161 results about "Human computer dialogue" patented technology

The invention provides a man-machine dialogue method based on a large language model, and the method comprises the steps: starting a dialogue from a starting node of a predefined dialogue process, and transmitting initial problem information configured by the starting node to a user; initial reply information input by the user for the initial question information is received, and a first intention recognition node is determined based on the initial reply information; displaying a node question corresponding to the current node to the user, and receiving intention reply information input by the user for the node question; determining subsequent nodes according to the intention reply information and a candidate option set predefined by the current node through a large language model; and displaying reply information corresponding to the reply node to the user until the determined node is the reply node. The combination of the graphical process and the large language model enables the dialogue path to advance according to the preset logic, reduces the risk of deviating from the core target, and enhances the naturalness and flexibility of the dialogue by using the semantic understanding capability of the large language model, so that the reply of the user can be better understood.

A human-machine conversation method and apparatus, a device, and a storage medium. The method comprises: acquiring a user statement of a current round; determining, from one or more candidate tables, one or more target tables that match the user statement; parsing the user statement of the current round to obtain a first query statement; according to the first query statement, querying from the one or more target tables to obtain target data; and generating a system reply of the current round according to the target data.

The invention provides a man-machine conversation-based software cost intelligent evaluation interaction method and system, and relates to the technical field of software cost evaluation, and the method comprises the steps: obtaining project description information, carrying out intention recognition and entity extraction, and carrying out semantic matching in combination with a knowledge graph to obtain candidate cost references; according to the method, the estimation reference value is generated based on multi-dimensional similarity calculation and weight self-adaptive adjustment, user feedback correction is supported, accurate evaluation and dynamic adjustment of the software cost are achieved, and the accuracy, transparency and efficiency of software cost evaluation are improved.

The embodiment of the invention provides a large model tool calling data enhancement method, system and device and a medium. The method is applied to the technical field of man-machine conversation systems and artificial intelligence data enhancement, and comprises the steps of obtaining historical service data and corresponding track data to form historical parameters; carrying out association binding among the historical parameters, and constructing dialogue chain data; preprocessing the historical parameters and the corresponding dialogue chain data, judging whether the dialogue chain data is complete or not, and if yes, entering the next step; comparing the historical parameters with the reference data, and dividing the data into completely matched data, partially matched data and unmatched data; performing similarity retrieval on the dialogue chain data corresponding to the partial matching data to obtain historical correction data; and correcting the partial matching data according to the historical correction data to obtain correction data, and outputting the correction data, the complete matching data and respective corresponding track data and session chain data. According to the efficient and controllable data enhancement method disclosed by the invention, the large model tool calling capability can be improved.

The application relates to the technical fields of artificial intelligence and man-machine natural language dialogue, and provides a general memory management method and system based on a language model, which extracts memory information needing to be memorized from dialogue content with the language model, generates temporary memory, integrates the temporary memory to obtain persistent memory about a dialogue user, and is integrated into a dialogue process of the dialogue user and the language model to form memory of the dialogue user, so that the dialogue of the dialogue system is controlled, memory type classification management is performed according to the importance of the memory information in the persistent memory, so that the persistent memory contains short-term memory and long-term memory, the importance of the memory information in the persistent memory is updated and managed, and the short-term memory and the long-term memory are forgotten and upgraded, so that flexible and efficient storage and calling of user memory independent of the model type are realized, and the humanized, continuous and personalized dialogue experience in the man-machine dialogue process is improved.

The invention discloses a simulated inquiry interaction analysis method, device and equipment based on man-machine conversation, and relates to the technical field of artificial intelligence and medical treatment, and the method comprises the steps: obtaining inquiry conversation data generated by interaction between a user object and a virtual user; performing multi-dimensional interaction analysis on the inquiry dialogue data based on a preset medical knowledge graph and preset diagnosis information to generate a corresponding evaluation index and a corresponding index score; and generating and outputting an evaluation report containing the index score and the user object-oriented inquiry dialogue improvement suggestion. According to the method, the objectivity and the accuracy of an evaluation result are ensured, prejudice and inconsistency caused by subjective experience difference in manual evaluation are overcome, the evaluation result is accurate, and a user can clearly know the expression level of the user in each dimension and can also obtain a targeted inquiry optimization direction.

