19 results about "Interaction agent" patented technology

The invention discloses a multi-modal interaction system based on large model driving, and relates to the field of multi-modal interaction. The system comprises an interaction agent terminal configured to receive multi-modal original data input by a user and output a fusion feedback result; the multi-modal gateway is in communication connection with the interaction agent terminal and is used for converting the fusion feedback result into a low-dimensional feature vector and eliminating a semantic gap between modals of the low-dimensional feature vector through a pre-trained attention mechanism; the large model driving engine is coupled with the multi-mode gateway and comprises a dynamically loaded multi-mode large model base supporting joint reasoning and a domain knowledge plug-in interface used for mounting a vertical domain fine tuning adapter; and the intention decision center is configured to analyze the output of the large model driving engine and generate an executable instruction. Unification of high precision, strong robustness and expandability of the industrial-grade multi-mode interaction system can be achieved.

The invention discloses an Agent-based multi-dimensional data quality intelligent evaluation method. The method comprises the following steps: establishing a multi-dimensional data quality evaluation index system; and an Agent agent model is constructed, the Agent agent model comprises a data perception Agent, an evaluation Agent, a decision-making Agent and an interaction Agent, and automatic evaluation of data quality is realized. According to the invention, comprehensive improvement of data quality evaluation, dynamic adaptability enhancement, intelligence and high efficiency can be realized.

Techniques for delegate data leakage protection using self-generated task-specific security policies are described. An interaction agent obtains a request to have the interaction agent perform a task (e.g., on behalf of a user) that involves interacting with an untrusted entity. The interaction agent generates a task-specific security policy via use of first machine learning (ML) model, applies the security policy for the execution of the task, and executes the task based on use of the ML model to identify actions to be performed for the task. The security policy governs which of the actions the interaction agent can perform or cannot perform during the execution of the task.

The invention discloses a vehicle-mounted system modular framework and a collaboration method thereof, the vehicle-mounted system modular framework comprises an agent module cluster, and the agent module cluster comprises an interaction agent and at least one function execution agent; a large language model is deployed in each agent, and all the large language models are coordinated through a standardized interface; the interaction agent is used for receiving a natural language instruction of a user and analyzing the natural language instruction by using a large language model deployed in the interaction agent to generate a task instruction; and the function execution agent is used for receiving the task instruction, analyzing and extracting key parameters by using a large language model deployed in the function execution agent, executing security rule check, and executing a corresponding specific task if the task instruction passes the security rule check. According to the method, the rule code quantity can be reduced, the interactive agent realizes cross-agent dynamic task distribution based on the big language model reasoning ability, new function access does not need to modify a bottom layer architecture, only peripheral agents need to be increased or decreased, the development period can be effectively shortened, and the development progress can be accelerated.

The invention discloses a rehabilitation assistive device assessment method based on multi-agent and RAG technology. The method comprises the following steps: constructing a multi-agent system which comprises a rehabilitation physician agent, an auxiliary tool engineer agent, a nurse agent and a user interaction agent; building a dynamic knowledge base based on an RAG technology, wherein the dynamic knowledge base comprises an auxiliary tool product database, a rehabilitation medicine knowledge base, a clinical case base and an industrial standard base; interacting with the user, and dynamically adjusting the question mode, the language style and the question sequence according to the user type and the answer content; retrieving in a dynamic knowledge base according to the obtained information, and respectively outputting professional evaluation suggestions; and according to the collaborative decision model, integrating professional evaluation suggestions of the rehabilitation physician intelligent agent, the auxiliary tool engineer intelligent agent and the nursing personnel intelligent agent to generate a multi-dimensional auxiliary tool evaluation report and recommendation scheme. According to the method, the efficiency of the evaluation process of the assistive device is higher, and the evaluation result is more accurate and effective.

The invention relates to the technical field of intelligent interaction, in particular to an interaction agent collaboration system and a control method thereof, and solves the problems that in the interaction process of existing artificial intelligence, emotion perception cannot be performed according to interaction data of a user, so that accurate capture of user requirements is inconvenient to realize, and user experience is poor. The system comprises a multi-port intelligent agent, the multi-port intelligent agent is composed of a software module and a hardware module, the software module is embedded in the hardware module, the software module is an M-Creation engine, and the hardware module is an M-Creation engine. The M-Creation engine comprises a data set, a storage module, a process arrangement platform, a plug-in tool set, a large language model, a back-end service platform and a cache module. Through a large amount of emotion cognition training, the multi-port intelligent agent can conveniently customize the preset role identity and the emotion setting operation in the interaction process, the user demand can be accurately captured, the response emotion and mood can be automatically adapted, and the application of different scenes can be met through the virtual digital human.

