1057 results about "Context data" patented technology

An example may receive at least one input via at least one device. An example may use the at least one input to determine an entity identity. An example may use the entity identity to create an automated agent and load context data associated with the entity identity into at least one layer of a multi-layer memory of the automated agent. An example may cause the automated agent to machine-learn a supervision level via the context data. The machine-learned supervision level may indicate a level of supervision of the automated agent by an entity associated with the entity identity. An example may configure the automated agent to execute a task on behalf of the entity and in accordance with the machine-learned supervision level.

An example may use an objective to retrieve first context data from at least one first memory layer of a multi-layer memory associated with an automated agent, and cause the first context data to be presented via at least one first conversational dialog element. An example may determine context feedback data in response to the first context data, and cause the context feedback data to be stored in at least one second layer of the multi-layer memory. An example may use the objective, the first context data, the context feedback data, and at least one workflow to configure a first prompt. An example may use the configured prompt and a machine learning model to generate a plan including one or more tasks executable by at least the automated agent to complete the objective. An example may cause the plan to be presented via at least one second conversational dialog element.

The present disclosure provides an example method that includes: (a) obtaining context data descriptive of an environment surrounding an autonomous vehicle, the context data based on map data and perception data; (b) generating, by a proposer and based on the context data: (i) a plurality of candidate trajectories, and (ii) a plurality of actor forecasts for a plurality of actors in the environment; (c) generating, by a ranker and based on the context data, the plurality of candidate trajectories, and the plurality of actor forecasts, a ranking of the plurality of candidate trajectories; and (d) controlling a motion of the autonomous vehicle based on a candidate trajectory selected based on the ranking of the plurality of candidate trajectories, wherein the proposer comprises a first machine-learned model and the ranker comprises a second machine-learned model, and wherein the first machine-learned model and the second machine-learned model use a common backbone architecture.

An example may determine an entity identity associated with an entity. An example may use the entity identity to create an automated agent including a multi-layer memory and a workflow. An example may store context data in a first layer of the multi-layer memory. The context data may be obtained using the entity identity. An example may store at least one machine-learned entity preference in a second layer of the multi-layer memory. The at least one machine-learned entity preference may be machine-learned using the context data. An example may use the at least one second layer of the multi-layer memory including the at least one machine-learned preference to configure or control execution of the workflow by the automated agent.

The invention discloses a fireman occupational health risk dynamic prediction method based on multi-modal data fusion, relates to the technical field of occupational health risk assessment, and aims to solve the problems of information loss, difficulty in capturing cross-modal complex dependence, lack of interpretability of prediction results and the like when unstructured data is processed in the prior art. The method comprises the following steps: intelligently analyzing a multi-modal document, and analyzing unstructured physical examination information into structured data; performing semantic standardization and knowledge graph dual verification of domain knowledge enhancement; the time-space-static cross-modal attention deep fusion network is used for modeling physiological, dynamic and situational data; and carrying out interpretable risk prediction and attribution. By adopting the technical scheme, the method can realize early, accurate, dynamic and high-credibility prediction of the occupational health risk of the firefighter, provides quantitative attribution, and improves the transparency and application value of the system.

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 invention provides a blood pressure dynamic monitoring system integrating overall risk and local anomaly detection, and relates to the field of electric digital data processing. Comprising a multi-source blood pressure data fusion and acquisition module, a dual-path feature learning and abnormity pre-detection module, a local and overall interactive abnormity accurate identification module and a dynamic risk assessment and intelligent early warning decision module, and the multi-source blood pressure data fusion and acquisition module is used for acquiring blood pressure and context data; the dual-path feature learning and anomaly pre-detection module is used for extracting time sequence features and performing preliminary anomaly screening, and the local overall interactive anomaly accurate identification module is used for detecting and analyzing local anomaly. The dynamic risk assessment and intelligent early warning decision module is used for comprehensively assessing the risk, forming a feedback optimization mechanism and outputting a personalized early warning decision; the system can accurately identify abnormal blood pressure, realizes accurate assessment and prediction of risks, and provides effective support for clinical decision and personalized health management.

Techniques and systems are described that perform automated identification and retrieval of contextual information for quick and accurate processing of user queries by artificial intelligence generative models. The techniques include receiving a natural language (NL) query associated with a user identifier (ID) and obtaining, using a first NL generative model, contextual data that is pertinent to the NL query and is associated with the user ID. The techniques further include generating an augmented NL query that is based on the NL query and the contextual data. The techniques include communicating the augmented NL query to a recipient that includes the first NL generative model, a second NL generative model, or a user session associated with the user ID.

