67 results about "Context sensitivity" patented technology

The invention discloses a multi-modal-based digital intelligent customer service system. Data compensation is carried out on initial multi-modal problem data by adopting a dynamic adaptive compensation mechanism through a multi-modal input module. And carrying out dynamic weight distribution on the target multi-modal problem data by adopting a cross-modal attention mechanism through a multi-modal comparison learning module. And performing intention recognition on the target multi-modal semantic vector through an intention recognition module according to the historical interaction data and a preset dynamic knowledge graph. And inputting the user intention data into the target reinforcement learning reply model through a reply generation module to construct reply data. And performing potential problem prediction by adopting the target multi-modal semantic vector, the historical interaction sequence corresponding to the user and the time data through a potential problem prediction module. And by utilizing the dynamic updating capability of the knowledge graph, the context sensitive identification of the user problem is realized, and the identification accuracy is improved. And the potential problem prediction module turns from passive response to active service, so that the interaction efficiency and the user satisfaction are remarkably improved.

The invention discloses a code vulnerability detection method, a code vulnerability rule configuration method, a static single assignment SSA code acquisition method, devices corresponding to the methods, and electronic equipment. The code vulnerability detection method comprises the following steps: acquiring a plurality of source code files and code vulnerability rules of an application program; generating an abstract syntax tree of the source code file, and obtaining a function call relationship; and traversing the abstract syntax tree, and obtaining a cross-function risk propagation path according to the function call relationship and the code vulnerability rule. By the adoption of the processing mode, the function calling relation and the abstract syntax tree are fused to conduct cross-function code vulnerability detection, cross-function vulnerability detection of context sensitivity, flow sensitivity and domain sensitivity is achieved, and vulnerability false report and missing report are avoided; therefore, the accuracy and recall rate of vulnerability detection can be effectively improved.

The invention discloses an interactive garden design method and system based on landscape garden simulation, particularly relates to the technical field of garden landscape design and computer graphic simulation, and relates to a plurality of modules of environmental data acquisition, three-dimensional modeling, plant growth simulation, water simulation, interactive design, microclimate prediction and the like. According to the method, terrain, climate and soil data are collected through a laser radar and multiple sensors, and a standardized environmental map is constructed; generating a plant topological structure based on a context sensitive type L system, and simulating a growth process by adopting a Logistic function in combination with an environmental factor; the method comprises the following steps: realizing dynamic simulation of a water body by utilizing an SPH particle method and a Navier-Stokes equation, and establishing a plant-water body feedback model; and real-time operation and design optimization are supported through a three-dimensional interaction interface. The system has the advantages of being high in ecological adaptability, high in visualization degree, perfect in feedback mechanism and the like, the intelligence and scientificity of garden design are remarkably improved, and the system is suitable for various scenes such as urban green lands and ecological restoration.

The invention discloses a mandatory access control method and device based on a process function context, and the method comprises the steps: collecting a security context associated with a system call initiated by a target process, so as to generate a standardized object description; mapping the object description into a target function classification identifier, so as to obtain a process function context view of the target process according to the target function classification identifier; constructing a target decision key for access decision based on the current policy era, the qualifier, the function classification identifier, the view identifier of the process function context view and the isolation domain abstract; and querying the multi-level cache according to the target decision key to determine a matched target access decision. Therefore, context-sensitive judgment and cross-component consistency taking the functional context as the center are realized.

The invention discloses an English text automatic proofreading system and method based on intelligent AI, and relates to the technical field of natural language processing, and the method comprises the steps: inputting an original English text, generating a syntactic graph through dependency syntax analysis, calculating a node syntactic centrality weight, and generating a triple description set after screening key nodes; on the basis of the triple description set, constructing a structured prompt template, executing English-in-English bidirectional conversion verification through a multi-language model, generating an initial image, and outputting an aligned final image through iterative optimization circulation; and searching a syntax compliance candidate word library for the conflict text node set, selecting a candidate word with the highest comprehensive score, and outputting a corrected text, a video stream and an error analysis report. According to the method, the text assertion and the visual assertion are synchronously verified through the differentiable grammar rule matrix, and the problem that context sensitive errors are difficult to detect through a traditional method is solved.

