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公开(公告)号：US20240256618A1
公开(公告)日：2024-08-01
申请号：US18336000
申请日：2023-06-15
IPC分类号： G06F16/9536 , G06F40/20
CPC分类号： G06F16/9536 , G06F40/20
摘要： A computing system is disclosed that includes a processor and memory. The memory stores instructions that, when executed by the processor, cause the processor to perform several acts. The acts include generating a prompt that is to be input to a generative language model. The prompt includes conversational input set forth by a user. The acts further comprise providing the prompt as input to the generative language model, and receiving conversational output from the generative language model, where the generative language model generated the conversational output based upon the prompt. Additionally, the acts comprise streaming the conversational output on one of a SERP or webpage to which the user has navigated from the SERP.
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公开(公告)号：US20220335051A1
公开(公告)日：2022-10-20
申请号：US17856257
申请日：2022-07-01
IPC分类号： G06F16/2457 , G06N20/00 , G06F16/93 , G06F16/332 , G06F16/951 , G06F40/205 , G06F40/258
摘要： A machine reading comprehension system (MRCS) can analyze a larger-sized document that includes multiple pages to predict an answer to a query. For example, the document can have two, five, tens, or hundreds of pages. The MRCS divides the document into multiple sections with each section including a portion of the document. Each section is processed separately by one or more processing circuitries to determine a score for that section. The score indicates how related the section is to the query and/or a probability that the section provides a possible answer to the query. Once all of the sections have been analyzed, the sections are ranked by their scores and a subset of the ranked sections are processed again to determine a predicted answer to the query.
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公开(公告)号：US20240256840A1
公开(公告)日：2024-08-01
申请号：US18213064
申请日：2023-06-22
发明人： Xia SONG , Kris K. GANJAM , Mahmoud ADADA , Justin D. HARRIS , Dominic MORIN , Bradley Moore ABRAMS , Peter POTASH
IPC分类号： G06N3/0475
CPC分类号： G06N3/0475
摘要： Methods, systems, and media for storing entries in and/or retrieving information from an object memory are provided. In some examples, a content item is received that has content data. The content data associated with the content item may be provided to one or more semantic models that generate semantic objects. From one or more of the semantic models, one or semantic objects may be received. The one or more semantic objects may then be inserted into the object memory. The semantic objects may be associated with respective indications corresponding to a reference to source data associated with the semantic objects. Further, the insertion may trigger a memory storage operation to store the semantic objects. A plurality of collections of stored objects may be received from the object memory, based on a provided input, to determine a result.
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公开(公告)号：US20190138613A1
公开(公告)日：2019-05-09
申请号：US15808540
申请日：2017-11-09
摘要： A machine reading comprehension system (MRCS) can analyze a larger-sized document that includes multiple pages to predict an answer to a query. For example, the document can have two, five, tens, or hundreds of pages. The MRCS divides the document into multiple sections with each section including a portion of the document. Each section is processed separately by one or more processing circuitries to determine a score for that section. The score indicates how related the section is to the query and/or a probability that the section provides a possible answer to the query. Once all of the sections have been analyzed, the sections are ranked by their scores and a subset of the ranked sections are processed again to determine a predicted answer to the query.
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公开(公告)号：US20240256784A1
公开(公告)日：2024-08-01
申请号：US18104251
申请日：2023-01-31
IPC分类号： G06F40/35 , G06F40/279 , H04L51/02
CPC分类号： G06F40/35 , G06F40/279 , H04L51/02
摘要： Disclosed is a system for composable chatbot extensions. Chatbot extensions are composed by providing the output of one extension as input to another. This defines a pipeline of extensions that accepts a prompt as input and provides a response as output. Composability makes it easier to leverage functionality provided by other extensions, log output, execute tasks in parallel, and test extensions. In some configurations, each extension declares the inputs it accepts, the outputs it produces, and any modifications it makes to data being passed through the pipeline. An extension may also declare a preferred location in the pipeline, enabling developers to choose whether to respond to a raw prompt as quickly as possible or to wait and receive intermediate results generated by other extensions. At the end of the pipeline a response is provided to the user via the chatbot.
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公开(公告)号：US20240248902A1
公开(公告)日：2024-07-25
申请号：US18429875
申请日：2024-02-01
IPC分类号： G06F16/2457 , G06F16/332 , G06F16/93 , G06F16/951 , G06F40/205 , G06F40/258 , G06N20/00
CPC分类号： G06F16/24578 , G06F16/3329 , G06F16/93 , G06F16/951 , G06F40/205 , G06F40/258 , G06N20/00
摘要： A machine reading comprehension system (MRCS) can analyze a larger-sized document that includes multiple pages to predict an answer to a query. For example, the document can have two, five, tens, or hundreds of pages. The MRCS divides the document into multiple sections with each section including a portion of the document. Each section is processed separately by one or more processing circuitries to determine a score for that section. The score indicates how related the section is to the query and/or a probability that the section provides a possible answer to the query. Once all of the sections have been analyzed, the sections are ranked by their scores and a subset of the ranked sections are processed again to determine a predicted answer to the query.
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