A system and method for field service assistance for telecommunications operations are described, which utilize a data acquisition module configured to receive multimodal data inputs including structured and unstructured data from field operations. A preprocessing module normalizes the multimodal data inputs to generate pre-processed data. A vectorization module transforms the pre-processed data into numerical vector representations using domain-specific embedding models trained on telecom equipment data, implementing convolutional neural networks for image feature extraction and transformer-based encoders for text vectorization. A contextual retrieval module retrieves contextually relevant historical data from a vector database by computing similarity metrics between current job vectors and stored job completion vectors. A response generation module processes the numerical vector representations and retrieved contextual data using an evolutionary algorithm engine to generate structured job summaries and real-time field recommendations.

Aspects of the disclosure include methods for leveraging retrieval augmented generation (RAG) over a graph neural network (GNN) for edge building and the generation of reason-aware graph recommendations. A method can include constructing a graph neural network from an input graph having a plurality of nodes and one or more edges. The graph neural network includes one or more internal layers, each internal layer having one or more node vectors encoding a K-hop neighborhood for a target node of the plurality of nodes. RAG data including non-graph contextual data is retrieved for each of the plurality of nodes and transformed into embeddings using a large language model encoder. The RAG embeddings are encoded into node vectors of the graph neural network. The graph neural network generates a representation for the target node that is transformed by a feed forward neural network tower into an output vector.

The invention provides a user marking rule management method and system supporting a dynamic strategy, and relates to the technical field of user portrait analysis, and the method comprises the steps: carrying out the multi-modal feature fusion of a user behavior data flow and environment context data, obtaining a dynamic user portrait vector, carrying out the strategy intention analysis of a business target of a user, and obtaining a strategy parameter set; constructing a strategy evolution graph based on a semantic association network and a historical strategy, and performing dominant rule reasoning and implicit demand mining on the dynamic user portrait vector and the strategy parameter set through the strategy evolution graph to obtain a candidate strategy packet; performing simulation prediction on the candidates to obtain a deduction efficiency matrix, and performing confidence coefficient calibration on the deduction efficiency matrix to obtain a dynamic marking rule; according to the method and the device, closed-loop feedback of the user marking strategy can be realized based on iterative optimization and confidence guarantee of the strategy evolution graph, so that the dynamic adaptability of a user portrait is improved.

The invention provides an intelligent e-commerce behavior event decision-making method and system fusing multi-source perception, and the method comprises the steps: obtaining a user behavior data set, a commodity attribute data set and an environment context data set through a preset data collection interface, and carrying out the intention recognition processing of multi-source data; generating an intention feature set containing preliminary intention features and refined intention features, then generating an interactive guidance strategy tree containing node branch weights and strategy execution priorities based on the intention feature set, and performing dynamic path adjustment processing on the interactive guidance strategy tree according to a real-time feedback data set to obtain an optimized strategy tree set; and finally, pushing the optimization strategy tree set to a target interaction interface to activate an interaction guide operation, thereby providing personalized and intelligent interaction guide for the user by fusing multi-source sensing data, deeply understanding the intention of the user and dynamically adjusting a decision strategy, and improving the operation efficiency of an e-commerce platform and the satisfaction of the user.

Architectures and techniques are described that can receive an indication that additional information about a dynamic element of a webpage is solicited or requested. In response to the indication, context data can be determined, comprising user context data and page context data. As a function of the context data, prompt data can be generated. The prompt data can be indicative of a natural language query. The prompt data, which was automatically generated, can be input to a model such as a large language model in order to obtain the additional information about the dynamic element.

The invention discloses a large language model agent security test method and device based on a model context agreement, and the method comprises the steps: firstly deploying a test tool in a test server, defining an application program interface which comprises function parameters and test probe parameters, describing the test probe parameters as necessary technical requirements for executing nominal functions, and executing the nominal functions according to the test probe parameters; and inducing the tested LLM agent to transmit complete session context data when calling. The test server receives a tool call request including a function parameter value and session context data, a back-end concurrently processes the request, executes a nominal function to generate a benign result, and extracts the session context data at the same time. Afterwards, a benign result is returned to the tested LLM agent, and the extracted data is asynchronously transmitted to a remote log server for recording as a test log. And finally, comparing the test log with the actual operation history, judging whether a session context data leakage vulnerability exists or not, and quantifying the severity level so as to detect whether the LLM agent leaks session memories such as the user interaction history or not.

