322 results about "User intent" patented technology

Methods and systems for artificial intelligence (AI) assisted automation of testing of a software functionality related to a user intent on a software platform is provided. In one embodiment, the method includes receiving user action data indicating the user intent related to a software platform and generating a human-readable test scenario. The test scenario, which includes a sequence of human-readable testing steps and an expected outcome, is generated using a scenario machine learning (ML) model. The method includes generating an interpretable test script based on the test scenario using a script ML model. The test script is interpreted to implement testing steps on the software platform, generating a test outcome. A human-readable test report is also generated to evaluate the test scenario on the software platform, based on the test outcome and the expected outcome.

The application discloses a government affair text auxiliary writing system and method based on a multi-modal large model, and relates to the technical field of artificial intelligence and informationization; comprising: step 1: converting heterogeneous government affair data into unified semantic representation, realizing cross-modal deep alignment through fine-grained contrast learning; encoding text, image and voice features by using an encoder respectively, and constructing a training set containing 100,000 pairs of government affair image-text samples; weighting and fusing the text, image and voice features, dynamically adjusting the weight through an attention mechanism, training a large model, step 2: performing lightweight deployment of the large model, and step 3: using the large model to understand and identify user intention, and outputting government affair text.

This invention provides a method and agent-based approach for identifying and scheduling computing resource requirements, relating to the field of cloud computing resource management technology. The method includes: receiving a user's fuzzy description of computing resource requirements; parsing the user's requirements using natural language processing (NLP) technology to identify user intent and key entities; proactively guiding the user to complete missing parameters through multi-turn dialogue based on key entities and a built-in knowledge base; calling a resource evaluation model to quantify the parameters and generate a list of computing resource requirements; obtaining a set of candidate resources that meet the list of requirements; calculating multi-dimensional utility scores for each candidate resource; generating multiple recommendation schemes based on different weighting strategies across multiple dimensions, and generating natural language descriptions of recommendation reasons for each scheme; and executing resource allocation based on the user's selection. This invention improves resource matching accuracy and scheduling efficiency by accurately understanding and quantifying the user's computing resource requirements through an agent, thereby generating a computing resource scheduling scheme.

This invention belongs to the field of intelligent cockpit technology, and relates to a six-dimensional screen adaptive following system and method for vehicle cockpits. The invention includes a perception module for collecting multimodal data within the cockpit; a decision control unit connected to the perception module for processing the multimodal data, identifying user intent and the current scene, and generating corresponding six-dimensional screen following control commands; a six-dimensional screen actuator connected to the decision control unit for receiving the following control commands and driving the screen to translate forward and backward along the Z-axis, rotate around the X-axis, and rotate around the Y-axis; and a human-computer interaction output module connected to the decision control unit, including a screen UI rendering unit and an audio output unit. The screen UI rendering unit outputs a corresponding virtual avatar animation while the screen moves, and the audio output unit outputs corresponding voice feedback while the screen moves.

We provide the system. [Solution] A means of analyzing user inquiries using natural language processing, A means for referencing user attribute information and generating personalized information, A means of obtaining information from external sources and selecting information according to the user's intent, A means of displaying the selected information on the user interface, A means of converting voice input to text, A system that includes this.

The application discloses a knowledge graph-based semantic analysis method, a storage medium and an electronic device, relates to the technical field of smart home / smart home, and the knowledge graph-based semantic analysis method comprises the following steps: obtaining a voice instruction to be recognized; adopting a pre-trained recognition model to analyze the voice instruction, determining a user intention and slot information contained in the voice instruction; according to the user intention, determining a device to be controlled, and calling a knowledge graph of the device to be controlled in a preset knowledge graph library; according to the knowledge graph, analyzing the slot information, determining a matching degree of the slot information and the knowledge graph, and determining semantic information corresponding to the voice instruction based on the matching degree. In conclusion, the application can more accurately recognize semantic information by combining a deep learning model with a knowledge graph.

