465 results about "Context awareness" patented technology

The invention provides a Beidou electric power data intelligent acquisition and dynamic communication scheduling method and system. The method comprises the steps that a master station constructs a database and trains a data priority classification model, a channel quality prediction model and an intelligent compression model; a substation collects power data through an edge calculation unit, constructs a skyline candidate set to perform data screening, and performs intelligent classification marking by using a data priority classification model; performing reliability evaluation on the data stream by using a Gaussian mixture model, selecting a compression strategy according to a reliability score, a data type and a priority, and packaging into a data frame; determining a transmission strategy in combination with a channel quality prediction result and a context-aware intelligent switching protocol, and sending a data frame; the master station receives the data frame, performs integrity verification, decompresses and reconstructs the data frame, and feeds back a communication state for model updating; and monitoring the operation state, performing early warning based on the anomaly detection model, and triggering a self-healing strategy. According to the invention, the Beidou communication resource utilization rate and the system reliability are improved.

A system for improving employee engagement and automating the integration of business processes. The system includes: a user interaction module configured to receive multimodal inputs, including natural language text and voice requests from employees via enterprise portals, mobile applications, voice-activated devices, and collaboration platforms, and to generate real-time responses via conversational output channels; a context processor that is operationally coupled with the user interaction module, wherein the context processor stores the interaction history, employee preferences, organizational role metadata, and task results in both short-term and long-term memory and dynamically derives context for controlling responses and initiating workflows; A natural language and intent processor that is communicatively linked to the context processor. The intent engine consists of large language models trained on company-specific lexicons to perform intent detection, entity extraction, ambiguity resolution, and sentiment or urgency classification from employee queries; a workflow orchestration layer in communication with the intent engine and the context processor, wherein the workflow orchestration layer includes a rule-based and AI-powered process execution engine configured to automatically initiate, route, and complete cross-functional business tasks, including approvals, escalations, and compliance checks, with workflows defined using modular templates that include conditional logic and time-based triggers; Webhooks and authentication protocols provide secure interoperability between the workflow orchestration layer and external enterprise platforms; a feedback and learning module that is operationally coupled with the workflow orchestration layer and the natural language understanding engine, wherein the feedback and learning module is configured to analyze task completion rates, response accuracy, latency metrics, and user feedback signals, and to retrain underlying language and workflow models for adaptive improvement in real time; and an administration console with role-based access controls, compliance dashboards, audit trails and interfaces for workflow configuration, whereby the administration console enables authorized personnel to monitor system operations, adjust interaction rules and enforce data protection restrictions across departments.

The invention discloses a large-model multi-scene antagonism dynamic evaluation system and method based on context perception strategy optimization, relates to the technical field of artificial intelligence safety evaluation, and aims to solve the problems of lack of multi-round context modeling, poor confrontation sample semantic consistency and lack of a feedback-driven optimization mechanism in the prior art. According to the method, a state space and an action space are constructed, an adversarial sample is dynamically generated by adopting a reinforcement learning strategy network, a high-quality sample set is expanded in combination with a semantic disturbance and screening mechanism, and a vulnerability knowledge base is constructed based on an interaction log to guide strategy optimization and security evaluation. The method can effectively improve the security evaluation coverage and vulnerability discovery capability of the model in a high-risk multi-round interaction scene, and is mainly used for security reinforcement and deployment support of large language models in the fields of medical treatment, customer service and the like.

The invention relates to the technical field of medical decision, and discloses a medical decision support system based on a knowledge graph, and the system comprises a knowledge graph construction module which constructs an initial knowledge graph based on a medical ontology library, and the knowledge graph comprises entities and association relationships of diseases, symptoms and drugs; the data acquisition module is used for acquiring data from an electronic medical record, wearable equipment, a medical literature library and a hospital information system and normalizing the data through a standardized protocol; the dynamic knowledge updating module is used for processing normalized data through an incremental graph neural network; a multi-source knowledge fusion module; a context awareness module; a dynamic deduction module; and a decision optimization closed loop module. And triggering a preset clinical rule in real time based on the pathological state of the patient, dynamically adjusting the intensity value of the related edge in the factor graph, and persistently storing the intensity value back to the knowledge graph, so that logic adaptation and individualized experience precipitation of general medical knowledge in a special pathological state are realized, and the individualized treatment accuracy is ensured.

