8 results about "Dialogue management" patented technology

This invention discloses a digital human anchor interaction method, electronic device, and storage medium based on a large-scale model of news-related linguistic data. The method includes: a corpus construction step, collecting news-related linguistic data to construct a labeled text corpus oriented towards news scenarios; a model training step, fine-tuning and training a basic large-scale language model based on the corpus to obtain a vertical large-scale language model for the news domain; a dialogue management step, organizing user input, historical dialogue summaries, and news content summaries into structured prompt text, inputting it into the model to obtain response text; a multimodal presentation step, synthesizing speech based on the response text and driving the digital human anchor's lip movements, facial expressions, and body movements; and a continuous optimization step, constructing a reward mechanism based on user behavior data and feedback information for continuous optimization. This invention enhances professionalism through a vertical large-scale model trained on news-related linguistic data, achieves natural interaction through multimodal presentation, and continuously improves user experience through a continuous optimization mechanism.

Providing a program that solves at least one of the problems of conventional technology. [Solution] A program that enables a computer to implement a dialogue management function that generates dialogue content to be provided to a reception terminal and manages the dialogue state based on the visitor's selection input to the dialogue content; a notification management function that generates visitor notification information to be provided to the staff terminal in accordance with the progress of the dialogue; a dialogue update function that, upon detecting a first operation on the visitor notification information on the staff terminal, updates the dialogue content based on information stored in advance and linked to the first operation; and a browser provision function that provides the dialogue content and the visitor notification information as data in a format accessible by a web browser from the reception terminal and the staff terminal.

The present application discloses a voice dialogue processing method and apparatus. The voice dialogue processing method includes: determining a voice semantics corresponding to a user voice to be processed; determining a reply sentence for the voice semantics based on a dialogue management engine, a training sample set of which is constructed from a dialogue business customization file including at least one dialogue flow, and the dialogue flow includes a plurality of dialogue nodes in a set order; and generating a customer service voice for replying to the user voice according to the determined reply sentence.

The application relates to the technical field of Internet, and provides a dialogue management method, device, equipment, storage medium and program product, the method comprises the following steps: determining reply information for feedback information of a user based on the feedback information, historical dialogue information of the user and a question and answer model; outputting the reply information for the feedback information; wherein the question and answer model is obtained by strengthening training a first model based on a first sample data set; any first sample data in the first sample data set contains at least one round of dialogue information; reward information required by the strengthening training is determined based on a final satisfaction degree of dialogue output by a second model; and the final satisfaction degree of the dialogue is generated by the second model based on the first sample data set. The application accurately determines the reply information for the feedback information and outputs the reply information, so that the user can obtain accurate reply content, thereby effectively solving the problem of the user and improving the user experience.

The application relates to the technical fields of intelligent voice interaction and medical informatization, and discloses an intelligent interaction self-service machine and system based on voice recognition, wherein the intelligent interaction self-service system based on voice recognition comprises an audio acquisition module, a voice recognition module, a user identity recognition module, a semantic understanding module, an intelligent reasoning module and a dialogue management module; the audio acquisition module acquires multi-channel audio signals, extracts voice signals of a target user and carries out signal processing; the voice recognition module carries out voice text recognition and confidence evaluation; the user identity recognition module recognizes the user identity; the semantic understanding module carries out semantic understanding and intent recognition; the intelligent reasoning module carries out intelligent reasoning and decision-making, recommends a department and formulates a treatment plan; the dialogue management module executes multi-round dialogue management and information completion; the business execution module carries out privacy protection processing, and carries out task distribution and execution; the application adopts a cloud-edge-end collaborative architecture and privacy protection technology, and realizes efficient business processing under the premise of guaranteeing data security.

The application discloses an AI chat robot system based on multi-agent cooperation and dynamic tool calling, and belongs to the technical field of AI chat robots, and comprises a user interface module, a dialogue management module, a multi-agent cooperation engine including a master control agent and multiple function agents, a tool library management module, the master control agent is used for generating a dynamic workflow based on a query instruction input by a user and a dialogue context, and sending the dynamic workflow to a workflow execution engine, the workflow execution engine is used for coordinating multiple function agents to call tools in the tool library management module to execute subtasks according to the dynamic workflow, and collecting subtask results and returning the subtask results to the master control agent, and the master control agent is further used for fusing the subtask results to generate an answer, and outputting the answer through the user interface module. Through the above scheme, the performance, reliability and explainability of the AI chat robot in a complex task scene are significantly improved.

Embodiments of the present application provide a dialogue management method and device, computer equipment and a storage medium. The method comprises obtaining voice stream data sent by a user, and determining an initial user intent according to the voice stream data. After the initial user intent is determined, a target state machine flowchart can be obtained according to the initial user intent. Then, the target state machine flowchart is executed by a state machine to control a user say state node, a robot think state node and a robot say state node to determine a reply result of the initial user intent. Since the target state machine flowchart is controlled by the state machine and only includes three state nodes, the execution logic is simple, and the target state machine flowchart is completely decoupled from a flowchart constituted by complex nodes published by an operation side platform, thereby simplifying the operation and maintenance complexity.

A multi-language voice-to-voice translation method and a system is disclosed using a uniquely designed conversation manager module. According to an embodiment, the conversation manager module converts each of the received one or more utterances into text data. The conversation manager module recognizes a language corresponding to each of the one or more utterances. Further, the conversation manager module segments, the converted text data, into one or more segments. A language processing model then translates the one or more segments into an output language. The conversation manager module fetches a tone style embedding similar to the received one or more utterances from a database. Audio output is generated in the output language along with the tone style embeddings. Thus, the generated output is the translated output in the output language having a style of a user who is uttering it.