96 results about "Context sensitivity" patented technology

Systems and methods for context sensitive user interfaces for enhanced vehicle operation. An example method is method implemented by a system of one or more processors, the system being included in a vehicle. The method includes causing presentation, via a display of the vehicle, of a unified user interface comprising a combined view which aggregates an autonomous visualization and map information, the autonomous visualization and map information being related to a zoom level, with the unified user interface being generated based on first contextual information indicative of operation of the vehicle. Second contextual information indicative of subsequent operation of the vehicle is accessed. The unified user interface is updated based on the second contextual information, with the combined view being adjusted, and with adjusting the combined view including adjusting a size of the combined view in the unified user interface or adjusting the zoom level.

Computerized systems and methods are disclosed to generate a document from one or more first and second text prompts, generating one or more context-sensitive text suggestions using a transformer with an encoder on the text prompts and a decoder that produces a text expansion to provide the context-sensitive text suggestions based on the one or more first and second text prompts by applying generative artificial intelligence with token biased weights for zero-shot, one-shot or some-shot generation of the artificial intelligence context-sensitive text suggestions from the one or more first and second text prompts.

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.

Herein from tabular data in a text document, a machine learning text classification pipeline infers natural language syntax and semantics to prepare the tabular data for graph analytics. In an embodiment, a computer infers a respective classification of each column in a table in a text document that contains natural language. Based on the classifications of the columns in the table, the vertices of the knowledge graph are automatically generated. Based on those column classifications and automatic analysis of a particular document portion that does not contain the table, the edges of the knowledge graph are automatically generated. The knowledge graph may be generated and operated as a property graph. Pipeline subsystems herein include column classification, edge type identification, and subject / object detection that provide sufficient semantic enrichment and context sensitivity to faster generate a more accurate knowledge graph than the state of the art.

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.

Infusion devices and related medical devices, patient data management systems, and methods are provided for monitoring a physiological condition of a patient. An exemplary infusion device includes an actuation arrangement operable to deliver fluid to a user, a communications interface to receive measurement data indicative of a physiological condition of the user, a sensing arrangement to obtain contextual measurement data, and a control system coupled to the actuation arrangement, the communications interface and the sensing arrangement to determine a command for autonomously operating the actuation arrangement in a manner that is influenced by the measurement data and the contextual measurement data and autonomously operate the actuation arrangement in accordance with the command to deliver the fluid to the user.

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 invention relates to the technical field of user abnormal behavior recognition, and discloses an intelligent keyboard and mouse control system based on the Internet of Things, which comprises a data acquisition module used for acquiring operation data, environment data and a screen image when a user uses a computer; the data processing module is used for preprocessing the operation data and the environment data to obtain first feature data; the user behavior analysis module is used for analyzing the first feature data and judging whether the current behavior of the user is abnormal or not based on a rule threshold value and a support vector machine; and the intervention evaluation module is used for obtaining a behavior feature score according to the first feature data, obtaining a context sensitive feature score according to the operation data and the environment data, calculating the behavior feature score and the context sensitive feature score through a nonlinear combination formula to obtain a comprehensive risk score, and further taking intervention measures. According to the invention, real-time identification, accurate prediction and dynamic intervention of abnormal behaviors when a user uses a computer are realized.

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 invention provides an automatic security vulnerability detection method and system for multiple types of scripts in an embedded system, electronic equipment and a readable storage medium, the method comprises the following steps: unpacking and analyzing firmware of a to-be-detected embedded system to identify a script file and a template file; identifying a dependency relationship between script files and template files and interaction and dependency relationships between different types of script files, marking sensitive input source points and potential vulnerability sink points, and generating a control flow graph and an abstract syntax tree by adopting a context-sensitive control flow analysis algorithm; constructing a power set lattice according to an assignment statement in the control flow graph, and performing data flow analysis by using a fixed point algorithm to obtain a data flow constraint relationship; and performing taint analysis to identify all dangerous execution paths. The script security vulnerability can be automatically identified, the security of the embedded system in the script execution process is improved, and potential security threats are effectively prevented.

