283 results about "Context awareness" patented technology

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 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 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.

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.

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 intelligent context perception English adaptive learning system and method, and belongs to the technical field of acknowledgement science and language education. According to the system, physical scenes, social contexts, emotional physiological states and cognitive load data of a user are comprehensively collected and fused through a multi-dimensional context perception and cross-modal analysis module; constructing a real-time user model containing deep cognitive features by means of a user dynamic cognitive modeling module in combination with a meta-learning algorithm; through a dynamic knowledge graph and a generative content engine, real-time generation and compliance verification of scenarized learning content are realized. According to the method, the problems of one-sided context perception, static content stiffness, single algorithm adaptation and shallow cognitive modeling of an existing system are solved, deep adaptation of learning contents and real-time states of users is realized, the scene practicability of learning is remarkably improved, and an efficient and flexible English learning solution is provided for users with different requirements.

The invention discloses a thermal power plant multi-source heterogeneous data quality evaluation method and system, and relates to the technical field of data evaluation, and the method comprises the steps: carrying out the comparison based on an alignment path length, a preset path length threshold value and a deviation threshold value, obtaining a consistent data flow and a conflict data flow, the method comprises the following steps: respectively calculating an accuracy index, an integrity index and a timeliness index for consistent data flow and equipment log data, obtaining a three-dimensional quality index matrix, weighting the three-dimensional quality index matrix through an entropy weight method, and calculating a comprehensive quality score of the multi-source heterogeneous data in combination with a real-time weight value of the multi-source heterogeneous data, and comparing the comprehensive quality score of the multi-source heterogeneous data with a preset score threshold to obtain a quality evaluation result, and directly generating a conflict alarm signal for a conflict data flow. According to the evaluation method based on the data indexes, not only is the sensitivity to data logic contradictions enhanced, but also context awareness and weight optimization in the evaluation process are realized.

A system identifies a target device within a network and monitors interactions between a support device and the target device. Based on the monitored interactions, the system generates a support score for the support device, representing its contribution to the functionality of the target device. The system prioritizes a remediation action corresponding to the support device based at least in part on the support score for the support device.

The invention discloses an event causal identification method and device based on an iterative graph neural network, equipment and a medium, and relates to the technical field of artificial intelligence and machine learning, and the method comprises the steps: carrying out the sentence coding and event extraction of an input text, and obtaining a sentence embedding and event mention result; then, sentence-level embedding and document-level embedding of the event are generated using a multi-granularity context awareness mechanism. Then, constructing an initial event causal graph structure, and encoding the initial event causal graph structure to obtain graph embedding; and finally, dynamically updating an event causal graph structure by combining sentence-level embedding, document-level embedding and graph embedding through an iterative graph optimization mechanism, and realizing accurate identification of the event causal relationship. Through a multi-granularity context perception mechanism and an iterative graph optimization mechanism, local and global context information is effectively integrated, the accuracy and robustness of document-level event causal relationship recognition are improved, and the method is particularly excellent in performance when processing long texts and cross-sentence causal relationships and can better adapt to complex document structures.

Systems and methods for providing context-aware emergency information during an electronic communication are disclosed. The system can include an electronic communication device that includes a virtual assistant. The virtual assistant can autonomously provide information to a user and / or to an emergency response service provider during an emergency communication. Additionally or alternatively, the virtual assistant can automatically manipulate one or more functions on the electronic communication device and / or send a signal to manipulate one or more functions on a responder device.

The invention discloses a network information security access control system based on dynamic trust evaluation, and the system comprises a behavior collection module which collects original behavior log data, and builds a behavior feature sequence reflecting an entity security state through a preset streaming processing engine; the behavior analysis module is used for carrying out long and short term behavior modeling analysis on the behavior feature sequence to obtain long and short term behavior pattern representation, outputting potential causal chain data based on the long and short term behavior pattern representation and an abnormal causal inference model combined with context perception, and constructing a dynamic trust portrait according to the potential causal chain data; the long and short-term behavior modeling analysis comprises long behavior modeling analysis and short behavior modeling analysis; and the strategy generation module is used for driving a strategy engine in real time based on the dynamic trust portrait, carrying out attribute extraction and dynamic attribute coding based on a behavior sequence in a software defined boundary frame, and dynamically generating and issuing an optimal dynamic access strategy with attributes according to a minimum permission principle.