The embodiment of the invention discloses a multi-agent collaborative hospital guide method and device, equipment and a storage medium. According to the technical scheme provided by the embodiment of the invention, the user inquiry data is obtained through the user interaction agent, the symptom information and the user intention are determined according to the user inquiry data, and when the user intention is the triage request, the candidate department list is generated through the triage decision agent according to the symptom information; determining a key discrimination symptom of each candidate department in the candidate department list through a medical knowledge agent, and performing symptom query processing according to the key discrimination symptom through a user interaction agent to obtain user reply data, the medical knowledge agent determines the department confidence coefficient of each candidate department according to the user inquiry data and the user reply data, the dynamic scheduling agent determines the target department from the candidate department list according to the department confidence coefficient, and the user interaction agent performs hospital guide feedback processing according to the target department. The personalized hospital guide appeal of the user can be accurately responded, and the user experience is improved.

The invention discloses an emotion interaction agent interaction method based on a scene and modal double attention mechanism, and relates to the technical field of man-machine interaction. Comprising the steps that interaction data and scene context data in the interaction process of an emotion interaction agent and a user are acquired, and an interaction data set and a scene data set are acquired; performing multi-modal feature extraction on the interaction data set to obtain demand features and emotion features, and performing association analysis on the emotion features to obtain comprehensive emotion features; performing feature extraction and analysis on the scene context data to obtain scene features; fusing the comprehensive emotion features and the scene features, carrying out user state recognition, and obtaining user state data; and based on the user state data in combination with the demand data, mapping to obtain an agent response scheme, and performing agent interaction. According to the invention, the recognition accuracy of the emotion interaction agent on the user state is effectively improved.

To smoothly interact with a user by using an interaction agent.SOLUTION: An interaction device that responds to a user's utterance by using an interaction agent includes: an utterance detection part for detecting a first utterance by the user; and an utterance output part for controlling to output a response from the interaction agent to content of the first utterance detected by the utterance detection part. The utterance output part controls to output a second utterance for making interaction with the user smooth from the interaction agent before outputting the response when a prescribed condition is met.SELECTED DRAWING: Figure 6

The invention belongs to the technical field of man-machine interaction, and discloses a plug-and-play desktop interaction agent module and an interaction method thereof.The module carries an embedded AI computing platform to achieve desktop interaction, and the interaction process comprises the steps that a GUI view of a desktop is obtained through an HDMI acquisition card, and a voice instruction is obtained through a microphone and converted into an instruction text; analyzing the GUI view by using a deep learning algorithm, and constructing a structured GUI view text representation; analyzing the interaction intention based on the large language model, and formulating an execution scheme based on a GUI control; a keyboard and mouse event is sent to a computer system through a USB interface and a keyboard and mouse protocol, and autonomous execution of interaction is achieved; the module serves as a plug-and-play external device, and an embedded AI computing platform is carried in the module, so that a desktop interaction agent gets rid of dependence on a network and computing power of a computer system, extra computing burden cannot be caused to the computer system, and the system is more convenient to use. And the large-scale application of the desktop interaction agent on the existing computer system is facilitated.

The application discloses a rehabilitation auxiliary tool evaluation method based on multi-agent and RAG technology. The method comprises the following steps: constructing a multi-agent system, including a rehabilitation doctor agent, an auxiliary tool engineer agent, a nursing staff agent and a user interaction agent; building a dynamic knowledge base based on RAG technology, including an auxiliary tool product database, a rehabilitation medical knowledge base, a clinical case database and an industry standard database; interacting with the user, and dynamically adjusting the questioning method, language style and question order according to the user type and answer content; retrieving in the dynamic knowledge base according to the obtained information, and respectively outputting professional evaluation opinions; and integrating the professional evaluation opinions of the rehabilitation doctor agent, the auxiliary tool engineer agent and the nursing staff agent according to a collaborative decision-making model to generate a multi-dimensional auxiliary tool evaluation report and a recommended scheme. The above method can make the evaluation process of the auxiliary tool more efficient, and the evaluation result more accurate and effective.

The embodiment of the invention provides an auxiliary method for direct communication between a vehicle voice assistant and WeChat in a virtual machine, and belongs to the technical field of communication, and the method specifically comprises the steps: 1, collecting and storing the component coordinate information of a WeChat application interface in the virtual machine; step 2, the vehicle voice assistant receives a user voice instruction, and generates a standardized interaction instruction with an authority label after semantic analysis; step 3, performing security verification on the standardized interaction instruction through a security unvarnished transmission layer running between the vehicle machine host system and the virtual machine, and then issuing the standardized interaction instruction to the virtual machine; 4, an intelligent auxiliary interaction agent module in the virtual machine receives the verified instruction and executes a corresponding target operation; and step 5, generating a receipt containing verification information according to an execution result of the target communication operation, feeding back the receipt to the in-vehicle voice assistant through the safe unvarnished transmission layer, and performing result broadcasting or multi-round interactive guidance by the in-vehicle voice assistant according to the receipt. Through the scheme of the invention, the security and reliability of communication are improved.

The invention provides an interaction method and device based on a large model, an interaction agent and electronic equipment, relates to the technical field of computers, in particular to the technical fields of artificial intelligence, the large model, the agent and the like, and can be applied to scenes of data mining, system analysis, system testing, system optimization and the like. According to the specific implementation scheme, X target feature dimensions are determined; wherein X is greater than or equal to 2 and is an integer; based on the X target feature dimensions, constructing different N user feature groups; wherein N is greater than or equal to 2 and is an integer; and interacting with the target system based on the user feature group by using the target large model to obtain an interaction result. According to the invention, the cold start difficulty of the target system can be reduced.

