8 results about "Dialog management" patented technology

The dialog management method comprises: performing semantic analysis on current input content of a user to obtain corresponding current user semantic information; obtaining current dialog state information of the user from stored state information of the user according to the current user semantic information; determining a current node in which the current dialog is located from a stored dialog resource architecture by querying, and jumping to a corresponding destination node when a jump condition of the current node is triggered, and obtaining output content of the destination node from the dialog resource architecture; the dialog resource architecture is a multi-level finite state machine structure, comprising a plurality of dialog nodes, and the dialog nodes jump based on a preset jump condition; and generating reply content corresponding to the current input content of the user according to the current dialog state information of the user and the output content of the destination node. The above scheme can effectively control the entire dialog process.

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.

Systems and methods for automatically and continuously processing communications exchanged among users and automated agents and corresponding user behaviors are provided to dynamically generate topical communications sessions from ongoing communications sessions. The topical communications sessions are implemented to reduce the cognitive effort of conversation management within existing communications sessions where topical drift may occur. The framework allows users to submit multimedia inputs that are classified by the systems and methods and automatically routed to appropriate communications containers.

An object of a system according to an embodiment is to reduce child's repulsion to homework and to effectively manage progress.SOLUTION: A system includes a homework progress confirmation unit, an interaction unit, a progress management unit, and a reminder unit. The homework progress check unit checks the progress of the homework using the generated AI. The interaction unit performs an interaction to reduce the child's repulsion based on the homework progress confirmed by the homework progress confirmation unit. A progress management part manages the progress of homework on the basis of the interaction performed by the interaction part. The reminder unit sends a reminder at an appropriate timing based on the homework progress managed by the progress management unit.SELECTED DRAWING: Figure 1

A dialog management system that coordinates system dialog responses based on natural language guidelines which provide non-deterministic ways for the system to properly respond to a dialog input based on the dialog history / context. For each input, an appropriate guideline is selected by a machine learning component based on the dialog history. The guideline is then sent, along with the dialog history, to a downstream machine learning component to determine an appropriate dialog system response.

Systems and methods for automatically and continuously processing communications exchanged among users and automated agents and corresponding user behaviors are provided to dynamically generate topical communications sessions from ongoing communications sessions. The topical communications sessions are implemented to reduce the cognitive effort of conversation management within existing communications sessions where topical drift may occur. The framework allows users to submit multimedia inputs that are classified by the systems and methods and automatically routed to appropriate communications containers.

A computer-implemented method dynamically routes and processes user queries in a bot-builder system. The method involves receiving a user query and routing the user query to a Personal Info Retrieval Module, a Document Retrieval Module, and a Tool Manager. The Personal Info Retrieval Module retrieves and updates personal information in real time through API calls. The Document Retrieval Module and Tool Manager retrieve document and tool embeddings from corresponding vector stores. An AI model processes the user query to generate query embeddings, performing similarity searches against the document and tool embeddings. The retrieved information is combined with the user query and chat history to form a comprehensive input, which the AI model processes to determine an appropriate response. The response is generated using existing knowledge or by invoking an external tool. The system includes real-time updates, leveraging historical interaction records and external services and integration of external tools.