A system (100) for multi-channel purchase authorization with dynamic role validation, comprising: (a) a multi-channel request capture module configured to receive and standardise purchase requests initiated through a variety of communication channels, including corporate portals, mobile applications, emails and messaging platforms; (b) a role identification and contextual mapping module configured to dynamically retrieve user roles and contextual information based on real-time access management data and organizational hierarchy; c) a policy and rules module configured to evaluate purchase requests based on pre-configured and context-sensitive authorization policies, thresholds and compliance rules; (d) a dynamic role validation and escalation module configured to validate whether the requester has the authority to initiate or authorise a transaction and, if not, to automatically escalate the request to an appropriate authorised role; (e) an authorization workflow orchestration module configured to sequence approval steps, apply business logic, manage parallel or sequential workflows, and interface with external procurement systems; f) and an audit, logging and analytics module configured to record all request, validation and authorization actions in a tamper-proof log and generate real-time insights and compliance metrics, g) where the system enables real-time, role-based purchasing authorization across multiple channels with dynamic policy enforcement and escalation handling.

The invention discloses an AI medical comment hierarchical classification method and system based on hyperbolic space attention, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining a to-be-classified original AI medical comment text, inputting the preprocessed AI medical comment text into a BERT model, and carrying out the coding and modeling of a multi-layer Transform self-attention mechanism, thereby obtaining an AI medical comment hierarchical classification model. Extracting context-sensitive semantic features; inputting the semantic features into a bidirectional LSTM network, capturing a long dependency relationship and grammatical logic in the semantic features, and extracting semantic enhancement features; based on a hyperbolic space attention module, generating attention weights according to structural distances among the semantic enhancement features in hyperbolic geometry, and calculating to obtain the semantic enhancement features subjected to attention weight weighted aggregation; and performing classification based on the weighted and aggregated features, and outputting a multi-level category label of the text. According to the method, accurate hierarchical classification and semantic aggregation of the AI medical comments can be realized.

The invention relates to the technical field of voice signal processing and synthesis, and particularly discloses an information processing method and system for voice conversion, and the method comprises the steps: extracting a language feature vector of each word from an input text; calculating posterior probabilities of the words in different languages by combining a Bayesian reasoning mechanism, and generating language attribution confidence coefficient characteristic values; a Monte Carlo sampling method is adopted to carry out multiple times of context sensitive simulation, and pronunciation path selection probability distribution characteristic values are generated; further fusing the feature values into a multi-language pronunciation decision vector, inputting the multi-language pronunciation decision vector into a multi-language end-to-end speech synthesis model, calling a phoneme mapping rule of a corresponding language and an acoustic parameter prediction module, and generating a high-quality target speech spectrogram; and finally, dynamically adjusting Bayesian prior distribution and a language recognition threshold according to the output speech spectrogram.

The invention relates to the field of multi-modal neural network model compression, and particularly discloses a multi-modal model compression and distillation method and system based on causal reasoning, and the method comprises the steps: constructing a comprehensive causal discovery module, identifying a causal dependency relationship among the multi-modal features through information theory measurement, Granger causal analysis and intervention-based verification; executing an adaptive compression engine, and performing pruning, mixing precision quantification and low-rank decomposition based on a causal relationship; a cross-modal distiller is applied, and multiple loss function combinations are adopted to maintain the relationship between modals; and implementing a dynamic optimizer to carry out hardware perception and context-sensitive reasoning optimization. According to the method, the compression decision is guided through causal reasoning, the high compression rate is achieved while the key causal path is kept, and the deployment problem of the multi-modal model in the resource-constrained environment is effectively solved.

An example of a context sensitive guidance (CSG) system for guiding caregivers providing medical care for a victim, for example a trauma victim, includes a CSG engine and contextual data sources communicatively coupled to the CSG engine. The contextual data sources include a medical device configured to collect physiologic data and provide the physiologic data to the CSG engine and an emergency environment data source configured to receive emergency environment data and provide the data to the CSG engine. The CSG engine includes hardware logic and / or software logic configured to receive the contextual data, evaluate protocols based on the contextual data, select at least one action item based on the protocols, generate at least one instruction based on the action item(s), the instruction(s) including a caregiver instruction and / or a medical device instruction, and provide the instruction to a caregiver interface device and / or the medical device.

The invention belongs to the field of micro-architecture design of an integrated circuit processor. The invention provides an H2P branch prediction chip circuit architecture based on BrPerceptron, a variable operation track of a program is constructed based on a context-sensitive variable tracking mechanism, a program execution path topology is constructed based on a basic block division algorithm according to the variable operation track, multi-dimensional correlation detection is performed according to the program execution path topology to judge whether an H2P branch exists or not, and if yes, the H2P branch is predicted. And when the H2P branch instruction is judged to be the H2P branch instruction, enabling the BrPerceptron predictor to output, performing multi-stage feature fusion and outputting a prediction result, and realizing high-precision prediction for the H2P branch instruction.