The invention provides a multi-agent cooperation interaction method and system, and the method comprises the steps: firstly, enabling a decision agent to obtain an execution environment and a subtask set corresponding to a to-be-executed instruction in response to the received to-be-executed instruction; the decision-making agent generates a target dynamic interaction graph corresponding to a to-be-executed instruction based on a preset interaction graph and the sub-task set, and decomposes the target dynamic interaction graph into interaction sub-tasks which execute at least one interaction operation with at least one element feature in an execution environment in sequence; and finally, sequentially executing the page interaction agent according to the sequence of the interaction subtasks so as to complete the execution of the to-be-executed instruction. According to the method and the system, compared with a traditional browser automatic script which only depends on a preset rule, complex tasks of multi-step decision making can be processed, the intelligent level of man-machine interaction is remarkably improved, the task application range is remarkably widened, and meanwhile the response speed of task processing is also improved.

The application discloses an unmanned parking interaction system and method based on multi-modal and chain reasoning, and relates to the technical field of unmanned parking interaction systems.The system comprises: after an abnormality is triggered, an interaction agent corresponding to the trigger signal is materialized, and the interaction agent is configured to perform the following interactions: calling a camera to obtain a vehicle image, querying a vehicle information database for vehicle information to obtain an abnormal event, calling a chain reasoning engine to determine an abnormal situation according to the abnormal event and the vehicle information, querying a strategy sequence library for a matching rule corresponding to the abnormal situation, and calling an audio-video interaction device, a multi-modal interaction executor, an OCR recognition device, a billing engine and a payment interface to execute the matching rule; after payment is completed, the vehicle information and transaction information are updated, and the interaction agent is destroyed.The application constructs a corresponding interaction agent for each abnormality, greatly reduces system resource consumption and response delay, and realizes autonomous operation of the interaction agent.

The invention relates to the technical field of task processing, and provides a multi-task processing method and device for a map interaction agent, equipment and a medium, and the method comprises the steps: receiving task processing demand information sent by a client; performing task planning according to the task processing demand information to obtain a task planning result; and according to the task planning result, controlling the intelligent agent to execute the corresponding task, and streaming and transmitting the task execution state to the client for display. The task processing demand information sent by the client is received, task planning is carried out based on user natural language input of the client, the intelligent agent is controlled to execute the corresponding planning task, unified scheduling of client tools and server planning capability is combined, deep fusion of front and back end capabilities is realized, and the task planning efficiency is improved. The interactive experience of the user is improved, the closed-loop process of interaction between the user and the map is completed, and manual intervention is reduced.

The invention discloses an intelligent customer service interaction system, method and equipment based on a multi-dimensional state sequence. The intelligent customer service interaction system comprises a behavior judgment agent, a session agent, a task generation agent, an external system interaction agent, a reply interruption agent, a task interruption agent and an external system message receiving agent. According to the invention, decision making is carried out on customer service behaviors based on a multi-dimensional state sequence, and cooperation of various customer service behaviors is completed through a behavior judgment agent, a session agent, a task generation agent, an external system interaction agent, a reply interruption agent, a task interruption agent and an external system message receiving agent. The user intention can be accurately understood, triggering and interruption of various customer service behaviors can be controlled in real time, the interaction experience of the user is improved, the misoperation risk of the intelligent customer service is reduced, and the method can be widely applied to the technical field of artificial intelligence.

The present invention discloses a diversified interactive agent construction system based on offline learning. The system includes: an offline loop system and an agent interaction system. The offline loop system includes a process splitting unit, a self-evolution unit, a storage processing unit, and a model calling unit. The agent interaction system includes an information sharing unit and a collaboration unit. The offline loop system uses an offline reinforcement algorithm to process the basic functions of an agent under the condition of a single agent's independence, including thinking function, perception function, and execution function. The agent interaction system uses an interactive adjustment learning algorithm to enhance the information in the offline loop system, and uses a distance contraction adjustment algorithm to improve the collaboration efficiency of the agent interaction system, and conducts information interaction with other single agents to effectively complete complex tasks. The present invention realizes efficient learning of agents in an offline state and improves the data processing efficiency of diversified agents in completing complex tasks.

The invention discloses a PBL teaching auxiliary system based on a double-agent system, and relates to the technical field of education, and the PBL teaching auxiliary system comprises a retrieval database, an auditing agent, an interaction agent and an interaction module; the invention also discloses a PBL teaching assistance method based on the double-agent system. The method comprises the following steps: S100, preparation work; s200, inputting student questions; s300, generating a preliminary reply; s400, auditing the preliminary reply; s500, generating a preliminary reply; and S600, presenting the formal reply. According to the method, while direct answers are avoided and out-of-line output is prevented, personalized guidance is provided, the supervision burden of teachers is remarkably relieved, and the technical blank of AI teaching tools in the aspects of compliance, suitability and process evaluation is filled.