7 results about "Dialog manager" patented technology

The present application provides a mental health chat robot system, which comprises a user interface and input layer, a natural language processing and classification layer, an intention recognition and emotion analysis layer, a user database layer, a dialogue manager and context memory layer, a large language model integration layer, a user response generation and post-processing layer, a learning and feedback layer. The present application is reasonable in concept, and proposes "promoting mental health through dialogue - safe and adaptive ethical framework, which is a hierarchical conceptual framework, integrating evidence-based treatment model, adaptive technology and structured ethical guarantee measures, providing practical guiding principles for the responsible design of next-generation artificial intelligence mental health chat robots.

In various examples, contextual data may be generated using structured and unstructured data for conversational AI systems and applications. Systems and methods are disclosed that use structured data (converted to unstructured form) and unstructured data, such as from a knowledge database(s), to generate contextual data. For instance, the contextual data may represent text (e.g., narratives), where a first portion of the text is generated using the structured data and a second portion of the text is generated using the unstructured data. The systems and methods may then use a neural network(s), such as a neural network(s) associated with a dialogue manager, to process input data representing a request (e.g., a query) and the contextual data in order to generate a response to the request. For instance, if the request includes a query for information associated with a topic, the neural network(s) may generate a response that includes the requested information.

Methods and systems are disclosed for improving dialog management for task-oriented dialog systems. The disclosed dialog builder leverages machine teaching processing to improve development of dialog managers. In this way, the dialog builder combines the strengths of both rule-based and machine-learned approaches to allow dialog authors to: (1) import a dialog graph developed using popular dialog composers, (2) convert the dialog graph to text-based training dialogs, (3) continuously improve the trained dialogs based on log dialogs, and (4) generate a corrected dialog for retraining the machine learning.

A conversational AI platform with closed domain and open domain conversational integration is disclosed. In various examples, the systems and methods of the present disclosure combine an open dialog system and a closed dialog system into an intelligent dialog management system. The textual query may be processed by a natural language understanding model trained to associate the textual query with domain tags, intent classifications, and / or input slots. Using the domain tags, the natural language understanding model may identify information in the textual query corresponding to the input slots required to answer the textual query. The textual query and related information may then be passed to a dialog manager to direct the textual query to an appropriate domain dialog system. The response retrieved from the domain dialog system may be provided to the user via a text output and / or via a text-to-speech component of the dialog management system.

An automated software framework migration system modifies a project built using components of a legacy software framework with components of a newer version of the software framework. The system uses a conversation manager, agents and a machine learning model to perform the migration task. A developer agent is used to develop the migrated code and the reviewer agent verifies the migrated code for build errors. The machine learning model determines the actions that an agent takes to process the migration task. The conversation manager coordinates the communications with the agents and the machine learning model to generate a dynamic migration workflow.

A method and a system for automatic generation of conversation samples for training machine learning-based dialogue managers by using conversation graphs are provided. The method includes: receiving a conversational flow that relates to a conversation; converting the conversational flow into a graphical representation of the conversation; using the graphical representation to determine a set of paths that relate to possible realizations of potential user interactions with respect to the conversation; and generating conversation samples that correspond to the set of paths.

The invention relates to a business data processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring multi-modal data input by a user, and converting the multi-modal data into high-dimensional features; based on the high-dimensional features, maintaining a session state through a session manager, and generating a recommendation task; based on the recommendation task, performing recall processing on the candidate content of the product to obtain a recommendation result; fusing the environment-related characteristics of the recommendation results and then scoring to obtain a score of each recommendation result; taking a recommendation result corresponding to the highest score as a final recommendation result of the digital person; and outputting a final recommendation result to the user in at least one mode of voice, expression and action based on the digital human, and outputting an interpretable text of the final recommendation result. By adopting the method, the accuracy of digital person recommendation can be improved.