9349 results about "Personalization" patented technology

An online social wager-based gaming system is disclosed, featuring dynamic cross-provider game filtering and persistent cross-provider voice-interactive group play. The system allows for automated multi-seat group game reservations and incorporates a distributed ledger for bet verification. Features include a Group Connect module for dynamic group formation and live audio across various game providers, enabling real-time play. A Professional Companion Connect module facilitates live webcam or microphone gambling sessions with professional companions, under contractual agreements. The User Games module enhances personalization by integrating user-uploaded images into the gaming environment. A cryptocurrency / blockchain-based bet tracking system ensures transparency and security in managing bet transactions through a controlled digital wallet system. This integrated platform aims to provide a cohesive, interactive, and personalized online casino experience, addressing limitations of traditional solitary online gaming by fostering social connections and enhancing user trust and engagement.

A computing system for adaptive electronic message classification employs a multi-agent architecture comprising a media feature analysis system, a user context refinement system, and a response synthesis system. The media feature analysis system generates pillar scores including message type, intent, and link risk scores with associated confidence values using trained classification models. When pillar scores and confidence values do not satisfy predetermined threshold conditions, the user context refinement system dynamically constructs contextual prompts using the pillar scores and confidence values as input parameters. User responses generate score modification data that refines the pillar scores and contextual response data for recommendation generation. The response synthesis system generates refined classifications and personalized recommendations using the refined pillar scores and contextual response data. An orchestration system coordinates agent interactions using learned uncertainty points and implements asymmetric influence algorithms with variable weighting based on content and URL analysis concordance.

In embodiments, systems and methods for configuring and deploying artificial intelligence driven transacting agents that are permitted to autonomously execute transactions on behalf of the individual or organization and configuring the transacting agent based on the agent configuration instructions and a set of predefined system prompts. The method further includes granting the transacting agent access to a digital wallet associated with the individual or organization and deploying the transacting agent to a public network, such that the transacting agent executes transactions on behalf of the individual or organization via one or more digital marketplaces using the digital wallet to which the transacting agent was granted access. In some embodiments, the transacting agent is granted access to the digital wallet using a consent token. In some embodiments, the method also includes hyper-personalizing and / or fine-tuning the agent.

An AI-powered content generation system that creates consistent, coherent, and engaging multi-modal content by integrating multiple specialized AI components. The system analyzes user input, identifies key elements, and maintains continuity throughout the generation process. It incorporates a feedback loop to learn and adapt based on user preferences, enabling personalized content experiences. The modular architecture allows for seamless integration of AI components focusing on text, images, audio, and interactive elements. The system ensures consistency across modalities and over extended periods, while managing rights, licenses, and royalties using blockchain technology. This advanced platform revolutionizes content creation, consumption, and management in the digital age.

An autonomous environmental regulation and behavioral monitoring system is disclosed, configured to adapt temperature, lighting, and acoustic conditions based on real-time emotional and physiological data. The system includes a dual-redundant central processor, hierarchical communication networks, multi-angle visual acquisition units, infrared thermometers, and modular environmental subsystems. It detects posture, gestures, facial expressions, and thermal signals to classify user states and apply individualized airflow, light, and sound modulation without relying on external internet connectivity. The system also monitors connected appliances using voltage-based pressure analysis to forecast device degradation. With integrated gesture recognition, privacy-preserving data handling, and predictive adaptation, the invention enables multi-user personalization, long-term learning, and uninterrupted operation within residential, administrative, or healthcare infrastructures.

