13471 results about "Personalization" patented technology

The invention discloses a hydraulic engineering equipment data intelligent management system based on digital twinning, and belongs to the technical field of hydraulic engineering. Comprising an intelligent perception and data fusion module for realizing real-time acquisition and standardized processing of cross-modal data; the knowledge graph construction and causal reasoning module is used for constructing an intelligent knowledge system capable of autonomously learning and semantic reasoning; the digital twin modeling and simulation module is used for realizing dynamic simulation and scene deduction of a full life cycle and providing limit working condition simulation and risk assessment support; the intelligent prediction and health management module is responsible for performing real-time monitoring, fault early warning and residual service life prediction on the equipment state, and generating personalized intelligent maintenance strategies for different working conditions; the visualization and decision support module is used for visually presenting the equipment operation data and the analysis result and providing intelligent decision recommendation; and the cloud edge collaboration and system integration module realizes cross-platform interoperation and continuous integration through distributed computing and micro-service architecture.

A system and method for AI based user experience curation across multiple scenarios and finite time horizons of interest. The present invention integrates connectionist and symbolic AI techniques to generate coherent, relevant, and personalized content across various domains. The invention bridges the gap between connectionist AI and symbolic AI, enabling more adaptive, immersive, and engaging user experiences while prioritizing security and traceability and contextualization considerations behind recommendations or content generation. The platform furthers dynamic and tailored user interactions with digital systems across individual interactions, preferences, sequences and ongoing engagements across sessions by leveraging the power of analytics, deep learning, and AI to create truly intelligent, dynamic, and responsive user experiences enhanced with optimization and planning faculties.

An artificial intelligence driven system of systems may include a layered architecture for providing transaction support to various types of enterprises. A governance layer implements automated governance and policy enforcement through specialized governance modules utilizing generative AI technology. An enterprise layer supports enterprise functions by integrating management and control platforms with digital infrastructure. An offering layer creates and manages system offerings via content generation, personalization, and smart product modules. A transactions layer enables automated transaction orchestration through API integration, execution, and fulfillment modules. An operations layer manages AI systems through generation, training, verification and orchestration modules. A network layer provides adaptive networking capabilities through routing, protocol selection and communication modules. A data layer processes fused data from multiple sources using machine learning and AI systems. A resource layer manages computing, storage, and other resources through specialized resource modules.

The invention relates to the technical field of voice semantics, can be applied to business scenes of financial science and technology, medical health, poster design and the like, and discloses a visual sense generation method and device based on semantic association modeling, equipment and a medium. Generating a demand text containing theme and style parameters; semantic features in the demand text are extracted, semantic association weights are constructed, and element layout coordinates are optimized in combination with spatial distribution constraints; and encoding the layout information into a control matrix, fusing the control matrix with the initial noise, adjusting a noise reduction process through an encoding and decoding network, and generating target visual content highly matched with the semantic meaning of the user instruction. According to the method, the layout optimization function is constructed, the diffusion model is guided to focus the semantic salient region in space, language model output and the visual generation process are closely combined, structured response and space mapping of user semantic requirements are achieved, and the expression consistency and personalized adaptation capacity of visual content generation are improved.

The invention discloses a labeling task assignment method and device based on artificial intelligence, and the method comprises the steps: obtaining historical behavior data, and constructing a multi-dimensional user portrait; receiving a task description document, a data sample and a quality requirement document to obtain a multi-dimensional task feature vector; based on the multi-dimensional user portraits and the multi-dimensional task feature vectors, a matching degree score is calculated through a multi-objective optimization algorithm, and an optimal task allocation scheme is generated; optimizing the task structure through a fireworks algorithm based on student t distribution, and generating an optimized task unit structure; real-time monitoring is carried out through the anomaly detection model and the quality prediction model, and quality control measures are triggered; model parameters are updated through a reinforcement learning algorithm, and a personalized feedback and capability improvement strategy is generated. According to the method, accurate matching between the annotators and the tasks is realized, the processing efficiency of complex tasks is improved, the annotation quality is improved, the expansibility and the response speed of a platform are enhanced, and an effective solution is provided for large-scale and high-quality data annotation.