The invention discloses a virtual historical character dialogue method and system with role knowledge and context awareness, and relates to the technical field of man-machine interaction, and the method comprises the steps: constructing a multi-level role depth model; when a question of a current user is received, identifying information of a virtual scene where the current user is located, analyzing micro-expressions of the face of the user and voice rhythm characteristics of speech of the user, analyzing an emotional state and an interaction intention of the user based on a multi-modal fusion algorithm, and generating a user state vector; executing a dynamic Prompt construction program, extracting related information from the multi-level role depth model and the user state vector, and generating a structured Prompt; and inputting the structured Prompt into a large language model, generating a reply text conforming to role features based on questions of the current user, and driving a virtual character model. The method solves the problem that in the prior art, virtual historical figures cannot provide real immersion and credible interactive experience with emotional connection.

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 provides an agent collaborative question-answering method and system based on context awareness and authority control, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring user query, and triggering a user context manager to generate a session state user context object containing a permission label and preference; the central scheduler performs task decomposition based on the query intention and the context, dynamically selects an intelligent agent through a multi-dimensional scoring model and binds a knowledge source; the authority-aware agent execution engine executes triple authority verification and intermediate result desensitization processing in the whole process, and supports agent fault tolerance and audit log recording. The method realizes unification of knowledge security and access flexibility, supports multi-scene adaptive question and answer, and has good expandability and fault-tolerant capability.

The invention discloses an intelligent class case retrieval method and system based on reinforcement learning self-feedback, and the method comprises the following steps: S1, constructing a multi-level semantic understanding framework, carrying out the semantic understanding of a query case through the multi-level semantic understanding framework, and generating a complete semantic representation; s2, carrying out multi-dimensional similarity calculation and sorting optimization on the query case and historical discriminants in the candidate case library; s3, constructing a dynamic user portrait and optimizing a recommendation strategy; and S4, establishing multiple rounds of dialogues and context awareness, and carrying out visual display and feedback mechanism optimization. According to the intelligent class case retrieval method and class case retrieval system based on reinforcement learning self-feedback, a more accurate, efficient and reliable law intelligent retrieval system is finally achieved, judicial practice requirements are met, and quantitative and measurable technical progress is achieved in the aspects of retrieval accuracy, sorting quality, personalized service, law adaptability and the like.

The invention relates to the technical field of structured retrieval, and discloses a retrieval enhancement generation method and system based on LLM structured index and vector hybrid retrieval, and the retrieval enhancement generation method based on LLM structured index and vector hybrid retrieval comprises the steps of processing and analyzing a non-structural document, generating a hierarchical tree data extraction with document logic, and generating a hierarchical tree data extraction with document logic. The method comprises the following steps: obtaining a query demand, analyzing and identifying the query demand, self-matching any one or a combined retrieval strategy to quickly and accurately obtain a retrieval result in the complete logic positioning information, optimizing hierarchical tree data by adopting context perception, and establishing the complete logic positioning information in the hierarchical tree data through a reasoning path generation method. According to the method, through structured indexing, accurate positioning of specific chapters of the document, improvement of retrieval accuracy and support of multi-step logical reasoning, the long tail problem which cannot be processed by traditional RAG is solved, a clear and transparent retrieval path can be provided, and the system credibility is enhanced.