The invention relates to the technical field of network security and data protection, and discloses a data leakage prevention method and system based on user behavior perception, and the method comprises the steps: obtaining historical operation data, and constructing a personalized behavior reference library; monitoring a current access behavior in real time by sliding a time window, calculating a deviation degree and triggering anomaly detection; performing multi-level feature analysis on the abnormal behavior and calculating a comprehensive abnormal score; dynamically adjusting the access authority according to the score, recording an abnormal behavior and carrying out relevance matching; optimizing the reference model through feedback learning; and evaluating the credibility and the risk level based on an accurate modeling result, and adaptively adjusting permission configuration. According to the method, the user behavior rule can be accurately captured, the data leakage risk can be efficiently identified, the access permission can be dynamically adjusted, the false alarm rate can be reduced, adaptive protection can be realized, and data security and business smoothness can be guaranteed.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a protocol text generation and verification method and device, equipment and a medium. The method comprises the following steps: acquiring multi-modal data related to a target object, constructing a dynamic abnormal portrait, matching the dynamic abnormal portrait with a clause knowledge graph, screening candidate clauses to form a candidate clause pool, generating a protocol text according to the candidate clause pool, carrying out logic conflict detection on the protocol text, and carrying out compliance verification when a logic conflict is not detected. And after the compliance verification is passed, generating a record from the protocol text, storing and outputting. According to the method, multi-source clause knowledge and dynamic abnormal portraits are fused, automatic clause screening and protocol generation based on risk features are achieved, logic conflict detection and compliance verification are carried out, the generation efficiency and personalized suitability are improved, and the protocol compliance is enhanced.

The invention discloses a personalized learning resource recommendation method and system based on multi-agent collaboration and a dynamic knowledge graph, and the method comprises the steps: constructing the dynamic knowledge graph, enabling nodes to be associated with teaching resources (videos, test questions, teaching plans, PPT and the like), and enabling edges to represent the logic relation between the resources; collaborative decision is made through four layers of agents: a target determination agent generates a learning target based on student historical learning data and a graph node state; the path planning agent plans a learning path in combination with the target and the learner model; the resource screening agent matches personalized resources from the path nodes; the user portrait intelligent agent updates the learner model in real time; and finally, generating a dynamic recommendation result and feeding back the optimized knowledge graph. Through multi-agent hierarchical collaboration and dynamic interaction with the knowledge graph, the problems of cold start, incomplete resource coverage and path stiffness of a traditional recommendation system are solved, and precise and adaptive learning resource recommendation is realized.

A system for affinity-based user acquisition is disclosed. An affinity analysis agent analyzes affinity patterns from existing customers and automatically identifies potential referrals without accessing message content. A relationship mapping agent calculates relationship strength between customers and non-customers based on communication frequency, interaction duration, timing patterns, and automatically finds the high-probability network candidates by scoring these relationships. An outreach and engagement agent creates personalized referral offers using relationship information and delivers them through optimal communication channels at ideal times. When someone accepts a referral offer, a billing integration AI agent may be initiated for automated account creation and service activation through identity verification, service selection, and account activation. Each new individual becomes part of the network. The AI communication agents analyze affinity patterns associated with the new individuals for discovering additional compatible individuals through graph traversal algorithms and centrality analysis.

The embodiment of the invention provides a vehicle-mounted emotion interaction method and device based on multi-dimensional recognition, and the method and device achieve the precise judgment of the emotion of a driver through innovatively constructing an emotion fusion recognition model and integrating the facial expression, voice emotion, driving behavior and physiological state features. And designing a scene-based self-adaptive interaction strategy, and establishing an interaction triggering threshold value for intelligent matching in combination with external environment data and a danger level. An interaction effect evaluation mechanism is introduced, an interaction strategy model is continuously optimized through an online learning module, and dynamic adjustment of personalized interaction content is achieved. According to the method, the defects of the traditional technology in the aspects of emotion recognition, interaction strategies, effect evaluation and the like are effectively overcome, and the intelligent level and the user experience of vehicle-mounted emotion interaction are remarkably improved.

