208 results about "Context data" patented technology

Technology is disclosed for dynamically creating and / or validating new URLs based on a user's query and / or contextual data. This enables the discovery of relevant content that may not be pre-existing in the search engine's index. This process enhances the search engine's ability to deliver the most current and relevant results by generating potential content sources in real-time, validating them, and incorporating the discovered content into the index for immediate query execution. Various embodiments are additionally or alternatively directed to a process that involves iteratively discovering, crawling, and indexing new content by following links from seed URLs, which may be either pre-existing or generated. This process deepens the search engine's index by continuously exploring outlinks, fetching new content, and updating the index in real-time.

Various embodiments described herein relate to real-time proximity-based contextual information for an industrial asset. In this regard, a request to establish a remote collaboration environment associated with an industrial asset in an industrial environment is received. The request includes an asset identifier provided by a mobile computing device proximate to the industrial asset. The request also includes location data associated with the industrial asset. In response to the request, contextual data for an industrial task associated with the industrial asset is determined based on the asset identifier and the location data. Furthermore, one or more interactive media elements associated with the industrial task is generated based on the contextual data. The one or more interactive media elements are communicated via the remote collaboration environment.

A method includes determining a target destination at an airside of an airport and of an aircraft, and receiving aircraft operating context data indicating a current state of the aircraft and including at least a current position of the aircraft. The method also includes receiving real-time ground operations data for the airport including instructions to move the aircraft to the target destination. The method includes determining ground navigation instructions by using both the real-time ground operations data and the aircraft operating context data. The ground navigation instructions recite a planned state of one or more engines of the aircraft. The method includes providing the ground navigation instructions in time to implement the ground navigation instructions so that the one or more engines of the aircraft are in the planned state at or before reaching the target destination as instructed by the ground navigation instructions.

The application belongs to the field of network security operation, and particularly relates to a network security alarm accurate identification and grading method based on AI.The method is connected with multiple types of security systems to collect alarms and context data, generate a unique alarm identifier and an entity identifier set, and construct an alarm entity graph through entity disambiguation alignment;secondly, multi-dimensional deep features such as time sequence self-excitation and topological persistence are extracted, normalized and spliced into feature objects and stored in a feature warehouse;then, corresponding data is obtained through a normal behavior self-supervision and attack technology identification double-track model, and a true alarm probability is calculated through an evidence ratio fusion model;and finally, alarm dynamic grading and differentiated disposal are realized based on attack features and risk budget.The application reduces the alarm false alarm rate, solves the problem of disposal resource mismatch, and enhances the security operation efficiency.

A method comprising receiving audio data 102, generating a transcription 151 comprising a sequence of terms 152, such as “Buy some tomatoes and bananas. Change tomatoes to potatoes”, parallel processing the audio data and the transcription using a multimodal large language model (LLM) 150 to identify one or more revision terms 152R, for example “Change”, specifying a revision action to perform on at least one other term in the sequence, in this instance “tomatoes”, and modifying the transcription 151M accordingly – “Buy some potatoes and bananas”. Identifying the revision term(s) may be based on a corresponding user intent 154 determined for each respective term in the sequence, for example, the user 10 does not intend the final transcription to include “tomatoes”. For each term in the sequence, parallel processing may comprise correlating its speech characteristics 156 such as pitch, tone or prosody information determined from the audio data with its corresponding linguistic context 158 determined from the transcription. Transcription correction may be based on a revision token inserted into the sequence, the token indicating an N number of terms for replacement and their corresponding replacement terms. User context data 104 may be obtained to tailor the LLM to a particular user. [Figure 1A]

Systems and methods are disclosed herein for adjusting a demura configuration in a display. In an example system, input pixel data is analyzed to determine pixel data characteristics. The input pixel data is received from a display controller of a computing device, such as a computing device coupled to the display. Contextual data corresponding to a generation of the input pixel data is analyzed. A demura mode is determined for the input pixel data based on the pixel data characteristics and the contextual data. Elements of a pixel array of a light emitting diode (LED) display are energized based on the demura mode and the input pixel data. In this manner, a demura correction can be dynamically controlled in a display, leading to various improvements such a reduced processing and power consumption.

This application discloses a task migration method and a mobile terminal, relating to the field of artificial intelligence technology. In response to changes in the screen form of the mobile terminal, task context data is acquired; based on the task context data, task planning information is generated; wherein, the task planning information includes inference information of the current task and at least one of the predicted subsequent executable tasks; thereby, based on the task planning information, a corresponding task management window is output in the changed screen form to form an interactive interface associated with the current task.