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 platform for coordinating networks of specialized AI agents that enables secure collaboration through token-based communication and real-time result streaming. The system features a central orchestration engine managing interactions between domain-specific expert agents, with memory management and optional encryption for secure data handling. The platform uses efficient communication protocols for knowledge compression and faster reasoning, while a standardized agent interface system handles security, privacy, and policy requirements. It scales across distributed computing environments to enable complex collaborative tasks like personalized content creation, materials discovery, and drug development while optimizing resource usage and maintaining data privacy.

The invention provides a lightweight digital human lesson preparation system based on an intelligent agent, and belongs to the field of intelligent teaching. Through collaborative operation of four core modules of knowledge graph construction and reasoning, multi-modal cognitive agent, lightweight digital human generation and intelligent teaching plan assistance, the problems of low efficiency of resource integration, teaching content homogenization, insufficient digital human interaction experience and the like in traditional lesson preparation are solved. The knowledge graph construction and reasoning module is used for constructing a structured knowledge graph and realizing knowledge point association mining and teaching logic reasoning; the multi-modal cognitive agent module is used for generating personalized explanation content according with a teaching target by fusing multi-modal courseware analysis, semantic understanding and lecture style dynamic adaptation functions; the lightweight digital human generation module is combined with model pruning and emotion modeling technologies to synchronously output natural voice and a high-simulation digital human image; the intelligent teaching plan auxiliary module helps the teacher to intelligently generate a teaching plan and a teaching outline according to the courseware content based on the knowledge graph.

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.

The invention discloses a teaching data management method and system based on artificial intelligence, and the method comprises the steps: obtaining a standardized time series data stream according to a heterogeneous data stream generated by a multi-source teaching platform in real time; based on the standardized time sequence data stream, performing classified encryption on the teaching data through a dynamic hierarchical storage strategy based on attribute-based encryption to obtain a security hierarchical storage topological structure; according to a user query request and a teaching scene label, extracting a target data set from the security hierarchical storage topological structure to obtain an enhanced multi-modal teaching data set; based on the enhanced multi-modal teaching data set, generating an interpretable teaching mode graph through a dynamic sub-graph evolution algorithm; and according to the teaching mode map and the real-time teaching feedback data, generating a personalized teaching recommendation strategy through a course-learner dual-channel adaptive recommendation model. According to the embodiment of the invention, the utilization efficiency of teaching resources can be improved, and personalized and intelligent teaching recommendation and decision can be realized.

The invention discloses an adaptive teaching real-time feedback method based on multi-modal fusion, and the method comprises the following steps: synchronously collecting text modal information, voice modal information and image modal information generated by students in a teaching process, forming multi-modal original data information, and extracting historical student interaction behavior data; preprocessing the multi-modal original data information, and respectively generating corresponding text, voice and image sequence features; a visual feature encoder and a sequence feature encoder are adopted to encode each modal sequence feature to obtain a high-dimensional feature; inputting the modal high-dimensional features into a cross-modal fusion network for deep fusion; parameters of the feedback model are optimized through a model-independent element learning feedback regulation and control algorithm, and a personalized feedback strategy is generated; generating comprehensive feature representation according to the fusion features, and outputting personalized teaching feedback; and the interaction information is updated based on the feedback behavior data to realize closed-loop optimization.

The invention relates to the technical field of artificial intelligence assisted medical rehabilitation, in particular to a peripheral nerve injury personalized rehabilitation system and method based on a multi-modal large model, and the method comprises the steps: a feature fusion module employs space-time attention to fuse multi-modal time series data, and constructs an evaluation map; the personalized generation module is combined with historical data and reinforcement learning to generate a scheme containing virtual scene parameters; the interaction feedback module collects data through mixed reality and calculates action deviation; and the adaptive adjustment module adopts a meta-learning optimization model and a distributed iterative output scheme. According to the method, the cross-modal association precision of the motion features and the mechanical parameters is improved, dynamic matching of the training scene and the motion ability of the user is achieved, the virtual environment and the mechanical feedback threshold are optimized by dynamically adjusting the rehabilitation scheme parameters, the scheme optimization period is shortened based on an online iterative optimization mechanism, and the training efficiency is improved. The core defects of personalized adaptation lagging and low utilization efficiency of multi-modal data are overcome.