The invention discloses a multi-modal X-ray image diagnosis report generation method based on reinforcement learning optimization. The method comprises the following steps: acquiring a multi-source public data set and a private data set in a specific range; performing format standardization processing on the X-ray image, performing normalization processing on text information, and constructing a structured public training data pair and a private enhanced data pair; training an end-to-end multi-modal neural network by using the public training data pair in a supervised fine tuning mode; carrying out reinforcement learning on the first-stage end-to-end multi-mode neural network by adopting a strategy gradient algorithm optimization mode of GRPO and using candidate reports and weighted rewards to obtain a second-stage end-to-end multi-mode neural network; and generating a diagnosis report by using the two-stage end-to-end multi-modal neural network. The method is suitable for personalized application scenes of X-ray image diagnosis, can quickly adapt to specific clinical requirements on the basis of limited professional data, and has good practical value and popularization prospect.

The invention specifically relates to the technical field of natural language processing, and discloses an intelligent interactive customer service system based on an AI large model, which comprises a multi-modal input analysis module, an intention preliminary detection module, a large model semantic understanding module, a dynamic knowledge base association module, a dialogue strategy generation module, a natural language generation module and a service feedback work order module, through deep cooperation of a multi-modal input analysis module and a large model semantic understanding module, entity and attribute information is accurately extracted, context semantic association information is mined, and through the cooperative effect of an intention preliminary detection module, a dynamic knowledge base association module and a dialogue strategy generation module, based on a semantic analysis result output by a large model, a dialogue strategy is generated. According to the method and the system, matching calculation is carried out with the constructed dynamic knowledge base, rich knowledge support is provided for solving complex problems, personalized knowledge content is recommended for different users through the natural language generation module and the service feedback work order module, and the reply matching degree and the complex problem processing capacity are greatly improved.

The invention discloses an illness state tracking management system and method based on a lingual face diagnosis instrument, and belongs to the technical field of traditional Chinese medicine tongue diagnosis and modern information technology fusion. The system comprises a multi-modal data acquisition module, a data processing and analysis module and an augmented reality visualization module; according to the method, illness state tracking is achieved through the steps of multi-modal data acquisition, data preprocessing and feature fusion, personalized digital twinborn model establishment, augmented reality visualization presentation and the like, multi-dimensional data such as tongue picture macroscopic features and tongue surface microorganism distribution can be integrated, dynamic association between the data is revealed, the health state and the intervention effect are visually displayed, and the method is suitable for being popularized and applied. The method is suitable for the fields of traditional Chinese medicine health management and chronic disease monitoring.

The invention discloses a 3D intelligent simulation generation method for clothing styles based on a model library, and relates to the technical field of digital twinning, and the method comprises the steps: carrying out the entity relation extraction and semantic mapping of multi-source clothing data, constructing a cross-modal knowledge graph, and extracting a physical constraint rule of the cross-modal knowledge graph; performing association reasoning and cross validation on the physical constraint rule and the version library to obtain a basic version template set; inputting the basic model template set into an intelligent clothing model, performing structured feature decoupling by the parameter analysis layer, dynamically adjusting a topological structure by the geometric generation layer, and generating 3D simulated clothing; and carrying out digital modeling on the basic model template set through a digital twinning algorithm, constructing a digital twinning body, carrying out physical simulation and optimization verification on the 3D simulation clothing by using the digital twinning body, and outputting a clothing style 3D simulation scheme. According to the invention, by constructing the pattern library and the intelligent garment model, the efficiency, accuracy and individuation level of garment 3D simulation generation are improved.

The invention discloses a personalized federated learning optimization method and system, and mainly solves the problem of poor performance of an existing personalized federated learning model. The method comprises the following steps: establishing a communication link between a client and a server; each client receives a current global sharing model parameter broadcasted by the server, loads the current global sharing model parameter to a local model, and introduces a total loss function of a dynamic alignment strength definition model; training and optimizing local model parameters, and updating shared parameters by using the parameters; the client side calculates a self-adaptive aggregation weight based on the parameter updating quantity norm, the data volume weight and the synchronous frequency weight of the client side, and uploads the self-adaptive aggregation weight and the updated shared parameters to the server; and the server receives the parameter and weight information uploaded by the server, executes global model aggregation to obtain an updated global model, and outputs the global model reaching accuracy convergence or a preset training round on the verification set. According to the method, local personalization and global consistency can be balanced, the robustness and efficiency of global aggregation are improved, and the method can be used for processing scenes of high data isomerism and dynamic change of client participation states.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