The invention relates to the technical field of natural language processing, in particular to an NLP-based multi-source data fusion system and method.The NLP-based multi-source data fusion system comprises a data collecting and processing unit, an NLP processing unit, a data fusion unit and an output unit.The data collecting and processing unit collects multi-source heterogeneous data and executes standardization processing to obtain preprocessed data; the NLP processing unit extracts entity, relation and emotion features through deep semantic analysis, a context-aware entity relation graph is constructed by adopting a bidirectional attention mechanism model and a graph attention network, and the data fusion unit realizes cross-source entity ambiguity resolution through an iterative graph neural network based on a graph topological structure. The emotion confidence weight is dynamically distributed to generate a fusion vector, a rule feedback reconstruction map is extracted, and the output unit converts the fusion vector into a target format for output, so that the problem of semantic conflict of multi-source data is solved, and semantic coherence and availability of fusion data are improved.

The invention discloses an intelligent situation awareness vehicle-mounted atmosphere lamp control method and system and electronic equipment. The method comprises the following steps: step 1, scene state modeling of multi-source data fusion; step 2, coupling control of the dynamic spectrum and environmental parameters; step 3, generating an emotion-driven light sequence; step 4, dynamically adjusting the noise-coordinated lamplight; step 5, storing and calling personalized parameters; and step 6, optimizing a control strategy of feedback driving. Through multi-source data fusion modeling and comprehensive analysis of the driving scene, the user state and the environmental parameters, the state recognition accuracy is improved, misjudgment is avoided, dynamic spectrum and environmental parameter coupling, emotion driving and noise collaborative light adjustment are achieved, diversified requirements are met, a personalized atmosphere is created, fatigue is relieved, and safety is improved. User parameters can be stored and called, personalized services are enhanced, and more comfortable and convenient driving experience is brought.

Various examples, systems, and methods are disclosed relating to an agentic curation pipeline. One system can process questions and other inquiries about video content by using a combination of models and stored information. The system can receive a query related to an event in a video, selects relevant portions of the video using embeddings, and apply the selected video data and a related sub-query to a video model. The output from the video model can be used by a language model, along with stored context, to generate an answer to the original query. The system can returns the answer to the requester.

The invention discloses an intelligent customer service dialogue management method and system fused with multi-layer memory, and belongs to the technical field of computers, and the method obtains a three-dimensional context through multi-mode context awareness, achieves the dynamic scheduling of a memory system through a memory coordinator, and combines a dynamic construction mechanism of a service situation portrait, thereby achieving the intelligent customer service dialogue management. The problem that the response of the intelligent customer service system is not matched with the complex and dynamic service demand of the user due to the single situation awareness dimension and the rigid memory scheduling mechanism is effectively solved, and the adaptive personalized service response can be generated according to the real-time change of the dialogue situation.

The invention discloses a time series data lossless compression method and system based on dynamic context awareness, and relates to the field of data lossless compression, and the method comprises the steps: extracting target data from a device port, and carrying out the preprocessing of the target data through a data processing technology according to the business dimension corresponding to the target data, constructing a structured data set based on a preprocessing result; capturing local time sequence features based on the structured data set, distributing attention weights in combination with a hidden state, generating a feature vector used for sensing a context, and outputting a dimension vector by using a three-layer full-connection network; and according to the dimension vector, analyzing the compression performance score value of each compression algorithm in the resource library, sorting the compression performance score values, and selecting the compression algorithm meeting the target requirement to compress the target data. According to the method, the global periodic features and the local mutation features of the time series data are analyzed through the space-time attention mechanism, and real-time driving algorithm switching is achieved.

The invention relates to the technical field of data security, and particularly discloses a real-time data desensitization method and system based on context awareness, and the method comprises the steps: intercepting an HTTP / HTTPS request through a gateway, and extracting a user authentication token, URL parameters, an IP address, a timestamp and request body data in a request header; analyzing user role information based on the user authentication token, extracting access environment characteristics based on an IP address and a timestamp, and performing sensitive field identification and grading on request volume data to determine a data sensitive level; according to the user role information, the access environment characteristics and the data sensitivity level, matching a predefined desensitization strategy template through a rule engine, and generating a real-time desensitization strategy containing a field-level desensitization instruction; verifying the access authority based on the user authentication token, and dynamically adjusting the desensitization degree in combination with the access environment characteristics; and according to the real-time desensitization strategy and the desensitization strength, performing real-time desensitization processing on the sensitive fields in the corresponding data.