The invention discloses a cockpit active recommendation system and method based on a knowledge graph and semantic reasoning, and relates to the technical field of intelligent cockpits. The system receives natural language voice input of a user, executes voice recognition and semantic analysis, extracts user intention, keywords and slot entities, generates structured semantic information, constructs or calls a knowledge graph structure with semantic relation edges in combination with environment context information, and obtains the knowledge graph structure with the semantic relation edges. Semantic path reasoning is carried out based on the path dependence weight and the semantic similarity, a semantic edge label guided graph attention mechanism is introduced to calculate a path consistency score, a candidate recommendation set is generated, the semantic fitting degree and the path score are fused to sort and output recommendation content, and the graph edge weight and the user portrait are updated based on user feedback. According to the method, semantic understanding precision, recommendation path interpretability and system adaptive capacity are improved, and the method is suitable for personalized voice recommendation, man-machine interaction and scene linkage control tasks in an intelligent cockpit.

The invention relates to a remote education data processing system which comprises the following steps: under a remote teaching task, pre-defining a task intention and a data expectation point; a semantic timestamp and a task binding label are printed on each data fragment; mapping the collected confusion data including silence, eye movement drift and prediction into a unified learning semantic vector; a micro-expression + interactive behavior + time sequence decision path ternary modeling mode is introduced, and a potential cognitive intention corresponding to the feature combination is recognized; teaching context information is fused; constructing a cognitive state mapping model; reasoning a current cognitive state label from multi-modal sensing data; searching intervention track VS effect feedback data in a historical database; generating a predicted intervention behavior sequence by using a sequence modeling algorithm; a dynamic combination suggestion chain including light prompt, content reconstruction, personalized practice and tutoring invitation is adopted; and superposing the cognitive state sequences of all students into a group cognitive trajectory map.

The invention discloses an AI virtual coach training system based on standard action matching and deviation feedback, which relates to the technical field of AI virtual coach training systems and comprises a user modeling module, an action acquisition module, a template matching module, a deviation calculation module, a feedback generation module, an interactive presentation module and a learning optimization module. The user modeling module is used for modeling a registered user by adopting a body parameter acquisition and health data analysis method to obtain a user personalized feature vector; the action acquisition module is used for capturing actions executed by a user in real time by adopting a multi-source sensor fusion method to obtain a time sequence containing key point coordinates; and the template matching module is used for comparing the time sequence of the key point coordinates with corresponding actions in a preset standard action template library by adopting an improved dynamic time warping (DTW) algorithm to obtain an optimal matching path and a corresponding minimum matching cost.

The invention discloses a large model recommendation method and system based on comparative learning enhancement and model fine tuning, and relates to the technical field of personalized recommendation, and the method comprises the steps: obtaining historical interaction behaviors of a user, and constructing text modal representation and structured modal representation of the user and an article; performing representation enhancement through cross-modal contrast learning, and generating preference representation information of the user and the article through adaptive multi-modal fusion; generating a dynamic prompt according to the real-time behavior of the user and the preference representation information of the user and the article, fusing the preference representation information of the user and the article with the dynamic prompt through cross attention, and inputting the fused information into a preset large language model for model fine tuning; and when a user initiates a request, calling the fine-tuned large language model to generate a recommendation result, and providing an interpretable recommendation reason in combination with the inference ability of the comparative learning representation and the large language model. According to the method, technical paths of comparative learning and model fine tuning are fused, and the performance and personalization of article recommendation are improved.

The invention relates to the technical field of multi-modal data, provides a pilot competency dynamic evaluation method, system and device based on multi-modal data and a storage medium, and solves the problems of low accuracy of pilot competency evaluation and poor pertinence of training guidance. The method comprises the steps of collecting flight control data, physiological signal data, psychological assessment data, subjective scale data and international civil aviation organization core competency information, performing alignment fusion on multi-source data by adopting a time synchronization algorithm, and identifying an attention fixation mode based on a hidden Markov model. And constructing a workload index by fusing a subjective scale and a physiological entropy value, inputting the multi-modal features into a pre-trained competency assessment model, outputting three levels of psychological assessment indexes including a basic ability layer, a dynamic presentation layer and a risk early warning layer, and finally generating a personalized training report. According to the invention, the accuracy of pilot competency evaluation and the pertinence of training guidance are improved.

