36 results about "Dialog management" patented technology

Techniques for conversational-based searching are described. A system may receive a first spoken user input, and may determine that the first spoken user input corresponds to a request for information associated with an entity. The system may retrieve item results corresponding to the entity. The system may determine a suggested user input based on the retrieved item results, and may determine output data corresponding to the suggested user input. The system may send output data to a user device, where the output data includes the item results and the suggested user input. The system may receive a second spoken user input, and may determine that the second spoken user input corresponds to the suggested user input. In response, the system may send the previously determined output data to the device.

Systems and methods are provided for facilitating automated administration of email messages received by a customer service representative including a service email administrator system that reviews incoming email messages, analyzes the content, assesses emotional sentiment, urgency, and determines the best course of action. The conversation administrator is configured to use natural language processing and machine learning algorithms to generate automated responses, provide email response drafts for customer service representative (CSR) review, or route emails to CSRs based on the sentiment or urgency.

The invention provides a vehicle enterprise APP intelligent assistant navigation method and system based on a large model. The method relates to collection of user navigation requirements and identification of key entities and user intentions by using a large language model. And when the navigation intention is identified, generating a navigation route through map data and a path planning algorithm in combination with the current position and the target position. And when the vehicle control intention is identified, remotely controlling the vehicle through a vehicle control interface. And when the intention is identified as the information query intention, relevant information is retrieved and displayed from the vehicle information base. And collecting user feedback, an optimization model and a navigation strategy. The navigation demand information comprises text and voice information. The navigation requirement understanding of the user is improved through the large language model, tedious operation is avoided, and the access efficiency and the user experience are improved. Personalized and real-time recommendation is realized, real-time information is provided according to requirements and environments, and travel route planning is optimized. Dialogue management supports multiple rounds of dialogues and deep interaction, complex requirements are effectively processed, and the ability to deal with complex scenes is improved.

A method in a contact center for generating an action classifier model and use thereof in selectively initiating turn set queries of a knowledge base to assist agents in real time during ongoing conversations with customers. The method includes: generating an action classifier model; receiving classification data that classifies a first plurality of the customer actions found in training samples as belonging to a first action category for which a knowledge base search is deemed needed, and a second plurality of the customer actions as belonging to a second action category for which a knowledge base search is deemed not needed; and using the action classifier model and the received classification data to perform a query filtering routine for selectively initiating a turn set query for a present turn set occurring in an ongoing conversation between an agent and customer.

A system for generating objective assessment criteria of social skills, comprising a speech to text system receiving audio data and converting the audio data into text data for a first speaker and a second speaker, a dialog management system processing text data for the first speaker and text data for the second speaker generating a strategic attention score for the first speaker based on responsiveness of the text data of the first speaker to text data of the second speaker, and a response verification system generating a user control for the strategic attention score and receiving feedback data from the second speaker to accept or modify the strategic attention score.

The present application relates to an integrated customer service method and system based on multiple agents, wherein the method includes: processing consulting questions raised by active incoming users through a dialogue management agent, obtaining multi-dimensional data information and generating dialogue responses to the consulting questions; based on the feedback of the active incoming users after the dialogue response, obtaining the consulting intention of the active incoming users through analysis by an intention recognition agent; based on the consulting intention and recent activity records of the active incoming users, determining the outgoing call operation to be performed through an outgoing decision agent; and then executing the outgoing call operation through an outgoing execution agent. Through the present application, integrated customer service of multiple agents is realized, and the collaborative division of labor of multiple different agents is achieved, each focusing on its specific business process, thereby providing better services to customers. It is also possible to proactively contact customers by analyzing customer data, track customer issues or conduct proactive marketing, and effectively improve customer satisfaction.

The invention relates to a multi-agent-based integrated customer service method and system, and the method comprises the steps: processing a consultation question proposed by an active incoming call user through a dialogue management agent, obtaining multi-dimensional data information, and generating a dialogue response of the consultation question according to the multi-dimensional data information; based on the feedback of the active incoming call user after the dialogue response, analyzing through an intention recognition agent to obtain the consultation intention of the active incoming call user; on the basis of the consultation intention and recent activity records of the active incoming call user, call-out operation needing to be executed is determined through a call-out decision agent; and the calling-out operation is executed through the calling-out execution agent. According to the method and the device, integrated customer service of multiple agents, collaborative division of labor of multiple different agents and specific business processes focused on the agents are realized, so that better service is provided for customers, customer data can be analyzed to actively call out to contact the customers, track customer problems or perform active marketing, and the satisfaction degree of the customers is effectively improved.