Various embodiments discussed herein relate to route optimization and query understanding for route and / or direction queries with complex user preferences. Each route candidate, for example, is treated as a richly annotated document. The routing engine, in addition to performing route optimization, acts as a retriever and ranker of route documents according to user intent. Various embodiments rank routes not just based on a simple cost model, but based on many more or alternative factors according to user preferences, user intent, and / or contextual data.

Determining user types from behavior is described. An artificial intelligence (AI) model is trained to classify user accounts of a payment service into different user types using contextual data associated with processed payments between the user accounts. The AI model is used to analyze additional contextual data associated with additional payments between additional user accounts to classify the additional user accounts, and, if a particular user account of the additional user accounts is associated with a user type of the different user types that requires an action to be performed, an instruction is sent to a user device associated with the particular user account to cause a payment application to present a user interface element prompting a user to perform the action, and, based on whether the action was performed, account data indicating whether the particular user account is an authorized account is stored in a datastore.

The invention relates to the technical field of data processing, and discloses a research report text extraction optimization method and device, equipment and a medium, which can be applied to the financial field, and the method comprises the following steps: obtaining research report text data, and processing to obtain a segmented text fragment set; establishing a fragment vector index database by utilizing the segmented text fragment set; receiving a query request based on the fragment vector index database and processing the query request to obtain candidate fragment sequence data; and processing the candidate fragment sequence data to obtain context data, injecting a cue word template to generate a target cue word, inputting the target cue word into a language model for reasoning, and outputting a text optimization result. In the invention, aiming at the problem that the output of the existing research and report text is lack of standardization, the target cue word can be generated by utilizing the context data obtained by calculation, and is input into the language model for reasoning, and finally the text optimization result is output, so that a multi-level abstract and key information summary can be quickly generated, the manual reading cost is reduced, and the efficiency is improved. The working efficiency of personnel is improved.

Systems and processes for operating an intelligent automated assistant are provided. An example method includes, at a computer system that is configured to communicate with a display generation component and an input device: detecting an audio input including a query; in response to detecting the audio input including the query: retrieving contextual data related to the query; in accordance with a determination that the query includes a request of a first type: converting the query to a rewritten query based on the contextual data related to the query; and providing the rewritten query to a first digital assistant component; and in accordance with a determination that the query includes a request of a second type different from the request of the first type, providing the query and the contextual data related to the query to a second digital assistant component different from the first digital assistant component.

The invention belongs to the technical field of Internet of Things security, and particularly relates to a multi-level Internet of Things equipment security management and access control method, which comprises the following steps of: performing bidirectional verification on a digital certificate and a hardware fingerprint through an edge node when equipment is accessed; calculating credibility in real time and dynamically dividing security levels by using a hybrid evaluation algorithm based on equipment operation context data; a fine-grained access strategy is generated in combination with equipment attributes and environmental risks, and cross-level collaboration is realized through a lightweight protocol; abnormal behaviors are detected in real time in the access process, and high-risk operation is blocked within milliseconds; and strategy conflict resolution and adaptive optimization are realized through formalized verification and deep reinforcement learning. According to the technical scheme, the identity authentication reliability, the authority dynamic adaptability, the strategy consistency and the threat response speed of the Internet of Things equipment are remarkably improved, and a systematic security guarantee is provided for large-scale Internet of Things applications with high security requirements.

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

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

The invention discloses a radiation safety management method and system based on cloud platform data driving, and relates to the technical field of cloud platform radiation management. The method comprises the following steps: collecting radiation field data and environment state data of a target area through a deployed intelligent sensing node; uploading the radiation field data, the environment state data and the context data from the service module to a cloud platform, performing energy compensation and radiation unmixing, and constructing a multi-dimensional feature vector; and processing the multi-dimensional feature vector by using a pre-trained situational radiation perception model, identifying radiation field features and an environment situation, and generating a situational radiation safety early warning signal. The technical problem that in the prior art, radiation safety monitoring depends on single-point measurement, comprehensive judgment cannot be carried out in combination with environment and service context data, and consequently the radiation field anomaly recognition capability is insufficient is solved, and the purpose that the radiation field anomaly recognition capability is improved through cloud platform data driving and context awareness model fusion is achieved. And the technical effects of high-precision identification and situational safety early warning of the radiation field state are realized.