The invention discloses a video editing method and related equipment, and the method comprises the steps: receiving an editing intention of a user, carrying out the semantic analysis of an intention analysis model, and generating an editing copywriting, a style and a structured instruction; the edited copywriting is split according to the edited copywriting; performing cross-modal analysis on video materials by using a video understanding model trained based on an open-source multi-modal model, and accurately positioning sub-lens segments; secondly, editing the sub-lens segments through an editing model to generate a preliminary sheet, performing quantitative scoring through a video scoring model, and obtaining a scoring result according to a multi-dimensional index; and finally, the result is fed back to the editing model, and iterative adjustment is carried out until a final slice is produced. According to the method, the editing efficiency is improved, the manual operation time is shortened, the material adaptability is enhanced, the personalized requirement is met, the editing effect consistency is improved, the video content understanding is deepened, the editing effect is optimized, the technical threshold is reduced, the existing technical problems are solved comprehensively, and an efficient, intelligent and personalized editing scheme is provided.

The embodiment of the invention provides an intelligent project text generation method and device, and the method achieves the precise screening of positive and negative training corpora through the construction of a multi-dimensional scoring mechanism, and the comprehensive evaluation of the text coverage degree, the technical correlation degree, the fluency and the professional term specification degree. And constructing a complete knowledge graph based on the relation extraction model and the external knowledge base, and fusing the enterprise technical feature matrix and the basic information to generate an enterprise portrait vector. By transforming a decoding layer structure of a pre-training large model, enterprise portrait vectors are used as condition vectors to be embedded, and personalized text generation is achieved. According to the method, the defects of the traditional technology in the aspects of training sample quality evaluation, knowledge graph integrity and enterprise feature fusion are effectively overcome, and the professionality and pertinence of project text generation are remarkably improved.

A spatiotemporal modeling system for Personal Health Database (PHDB) platforms integrates diverse health data types into a comprehensive 4D model of an individual's health status. By combining genomic, imaging, clinical, and real-time health data, the system creates a dynamic, time-based representation of the user's anatomy and physiology. This model enables real-time analysis, pattern recognition, and predictive forecasting of health outcomes. The system preprocesses and aligns data from various sources, constructs a detailed spatial framework, and continuously updates the model with new inputs. Through interactive visualizations, it provides users and healthcare providers with intuitive, personalized insights for improved health management and decision-making.

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.

The invention relates to the technical field of government affair service intellectualization, in particular to a multi-modal retrieval enhanced generation government affair intelligent system which comprises multi-level government affair knowledge base construction, retrieval enhanced agent collaborative fine adjustment, a credible generation mechanism of government affair agents and multi-modal fused personalized interaction application. Multi-source heterogeneous data semantic unification is realized through a dynamic cross-modal alignment framework and a knowledge graph; a dual-path parameter efficient fine tuning architecture is adopted to improve the retrieval and generation capability; the tool chain is verified by integrating logic, law and ethics to ensure the compliance of the generated content; and guiding and optimizing complex scene demand understanding by using distributed user portraits and progressive dialogues. According to the application, the intelligent level and the user experience of government affair services can be remarkably improved, and the government affair services are promoted to transition from passive response to active service.

The invention relates to the related technical field of digital data processing, in particular to a personalized federated learning method and system for a heterogeneous multi-source industrial internet, and the method comprises the steps: connecting a client, evaluating a load, time delay and modal similarity to generate a dynamic association table, deploying a hierarchical encryption protocol, and constructing a priority queue; a cache mechanism is set to coordinate distributed iterative optimization, so that the technical problem that network oscillation and computing resource waste are aggravated due to overhigh load of part of nodes caused by frequent access and exit of equipment and data volume difference in the industrial internet and repeated migration of clients and nodes is caused is solved, cross-equipment shared knowledge base vectors are extracted, and the computing efficiency is improved. The technical effects of reducing the influence of model isomerism on aggregation, dynamically scheduling high-frequency parameter local aggregation and low-frequency parameter cloud synchronization, optimizing the association weight of a client and a fog node in real time, realizing privacy protection and efficient personalized federated learning, and ensuring the privacy and security of user data in the training process are achieved.