The invention relates to the technical field of artificial intelligence, and discloses a smart park man-machine conversation intention recognition system based on natural language processing drive, comprising: step 1, a natural language processing module for receiving and preprocessing natural language input of a park user; 2, the intention recognition module communicates with the natural language processing module, modular design is adopted, preprocessing, intention recognition, RAG retrieval generation, chain reasoning and self-checking, interaction control and other parts are included, and parallel development and maintenance are facilitated; a multi-stage verification and rollback mechanism enables the result to be more reliable and the update to be more controllable; by means of weak / semi-supervised learning, intelligent word segmentation and multi-space embedding, rich semantics can be extracted even if labels are few; the RAG module gives consideration to high correlation and duplicate removal, and can dynamically access the newest laws and regulations and data; event triggering type updating is supported, regular increment updating is also achieved, and continuous self-adaption is achieved through closed-loop feedback; and the timely and stable delivery of the message is ensured by the channel reliability control and the push priority strategy.

1. Name of the product in this design: Dialog box body of a home decoration graphical user interface for electronic devices. 2. Intended use of this design: for use in electronic devices. 3. The key design features of this product are the graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. For electronic devices, which are conventional devices, the rear view, left view, right view, top view, and bottom view are omitted. 6. Purpose of the graphical user interface: The home decoration dialog graphical user interface is used for human-computer interaction, and the dialog box body is used for human-computer dialogue. 7. Human-computer interaction methods of graphical user interface: Human-computer interaction can be carried out through clicking, input, etc., to realize human-computer questioning and answering. 8. Description of the changing states of the graphical user interface: The main view displays the home decoration dialog graphical user interface. Questions can be entered through the input box at the bottom of the dialog box, and the questions and answers are displayed in the chat box in the middle of the dialog box. During the machine's response after a question is entered, the input box displays the current response progress, as shown in the changing state diagram 1. You can click on the status bar to view the changing state diagram 2, which displays the progress list in a pop-up window. 9. Other situations requiring explanation: This design is a partial design, wherein the pink-coated part is the part for which protection is not sought, and the other parts are the parts for which protection is sought.

The application is suitable for the technical field of man-machine conversation, and provides a multi-round conversation generation method and device based on emotion judgment and terminal equipment, the method comprising: obtaining conversation information; identifying first emotion information in the conversation information through an emotion classifier, wherein the emotion classifier identifies and classifies emotions contained in the conversation information based on a classification model, and the first emotion information represents the emotion of the conversation information; determining second emotion information based on the first emotion information by using an emotion transfer mathematical model, wherein the second emotion information is one kind of emotion information in multiple first emotion information, and the emotion transfer mathematical model is used to determine the second emotion information based on conversation time and conversation round; and outputting the current round of conversation containing the second emotion information according to the second emotion information through a conversation generation model. The application can improve the recognition accuracy of the emotion classifier and improve the experience of users using the man-machine conversation system.

The invention discloses a man-machine dialogue psychological stress identification and intervention method and system, a medium and a product, and relates to the field of natural language data processing, and the method comprises the steps: carrying out man-machine dialogue based on a man-machine dialogue strategy through employing a preset man-machine dialogue model; acquiring a multi-modal signal of the detected object in a man-machine conversation process in real time, and performing feature extraction and mapping on the multi-modal signal to obtain a multi-modal fusion answer space; performing psychological stress identification on the multi-modal fusion answer space to obtain a real-time psychological stress level of the tested object; a man-machine conversation strategy of the man-machine conversation model is adaptively adjusted according to the real-time psychological stress level; and executing corresponding operation based on the adjusted man-machine dialogue strategy by utilizing the man-machine dialogue model. The accuracy and robustness of psychological pressure recognition are improved, the man-machine conversation strategy can be automatically adjusted according to the recognition result, and the emotional stability and psychological assistance value of man-machine conversation are improved.

The invention discloses an intelligent fish tank for children enlightenment education, and the fish tank comprises a fish tank main body which is internally provided with a voice interaction module, an audio playing module, an image recognition module and a wireless transmission module; wherein the voice interaction module is used for carrying out dialogue interaction with children, the audio playing module is used for playing audio, the image recognition module is used for recognizing expressions and objects, and the wireless transmission module is used for carrying out data transmission with external terminal equipment. Intelligent upgrading and child-friendly design of the fish tank equipment are achieved, and the technical defect that traditional equipment lacks interaction ability is overcome. The system can recognize the emotional state of a user and make intelligent feedback, establishes a natural AI intelligent man-machine conversation mechanism, effectively improves the interaction experience of the child user, enables the child to carry out natural science popularization and accompanying conversation in the fish culture and observation process, is a child enlightenment education tool with interaction and accompanying functions, and improves the interestingness of the child. And the requirements of families on children education and accompanying are met.