The invention provides an agricultural large model system based on a multi-modal fusion technology. The agricultural large model system comprises a dialogue management module, a task scheduling engine, an agricultural knowledge base, a model and application library and a large voice model base, the dialogue management module comprises an intelligent interaction terminal, a voice converter, an image processor, a dialogue wrapper and a prompt template; the task scheduling engine comprises a task template, a task scheduler, a task executor and a task monitor; the data source of the agricultural knowledge base comprises an agricultural knowledge graph, an object database, a relational database and a text and graph database; the model and application library comprises a crop identification model, a pest identification model, a spectrum identification model and a plurality of business systems.

Implementations relate to dialog management of a large language model (LLM) utilized in generating natural language (NL) output during an ongoing dialog. Processor(s) of a system can: receive NL based input as part of the ongoing dialog, generate NL based output utilizing the LLM, and cause the NL based output to be rendered. Further, the processor(s) can receive subsequent NL based input as part of the ongoing dialog. In some implementations, the processor(s) can determine whether to modify a corresponding dialog context in generating subsequent NL based output, and modify the corresponding dialog context accordingly. For example, the processor(s) can restrict the corresponding dialog context, or supplant the corresponding dialog context with a corresponding curated dialog context. In additional or alternative implementations, the processor(s) can modify a corresponding NL based output threshold utilized in generating the subsequent NL based response to ensure the resulting NL based output is desirable.

The invention discloses an online interview system and method based on an AI large model and a digital human, and mainly relates to the technical field of online interview. Comprising a user interaction layer, a service layer, a model layer and a data layer. Wherein the user interaction layer comprises a video interview interface module, a digital human display module and a candidate motion capture module; the service layer comprises an interview dialogue management module, a candidate scoring module, a test question management module and a digital person management module; the model layer comprises a resume analysis model, an interview dialogue generation model and a scoring model; the data layer comprises a user data storage module, an interview record storage module, a score data module and an interview question bank; according to the method, the AI large model is combined with the digital human, so that the accuracy of the information obtained by online interview is improved, the interview experience of candidates is improved, the interview efficiency in a large number of interview scenes is improved, and the manpower consumption of recruiters is effectively reduced.

Techniques for executing system actions during conversations between a human user and an autonomous conversational system are disclosed. A first generative language model processes user messages to determine user intent, while a dialog management model analyzes the intent and conversation context to identify required system actions. The system executes actions by retrieving parameters from context, performing database queries or API calls to obtain response data, and storing results in conversation context variables. A second generative language model generates natural language responses using the action results. The system maintains conversation context including message history, action results, and state information, validates action execution, and initiates human agent handoff when needed. The system improves over time by detecting poor performance, gathering problematic conversations, and retraining using updated configurations.

A method in a contact center for generating an action classifier model and use thereof in selectively initiating turn set queries of a knowledge base to assist agents in real time during ongoing conversations with customers. The method includes: generating an action classifier model; receiving classification data that classifies a first plurality of the customer actions found in training samples as belonging to a first action category for which a knowledge base search is deemed needed, and a second plurality of the customer actions as belonging to a second action category for which a knowledge base search is deemed not needed; and using the action classifier model and the received classification data to perform a query filtering routine for selectively initiating a turn set query for a present turn set occurring in an ongoing conversation between an agent and customer.

A system for generating objective assessment criteria of social skills, comprising a speech to text system receiving audio data and converting the audio data into text data for a first speaker and a second speaker, a dialog management system processing text data for the first speaker and text data for the second speaker generating a strategic attention score for the first speaker based on responsiveness of the text data of the first speaker to text data of the second speaker, and a response verification system generating a user control for the strategic attention score and receiving feedback data from the second speaker to accept or modify the strategic attention score.

A method and system for managing information of an information provider based on a conversation with an information provider. An information management method may include providing a conversational information management function to an information provider that desires to provide content to a user of a search service; conducting a conversation between large language model (LLM)-based artificial intelligence and the information provider through the conversational information management function; and analyzing the conversation and managing a campaign of the information provider.

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.