The invention relates to the technical field of data mining, in particular to an enterprise financial data analysis and management cloud platform system and a programming method thereof. The method comprises the following steps: collecting multi-source enterprise financial data, and carrying out analysis pre-classification and compression coding to obtain a tense data collection event stream; performing context sensitive semantic alignment based on the temporal data acquisition event stream to obtain a high-dimensional semantic tensor; performing external economic entity semantic constraint according to the high-dimensional semantic tensor to obtain a financial entity relation graph; performing structured chain packaging and consensus verification on the logic relationship among the nodes of the financial entity relationship graph to obtain an encrypted verification financial chain packet; and performing programming logic constraint on the encrypted verification financial chain packet to obtain a pre-constructed logic execution unit set, and uploading the pre-constructed logic execution unit set to the enterprise private cloud platform to execute a platform construction task. According to the invention, the intelligent level and the management efficiency of enterprise financial analysis management are enhanced.

The present disclosure provides techniques for context-sensitive token-bucket rate limiting. A processing device obtains, in a kernel space of an operating system (OS), a message comprising a unique process identifier (UPID) and a message type. The processing device determines whether to send the message from the kernel space to a user space of the OS based on at least one of: the UPID, the message type, or a token count and a discrete time unit in an entry in a data structure in the kernel space. The processing device processes the message based on the determination of whether to send the message from the kernel space to the user space.

The application relates to the field of multi-modal neural network model compression, and specifically discloses a multi-modal model compression and distillation method and system based on causal reasoning, which comprises the following steps: constructing a comprehensive causal discovery module to identify the causal dependence relationship among multi-modal features through information theory measurement, Granger causality analysis and intervention-based verification; performing an adaptive compression engine to perform pruning, mixed precision quantization and low-rank decomposition based on the causal relationship; applying a cross-modal distiller to maintain the relationship among modes by using a variety of loss function combinations; and implementing a dynamic optimizer to perform hardware perception and context-sensitive reasoning optimization. The application guides the compression decision through causal reasoning, realizes high compression rate while maintaining key causal paths, and effectively solves the deployment problem of multi-modal models in a resource-limited environment.

A system and method for encoding and decoding optical codes with context-aware, multi-level security. Input data is classified into security levels with associated context sensitivity requirements. The system compresses data using public and private codebooks based on security classifications, then generates optical codes incorporating both compressed data and context requirements. When scanned, the system collects environmental contextual data (location, network environment, device security, user behavior), analyzes it against embedded context requirements, and dynamically determines which security levels are accessible in the current environment. Only authorized security levels are decoded using appropriate codebooks based on both user credentials and current contextual factors. This approach enables fine-grained, context-sensitive access control that adapts to changing environments while maintaining the compression benefits and capacity advantages of the multi-level security framework.

Embodiments include a system having processing circuitry to execute instructions to perform a variety of activities, operate in any of multiple operating modes, as well as run any of multiple virtual machines. A register stores operating state information of the processing circuitry. The processing circuitry is further able to selectively service requests issued by a controller coupled to the processing circuitry. Each service request includes one or more fields, each of which includes data to specify a respective qualifier for servicing. Each qualifier can be enabled to make the qualifier a condition of servicing the debug request. When all enabled qualifiers are satisfied, the processing circuitry services the service request. Servicing of the request may be performed in real-time without suspending the processing circuitry.

The invention relates to the field of industrial data processing, and particularly discloses an industrial data anomaly detection method and system.The industrial data anomaly detection method comprises the steps that firstly, an alarm event is responded to capture a real-time context snapshot representing a physical world state, and after preliminary vector retrieval is completed, a set of multi-dimensional fidelity scoring mechanism based on risk self-adaptive gating is introduced; the mechanism firstly evaluates the potential influence of knowledge, dynamically generates a situation sensitivity index and a collection threshold value on the basis of the potential influence of the knowledge, and then achieves strong override on low-situation consistency knowledge, especially high-risk knowledge through an indexed gating factor. Finally, only the verified knowledge set passing the strict verification is constructed into a high-fidelity cue word and input into a large language model, it is ensured that a generated diagnosis report is established on the basis of high-quality knowledge which is matched with the current working condition, reliable in source and subjected to risk adaptation evaluation, and safe, credible and traceable diagnosis is achieved.

A machine learning-based method for accelerating a detection and disposition of content anomalies in a target digital artifact includes identifying, by one or more computers, a digital artifact underpinning a digital artifact assessment request; detecting, via the one or more computers, a plurality of content deviations in the target digital artifact based on a context-sensitive artifact assessment protocol obtained from the context-sensitive artifact assessment protocol repository; identifying, via a digital artifact assessment user interface, a sequence of one or more inputs corresponding to a rejection of a first subset of the plurality of content deviations; and based on identifying the rejection of the first subset of the plurality of content deviations: computing, via the one or more computers, a system-generated adaptation proposal for each content deviation underpinning the first subset of the plurality of content deviations based on machine learning-derived policies underpinning the context-sensitive artifact assessment protocol.