The embodiment of the invention provides an online man-machine conversation optimization method, system and device, electronic equipment, a storage medium and a program product. In the scheme provided by the embodiment, when a first dialogue generation model executes a streaming response based on user input data, a currently generated first dialogue text fragment is subjected to repeated detection, and when it is detected that repeated content exists in the first dialogue text fragment, the first dialogue generation model is controlled to interrupt execution of the streaming response; in addition, reply generation constraint information is determined according to the repeated content in the first reply text segment, and then reply generation cue words are generated based on the reply generation constraint information and the non-repeated content in the first reply text segment. And after the dialogue generation cue word is input into the first dialogue generation model, the first dialogue generation model is triggered to determine a dialogue generation starting point according to the non-repeated content in the first dialogue text fragment, and streaming response is continuously executed from the dialogue generation starting point.

The invention relates to a control method and device of clothes processing equipment and the clothes processing equipment. The control method of the clothes processing equipment comprises the steps that clothes information of clothes to be processed is acquired based on man-machine conversation; obtaining a target processing program matched with the clothes information and a target processing parameter corresponding to the target processing program; and processing the to-be-processed clothes according to the target processing program and the target processing parameters. According to the embodiment of the invention, the method can guide the user to provide the expression related to the to-be-processed clothes through the man-machine conversation, converts the oral and fragmented expression of the user into the formatted clothes information, and recommends the target processing program and the target processing parameters suitable for the to-be-processed clothes for the user according to the clothes information. Therefore, the problem that the user does not know which processing program and parameter are selected to process the to-be-processed clothes in daily life is solved, and better clothes processing service is provided for the user.

The invention relates to the technical field of artificial intelligence, in particular to a papermaking employee virtual practical training method and system combined with a large model. According to the papermaking employee virtual practical training method and system combined with the large model, immersive interaction with a high-fidelity virtual papermaking production line is achieved through data collection, virtual simulation and large model interaction and reasoning. According to the method, a large-scale language model is introduced, so that the method has strong natural language understanding, deep reasoning and multi-modal information fusion capabilities, complex and changeable virtual environment states can be analyzed in real time, and intelligent diagnosis and guidance are performed in combination with natural language questions of employees; and even non-preset or multi-factor coupled fault scenes which cannot be processed by a traditional rule system can be processed, man-machine dialogue interaction similar to experienced experts is realized, and the intelligent level of the system on solving complex problems is remarkably improved.

The invention provides a man-machine conversation method and device, equipment, a medium and a program product, relates to the technical field of artificial intelligence, and aims to solve the problem of low accuracy of replying information of a user by an intelligent agent in related technologies. The method comprises the following steps: acquiring dialogue information of a user, wherein the dialogue information comprises a user video and a user voice; using a pre-trained emotion recognition model to carry out emotion recognition on the user limb movement in the user video to obtain a first emotion state of the user, and carrying out character conversion on the user voice to obtain voice recognition content; performing information fusion on the first emotional state, the voice recognition content and a preset instruction to obtain a prompt strategy; and generating reply information based on the prompt strategy by using a large language model. The accuracy of the generated reply information can be improved.

1. The name of the design product: AI intelligent dialogue machine (6). 2. The use of the design product: for voice recognition, voice playback, AI intelligent man-machine dialogue. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

Embodiments of the present application provide a contrastive learning, query and human-computer dialogue method, an electronic device and a storage medium. The contrastive learning method comprises: constructing a similar sample pair and a non-similar sample pair based on at least a similar enhanced sample and a labeled sample of a training sample; constructing a loss function of an encoder of a task model based on a similarity of the similar sample pair and a similarity of the non-similar sample pair, a function value of the loss function being directly proportional to the similarity of the similar sample pair and inversely proportional to the similarity of the non-similar sample pair; and training the encoder of the task model based on the loss function. In the scheme of the embodiments of the present application, based on the inventive concept of contrastive learning, the function value of the constructed loss function is directly proportional to the similarity of the similar sample pair and inversely proportional to the similarity of the non-similar sample pair, so that through such a loss function, the confidence of the training sample is improved, and the training sample itself is not only enhanced, and therefore the performance of the trained task model is improved.

