WITHIN-CONTEXT SEMANTIC RELEVANCE INFERENCE OF MACHINE LEARNING MODEL GENERATED OUTPUT
개요
발명자
Andrew Donald Yates
IPC 분류
CPC 분류
Methods and systems provide content searching and retrieval using generative artificial intelligence (AI) Models. The system is configured to receive a user search for content, media or item listings. The system receives a natural language-based input associated with a client device of a user. The system generates a search criterion for the received natural language-based input. The system, via the generative AI-bases search and retrieval system, generates a relevancy-ranked output listing of content items. The relevancy-ranked output listing content items responsive to the generated search criterion content items having an associated content identifier and a content description. The system causes portions of the relevancy-ranked output listing to be rendered at the client device of the user.
원문 (중국어)
Methods and systems provide content searching and retrieval using generative artificial intelligence (AI) Models. The system is configured to receive a user search for content, media or item listings. The system receives a natural language-based input associated with a client device of a user. The system generates a search criterion for the received natural language-based input. The system, via the generative AI-bases search and retrieval system, generates a relevancy-ranked output listing of content items. The relevancy-ranked output listing content items responsive to the generated search criterion content items having an associated content identifier and a content description. The system causes portions of the relevancy-ranked output listing to be rendered at the client device of the user.