The invention relates to the technical field of artificial intelligence and big data analysis, in particular to a data operation system and method based on a knowledge graph, and the method comprises the steps: extracting an entity and semantic relationship from multi-source business data, and constructing a dynamic evolvable initial knowledge graph; node features are aggregated, and multi-dimensional situation state vectors are generated in combination with gating loop unit modeling behavior path dependence; through a structure-semantic coupling attribution scoring mechanism, a statistical information gain and semantic similarity are fused to identify a core driving factor, and a causal regression model of the factor and an operation target is established; dynamically adjusting the edge weight and the structure of the atlas in real time, and triggering a new path discovery mechanism to continuously optimize the atlas; and according to a quantitative business target, reversely extracting a high-confidence influence path from the atlas, and generating a personalized strategy combination through intervention simulation and multi-target Pareto optimization, thereby realizing intelligent recommendation and decision closed loop driven by an operation target. According to the invention, higher-precision operation situation awareness and strategy generation are realized.

The invention discloses an educational scene-oriented knowledge graph enhanced large model personalized learning recommendation method and system, and the core thought of the method is that through the coupling of a knowledge dominant structure and a large model semantic capability, personalized learning resource pushing of triple driving of structure + semantics + behavior is realized. And deep, multi-dimensional and dynamic feedback-driven learning resource personalized intelligent recommendation can be realized in combination with student individual differences, learning paths, cognitive levels and teaching resource semantic structures.

Systems and methods are provided for machine learning models configured as zero-shot personalized text-to-speech models which comprise a feature extractor, a speaker encoder, and a text-to-speech module. The feature extractor is configured to extract acoustic features and prosodic features from new target reference speech associated with the new target speaker. The speaker encoder is configured to generate a speaker embedding corresponding to the new target speaker based on the acoustic features extracted from the new target reference speech. The text-to-speech module is configured to generate the personalized voice corresponding for the new target speaker based on the speaker embedding and the prosodic features extracted from the new target reference speech without applying the text-to-speech module on new labeled training data associated with the new target speaker.

This invention introduces an adaptive system for multi-modal media processing and delivery, addressing challenges in modern digital content distribution. The technology dynamically analyzes and processes media content in real-time, optimizing delivery across diverse devices, networks, and content types. Key features include adaptive processing that adjusts compression, encoding, and delivery protocols based on content characteristics and delivery constraints. The system incorporates artificial intelligence for continuous improvement, learning from historical data and user feedback. It addresses network variability and device diversity, adapting to changing conditions and optimizing content for different platforms. Security and personalization features enable protected content distribution and tailored user experiences. The invention's cross-media optimization approach allows efficient handling of various media formats within a unified framework. Its scalable, modular design suits applications from consumer streaming to enterprise-level distribution. This comprehensive solution aims to enhance content distribution efficiency and user experience in the complex, evolving digital media landscape.

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 a data processing method and device based on big data and advertisement pushing, and the method comprises the following steps: obtaining the historical behavior data of a user on a multi-channel platform, constructing a dynamic interest label map according to the historical behavior data, and depicting a user interest evolution process. Combining with a social relation network to analyze an interest propagation path, forming a user social interest diffusion trajectory, and introducing a time decay weighting mechanism to generate a dynamic interest decay curve. According to the method, user interests and advertisement materials are subjected to semantic similarity matching, a personalized advertisement recommendation list is generated, an optimal advertisement putting strategy is determined through multi-target optimization configuration and comprehensive consideration of display positions, opportunities and forms, accurate and efficient advertisement pushing is achieved, and the problems that a traditional user portrait method often depends on a static label system, and the user experience is poor are solved. The dynamic characteristic that the user interest changes along with time is difficult to reflect, so that the advertisement recommendation content lags behind the real intention of the user.