An improved context-aware security system may include an artificial intelligence (Al) reasoning security system. The context-aware security system may be configured to receive security input data from a plurality of input sources, generate a contextual security analysis, identify a security response, and send instructions to a security system output device to execute the security response. The context-aware security system may include a plurality of security input sources configured to provide security inputs and contextual data, including credential readers, physical access controllers, security sensors, environmental sensors, or external information sources. The context-aware security system may include a directive input subsystem configured to receive security directives from an authorized user interface. A plurality of security system output devices may be configured to execute the security response, including credential reading systems, biometric systems, alerting systems, logging systems, or notification systems.

Systems and techniques are described herein for a context-aware intelligent cockpit. For example, a computing device can capture, using a plurality of sensors, context data associated with user operation of a vehicle. The computing device can determine a distraction score based on the context data. The computing device can adjust a user interface of the vehicle based on the distraction score.

The invention relates to the field of block chains, in particular to an intelligent cabin digital twin recommendation system, method and device based on a block chain, and the method comprises the steps: obtaining data collected by an OBD sensor, a camera, an environment sensor and a biosensor; carrying out fuzzy processing on the collected data through edge calculation; performing feature extraction on the data after fuzzy processing, and generating a six-dimensional feature vector based on a context awareness recommendation algorithm; according to the scene label distribution weight of the six-dimensional feature vector, determining a recommendation priority; obtaining corresponding twinborn decision data according to the priority score; calculating a feature hash value of the twinborn decision data by adopting an SHA-3 algorithm, and generating a data fingerprint; dividing the data fingerprints into vehicle condition data and user data according to data types, and performing cross-chain storage; and performing credibility evaluation and screening on the multi-source data stored in a cross-chain manner, and feeding back the data to a user side. According to the scheme, a data island is broken, the security protection of privacy is enhanced, and the credibility and transparency of information recommendation are improved.

The invention relates to the technical field of network security, and discloses a data leakage real-time blocking method fusing a micro-isolation strategy and context awareness. According to the method, a micro-isolation strategy engine based on a data sensitivity level is constructed, a logic security domain is defined, and an independent access control rule is configured; collecting a network data flow in real time, identifying sensitive data by using a deep packet detection technology, and extracting context information; dynamically analyzing and generating risk metadata, calculating a real-time risk index, and establishing a behavior baseline model to detect behavior deviation; in combination with behavior deviation, a risk index and a data operation type, an access control rule is adaptively decided and dynamically updated, a high-risk or unauthorized data transmission session is blocked in real time, and the data leakage protection capability is improved.

A method includes: providing identification information for a provider of supplemental content; retrieving information on the provider of supplemental content from the internet using the identification information to generate a content source profile; retrieving a user profile about a user; computing, with a user mindset prediction language model configured to determine a user's mindset while reviewing content, the user's mindset based on primary content presented to the user; selecting, by a supplemental source selection language model configured to select supplemental sources, the provider of supplemental content from a plurality of supplemental sources, based on the primary content; and generating in real-time, with a supplemental content generation language model configured to generate supplemental content, one or more pieces of supplemental content for the provider of supplemental content at one or more locations that are contextually consistent with the primary content.

The invention relates to an intelligent sensing and trajectory prediction method for a low-altitude, slow and small target in a complex dynamic environment, and aims to solve the problem that accurate recognition is difficult due to the fact that a small-scale and high-maneuvering target is easily affected by strong electromagnetic interference and a complex background in the complex dynamic environment, and a low-altitude target fusion detection method is constructed based on a multi-source heterogeneous sensor. The bottleneck of accurate identification of the low-slow small target under strong electromagnetic interference and complex background is broken through; a low-slow small target detection method based on hierarchical feature distillation and dynamic context awareness is provided, multi-level features from low-level details to high-level semantics are extracted by designing a hierarchical feature distillation network, capture of small target features under a complex background is enhanced by using the dynamic context awareness, and the detection precision is improved; an unmanned aerial vehicle target motion trajectory prediction model based on space-time association is constructed, and in combination with a behavior pattern library, accurate evaluation and intention reasoning of a target threat level are realized, and the intelligent sensing and prediction capability of a low-speed small target in a complex environment is integrally improved.