The invention discloses an accompanying dialogue system and method based on real-time environment perception and knowledge graph enhancement. The system comprises a multi-mode real-time environment perception module, a knowledge graph construction and enhancement module, a natural language understanding and dialogue management module, a personalized recommendation and narration generation module and an immersive multi-mode interaction module. The method comprises the steps of multi-modal real-time environment perception and situation data generation; performing dynamic association, query and enhanced reasoning on the knowledge graph; natural language understanding and dialogue management, personalized recommendation and narrative generation, and immersive multi-modal interaction presentation and feedback reception. According to the method and the system, the concern point of the user, namely scenery or details, can be accurately positioned, and context information required by subsequent service intelligence is provided, so that the problems of poor environment perception ability, weak interaction immersion, dull knowledge service, lack of individuation, insufficient intelligent accompanying experience and the like in the existing tourism auxiliary technology are solved.

The present disclosure provides an example method that includes: (a) obtaining context data descriptive of an environment surrounding an autonomous vehicle, the context data based on map data and perception data; (b) generating, by a proposer and based on the context data: (i) a plurality of candidate trajectories, and (ii) a plurality of actor forecasts for a plurality of actors in the environment; (c) generating, by a ranker and based on the context data, the plurality of candidate trajectories, and the plurality of actor forecasts, a ranking of the plurality of candidate trajectories; and (d) controlling a motion of the autonomous vehicle based on a candidate trajectory selected based on the ranking of the plurality of candidate trajectories, wherein the proposer comprises a first machine-learned model and the ranker comprises a second machine-learned model, and wherein the first machine-learned model and the second machine-learned model use a common backbone architecture.

The invention discloses a multi-agent debate-based content auditing optimization method, which comprises the following steps of: 1, acquiring and preprocessing multi-source content data: integrating to-be-audited content and associated context data, and constructing a comprehensive and reliable input data basis through standardization processing and quality verification; 2, intelligent agent role design based on domain knowledge: designing a specialized intelligent agent role in combination with audit scene characteristics, and injecting a domain knowledge module to enhance the understanding ability for complex semantics and rules; and step 3, a multi-agent debate and dynamic optimization framework: adopting a debate stream and arbitration stream parallel multi-agent debate optimization framework, mining deep semantics through multiple rounds of structured debate, and combining a dynamic arbitration mechanism to integrate multi-party viewpoints. According to the method, accurate identification, fair judgment and efficient processing of complex contents are realized through multi-agent structured debate and a full-process optimization mechanism.

Various embodiments of a predictive fall event management system and a method of using such system are disclosed. The system includes a body-worn device and a controller operatively connected to the body-worn device. The controller is adapted to receive physiological data representative of a physiological characteristic of a wearer of the body-worn device over a monitoring time period; receive contextual data representative of context information of the wearer over the monitoring time period; and determine one or more future physiological states or contextual states based at least in part on one or more of the physiological data and the contextual data. The controller is further adapted to determine, for a future time, whether a fall condition is satisfied based upon the one or more future physiological states or contextual states and generate a fall prevention output responsive to satisfaction of the fall condition.

Systems and methods for generating Generative Artificial Intelligence (Gen AI) responses using context based hierarchical ontological representations are disclosed. In an aspect, input prompt data corresponding to an enterprise is received. A query embedding vector representing the received input prompt data is then generated. Further, at least one relevant node corresponding to the input prompt data is generated. Furthermore, a subset of nodes are selected based on the relevant node. Also, contextual data is generated by aggregating textual content from the selected subset of nodes. An ontological representation corresponding to the input prompt data is generated based on the generated contextual data. A Gen AI response to the input prompt data is then generated using at least one Gen AI model. The generated Gen AI response is then validated. Also, the Gen AI model and the ontological representation are fine-tuned based on results of the validation.

Provided is a system and method for controlling a plurality of devices. The method includes generating a command script by processing a text string with at least one model, the text string including a natural language input by a user, modifying the command script based on contextual data, the command script including a configuration for at least one device, generating at least one command signal based on the command script, and controlling at least one device based on the at least one command signal.

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 relates to the technical field of industrial data processing, and discloses a copper foil production monitoring method and system based on data storage optimization, and the method comprises the following steps: carrying out the periodic aggregation of high-frequency data when a production process is in a steady state, and carrying out the calculation of a newly recognized production process event when the production process is monitored to deviate from the steady state. Whether causal association exists or not is judged by calculating the correlation between the new event and the current activity event in time and physical dimensions, and if the association exists, a chained event ID inheriting a precursor event identifier is generated for the new event; if the event is an independent event, a brand-new root event ID is generated, context data before the event occurs is extracted in a backtracking mode, and the generated event ID is marked on a data set in a unified mode in combination with real-time data during the event occurrence period for permanent storage. According to the method, the storage configuration is obviously optimized, and the efficiency of fault causal chain tracing is improved through the structured chain ID.