To provide a method and system for managing, based on a conversation with an information provider, information of the information provider.SOLUTION: An information management method according to an embodiment includes: providing a conversational information management function to an information provider that desires to provide content to a user of a search service; conducting a conversation between large language model (LLM)-based artificial intelligence and the information provider through the conversational information management function; and analyzing the conversation and managing a campaign of the information provider.SELECTED DRAWING: Figure 4

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.

A first request to access a messaging conversation management service that permits the first user account to view and transfer text message conversations between other user accounts of multiple user accounts and multiple recipient devices is received by a communication services platform via a first application programming interface (API) call from a first client device associated with a first user account. Responsive to receiving the first request, whether the first user account is authorized to access the messaging conversation management service is determined. Responsive to determining that the first user account is authorized to access the messaging conversation management service, a first indication of the text message conversations between the other user accounts of the multiple user accounts and the multiple recipient devices is sent to the first client device for display at the first client device.

Systems and methods are provided for facilitating automated administration of email messages received by a customer service representative including a service email administrator system that reviews incoming email messages, analyzes the content, assesses emotional sentiment, urgency, and determines the best course of action. The conversation administrator is configured to use natural language processing and machine learning algorithms to generate automated responses, provide email response drafts for customer service representative (CSR) review, or route emails to CSRs based on the sentiment or urgency.

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.

The present invention discloses a dialogue management method and device, and relates to the field of computer technology. A specific implementation of the method includes: processing the user input data to be processed according to a natural language understanding model to obtain feature information of the user input data to be processed; determining the historical record information corresponding to the user input data to be processed, and determining the current dialogue state according to the feature information and the historical record information; and generating response data corresponding to the user input data to be processed based on a neural network algorithm according to the current dialogue state. This implementation uses a natural language understanding model to obtain feature information of the user input data, and then determines the current dialogue state according to the historical record information, and then generates response data, which reduces the design and maintenance costs of a large number of regular programs, improves the accuracy of identifying user input data, and enhances user experience.

Implementations relate to dialog management of a large language model (LLM) utilized in generating natural language (NL) output during an ongoing dialog. Processor(s) of a system can: receive NL based input as part of the ongoing dialog, generate NL based output utilizing the LLM, and cause the NL based output to be rendered. Further, the processor(s) can receive subsequent NL based input as part of the ongoing dialog. In some implementations, the processor(s) can determine whether to modify a corresponding dialog context in generating subsequent NL based output, and modify the corresponding dialog context accordingly. For example, the processor(s) can restrict the corresponding dialog context, or supplant the corresponding dialog context with a corresponding curated dialog context. In additional or alternative implementations, the processor(s) can modify a corresponding NL based output threshold utilized in generating the subsequent NL based response to ensure the resulting NL based output is desirable.

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

Systems and methods provides for dialog management in real-time rather than turn taking. An example method included generating first candidate responses to triggering event. The triggering event may be receipt of a live stream chunk for the dialog or receipt of a backend response to a previous backend request for a dialog shema. The method also includes updating a list of candidate responses that are accepted or pending with at least on of the first candidate responses, and determining, for the triggering event, whether the list of candidate responses includes a candidate response that has a confidence score that meets a triggering threshold. The method also includes waiting for a next triggering event without providing a candidate response when the list does not include a candidate response that has a confidence score that meets the triggering threshold.

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 are provided for facilitating automated administration of email messages received by a customer service representative including a service email administrator system that reviews incoming email messages, analyzes the content, assesses emotional sentiment, urgency, and determines the best course of action. The conversation administrator is configured to use natural language processing and machine learning algorithms to generate automated responses, provide email response drafts for customer service representative (CSR) review, or route emails to CSRs based on the sentiment or urgency.

1. The name of the present design product: smart assistant conversation management graphical user interface of electronic device. 2. The use of the present design product: the present design product is used for an electronic device. 3. The design points of the present design product: in the graphical user interface. 4. The picture or photo that best indicates the design points: design 1 front view. 5. The electronic device is a conventional design, and other views are omitted. 6. Design 1 is designated as the basic design. 7. The use of the graphical user interface: the interface is used for the interactive interface of smart assistant conversation management. The design 1 front view interface is an initial interface. Clicking the "focus mode" button in the design 1 front view interface, the interface changes to the design 1 interface change state diagram. The design 2 front view interface is an initial interface. Clicking the "idle chat mode" button in the design 2 front view interface, the interface changes to the design 2 interface change state diagram.