The present disclosure provides a kind of intelligent question answering method, device, equipment, storage medium and program product, it is related to big language model, artificial intelligence technical field such as intelligent question answering.The method comprises: the intent recognition of the question information of user is carried out in combination with the special dictionary of target field, and the intent recognition result is obtained;Recall the candidate answer information set matched with question information in the pre-set question and answer knowledge base based on the intent recognition result;Determine the target answer information corresponding to the question information based on the similarity of candidate answer information in candidate answer information set and question information.The present embodiment discloses that, in target field, the intent recognition of user is carried out in combination with natural language understanding algorithm, by understanding user intent, finding corresponding knowledge base and recalling corresponding target answer information, it can be more accurately generated answer information for the question information of user, improve the adaptability of intelligent question answering in specific field, and can be used for timely interaction with user.

This application discloses a method, apparatus, and computer program product for processing outbound call services. Relating to the field of artificial intelligence, the method includes: executing an outbound call task to a user; processing the target service according to a preset service processing procedure; collecting the user's voice signal during the target service processing; extracting voice features and voice content from the voice signal; determining the current service processing node corresponding to the voice content in the preset service processing procedure; determining the service path map corresponding to the preset service processing procedure; determining a path deviation index value based on the current service processing node and the service path map; inputting the voice features into a target model to obtain the user's behavior recognition result; determining a risk index value for the user interrupting the target service based on the behavior recognition result and the path deviation index value; and adjusting the preset service processing procedure according to the risk index value. This application solves the problem of poor accuracy in recognizing user intent in outbound call services in related technologies.

An exemplary computing system retrieves information from one or more applications using an application programming interface and generates personal context information for a user based on at least one section of that information. Upon receiving a natural language request to generate at least one graphical component, the computing system applies a machine learning model to the natural language request to determine at least one user intent. Using one or more pieces of information from the one or more applications and the user's personal context information, the computing system generates a set of instructions that include instructions for generating the at least one graphical component, such as a widget, based on the at least one user intent.

This invention relates to the field of natural language processing technology, specifically to a method and system for matching the intent of intelligent NPC dialogues in multimodal interaction. The method involves real-time acquisition of multimodal data, generating multimodal semantic features through submodal preprocessing; integrating the semantic features of each modality using a cross-modal fusion module based on semantic association, generating a candidate intent set based on semantic context representation and combined with a semantic parsing module and predefined intent templates; tracking changes in user intent in real time through a continuous semantic learning mechanism and an interactive memory module, combined with a Bayesian update method, dynamically adjusting the confidence level of each intent in the candidate intent set, and filtering the final intent; constructing an NPC semantic cognition model, and performing semantic consistency analysis to perform semantic checks on user input and NPC dialogue state; combining the final intent and a decision engine to generate a dialogue strategy and output synchronized response content; this invention improves the accuracy of dynamic matching of intelligent dialogue intents.

We provide the system. [Solution] A means for receiving voice input from a user and converting that voice input into text data, A means for analyzing the aforementioned text data and identifying the user's intent, A means for obtaining necessary information and generating an appropriate response based on the intent of the identified user, A means of converting the generated response into audio information and outputting it to the user, A means of analyzing information based on voice input, acquiring past health data, and providing health guidance, A means of notifying patients of the time to take their medication, A system that includes this.

The application discloses a Text-to-SQL intelligent agent coordination system and method for perceiving potential intention of a user in a multi-round context. The system comprises a memory bank, a prompt strategy pool, a strategy recommender, an SQL generator, an SQL refiner and a strategy optimizer. The memory bank is used for storing system running data. The prompt strategy pool is used for storing prompt strategies. The strategy recommender is used for receiving user information and strategy information of the prompt strategies, identifying the current potential intention of the user based on the user information and the strategy information, and matching the optimal strategy from the prompt strategy pool according to the potential intention to constrain the generation of an SQL query statement. The SQL generator generates an SQL query statement based on the current natural language query of the user, the system running data and the optimal strategy. The SQL refiner is used for adjusting and iteratively updating the SQL query statement. The strategy optimizer is used for updating the prompt strategies. The application increases strategy selection before SQL generation, so that the SQL generator can generate an SQL statement meeting the user demand at one time with a higher probability, and the cost is reduced.