The invention relates to an intelligent voice remote control system and method based on AI semantic analysis, belongs to the field of voice recognition, and particularly belongs to the application field of programs used in the man-machine conversation process. And the voice remote control module is used for intelligently analyzing an authorized user identifier and a voice remote control instruction corresponding to the field audio data of the intelligent remote controller of the television box based on the selected basic data by adopting an AI semantic analysis model designed by a dynamic structure, and providing multiple items of reference data for determining a voice remote control strategy. According to the invention, aiming at the technical problem that the reliability, the stability and the simplification level of intelligent voice remote control in the prior art cannot be considered at the same time, the AI semantic analysis model of the corresponding dynamic structure can be designed for the field audio data of the intelligent remote controller of the television box; and synchronous intelligent analysis of the voice remote control instruction corresponding to the field audio data and whether the corresponding source user belongs to the authorized user is completed, so that the technical problem is solved.

The invention relates to the field of artificial intelligence and man-machine conversation, and particularly discloses an emotion-enhanced historical session representation and drive response generation method, which comprises the following steps: S1, outputting a normalized historical session text sequence; s2, encoding a historical session text, and generating a text vector representation; s3, emotion recognition and coding are carried out, and emotion vector representation is converted; s4, identifying and coding a vertical field, and converting vertical field vector representation; s5, fusing the text vector, the emotion vector and the standing vector by adopting a splicing fusion mode, and outputting historical session vector representation of emotion enhancement; and S6, generating the response of the intelligent customer service, and adjusting a generation strategy according to the emotion intensity and the standing trend. According to the emotion-enhanced historical session representation and drive response generation method provided by the invention, the LLM structure does not need to be modified, the compatibility and expansibility are high, the deployment is convenient, and the pertinence of customer service response and the user satisfaction can be remarkably improved.

This invention discloses a human-computer interaction method for service robots, involving human-computer dialogue systems and mobile robot navigation control. The biggest drawback of current human-computer dialogue systems, which primarily use sequence-to-sequence models, is that these models tend to generate high-frequency, generic responses and lack sufficient useful information, making it difficult to provide practical assistance to users. This patent introduces external knowledge to enrich the information content of the model's generated responses, improve the diversity of responses, and alleviate the problem of high-frequency generic responses. Based on this model, a human-computer interaction method for service robots is designed. This method not only has dialogue capabilities but can also parse user navigation control commands, enabling fixed-point navigation control in home or office scenarios and improving the robot's human-computer interaction capabilities.

The invention relates to a multi-field dialogue model training and task processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring general dialogue data and at least one type of domain tag data; performing parameter fine tuning on the pre-trained large language model through the at least one type of domain tag data to obtain an initial professional model; according to a target sampling proportion, sampling each type of domain tag data to obtain target sampling data, and performing parameter fine tuning on the initial professional model through the target sampling data and the general dialogue data to obtain a target dialogue model; the target dialogue model is used for being deployed in task processing equipment, so that the task processing equipment processes the dialogue tasks of the target fields based on the target dialogue model. In this way, the target dialogue model has the universal capacity of processing all the target fields and man-machine dialogue related tasks, and occupation of storage resources in task processing equipment is reduced.

The present invention relates to the technical field of digital humans. Disclosed is a large-model-driven method for real-time interaction with a digital human, comprising the following steps: S1, inputting a question text by means of a human-machine dialog frontend and sending the question text to a large model backend; S2, the large model backend performing large model inference on the basis of the question text and a knowledge base to generate an answer summary and a full answer and respectively sending the answer summary and the full answer to a digital human backend and the human-machine dialog frontend for display; S3, a classifier generating a plurality of classification labels on the basis of the answer summary, and the digital human backend performing retrieval and matching on a preset video library on the basis of the classification labels to obtain a target preset video and sending the target preset video to the human-machine dialog frontend; and S4, a digital human processing engine generating a digital human summary video on the basis of the answer summary and pushing the digital human summary video to a player, and the player sequentially playing the target preset video and the digital human summary video. Smoother experience of real-time question-and-answer interaction with a digital human is finally achieved, thereby reducing loss of realism in the experience.

1. Name of the design product: children's companion robot (Doudou). 2. Use of the design product: for human-computer dialogue communication and interaction feedback. 3. Design points of the design product: in shape. 4. Picture or photo that best indicates the design points: perspective view.