The invention provides a medical scene optimization-oriented OCR neural network based on a lightweight CRNN architecture, and aims to significantly improve the medical text recognition precision while reducing the calculation overhead. The core innovation of the method is that on one hand, a lightweight convolution feature extraction module is introduced, and the model scale and reasoning energy consumption are effectively compressed; on the other hand, a dictionary constraint mechanism in the medical field is fused, medical terms and semantic structure priori knowledge are utilized, and the recognition capacity for professional symbols, abbreviations and context sensitive content is enhanced. According to the architecture, high accuracy is kept, meanwhile, the requirement for computing power is remarkably reduced, efficient deployment can be achieved on medical edge devices (such as portable terminals and basic diagnosis and treatment devices) with limited resources, feasible technical support is provided for large-scale application of medical OCR in scenes such as mobile medical treatment, basic health services and intelligent diagnosis and treatment terminals, and the application prospect is wide. Important clinical and industrialization values are realized.

This invention discloses a method, device, and medium for predicting public transport carbon emission factors per passenger. The method includes: constructing bus stop-level, bus link-level, and bus route-level influencing factors; using these factors as input factors, processing them through a routing mechanism, calculating the routing score for each input factor, and then assigning each input factor to a corresponding expert sub-model to predict the corresponding public transport carbon emission per passenger. The method also involves identifying key factors for public transport carbon emissions and / or determining the coupling effect between pairs of input factors. Context-sensitive frequency and call balance are introduced to evaluate the local and global load of the expert sub-models, and the impact of expert switching on the prediction output is quantified through expert perturbation sensitivity.

Device for the secure hybrid execution of context-aware Large Language Model (LLM) deployments, comprising: an edge execution unit (101) configured to execute at least a first part of an LLM inference process on a local device; a cloud inference processing unit (102) configured to execute at least a second part of the LLM inference process on a remote computing infrastructure; a context sensitivity analyzer (103) configured to analyze an input query and associated context data and generate a sensitivity classification for said context data;a hybrid execution orchestration engine (104) that functions with the context sensitivity analyzer (103), the edge execution unit (101), and the cloud inference processing unit (102), wherein the hybrid execution orchestration engine (104) is configured to dynamically split the LLM inference process into an edge-executed part and a cloud-executed part, based on at least the sensitivity classification; a secure communication interface (105) configured to transmit intermediate inference outputs between the edge execution unit (101) and the cloud inference processing unit (102) over an encrypted communication channel; a TEE module (106) integrated into the edge execution unit (101) to perform at least one privacy-relevant inference operation in an isolated, hardware-protected environment;a key management and encryption controller (107) configured to generate, store, distribute, and rotate cryptographic keys and to encrypt and decrypt intermediate inference outputs transmitted between the edge execution unit (101) and the cloud inference processing unit (102); a policy enforcement and access control unit (108) configured to validate user authorization and enforce execution policies that define whether at least some of the context data is restricted to local execution;and an audit logging and threat monitoring module (109) configured to record inference execution events, security decisions, policy enforcement events and communication events and detect anomalous behavior related to the LLM inference process, wherein the device securely generates an output response to the input query by performing context-aware LLM inference while preventing sensitive context data from being disclosed to unauthorized external environments.

Technologies are generally described for context sensitive display of information on a consumable delivery vehicle with en route preparation. An on-board controller may receive different sets of sensor data associated with time, location, temperature, humidity, captured images, and / or captured audio from sensors on the vehicle or at a destination location. The sensor data may be analyzed / processed at the on-board controller or by a remote control system and instructions may be generated for display of context sensitive information based on received sensor data and / or processed sensor data. One or more displays on the vehicle may display images are associated with a branding, a menu, a pricing, and / or an advertisement based on the generated instructions.

This invention relates to the field of enterprise office information security technology, and discloses a solution for office needs based on AI intelligent agents, including: context-sensitive semantic analysis, dynamic graph risk reasoning, multi-stage feature generation and dynamic learning, multi-level security policy generation, and feedback closed-loop optimization. Compared with existing technologies that rely solely on static keyword matching, lack dynamic risk reasoning and flexible permission adjustment, especially in handling complex conditions such as cross-network segment sharing, code names and coded messages, and abnormal operations, this invention addresses the technical problems of accurately identifying access to sensitive information and performing corresponding multi-level processing on sensitive access by using multi-stage feature modeling, path segmentation risk coding, context weighting, and improved XGBoost dynamic learning with AI intelligent technology. This achieves accurate labeling of sensitive information, forward prediction of leakage risks, and intelligent identification and control of permissions, thereby improving information security and the level of office intelligence.