The invention discloses a method and a system for generating an SQL (Structured Query Language) query based on a natural language question. The method comprises the following steps: converting mode information of a target database and the natural language question into semantic vector representation; based on the semantic vector representation, a simplified database mode set related to SQL query is screened out through an attention mechanism; based on the natural language problem, the simplified database mode set and preset database constraint information, generating an SQL structural skeleton, and filling specific elements of the SQL structural skeleton to form a preliminary SQL query; and performing dynamic correction and verification on the initial SQL query by utilizing a large language model, and outputting a final SQL query. According to the method, association mining between user query and a database mode is effectively enhanced by utilizing a context-aware cross-encoder mechanism, and an implicit corresponding relation between a natural language problem and a database table / column can be more accurately identified and utilized.

The invention provides a multi-vehicle cooperative sensing method based on context sensing, and the method comprises the steps: obtaining the sensing data of a vehicle and a cooperative vehicle, including the position and speed of the vehicle, and the context information of historical frame data; performing feature extraction on the acquired data by using a backbone network to obtain own vehicle features and cooperative features, and sharing feature information through compression and sparsification; the motion state information and the sparse feature mapping graph of the vehicle are utilized, and a graph attention mechanism is combined to dynamically select a cooperative vehicle capable of providing significant perception performance improvement in the current scene; fusing and aligning the sensing features of the historical frame and the current frame from the local of the vehicle by adopting a feature fusion algorithm based on Transform to form a time-space fusion feature of the vehicle; and carrying out multi-scale attention fusion on the self-vehicle space-time fusion feature and the cooperative vehicle feature, and carrying out a target detection task based on the fused global perception feature.

The invention belongs to the technical field of data visualization, and particularly relates to a dynamic visualization generation method, system and device based on user behaviors and context awareness and a medium, and the method comprises the steps: generating a structured operation sequence after standardizing original input data; performing time sequence modeling on the operation sequence to generate a user behavior semantic vector, and generating a joint feature vector by adopting an attention mechanism; pre-loading an input data set, and generating a candidate view set in combination with the user intention preference in the joint feature vector; calculating view scores through a layout optimization function, and screening an optimal view scheme with the highest score; dynamically simplifying or enhancing the chart component according to the equipment performance and context parameters in the joint feature vector; calling a rendering engine to output a view, and monitoring a user interaction event; and updating the strategy network through a PPO algorithm, and optimizing a subsequent view generation path. The problem of unsmooth interaction caused by a static template is avoided, and the user experience is remarkably improved.

The invention discloses a retrieval enhancement generation method and system based on context awareness, and relates to the technical field of information retrieval, and the method comprises the following steps: carrying out block processing on a knowledge base document, distributing a unique identifier and a sorting sequence number, and establishing an association relationship between text blocks to save a document structure; constructing a structure index of the document; according to a query request of a user, performing initial retrieval on query content based on the vector similarity to obtain an initial related text block set with the highest relevancy; performing context extension retrieval on the initial related text block set to obtain an extended preamble text block set; and carrying out duplicate removal, merging and reordering processing on the preamble text block set, and adopting a smooth transition technology to obtain a natural text as a retrieval result. Through the technical scheme of the invention, not only is the correlation ensured, but also the context integrity is ensured, the semantic fragmentation problem of a traditional RAG system can be effectively solved, and the retrieval quality is remarkably improved.