A computer-implemented method for regulating delivery frequency of outbound communications. The method includes receiving message context data comprising one or more of a user identifier, a client identifier, a campaign identifier, a channel identifier, a label identifier, and a trigger identifier. A counter-field identifier is determined that encodes the channel identifier, label identifier, trigger identifier, campaign identifier, and a temporal-interval indicator. A counter field identified by the counter-field identifier is created. A value stored in the counter field is incremented through an atomic operation. The value is compared with a limit stored in a rule set. An allow signal is output when the value does not exceed the limit and a suppress signal is output when the value exceeds the limit. An engagement-counter record is updated to the in-memory database.

Provided is a computer-implemented method, system, and computer program product for predicting data insufficiency related to performing autonomous vehicle actions and dynamically mitigating the data insufficiency by collecting external data from additional data sources. A processor may direct, for an autonomous vehicle, an action in response to external data. The processor may collect contextual data about an environment in which the autonomous vehicle is operating. The processor may predict, based in part on the collected contextual data, an insufficiency of external data for performing the action. In response to the predicting, the processor may direct collection of additional external data from a second data source.

In some implementations, a system may receive a request for a web page from a client device. The system may obtain user context data associated with a user profile, wherein the user profile is associated with the request for the web page. The system may obtain a set of user interface elements associated with the web page based on the user context data and the request for the web page. The system may obtain decision information defining one or more conditions for providing the user interface elements to the client device, wherein the decision information is obtained based on the request for the web page and the user context data. The system may select one or more user interface elements from the set of user interface elements based on the decision information. The system may provide the set of user interface elements to the client device.

The invention relates to a method and a system (1) for determining a self-position (2) of a motor vehicle (3), wherein a map data set (10) of the surroundings (11) of the motor vehicle (3) is provided to a data processing unit (4), and at least one surroundings element (12) in the surroundings (11) is detected at least partially by means of a sensor device (5) of the motor vehicle (3), and a sensor device data set (13) at least partially characterizing the surroundings (11) is provided to the data processing unit (4), so that the data processing unit (4) generates and provides a self-position data set (14) from the sensor device data set (13) and the map data set (10). According to the invention, a context data set (15) is provided to the data processing unit (4), by means of which the data processing unit (4) subjects the self-position data set (14) to a plausibility check. The invention also relates to a motor vehicle (3) equipped with such a system (1).

This application discloses an access control method, apparatus, device, medium, and product, relating to the field of communication technology. The access control method includes: when a terminal senses a switch from a trusted network environment to an untrusted network environment, the proxy terminal sends a long-connection request to a target business system; acquiring processing data of the target business system's response to the long-connection request, performing trustworthiness verification based on the processing data and the switching context data, and generating a restricted access credential for the terminal and enabling a one-way isolation mode for the terminal when the trustworthiness verification result is positive; with the one-way isolation mode enabled, authenticating the terminal's access behavior command to the target business system based on the restricted access credential, sending the access behavior command to the target business system after successful authentication, and filtering the response data received from the target business system before sending the filtered response data to the terminal.

A system and a method for generating contextually relevant prompts for Gen AI tools are disclosed. A user input corresponding to a data object is received for determining object characteristics. A plurality of pre-determined categories is determined from a pre-defined prompt library database based on the determined object characteristics, which is rendered for selection by a user. A plurality of pre-determined sub-categories is determined from the pre-defined prompt library database based on a selected pre-determined category from amongst the plurality of pre-determined categories and the plurality of pre-determined sub-categories is rendered for selection by the user. A selected pre-determined sub-category is transformed by inserting contextual data received from the user and the prompts are generated based on the transformed pre-determined sub-category.

This invention discloses a personalized recommendation system for reading software based on big data analysis, belonging to the field of computer software technology. Addressing the problems raised in previous technologies—that merely characterizing interests makes it difficult to depict the cognitive complexity users can process, that scene constraints are not transformed into quantitative requirements for cognitive load attributes and are not calculated in conjunction with user ability profiles, and that it is difficult to distinguish whether abnormal feedback stems from ability transfer or scene constraint distortion—this invention proposes the following solution, including data collection: the client SDK collects real-time environmental context data of the user's deep behavioral sequence during the reading process. This invention avoids recommending content that is short in form but cognitively overloaded, can distinguish whether abnormal feedback originates from user ability transfer or scene constraint definition deviation, and performs incremental calibration on the reading ability preference vector and scene constraint vector respectively, reducing the candidate size of the ranking model, reducing the amount of data transmission between the server and the client, and improving the overall system operating efficiency.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating trace log messages. A database server may include an agent or other feature to monitor and track performance of the database server to generate the trace log messages. The agent can use one or more identifiers associated with the database commands to generate the trace log messages. In particular, the agent can obtain such identifiers as part of the context data that is associated with a session established between a client application and the database server.