This invention pertains to the field of mobile device authentication and discloses a dynamic context-aware continuous authentication method for mobile devices. It constructs a two-dimensional behavior quantizer based on motion intensity and interaction density to define the physical boundaries of pure motion, pure interaction, and hybrid scenarios in real time. A gating unit controlled by MI / ID signals is introduced to automatically generate masks in extreme scenarios such as pure motion or pure interaction to suppress ineffective attention calculations for missing modalities, enabling full feature interaction only in hybrid scenarios. A deterministic mapping rule from physical context state to network sparsity is established. Based on the real-time calculated MI / ID physical metrics, the system directly indexes the corresponding sparse sub-paths in the pre-trained multi-pruning rate network, achieving millisecond-level zero-overhead state switching while ensuring security.

The invention discloses a code abstract generation method and system based on hierarchical context awareness, and belongs to the field of natural language processing and software engineering. The method comprises the following steps: generating hierarchical annotations for a code warehouse based on a function call graph, firstly extracting a call relationship in the graph and eliminating a ring structure, and then classifying leaf nodes and generating annotations; a topological sequence from bottom to top is adopted in the core step, the importance score of a child node is calculated for each father node, child node annotations, class function descriptions and code bodies of the child nodes are fused, and a large language model is input to generate annotations till the whole graph is covered. The method is suitable for an automatic document generation scene of a complex software project, and through a technical path combining function call graph analysis, child node importance measurement and class function description, the accuracy and semantic consistency of code annotation are improved; the method can be widely applied to the fields of software maintenance, code understanding, intelligent development tools, automatic document generation of enterprise-level code warehouses and the like.

The application relates to a context-aware intelligent retrieval method suitable for multi-domain heterogeneous data, relates to the technical field of intelligent retrieval and big data processing, combines domain semantic enhancement of context awareness with a cross-domain semantic hash retrieval model, and realizes effective alignment between a source domain and a target domain in a unified feature space. By constructing a domain-shared encoder and a domain-specific encoder, residual fusion strategy is adopted to integrate domain-shared features and domain-specific features, so that image detail information and context relationships are retained, and feature migration between domains is realized. In the training process, a domain classification loss and a distribution alignment strategy are used to ensure effective modeling of domain-specific information and domain-shared information.

The application belongs to the technical field of audio processing, and specifically provides a melody note sequence generation method and system based on tone features and machine learning, an electronic device, and a storage medium. The method comprises obtaining a tone sequence based on an original Chinese text; constructing a rich context feature vector; pre-training a gradient boosting model; inputting the context feature vector into the pre-trained gradient boosting model to obtain the probability distribution of each category under the melody mode; concatenating the probability distribution of each category under the melody mode in order to generate a melody note sequence separated by spaces; and obtaining slow, clear, and rhythm-exaggerated singing audio suitable for aphasia MIT rehabilitation based on the melody note sequence and the Chinese text. The application automatically extracts Chinese tone features and predicts the melody mode with context awareness, thereby realizing high-quality generation from Chinese text to natural melody note sequence.

The application discloses a user interest matching network marketing system fused with a knowledge graph, and relates to the technical field of computers. The system is used to solve the marketing decision deviation problem caused by the lack of dynamic context perception and causal reasoning capability in existing user interest modeling. First, a dynamic knowledge graph is constructed through distributed event stream processing, and the real-time context of users is structured as a graph sequence with timestamps. Second, a time series convolution graph network is used to extract user interest evolution features, and the influence weight of the context node is quantified through a multi-head attention mechanism. Then, a causal inference model is constructed to eliminate exposure and location bias, and an counterfactual learning framework is used to extract user essential interest representation. Finally, the knowledge graph structure is dynamically optimized based on a gradient propagation algorithm, forming a closed-loop learning system from decision-making to perception. The application realizes deep understanding and accurate matching of user interest, and improves the accuracy and interpretability of the marketing system.