A method of performing a data search in a data source is provided. Through the method, an operator optimization module using an intermediate representation of optimized and compiled operators optimizes and compiles operators of a data search pipeline in real time to generate optimized operators for the data search execution, taking into account runtime information and optimization rules. The method can be applied to one operator or multiple operators applied in any order or tree structure according to a query plan determined by the runtime information and the optimization rules.

A system and method for privacy-enhanced social networking may include dynamically managing user privacy and identity through multiple user profile aliases by defining each alias with a specific subset of user information and context parameters, such as location, proximity to other users, or participation in events. The system may dynamically select and display the appropriate alias based on the user's current context, thereby ensuring context-sensitive visibility of user information. Features include geofencing, proximity-based networking, and event participation tracking, supported by advanced system architectures such as graph databases and machine learning models. Additional functionalities include privacy-enhanced messaging with facial recognition for recipient verification, anonymous gifting, and automated will execution. A map interface and real-time adaptability further enhance user engagement and privacy management, fostering secure, meaningful interactions while maintaining robust privacy controls for applications in professional networking, community events, and confidential communications.

Techniques for auditory augmented reality using selective noise cancellation include receiving an input signal capturing an ambient auditory environment; separating the input signal into a set of audio signals that includes first and second component signals; and in response to generating the set of audio signals: generating a context-sensitive user interface that displays a plurality of first controls for modifying the first component signal and a plurality of second controls for modifying the second component signal, the plurality of first controls being independent from the plurality of second controls; receiving, from a user, a selection to remove the first component signal; and in response to receiving the selection: removing the first component signal from the set of audio signals to generate a modified set of audio signals that includes the second component signal; and driving an audio output device to generate sound based on the modified set of audio signals.

The invention discloses a demand-driven reusable binary pointer analysis method, which is used for determining a pointing set and an alias of a given target pointer variable, and comprises the following steps of: S1, recovering a pointer type in a process through a use mode of the variable, and extracting an instruction related to pointer calculation; s2, on the basis of the pointer representation and an execution result of the modeling instruction, abstract interpretation is carried out in the process, a pointer representation set of all pointer variables is obtained, and the result is stored in an intermediate result library; s3, on the basis of an analysis result in the process, determining a to-be-analyzed function as required, and obtaining different pointer representations of the target pointer variable in different functions; and if a certain function is not analyzed, the in-process analysis of the step S1 and the step S2 is triggered for the function. According to the method, a context sensitive strategy and a flow sensitive strategy are realized, the balance of analysis efficiency and precision is realized, and inter-process on-demand pointer analysis can be efficiently carried out.

The application discloses a method and device for enforcing access control based on process function context, wherein the method comprises: collecting a security context associated with a system call initiated by a target process to generate a standardized object description; mapping the object description to a target function classification identifier to obtain a process function context view of the target process according to the target function classification identifier; constructing a target decision key for an access decision based on a current policy epoch, a qualifier, a function classification identifier, a view identifier of the process function context view and an isolation domain digest; and querying a multi-level cache according to the target decision key to determine a matching target access decision. Thus, context-sensitive decision based on a function context is realized, and cross-component consistency is achieved.

A method of performing a data search in a data source by which an operator of a data search pipeline is just-in-time optimized and compiled, using an operator optimization module which optimizes and compiles an intermediate representation of the operator, considering runtime information, and optimization rules, to produce an operator that is optimized for the data search being performed. The method can be applied with one operator or with many operators applied in any sequence or tree structure according to a query plan, as determined by runtime information and optimization rules.

According to the context sensitive pointer analysis method provided by the invention, dependency and access relationships among modules are analyzed through keywords in the modules, modeling is carried out according to the dependency and access relationships, and a method calling statement for calling an interface is analyzed, so that the problem of completeness loss caused by the fact that interface access among the modules cannot be captured in a traditional pointer analysis method is solved. Secondly, an algorithm is designed, so that construction and updating of an object distribution graph and a module depth graph are completed in the statement analysis process, the pointer analysis efficiency is effectively improved, finally, key information is selected based on module depth information stored in the module depth graph to construct a context, the number of elements in the context is limited to be 1, and the algorithm is more efficient. Unnecessary analysis context elements are removed, and high precision of an analysis result is kept, so that the efficiency of an analysis process is remarkably improved.