The application discloses a virtual anchor intelligent idle chat system based on a dynamic knowledge graph, relates to the technical field of artificial intelligence and virtual anchors, and comprises data preprocessing, cross-mode fusion, intention distribution, response generation and knowledge updating modules. Current and historical dialogue data of a user is acquired first; dialogue text features and entity relationship features of a knowledge graph are extracted and fused; the correlation degree of user intention and knowledge entities is analyzed by using a graph neural network; idle chat responses fused with knowledge are generated accordingly; and finally, knowledge graph updating strategies are generated according to response feedback to drive the dynamic evolution of the knowledge base. The system realizes the deep dynamic fusion of idle chat dialogue and structured knowledge, and can autonomously optimize knowledge according to interactive feedback, thereby improving the knowledge, accuracy and long-term adaptability of responses of the virtual anchor.

A method comprising receiving audio data 102, generating a transcription 151 comprising a sequence of terms 152, such as “Buy some tomatoes and bananas. Change tomatoes to potatoes”, parallel processing the audio data and the transcription using a multimodal large language model (LLM) 150 to identify one or more revision terms 152R, for example “Change”, specifying a revision action to perform on at least one other term in the sequence, in this instance “tomatoes”, and modifying the transcription 151M accordingly – “Buy some potatoes and bananas”. Identifying the revision term(s) may be based on a corresponding user intent 154 determined for each respective term in the sequence, for example, the user 10 does not intend the final transcription to include “tomatoes”. For each term in the sequence, parallel processing may comprise correlating its speech characteristics 156 such as pitch, tone or prosody information determined from the audio data with its corresponding linguistic context 158 determined from the transcription. Transcription correction may be based on a revision token inserted into the sequence, the token indicating an N number of terms for replacement and their corresponding replacement terms. User context data 104 may be obtained to tailor the LLM to a particular user. [Figure 1A]

This invention discloses a method for generating intelligent shell commands in a Linux environment, comprising: acquiring input natural language in a Linux terminal; determining whether a corresponding ready-made command exists; if it exists, directly executing the corresponding ready-made command; if it does not exist, calling a cloud model to perform semantic parsing on the natural language, identifying the user's intent based on the semantic parsing, and performing knowledge retrieval in conjunction with the context; combining the user's intent and the knowledge retrieval results, calling a large model to generate a shell command, and displaying the shell command to the user; after user confirmation, performing sandbox pre-execution; if pre-execution fails, returning the pre-execution result; if pre-execution succeeds, executing the shell command, and returning the execution result to the user after execution. This invention also discloses an intelligent shell command generation system in a Linux environment. This invention uses natural language to operate the terminal, making it convenient to use.

The application relates to the field of big data, in particular to a model training method and a user intention prediction method. The method is applied to a cloud side device in a data processing system, the data processing system further comprises at least one end side device, and the method comprises the following steps: acquiring encrypted data reported by the end side device; wherein the encrypted data is data obtained by encrypting historical behavior data of a user by the end side device; generating a first event according to the encrypted data; wherein the first event is a general event which does not comprise user information; and training a first model according to the first event, wherein the first model is a general model used for predicting user intention. Through the above method, the cloud side device realizes the training of the prediction model of the user intention under the premise that specific user information cannot be obtained, thereby facilitating the protection of user privacy.

The application provides a network user intention recognition method, vectorization representation of text content and emotion is realized through two kinds of Bert pre-training models, efficient extraction of global and local features of the text is realized by adopting a Text-Inception module and a Bi-LSTM network, further, dynamic fusion of the local features and the global features of the text is realized based on an improved SENet network, and dynamic combination of the text and emotional features is realized through a residual cross-attention mechanism, understanding and recognition of the intention of the social network user are realized. Finally, the application can accurately recognize the intention of the user, and further provide support for a wide range of application scenarios, such as malicious user analysis of social media.

The application relates to the technical field of artificial intelligence, and provides a large model prompt word construction method, device, equipment, medium and program product, the method comprises the following steps: extracting query content and a business scene identifier from a user request; based on the business scene identifier, a target prompt word template is matched from a prompt word template database; and based on an intention category identifier associated with the query content and the target prompt word template, a large model prompt word is constructed. The large model prompt word construction method provided by the application can match a target prompt word template from a prompt word template database based on a business scene identifier, and can construct a large model prompt word in combination with an intention category identifier associated with query content, can dynamically construct a large model prompt word that is adapted according to real-time user intention and a business scene, has high flexibility, and can realize unified dynamic construction of prompt words for multiple businesses through scene-based adaptation and accurate intention guidance, thereby improving the expansibility and adaptability of business processing.