A system for the selective orchestration of confidential data processing in cloud environments, comprising an interface for receiving an application-layer-related transaction with input data, execution metadata, and context data; a decomposition module configured to decompose the transaction into multiple execution fragments; an evaluation module configured to determine a confidentiality criticality for each execution fragment based on at least one of the following: data content, semantic dependency, context information, and disclosure risk; an orchestration module configured to assign execution fragments with a specific confidentiality criticality to a confidential execution environment and other execution fragments to a non-confidential execution environment; and a merger module configured toto combine the partial results generated in the different execution environments into an overall result.

This invention belongs to the technical field of closed-loop management methods for health data in hospital convenience services, and particularly relates to a method and system for closed-loop management of health data in smart hospital convenience services throughout the entire process. It constructs a full-domain identity binding system based on blockchain technology, configuring identifiers and dynamic attribute tags for health data and assigning digital identity certificates to terminals / systems / roles; relying on a distributed graph database and tracking framework, it deploys a lightweight data collection agent and constructs a federated dynamic lineage graph based on medical parsing rules; it establishes a multi-dimensional operation traceability mechanism, expanding the collection of behavioral fingerprints and operation context data beyond regular logs, and adopts a dual-mode storage approach of blockchain evidence storage and local encrypted backup; it constructs an AI intelligent audit engine, transforming regulations into an audit rule base through a large language model, and combining unsupervised learning modeling to identify abnormal operations in medical scenarios.

System and techniques to sort and order a database of access credentials are described herein. A device can receive a signal indicating participation in an access transaction. The device can then obtain context data corresponding to the access transaction and search, based on the context data, a local data store of the device to locate a subset of access credentials from a set of access credentials stored in the local data store. The device can order members of the subset of access credentials based on a predefined metric to create an ordered subset of access credentials and render the ordered subset of access credentials to a user interface of the device for selection by a user of the handheld device to complete the access transaction.

This invention relates to an apparatus and control method for acquiring magnetic resonance imaging (MRI) images based on deep learning. One embodiment of the control method includes the following steps: acquiring an MRI signal; applying at least one of a plurality of elements, set for the quality of an MRI image corresponding to the MRI signal, to the MRI signal to acquire a training image corresponding to the MRI image; acquiring a training dataset that includes the MRI image as label data and the acquired training image as input data matching the label data; and enabling a neural network model to learn based on the training dataset and context data corresponding to the training image. The step of acquiring the training image may include the following steps: applying at least one of the plurality of elements in a K-space domain to distort the MRI signal and acquiring the training image based on the distorted MRI signal.

A method includes maintaining a record for a user associated with at least a first asset and a second asset; receiving a request associated with an exchange that identifies the record as a source and an amount of the exchange expressed in a third asset; dynamically determining an optimal conversion path for completing the exchange that specifies a timing for completing the exchange and whether to utilize the first asset or the second asset; when using the first asset, processing the exchange according to the timing and by sending a portion of the first asset to another record associated with the other user, wherein the portion of the first asset sent corresponds to the third asset; and when using the second asset: determining an amount of the second asset that corresponds to the third asset; converting a portion of the second asset to the third asset; and processing the exchange.

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.

The application discloses a pathogenic microorganism whole genome tracing library construction method and system for port quarantine, and belongs to the field of biological information and big data processing, which comprises the following steps: obtaining whole genome sequences and context data, generating knowledge units containing context enhancement and virtual variation fingerprints through a local trend prediction model, and mapping the knowledge units to a dynamic tracing knowledge graph; submitting the virtual variation fingerprints to a global mirror knowledge space for collaborative rehearsal to identify risk signals; carrying out directional depth analysis and updating the graph; submitting an encrypted knowledge abstract to the global space to drive consensus evolution; and optimizing the prediction model and identification rules based on the consensus results. The closed-loop mechanism combining local and global collaboration and virtual and real verification can realize active discovery of variation pathogens and automatic evolution of the knowledge base, and improve the rapid tracing and early warning capabilities of the port for sudden epidemics.

The present disclosure provides a blockchain contract processing method and device, electronic equipment and storage medium, comprising: loading N candidate smart contracts in batches from the to-be-processed smart contracts; in response to identifying that the candidate smart contract needs to perform a target external data read-write operation, judging whether the target external data exists in the local cache; in response to the target external data not existing in the local cache, performing saving processing on the first execution context data of the candidate smart contract, and determining a target contract from the to-be-processed smart contracts except the N candidate smart contracts; replacing the target contract with the candidate smart contract, and loading the target contract to the execution slot of the execution memory corresponding to the candidate smart contract, and executing the target contract. When it is found that the candidate smart contract needs to wait for the database to return, the context is saved and the execution is switched, avoiding the idling of the execution unit, improving the hardware utilization rate, and at the same time, by supporting batch preloading, the cache hit rate and execution continuity are improved.