The invention is suitable for the technical field of natural language processing, relates to a hierarchical memory and context awareness retrieval method of a large role model and a related product, and aims to solve the problems of limited model memory duration, insufficient retrieval correlation and insufficient personality consistency in a long dialogue. According to the invention, a short-term-middle-term-long-term three-level memory architecture is adopted, and a memory attenuation and migration mechanism is combined, so that dynamic metabolism of memory is realized; related memories are recalled accurately through a context semantics and role personality double-sensitive double-stage retrieval algorithm; relying on a personality-linked memory fusion and response generation strategy, the reply is ensured to fit personality setting; and a closed-loop adaptive learning mechanism of dialogue-memory-retrieval-generation-feedback is constructed, and the memory quality is continuously optimized. According to the method, the role large model can have the human-like continuous memory ability, the continuity, retrieval accuracy and personality consistency of long dialogues are remarkably improved, and the long-term personalized interaction requirements of scenes such as digital personality assistants and dialogue agents are met.

A controller can be used with a computing device to select and / or interact with content using user input devices on the controller. The content can be locally-stored on the computing device and / or streamed from a remote device. In one embodiment, a contextually-aware platform service switcher is provided. In another embodiment, a system and method for automatic content capability detection are provided. In yet another embodiment, a system and method for rich content browsing multitasking on device operating systems with multitasking limitations are provided. These embodiments can be used alone or in combination. Other embodiments are provided.

The invention provides a live broadcast advertisement effect real-time optimization method and system based on multi-modal AI, and relates to the technical field of advertisement optimization, and the method comprises the steps: collecting the multi-modal input information of a live broadcast scene, and extracting a feature vector after preprocessing; constructing a multi-modal-advertisement effect association graph, and integrating the multi-modal-advertisement effect association graph with the multi-modal knowledge graph to form a dynamically updated advertisement optimization knowledge network; based on real-time context awareness and a knowledge network, determining an optimal strategy through reinforcement learning evaluation sorting; the optimal strategy is converted into a specific instruction, and dynamic optimization is executed; collecting effect feedback data, and returning to a feature fusion and weight optimization link; the system comprises a multi-modal data acquisition and feature extraction module, a cross-modal feature alignment and knowledge network construction module, a reinforcement learning strategy generation and evaluation module and the like. According to the invention, the delay waiting time of the live broadcast advertisement optimization response is reduced, the real-time adaptability is improved, and the advertisement putting accuracy and conversion efficiency are improved.

The invention discloses an AI knowledge base retrieval method and system based on context awareness and dynamic fusion, belongs to the technical field of artificial intelligence, and aims to solve the technical problems of insufficient context understanding, weak model adaptability and low retrieval precision in a traditional retrieval method. Comprising the steps of collecting and preprocessing multi-dimensional context data; obtaining a context analysis result comprising a query semantic vector, a query intention type, a scene demand weight vector and a user preference model; constructing a plurality of basic retrieval models and registering metadata for each basic retrieval model; on the basis of context analysis results, the fusion weight of each basic retrieval model is calculated through a multi-factor decision algorithm, and the initial retrieval results are fused and sorted; and carrying out iterative optimization on a model algorithm involved in context data analysis and parameters of each basic model based on interaction feedback data of a user on a retrieval result.

Various embodiments discussed herein relate to using one or more language models and / or mapping platforms to generate a response to a natural language question or command regarding geographical information associated with a mapping platform. In response to receiving such natural language question or command, some embodiments first extract contextual data. Based at least in part on the extracting of the contextual data, various embodiments then provide the contextual data and the natural language command or question as input into one or more language models such that the one or more language models and / or mapping platforms generate a response. Some embodiments then cause presentation of an indication associated with the response.

The invention discloses a multi-agent-based context knowledge internalization, sharing and multiplexing system and method. The system comprises a multi-source knowledge capture module, a situation information extraction and structuring module, a knowledge internalization and incremental learning module, a situation awareness knowledge reuse and recommendation module and a knowledge quality evaluation and maintenance module, wherein each module is composed of a special intelligent agent or a functional unit and works cooperatively; according to the method, interaction, cooperation and process data and expert input of a task execution full cycle of a multi-agent system are captured through a multi-source module, preprocessing is carried out through a situation extraction module, a situation is associated to generate structured knowledge, and then the structured knowledge is fused into a dynamic knowledge system through an internalization module to achieve incremental learning. In a new task, matching knowledge is retrieved and recommended through the context awareness module, and finally the knowledge quality is periodically maintained by the evaluation module to ensure accuracy and timeliness; according to the method, the autonomous learning ability, the knowledge reuse efficiency and the intelligent level of solving complex problems of the multi-agent system are remarkably improved.