The embodiment of the application provides a kind of artificial intelligence native application ecosystem, and related method and product;Artificial intelligence native application ecosystem includes: big model application module and multiple application programs;Big model application module is used to determine user intent information, and according to user intent information, determine target application program from multiple application programs;With target application program carries out data interaction, to provide target application program corresponding service to user.By the embodiment of the application, the application program of third party can be called, so that AI application can provide more rich functions to user, to reach the performance ability of human assistant.

This application discloses a search error correction optimization method, apparatus, device, and computer program product. The method includes: acquiring user interaction behavior logs, including search behavior logs and click behavior logs; preprocessing the interaction behavior logs; performing user behavior analysis on the preprocessed interaction behavior data to obtain user behavior analysis results; making decisions on the preprocessed interaction behavior data based on the user behavior analysis results using a pre-set confidence decision threshold to obtain decision results; and updating the search error correction terminology in a closed loop based on the decision results. This application constructs a search error correction closed-loop optimization scheme based on user behavior feedback, enabling the error correction system to have self-evolution capabilities; it upgrades from "algorithm-driven" to "user intent-driven" based on massive amounts of real user behavior data, more accurately reflecting user needs; and it improves decision reliability through scientific quantitative decision-making using a confidence decision model.

The application relates to the technical field of artificial intelligence-based music melody automatic generation systems, and particularly discloses an artificial intelligence-based music melody automatic generation system. The system comprises an intention analysis and feature mapping module, which is used for converting a user intention into a structured semantic feature; an emotion evolution state machine module, which is used for dynamically generating a time-series emotion state vector according to the semantic feature; a hierarchical controllable generation network module, which is used for generating a note sequence with specific styles, coherent emotions and diversity based on the semantic feature and the emotion state vector; and a post-processing and music theory constraint module, which is used for performing compliance correction and optimization on the note sequence. The application can generate a melody with coherent emotion evolution logic, high matching of a user intention, various styles and compliance with music theory.

This application relates to a system and method for constructing a dialogue assistant for a coal mine monitoring system based on large-scale model technology in the field of coal mine safety production monitoring technology. The system includes a dialogue intent analysis module, an intelligent diagnosis module, and a large-scale model analysis module. The dialogue intent analysis module receives and processes user-input natural language questions. The intelligent diagnosis module, after clarifying the user's intent, performs multi-dimensional analysis and judgment, directly providing answers or guiding the large-scale model to organize diagnostic conclusions and suggestions for output. The large-scale model analysis module displays the answers to the user, ultimately achieving intelligent question-and-answer functionality. This invention solves the problem of insufficient intelligence in human-computer interaction in coal mine monitoring systems, overcomes the limitations of a single information display method, improves the system's intelligent diagnosis and analysis capabilities, and addresses the challenges of offline network environments in coal mines.

The invention provides a rules engine that manages user requests within an interface integrating multiple AI platforms and AI Models. Upon receiving a query, the engine assigns scores based on factors like management rules, user preferences, and contextual information. Determinative scores, such as those enforcing strict enterprise policies, can override others, leading the engine to block or reroute the query. If no score is determinative, the engine forwards the query and associated prompts to a specialized routing AI Model for contextual analysis. Based on this analysis, the rules engine directs the query to the most appropriate AI Model or defaults to the user-designated AI Model. This system balances user intent with rule enforcement, optimizing query processing across various AI platforms while ensuring compliance with enterprise guidelines.

The application relates to the technical field of massage equipment, in particular to a control method, system and equipment of massage equipment. The method comprises the following steps: receiving voice instruction information of a user; sending the voice instruction information to a cloud server in communication connection with the massage equipment; the cloud server comprises an artificial intelligence model, the artificial intelligence model is used for carrying out semantic analysis on the voice instruction information to obtain target semantic information, carrying out intention discrimination based on the target semantic information, and outputting a discrimination result; wherein the target semantic information comprises user intention information; receiving the discrimination result fed back by the cloud server; and regulating and controlling the massage equipment according to the discrimination result. The application can improve the voice recognition accuracy.