The application discloses a body turning motion prediction method, a model training method, an intelligent bed, a cloud platform and a body turning motion prediction system, and comprises the following steps: generating an enhanced state vector based on current sleep cycle acquisition of user physiological sensor data, static feature data and sleep environment context data; inputting the enhanced state vector into a personalized bed posture adjustment strategy model to obtain a bed body adjustment angle action; generating a control instruction to drive bed posture adjustment according to the adjustment angle action; obtaining the actual body turning motion frequency after the completion of bed posture adjustment, calculating a reward and punishment function value based on the actual body turning motion frequency, physiological sensor data and static feature data; generating interaction experience data of the current sleep cycle and reporting to the cloud platform to trigger the update training of the personalized bed posture adjustment strategy model. The application predicts the body motion trend through multi-modal perception, actively adjusts the sleeping posture based on the personalized strategy, effectively reduces the number of body turning of the user, and significantly improves the sleep continuity and overall sleep quality.

This application provides an option pricing method, system, apparatus, electronic device, computer-readable storage medium, and computer program product. The method includes: constructing pricing context data, which includes multiple pricing contexts; generating multiple pricing request messages based on the multiple pricing contexts; wherein each pricing request message includes each pricing context and a pricing request identifier corresponding to each pricing context; sending the multiple pricing request messages to a message queue, which is used by multiple second nodes to read the multiple pricing request messages; repeatedly reading pricing result messages from the message queue, and performing the following operations on each pricing result message: parsing the pricing result message to obtain a first pricing request identifier and a first pricing result; obtaining a first pricing context based on the first pricing request identifier; and performing pricing calculation processing on the first pricing result based on the first pricing context to obtain a second pricing result; thus, large-scale option pricing is achieved.

This invention discloses a context-aware adaptive tourism audio narrative method and system, relating to the field of interactive entertainment technology, including the following steps: collecting multi-dimensional context data through a multi-dimensional perception module; calculating team aggregation degree, average team movement speed, and environmental status based on the multi-dimensional context data; constructing a preset script through a dynamic plot engine module, processing multi-dimensional context data, and making dynamic plot decisions; generating and broadcasting adaptive audio through an adaptive mixing module; and achieving real-time adaptive linkage between plot content and real-world scenes through deep integration of multi-dimensional environmental perception, dynamic plot decisions, and intelligent mixing technology. It can automatically select and broadcast matching narrative branches based on multiple context data such as weather, crowd flow, and team status. Simultaneously, through spatial audio rendering and intelligent fusion of ambient sound, it breaks down the auditory boundaries between the plot and reality, significantly enhancing tourists' immersion and emotional resonance.

Techniques for cache management for LLM processing are described. Example embodiments include a signal hashing model that generates a key for particular context data. An LLM output corresponding to the context data is stored in a cache along with the key. For a user input received by the system, a cache lookup is performed using a key for context data corresponding to the received user input. For a cache hit, the stored output is used to respond to the user input. For a cache miss, a LLM processes the context data and the user input to generate an output within a first timeout. If the LLM is unable to generate an output within the first timeout, then in some cases, the LLM is allowed to continue processing until a second timeout, and a final or partial output from the LLM is stored in the cache.

The present disclosure provides a transaction data processing method and device, the method comprising: receiving a transaction request sent by a user account; preprocessing original transaction data in the transaction request to obtain target transaction data; wherein the target transaction data comprises context data of the original transaction data; performing preliminary risk identification on the target transaction data according to a set risk identification rule to obtain a preliminary risk identification result; in response to the preliminary risk identification result indicating that the target transaction data is normal transaction data, performing secondary risk identification on the target transaction data using a risk identification model to obtain a secondary risk identification result; and in response to the secondary risk identification result indicating that the risk level of the target transaction data is uncertain, performing risk analysis on the target transaction data using a large language model to generate a risk analysis result of the target transaction data; through hierarchical and progressive risk identification and analysis of transaction data, the accuracy and comprehensiveness of risk judgment can be improved.

The application discloses a kind of robot task planning system, method and device, it is related to embodied intelligence field, to improve the success rate of robot long-range task planning efficiency and implementation.System includes expert skill base, encapsulates multiple independent skill modules;Vector knowledge base, maintain historical scene data;Decision brain, receive and understand natural language instruction, implement instruction based on historical scene data;Determine the skill module required to be called in implementation instruction;In the process of implementing instruction, according to current task progress, only activate the skill module required currently, load required resource for the activated skill module immediately, resource includes at least one of historical scene data, historical context data, current scene data;Current activated skill module and the operation result of current skill module are written into historical context data.The application greatly reduces the reasoning burden, improves the success rate of long-range task planning efficiency and execution.