according to supervised learning. A particular order of execution can be selected to mitigate waste of processing, memory, and network resources—at least relative to other possible orders of execution. Using interaction data that characterizes past performances of automated assistants, certain orders of execution can be adapted over time, thereby allowing the automated assistant to learn from past interactions with one or more users.
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9.发明公开公开(公告)号：US20240046929A1
公开(公告)日：2024-02-08
申请号：US18382353
申请日：2023-10-20
Applicant: GOOGLE LLC
Inventor： Joseph Lange , Marcin Nowak-Przygodzki
IPC: G10L15/22
CPC classification number: G10L15/22
Abstract: Implementations set forth herein relate to an automated assistant that can operate as an interface between a user and a separate application to search application content of the separate application. The automated assistant can interact with existing search filter features of another application and can also adapt in circumstances when certain filter parameters are not directly controllable at a search interface of the application. For instance, when a user requests that a search operation be performed using certain terms, those terms may refer to content filters that may not be available at a search interface of the application. However, the automated assistant can generate an assistant input based on those content filters in order to ensure that any resulting search results will be filtered accordingly. The assistant input can then be submitted into a search field of the application and a search operation can be executed.
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公开(公告)号：US20230393810A1
公开(公告)日：2023-12-07
申请号：US18234760
申请日：2023-08-16
Applicant: GOOGLE LLC
Inventor： Joseph Lange , Asier Aguirre , Olivier Siegenthaler , Michal Pryt
CPC classification number: G06F3/167 , G06T7/70 , G06V20/00 , G10L15/26 , G06T2200/24
Abstract: Implementations are described herein for analyzing existing graphical user interfaces (“GUIs”) to facilitate automatic interaction with those GUIs, e.g., by automated assistants or via other user interfaces, with minimal effort from the hosts of those GUIs. For example, in various implementations, a user intent to interact with a particular GUI may be determined based at least in part on a free-form natural language input. Based on the user intent, a target visual cue to be located in the GUI may be identified, and object recognition processing may be performed on a screenshot of the GUI to determine a location of a detected instance of the target visual cue in the screenshot. Based on the location of the detected instance of the target visual cue, an interactive element of the GUI may be identified and automatically populate with data determined from the user intent.
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