The invention provides a sensitive word dynamic monitoring method and device in Tibetan complex environment speech recognition, and the method comprises the steps: carrying out the segment segmentation of a to-be-monitored Tibetan speech input stream in a complex environment, and obtaining the framed speech signals of the Tibetan speech in the complex environment; performing multi-dimensional perceptual fusion on the phoneme-level score, the semantic-level score, the scene-level score and the emotion-level score of each candidate sensitive word in the framing voice signals to obtain perceptual fusion features of the context corresponding to each candidate sensitive word; determining the decoding confidence coefficient of the Tibetan syllable sequence through the path stability of a decoding path in speech recognition and the posterior probability of each Tibetan syllable in the Tibetan syllable sequence; and determining the context sensitivity of each candidate sensitive word based on the decoding confidence and each perceptual fusion feature, and carrying out graded early warning on the Tibetan speech input stream in the complex environment through the context sensitivity. Based on the scheme, multi-dimensional fusion scoring of sensitive words in Tibetan complex environment speech recognition can be realized.

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 (100) for context-sensitive language translation, comprising: an input interface module (102) configured to allow the user to input text for language translation, wherein the input interface module (102) includes an interface that allows the user to input text and select the language for translation; a context processing module (104) connected to the input interface module (102) and configured to enable context-dependent extraction of information from the provided input text, wherein the context processing module (104) comprises the following: an input processing module (106) configured to decompose the input text into tokens and identify context-sensitive segments; and a context extraction module (108) connected to the input processing module (106), wherein the context extraction unit (108) comprises an extraction processor configured to generate context vectors from source input text; a translation processing unit (110) comprising a machine learning-based processor configured to translate the provided input text, wherein the translation processing unit comprises: an attention mechanism (112) configured to select words based on their relevance to the context; and a translation generation module (114) configured to generate translations using a neural network that incorporates the attention-weighted context vectors; a post-processing unit (116) connected to the translation processing unit (110) and configured to refine the translated output to ensure fluency and coherence, refining the translation grammar, ensuring semantic consistency, validating contextual appropriateness, and generating a fluency score; and an output module (118) connected to the post-processing unit (116) and comprising a display with an output interface on which the input text and the translated output text are displayed to the user.

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.

A system for performing functions in AR experiences is provided having a computer receiving an image of a monitored device having at least one object; the computer processes said image to determine a plurality of feature points; a database is in data communication with said computer and stores a plurality of reference images with reference feature points and reference object zones, each having a property; the computer matches at least some of the plurality of feature points with at least some of the plurality of reference feature points to compute a homography matrix; the computer transforms the image into a transformed image using the homography matrix; the computer identifies an object zone in the transformed image based on the reference object zone; the computer determines an object state by processing a sub-image in the object zone of the transformed image based on the property associated with the reference object zone.

The present invention relates to the technical field of user abnormal behavior recognition, and discloses a keyboard and mouse intelligent control system based on the Internet of Things, including: a data acquisition module, which is used to obtain operation data, environmental data and screen images when a user uses a computer; a data processing module, which is used to pre-process the operation data and environmental data to obtain first feature data; a user behavior analysis module, which is used to analyze the first feature data, and judge whether the user's current behavior is abnormal based on rule thresholds and support vector machines; an intervention assessment module, which is used to obtain a behavior feature score according to the first feature data, obtain a context-sensitive feature score according to the operation data and environmental data, and calculate the behavior feature score and the context-sensitive feature score through a nonlinear combination formula to obtain a comprehensive risk score, and then take intervention measures. The present invention realizes real-time recognition, accurate prediction and dynamic intervention of abnormal behaviors of users when using computers.

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 context sensitive avatar subtitles. Systems and methods are provided for performing operations that include: receiving, by a messaging application, an input that selects an option to generate a message using a captioned avatar; presenting the avatar and a subtitle input area close to the avatar by the message receiving and sending application; populating, by the messaging application, the subtitle entry area with a text string comprising one or more words; determining, by the messaging application, a context based on one or more words in the text string; and modifying, by the messaging application, the expression of the avatar based on the determined context.

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.

A security appliance samples data about software defined infrastructures (SDIs) of a cloud computing environment to incrementally build models that map resource attributes indicated in fields to data types. The security appliance uses the model(s) to provide context sensitive help in policy rule constructions.