Embodiments of the disclosed technology relate to improvements in location estimation and prediction for mobile devices while preserving energy. The use of classifiers is provided which preserves energy usage of the mobile device while providing contextually aware information for various stages. The use of a motion classifier and ranging in the context of access devices, the use of directional motion classifiers to reduce the latency in detection that a mobile device has entered a geofence area, and methods of improving location estimates and increasing geofence boundaries to prevent false exits from a geofenced area are disclosed.

Embodiments of the disclosed technologies are capable of generating, using a machine learning model and a prompt, first content recommendations. The prompt comprises a search query and historic information associated with an entity. The first content recommendations are presented. The embodiments describe receiving a selection of a content recommendation of the first content recommendations. The embodiments describe generating, using the machine learning model and a second prompt, second content recommendations. The second prompt comprises a second search query and second historic information associated with the entity. The embodiments describe generating a ranked order of the second content recommendations using a history of entity interactions including the selection of the content recommendation of the first content recommendations. The embodiments describe determining context-aware recommendations by optimizing a permutation of the ranked order of the second content recommendations. The embodiments describe causing the context-aware recommendations to be presented.

The invention discloses a vertical federal learning feature selection method based on context awareness, and belongs to the technical field of artificial intelligence and data privacy protection. According to the method, firstly, an unsupervised sparse network is utilized at a client to initialize the importance of local features so as to accelerate convergence and reduce calculation complexity; and then, obtaining the embedded representation of each client in a pre-training stage, and screening the embedded representation by combining context features through a server side, thereby indirectly identifying key features. In the feature selection stage, the client side performs local feature screening according to the significant embedded index issued by the server, and the influence of irrelevant features on calculation and communication is further reduced. According to the invention, an attention mechanism is introduced to dynamically evaluate contributions of different participants, so that fair weight distribution is realized. According to the method, through staged joint optimization, the communication and calculation cost in the federation training process is effectively reduced, and meanwhile, the prediction precision and interpretability of the model are improved.

Certain aspects of the disclosure provide for information retrieval that exploits context derived from document structure. Source documents can be preprocessed to identify fields and determine context attributes related to each field based on the structural layout of a source document. Resource documents can also be preprocessed to segment a resource document into passages and determine context related to the passages based on structural layout. Queries pertaining to a field can be enhanced by adding context metadata associated with the field. A query embedding can be generated and compared with previously generated passage embeddings to locate candidate matches based on similarity. A machine learning model can be provided with the top-ranked passages and tasked with re-ranking the passages based on relevancy to the original query. The highest re-ranked passage or set of passages can be output in response to the query.

The invention discloses an SQL (Structured Query Language) statement review method, system and equipment based on a large model and a storage medium, which are applied to the technical field of SQL statement review, after a to-be-reviewed structured query language statement is obtained, the to-be-reviewed structured query language statement is reviewed according to the type of a to-be-reviewed structured query language statement database; the method comprises the following steps: selecting a corresponding analysis tool to analyze a to-be-audited structured query language statement to obtain a syntax tree structure, extracting a table object dependency graph on the basis of the syntax tree structure, and forming an examination cue word by utilizing a constructed multi-dimensional cue word framework and combining the table object dependency graph and metadata. The review prompt words are input into the preset large model for risk identification, the review result is obtained, semantic analysis of context awareness is achieved, according to the method, after deep semantic extraction is conducted on the SQL statements, the large model is used for logical reasoning and identification of potential logic defects, and the review accuracy is effectively improved.