This invention discloses a multimodal perception-based immersive digital interaction system and method, relating to the field of digital interaction technology. The system includes generating user state representations; constructing a dynamic contextual framework; and optimizing interaction strategies through a multi-channel feedback mechanism. This invention achieves deep fusion and dynamic understanding of the user's multidimensional human state and environmental context, significantly improving the system's accuracy in recognizing user intentions and its real-time response; enhancing the naturalness and immersion of the interaction; and enabling the system to continuously adapt and optimize, generating highly personalized, smooth, and context-consistent immersive interactive experiences. This comprehensively solves the key problems of mechanical interaction, delayed feedback, and insufficient immersion in existing technologies.

A computer system is provided to assist a user in performing a task within a technical system. The computer system includes a user intent detection module configured to detect the user's intentions when interacting with the technical system, a user request clarification module configured to determine chat texts to assist the user in the task, and a task assistance module.The task assistance module is configured to receive information from the user, in which the information relates to one or more natural language chat messages and / or an observation of the user's interaction with the technical system, to determine, by applying the user intent detection module, a task execution process based on the information, and to interact, by applying the user request clarification module, with the user via chat messages in accordance with the process determined to assist the user in the task.

The application provides a control method and system of a smart home appliance, and relates to the technical field of large language models, which comprises the following steps: in response to receiving a control request sent by a user terminal, extracting a user identifier carried by the control request; if the user of the user identifier is verified as a legal user, calling an intelligent agent configured by the smart home appliance to analyze the control request to obtain a user intention and at least one target home appliance; querying model information corresponding to the target home appliance; inputting the model information and the user intention into a preset large language model, outputting a device control instruction corresponding to each target home appliance by the large language model, and sending the device control instruction to the target home appliance to control the target home appliance. The control method and system of the smart home appliance can effectively reduce the risk of illegal control of the smart home appliance, thereby improving the security and stability of the entire control system.

Biometric metric wearable systems and methods are disclosed for detecting intended limb motion and invoking pain mitigation. Biometric signal data of a user is sensed by biosensor(s) that form part of respective sensor pod(s) of a biometric wearable device while the user performs an intended limb motion. The sensor pod(s) include biofeedback indicator(s). Processor(s) communicatively connected to the biometric wearable device analyze the biometric signal data to learn in real time or near real time user-specific biosignal pattern(s) detected when the user performs the intended limb motion, and generates therefrom learned user intent data configured for at least one of: (1) visualizing a virtual limb, (2) visually controlling a graphical aspect, and / or (3) visualizing the one or more user-specific biosignal patterns on a display device. The processor(s) then visualize the learned user intent data on the display device for invoking a pain mitigation effect with the user corresponding to the given limb.

The application discloses a kind of equipment knowledge management system and method;The system comprises: multi-modal data access module, for accessing multiple format files and extracting multi-modal elements;Knowledge processing module, for the structural treatment of extracted elements and constructs knowledge graph;Retrieval question and answer module, for executing user intent judgment based on user question input;According to the intention recognition result, when judging triggering question and answer, generate answer and its generation basis;Fault diagnosis module, for determining triggering fault diagnosis according to the intention recognition result, constructing fault tree according to knowledge graph and outputting troubleshooting scheme;Process application module, for determining triggering process application according to the intention recognition result, extracting BOM structure and process elements, generating process outline, multi-level process and SOP content, recommending associated resources based on process;Authority management module, for realizing access control based on directory and secret level.The application provides efficient and accurate equipment knowledge management platform for users.

The application discloses a kind of intelligent decision control system, method and mobile terminal based on camera shooting, system is deployed in mobile terminal, using the three-layer architecture of input layer, decision layer, execution layer;Input layer is collected image, motion, user behavior and social label data by multi-modal perception module and is preprocessed;Decision layer extracts image features by improved ResNet network, and combines attention mechanism to fuse multi-modal features, then by LSTM network Time series decision, output scene classification result and optimal shooting parameter set;Execution layer dynamically allocates lens task and computing power resources by cross-camera cooperative unit, and executes parameter adjustment and feedback fine tuning by parameter control unit;The application solves the problems of poor scene adaptability, user intention fragmentation and inefficient hardware collaboration in the prior art, realizes global collaboration of environment, user intention and hardware resources, significantly improves the success rate of complex scene shooting, reduces system power consumption, and does not require user manual operation.