The invention relates to the technical field of intelligent customer service, and provides an AI intelligent customer service system based on a large model, and the system is characterized in that an unstructured text processed by a multi-source knowledge fusion subsystem is reconstructed into structured knowledge entries with a multi-dimensional label system, intention classification and context association, and the structured knowledge entries are dynamically integrated into an enterprise knowledge graph; the response generation subsystem is used for receiving the content queried by the user, analyzing the intention through a deep semantic understanding model in combination with an enterprise knowledge graph, and dynamically maintaining the context in combination with a multi-round dialogue state tracking technology; meanwhile, emotion dimension analysis is carried out on the content, and a personalized response is generated by fusing user service feedback; and the autonomous optimization subsystem dynamically adjusts the weight distribution of structured knowledge entries in the enterprise knowledge graph and the priority of process nodes in combination with an attribution analysis result, and triggers a knowledge updating and API process reconstruction mechanism. The method has self-learning and continuous optimization capabilities, and continuously improves the service quality and the user experience.

The invention relates to the technical field of physiological parameter monitoring of intelligent wearable equipment, in particular to an intelligent ring health monitoring method based on multi-sensor cooperation and related equipment. The method comprises the following steps: acquiring motion, optical and temperature sensing data, determining a scene in combination with a multi-dimensional rule and a user preset log, executing differential data acquisition, and generating a health assessment result matched with the scene after signal noise reduction, feature extraction and fusion analysis. According to the invention, monitoring accuracy, low energy consumption and individuation can be considered, and the effectiveness of health monitoring is improved.

A system and method for real-time, identity-free personalization using deformable emotional trait vectors to dynamically adapt digital and voice-based experiences. Each user session is modeled as a behavioral object known as a Vectra, composed of fluidic traits—such as mass, viscosity, temperature, volatility, and texture—that evolve continuously in response to live behavioral, contextual, environmental, and voice-derived signals. These Vectras traverse a dynamically warped emotional space, the Vectraverse, influenced by ambient conditions including time of day, noise level, inventory urgency, and engagement rhythm. Gravitational pull toward predefined emotional goal attractors modulates system behavior, while a goal mutation engine reclassifies session intent when confidence decays or friction spikes. Outputs include tone modulation, content pacing, offer framing, and gamified reward logic—all executed without storing identity, login credentials, or historical profiles. The system supports modular deployment across voice, screen, signage, mobile, and in-room environments, and integrates with large language models, AI agents, and third-party personalization stacks via privacy-safe APIs and federated learning. Designed for zero-ID personalization, the platform enables emotionally intelligent, context-aware engagement across any surface or session.

The invention discloses a consultation method and system based on natural language processing and a legal knowledge graph, and relates to the field of data processing, and the method comprises the steps: receiving a multi-format legal consultation demand of a user, converting the multi-format legal consultation demand into a text, inputting the text into a BERT law NLP model, and analyzing key information through word segmentation, intention recognition and entity extraction; based on a pre-constructed multi-level legal knowledge graph, carrying out accurate and fuzzy retrieval and domain filtering in combination with an analysis result, and obtaining an associated law article, a case and a legal relationship; screening conflict law articles and similar cases, and inputting the conflict law articles and the similar cases into a graph neural network reasoning model to generate a preliminary conclusion; the conclusion is converted into a spoken consultation report through a natural language generation module, and output is customized according to a user scene; and if the user feedback satisfaction degree is less than the threshold value, iteratively optimizing the storage data to the historical library. The method has the advantages that accurate retrieval is realized based on the BERT model and the multi-level knowledge graph in the legal field, the oral personalized conclusion combined with the user scene is generated through GNN reasoning, and iterative optimization is performed through user feedback.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

