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公开(公告)号:US20240281472A1
公开(公告)日:2024-08-22
申请号:US18443903
申请日:2024-02-16
Applicant: Snowflake Inc.
Inventor: Cooper Paul LaRhette , Seth Michael Li , Muhua Ngan , Vivek Raghunathan , Sridhar Ramaswamy
IPC: G06F16/9032
CPC classification number: G06F16/90328
Abstract: An interactive search method is disclosed, utilizing a browser-based interface and generative artificial intelligence to enhance user search experiences. The method involves receiving an initial search query from a user, generating a proposed search result via hardware processors, and displaying the result to the user. To refine search accuracy, the method recommends clarifying questions, soliciting additional search parameters. Upon receiving an updated search query, the system interactively refines the initial query and displays an updated search result. This process allows for dynamic query adjustment and improved search result relevance in real-time.
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公开(公告)号:US20240281487A1
公开(公告)日:2024-08-22
申请号:US18443838
申请日:2024-02-16
Applicant: Snowflake Inc.
Inventor: Rahil Bathwal , Daniel Fernando Campos , Ashwin Devaraj , Seth Michael Li , Muhua Ngan , Vivek Raghunathan , Sridhar Ramaswamy , Rajhans Samdani , Chiu Wah So , Nitya Kannan Tarakad
IPC: G06F16/9538 , G06F16/93 , G06F16/955
CPC classification number: G06F16/9538 , G06F16/93 , G06F16/9558
Abstract: Enhanced search results are generated using multi-document summarization. A multi-document summarization system receives a search query from a user and retrieves a plurality of search result documents based on the search query. The summarization system generates a summary of each of the plurality of search result documents using distinct per-document summarization machine learning models, where the distinct per-document summarization machine learning models are trained on a training dataset. The summarization system synthesizes the summary of each of the plurality of search result documents into a single-consolidated answer responsive to the received search query. The multi-document summarization system formats the single-consolidated answer to include citations to the plurality of search result documents.
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