The invention relates to the technical field of edge bonding machines, in particular to a control method and a control system of a single-tool head and tail cutting device of an edge bonding machine, and the system comprises a man-machine conversation unit, a CPU control unit, a conveying mechanism control unit and a single-tool head and tail cutting control unit. The conveying mechanism control unit comprises a conveying motor frequency converter, a conveying motor, a rotary encoder and a conveying mechanism position sensing switch; a signal of the rotary encoder is output to the CPU control unit so as to provide plate moving position data; the single-tool head-tail cutting control unit comprises a head-tail cutting horizontal moving module; a head and tail cutting lifting module; the invention discloses a head-tail cutting blocking module. Precise control and machining of the head and the tail of the plate are achieved, rotary cutting is conducted on reserved edge sealing strips at the front end and the rear end of the plate through the head and tail cutter, the edge sealing machine is simpler in structure, the machine length size of the whole edge sealing machine is shortened, use is convenient, the structure of the edge sealing machine is simplified, and control units are reduced; and meanwhile, the stability and the reliability of the equipment are enhanced.

The invention discloses an intelligent body structure for realizing man-machine conversation based on a large model, and the intelligent body structure comprises an intelligent body structure body which can autonomously move and operate outside; and the large language model is deployed on a central processing unit in the body structure and is used for understanding a user language instruction and driving the body structure to act. The large language model understanding instruction comprises the steps that a user sends out an instruction through voice, the instruction is converted into a text instruction after being collected and preprocessed through a voice recognition microphone array, a central processing unit inputs the text instruction into the large language model, the model conducts semantic understanding and intention recognition and generates a decision instruction, and the central processing unit drives the body structure to act in sequence. The method has the beneficial effects that efficient man-machine conversation based on a large model is realized, the intelligent agent with the body can autonomously move and operate, and the man-machine interaction experience and the working efficiency of the intelligent agent are improved.

1. The name of the design product: man-machine dialogue dynamic graphical user interface for electronic device. 2. The use of the design product: an electronic device. 3. The design points of the design product: the graphical user interface content in the electronic display screen. 4. The picture or photo that best indicates the design points: front view. 5. The design points are in the dynamic graphical user interface, and the omitted views do not involve the design points, and the rear view, left view, right view, top view and bottom view are omitted. 6. The use of the graphical user interface: to show dynamic expressions of man-machine voice interaction. 7. The man-machine interaction mode of the graphical user interface: man-machine dialogue. 8. Other circumstances that need to be explained: the front view is the initial screen of the graphical user interface. During the man-machine dialogue interaction process, the graphical user interface jumps from the initial screen to change state diagram 1 to change state diagram 8 to show different dynamic expressions. In each change state diagram, the eyes slightly rotate and the mouth slightly changes shape, representing different emotions.

This disclosure relates to context-aware human-to-computer dialog. Methods, devices, and computer-readable media are described that involve utilizing the context of an ongoing human-to-computer dialog to enhance the ability of an automated assistant to interpret and respond when a user abruptly transitions between different domains (topics). In various implementations, natural language input from a user can be received during an ongoing human-to-computer dialog with an automated assistant. A grammar can be selected to parse the natural language input. The selection can be based on a topic that is stored as part of a context data structure associated with the ongoing human-to-computer dialog. The natural language input can be parsed based on the selected grammar to generate a parse. Based on the parse, a natural language response can be generated and output to the user by using an output device. Any topic raised by the parse or the natural language response can be identified and added to the context data structure.

1. Name of the product in this design: Intelligent Conversational Robot. 2. Purpose of this design: This design is mainly used for human-computer interaction. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

The application provides a man-machine conversation method, device and equipment and a readable storage medium, and relates to the technical field of artificial intelligence. The man-machine conversation method comprises the following steps: determining the category of an incoming call; determining the conversation mode corresponding to the category of the incoming call; acquiring language information of a conversation object in the conversation mode and identifying first key information of the language information; determining a reply strategy based on the first key information and a preset deep reinforcement learning model; generating reply language information corresponding to the first key information based on the reply strategy, and returning the reply language information to the conversation object. Thus, the problem of high complexity of the existing man-machine conversation can be solved.

The application discloses a kind of human-computer conversation scene construction systems based on generative language model, belong to human-computer interaction technical field.The application includes scene preset module, script generation module, user intent processing module, routing logic processing module, data generation and processing module and knowledge base node processing module;Above module generates the script of machine node according to user preset scene description information, judges whether there is similarity user intent according to user reply content, and carries out merging or generates new user intent, determines machine content and user content in next interaction according to preset information and language model, and forms routing chain, generates machine content based on generative language model, judges whether there is similarity intent, and forms the routing chain of machine node and user intent, triggers discrete knowledge point output node content, and realizes process memory regression, to build out general closed human-computer conversation scene.