A computer-implemented method is disclosed for transforming academic test performance into personalized feedback and learning recommendations. The method involves presenting an academic test to a user via a user interface of an online learning platform and receiving the user's submitted answers. The system accesses input parameters including historical user-performance data, correct answers, and coaching session data. The user's responses are compared with the correct answers to identify incorrect responses. A prompt generator creates a prompt to guide and constrain an AI engine in analyzing the test responses. The AI engine correlates the incorrect responses with historical performance data and coaching session information to detect learning patterns or recurring errors. Based on the identified patterns, the system generates personalized feedback and targeted learning recommendations to address specific learning gaps. The method enables adaptive, AI-assisted post-assessment guidance, improving learning outcomes through individualized support.

The invention discloses a human resource data natural language query and SQL generation method and system, and relates to the technical field of SQL generation, and the method comprises the steps: preprocessing a natural language query text input by a user, and constructing a query semantic tree; traversing the query semantic tree to generate a candidate entity set, determining an unambiguous entity through a three-layer ambiguity resolution mechanism of semantic scoring, service verification and cross validation, identifying a composite intention, disassembling the composite intention into a sub-intention sequence, and generating an SQL operation instruction set in combination with a preset SQL operation mapping rule; based on the SQL operation instruction set and the human resource data association model, determining a data table set and an association path, generating a dynamic calculation logic and structured condition expression, and constructing a structured query skeleton; according to the method, the personalized SQL template is dynamically generated according to the query skeleton, after filling, grammar verification, strategy optimization and logic consistency verification are performed, and the optimized SQL statement is output, so that the query accuracy and performance are remarkably improved.

The invention relates to the technical field of health management systems, and particularly discloses a personalized AI intelligent health management system based on multi-source data fusion. Comprising a multi-modal health data dynamic acquisition and fusion module, a personalized dynamic health portrait construction module, a hierarchical risk early warning and root cause inference module, a self-adaptive personalized intervention strategy generation and closed-loop optimization module and a privacy protection and federated learning module which are connected in sequence. According to the system, standardized acquisition, quality verification and feature level fusion of different types of health data are realized through a multi-modal health data dynamic acquisition and fusion module, the problems of multi-source data dispersion and poor fusion effect in the prior art are solved, and a high-quality data basis is provided for subsequent health analysis; through an embedded dual adaptive calibration mechanism and a dynamic updating unit, portrait calibration is carried out in combination with a user individual historical baseline and a group generality mode, and the change of a user health state can be adapted in real time.

The invention discloses a multi-user sharing-oriented multimedia network video recommendation method, which comprises the following steps: firstly, constructing multi-user characteristics by utilizing collected program information in a multi-user sharing environment, and constructing a leading user label according to the similarity of the program characteristics and the continuity of user watching behaviors, so that separation of multi-user mixed logs is realized; performing periodic multi-user identification prediction of future sessions; secondly, building a user interest mining model based on a time-varying LinUCB algorithm to learn interest changes of a user for each program theme, and enhancing the personalized ability and efficiency of a recommendation system from three aspects of parallel calculation, adaptive control of an exploration coefficient and incremental updating based on LSTM; and finally, establishing an article quality model based on a non-time-varying LinUCB algorithm to further ensure the program quality, and integrating the two algorithms into a final recommendation system model by adopting a cross weighting strategy to form a final program recommendation list. The novelty and accuracy of the recommendation result are ensured.

The present invention relates to the technical field of chemical and plastics product recommendation, provides a chemical and plastics product personalized recommendation method based on deep reinforcement learning, comprising: S1 receiving a search condition of a user; S2, constructing a user state vector; S3, obtaining a candidate product set on the basis of the user state vector and the search condition; S4, using a pre-trained deep reinforcement learning model to sort candidate products, and generating a recommendation result; S5, displaying the recommendation result to the user, and acquiring user feedback; and S6, determining whether a recommendation stop condition is met, if yes, stopping recommending, and outputting a final recommendation result, otherwise, updating parameters of the deep reinforcement learning model and the user state vector on the basis of the user feedback, and returning to step S3. The present invention can improve the correlation, diversity and timeliness of the recommendation result, better meet the personalized needs of a user in the chemical and plastics industry, and can continuously learn and adapt to changes in user needs.

