5results about How to "Correct answer" patented technology

The invention relates to the technical field of medical question answering, and discloses a medical dialogue question answering method and device, computer equipment and a storage medium. Obtaining audio coding detail features including semantic information, emotion colors and contextual relationships; secondly, performing vectorization representation on the audio coding detail features through a coding vectorization model to obtain problem vector representation data; and finally, performing answer generation on the question vector representation data through a multi-head attention big model to obtain a medical answer voice signal corresponding to the medical question voice signal. The multi-head attention big model has the beneficial effects that the voice detail information required for accurately understanding the problem of the patient is input into the multi-head attention big model, so that the obtained answer is not only accurate, but also can emulate and embody the same principle, and fully embody humanistic care for the patient, so that the user can be better assisted and guided.

The application discloses a kind of based on the continuous named entity recognition method of generative paradigm, it is related to artificial intelligence algorithm technical field.The application includes constructing generative task framework, using Instruction learning technology, according to different NER application scene and demand custom task instruction, according to general entity category and specific field requirement flexible adjustment entity option, obtain input sentence from the text data source of webpage text, document library, social media text, determine output entity format as [(entity type 1: entity mention 1), (entity type 2: entity mention 2)... ], to guide generative model to complete NER task.By using Instruction learning technology to construct generative task framework, can customize task instruction according to different NER application scene, adjust entity option, guide T5 language model to convert traditional task into generative task, enhance the flexibility and practicality of model processing different tasks, so that it can better adapt to various text scenes, improve the processing capacity to complex and various text.

This application discloses a question-answering interaction method, apparatus, computer device, and storage medium based on multi-source fusion, relating to the field of Internet technology. It can significantly improve the quality and reliability of answers to complex questions, and enhances the overall efficiency and intelligence level of question answering through parallel retrieval and intelligent fusion. The method includes: acquiring a natural language question input by a user; standardizing the natural language question to obtain structured intent information; determining multiple candidate retrieval sources based on the question characteristics of the natural language question and the interaction with the user; calling the multiple candidate retrieval sources to retrieve the structured intent information, and fusing the multiple retrieval results from the multiple candidate retrieval sources to generate a candidate answer set; summarizing, reasoning, and optimizing the candidate answer set to obtain a target answer; and providing feedback of the target answer to the user.

The invention relates to the technical field of data exchange, in particular to a virtual digital human interaction management method and system based on artificial intelligence. Comprising the following steps: S1, establishing a portrait identity database, and independently storing a consultation record of each user according to portrait data; s2, collecting historical business consultation data of a bank, and then carrying out statistical analysis on the historical business consultation data by utilizing artificial intelligence so as to obtain the number of first consultation times of each business type; through different processing modes for new users and old users, services can be provided for the users more efficiently, input voices of the new users are analyzed in combination with historical consultation service data, service matching is performed in combination with the number of times of first consultation, accurate information of related services can be provided for the new users quickly, and user experience is improved. And the old user can carry out business matching according to the historical consultation record in the association effective time threshold value and the business type associated with the consultation sequence, so that more personalized services are provided.