The application provides a system and method for optimizing maintenance procedures using natural language interaction and structured knowledge representation. The system includes a natural language processing module, a knowledge graph, a database, a retrieval analysis module, and an interactive user interface. With the system, maintenance personnel do not need to manually search for various types of maintenance information, do not need to be very familiar with the content of the information, can use the context-aware maintenance guidance provided by the system to gradually narrow down the possible causes of the fault and ultimately lock the fault cause, and can perform processing according to the repair scheme provided by the system. Therefore, the system greatly reduces the experience requirements for maintenance personnel and the familiarization procedures for various types of information related to repair experience, saves the time of maintenance personnel for repeatedly searching various types of information, and improves the accuracy and work efficiency of the maintenance personnel for troubleshooting.

A system is disclosed for generating context-aware, domain-specific responses using a pre-trained language model in combination with a semantic search engine and specialized processing modules. The system includes a classification model to identify user intent, an extraction model to determine parameters, and a plurality of parameter functions that generate outputs such as sentiment classifications, semantically similar content, and dynamically constructed prompts. By integrating retrieved structured and unstructured content into tailored prompts, the system provides accurate, domain-specific query responses without requiring retraining of the underlying language model. The architecture supports efficient and scalable workflow management in dynamic environments while reducing computational overhead compared to conventional approaches.

This application discloses an AOI matching method based on spatial context awareness and adaptive multimodal feature fusion, comprising: extracting geometric and textual features from AOIs from two different map platforms; constructing a heterogeneous spatial map; using a heterogeneous graph Transformer model to perform context aggregation and feature updates on the heterogeneous spatial map to obtain the spatial context embedding features of the AOI; constructing a global spatial context vector of the AOI, and obtaining multimodal refined features through a GRU gating fusion mechanism; fusing the multimodal refined features through an intra-source attention mechanism to obtain intra-source embedding features, and performing bidirectional cross-attention calculation through a cross-source attention mechanism to obtain the inter-source embedding features of each of the two map platforms; and determining the AOI matching probability based on the inter-source embedding features and the spatial embedding vectors of candidate AOI pairs. This method solves the problem of poor AOI matching performance under special data such as missing names and coarse contours, and in complex heterogeneous scenarios, thereby improving the matching accuracy of AOIs.

This application relates to a vehicle-mounted user intent recognition and task generation system based on a large model, belonging to the technical field of automotive interaction. It includes: a user intent recognition module, used for: acquiring voice input from an in-vehicle user and parsing the voice input to obtain multiple user intents; a context awareness module, used for: collecting current environmental data of the vehicle and obtaining the current context based on the current environmental data using a large model; a task generation module, used for: searching the vehicle's task capability interfaces to generate task combinations based on the user intents and the current context; the task combinations involve calling different intelligent agents; a reflection module, used for: evaluating whether the generated task combinations are consistent with the user intents and the current context and adjusting inconsistent task combinations; and a task sequence orchestration module, used for: confirming the control order of different serial and parallel tasks based on the task combinations and calling the corresponding intelligent agents.

In some embodiments, a computer-implemented method, includes capturing, for a payment network associated with a payment card of a user of a digital platform, context-aware image generation data; generating, at the payment network associated with the payment card, a context-based personalized prompt based upon the context-aware image generation data, the context-based personalized prompt being associated with the user of the digital platform; utilizing the context-based personalized prompt to generate a context-aware image, the context-aware image being a transformer-based context aware image; and providing the context-aware image to the digital platform for dynamic view by the user of the digital platform. In some embodiments, the computer-implemented method further includes transforming the context-based personalized prompt into a context-based text embedding in order for the context-aware image to serve as the transformer-based context aware image.

This invention belongs to the field of medical wristband technology, specifically disclosing a translation method for bidirectional communication between medical staff and patients based on multimodal interaction, including the following steps: Step S1, the multimodal interaction unit receives multimodal input signals; Step S2, the multimodal interaction unit fuses the multimodal input signals to generate comprehensive input information; Step S3, the comprehensive input information is identified and translated based on a medical-specific translation engine; Step S4, the context-aware module analyzes the dialogue content, identifies the scenario, and dynamically adjusts the translation results; Step S5, the adjusted translation results are synchronously output bidirectionally and broadcast via voice. This invention achieves three core breakthroughs in clinical cross-language communication through a translation mechanism combining multimodal signal fusion and context awareness: enabling real-time, accurate, and private barrier-free communication between doctors and patients, reducing the risk of medical errors due to language barriers, improving consultation efficiency and patient satisfaction, and reducing the risk of misdiagnosis.