The invention discloses a dynamic self-adaptive recommendation strategy optimization method for a business handling failure scene, and relates to the technical field of business handling recommendation and intelligent decision making, and the method comprises the steps: firstly collecting various types of data of a whole business handling process, and guaranteeing the integrity and real-time performance at a frequency of 100 milliseconds per time; a decision tree and Bayesian network fusion algorithm is used for attribution, and direct and indirect reasons are clarified; integrating data to construct a user portrait, and mining potential and subsequent demands; generating a recommendation scheme set based on attribution and portraits, and adjusting priorities and forms in combination with scene features; feedback data is introduced, a strategy weight is optimized by using a gradient descent algorithm, and the scheme is updated regularly; a multi-dimensional index weighted evaluation effect is set, and emergency optimization is carried out if the evaluation result does not reach the standard; and establishing a distributed strategy library, and reusing the optimal strategy of the similar scene by using a K-nearest neighbor algorithm. According to the method, failure reason accurate positioning and personalized recommendation are realized, the recommendation effect is continuously optimized along with data accumulation, and the method is adaptive to multiple service types and user groups.

The embodiment of the invention discloses a self-adaptive air conditioner heating control method and system based on multi-source perception.The method comprises the steps that multi-source environment data such as indoor environment data, outdoor environment data and air conditioner operation data are collected in real time, and active adjustment behaviors and environment state data of a user are combined; a personalized comfort model is established and continuously updated through a machine learning algorithm, and a dynamic target comfort temperature interval is output to reflect comfort temperature preferences of a user in different environments; the multi-source environment data and the target comfortable temperature interval serve as input, maximization of user comfort, minimization of energy consumption and reduction of compressor starting and stopping times serve as targets, rolling optimization calculation is conducted through an optimization algorithm, and an air conditioner control instruction set containing a dynamic target temperature set value, a compressor operation instruction and a fan rotating speed instruction is dynamically generated; and executing an instruction set, monitoring an effect and user feedback, and feeding data back to the model establishment and optimization calculation step to realize self-adaptive control.

System, process, and method for gamifying physical assets, digital assets, and virtual assets through advertising and e-commerce, and system and method for personalized digital twin LLM chatbot gamification in e-commerce and emotional intelligence development. The first aspect of the invention is a system and process for creating and training a personalized digital twin LLM chatbot assistant. The digital twin is personalized for a given human user using training data regarding the human user. A second aspect of the invention is a system and process of creating and playing an e-commerce game, including the mechanics of the game. The e-commerce game may be created and / or played by employing personalized digital twin LLM chatbot assistant such as that described in the first aspect of this invention. State of the art technologies such as sensors, IoT devices, wearable devices, and VR / XR / AR can be integrated into the game.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a personalized scheme generation method, device, equipment and medium, and the method comprises the steps: collecting multi-modal data, and carrying out the preprocessing of the multi-modal data to generate a preprocessed data set and a data gateway lead; the fusion processing module performs cross-modal interaction and attention interaction processing on the preprocessed data set based on the data gateway association to generate a comprehensive feature vector; inputting the comprehensive feature vector into a recognition model to generate an object dynamic portrait; generating a personalized optimization path set based on the dynamic image of the object; and extracting a demand intensity item, a preference weight item and an emotional state item, and generating a personalized scheme in combination with the personalized optimization path set. According to the method, the object dynamic portrait containing the demand, preference and emotional state is generated through multi-modal data fusion and feature extraction, and a personalized scheme is output in combination with an optimized path mechanism, so that demand description comprehensiveness and scheme generation precision are realized.