1430 results about "Context data" patented technology

An example operation may include one or more of training an artificial intelligence (AI) model to recommend numerical goals based on execution of the AI model on historical profile data and contextual data that is associated with the historical profile data, storing profile data of a user profile within a data store of a software application, receiving a request via a user interface of a user profile page of the software application, wherein the request comprises context of a user of the user profile, determining a numerical goal for the user profile based on execution of the AI model on the profile data of the user profile and the context, and displaying the numerical goal via the user interface of the user profile page within the software application.

An example assistant system can use a multimodal multitask medical machine-learned model to perform image processing to answer natural language queries. A device can process speech data or other natural language inputs to obtain a query. The query can be processed alongside image data that provides context for the query. The example system can receive a query associated with a particular task domain; generate, based on the query, a query input that comprises query instruction data from a first modality and query context data from a second modality; generate a combined input comprising the query input and an exemplar input, wherein the exemplar input comprises exemplar instruction data from the first modality and an exemplar context placeholder in lieu of exemplar context data from the second modality; process the combined input with a multimodal machine-learned model to generate output data; and output a query response based on the output data.

An example operation may include one or more of receiving context of a user during an inquiry of a feature via a software application, executing a waiting period via the software application, during the waiting period, selecting an animation to display via the software application based on the context of the user and the feature inquiry wherein the animation provides contextual data associated with the feature, wherein the contextual data is based on a determined need of the user, displaying the animation via the software application during the waiting period, and determining if the user has accepted the feature via the software application. At least one portion of the example operation: integrates with an artificial intelligence (AI) chatbot, interacts with the AI chatbot, is performed by the AI chatbot, and / or is associated with an AI model.

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.

In one implementation, a computer-implemented method involves receiving a user message corresponding to a query or a statement to AI chatbot, performing preprocessing operations resulting in generation of initial context of the user message by extracting text of the user message, metadata of the user message, and a conversation identifier, obtaining historical context pertaining to the user message from a plurality of storage mechanisms provided in differing formats including a knowledge graph, a vector database comprised of vector embeddings, and a database comprising text summaries of prior conversations between the user and the AI chatbot, generating a prompt for a LLM that instructs the LLM to generate a response to the user message that is based on and consistent with the user message, the initial content, and the historical context, and providing a final response to the user that is corresponds to an LLM-generated response.

Systems, devices, and methods are provided for determining contextually relevant user-based product recommendations based on scene information. In at least one embodiment, techniques described herein may be used to determine, using a first machine-learning model, first information associated with a first object within a first image of digital content, determine, using a second machine-learning model, similarity scores between the first object and a first plurality of products of an online purchasing system, detect, in association with the first image of the digital content, performance of a first computer-based action by a user, determine, using a third machine-learning model and based on contextual data of the user, one or more affinity scores for the user, select a first product based on the one or more affinity scores, and present a recommendation to the user to perform a second computer-based action in association with the first product.

The invention relates to the technical field of anomaly detection, and discloses a context-aware-driven multi-dimensional anomaly detection early warning method. The method comprises the following steps: collecting real-time context data in a target monitoring scene, and generating an initial feature set containing an environment parameter sequence and a behavior pattern map; a first detection model and a second detection model matched with the scene type are constructed according to the scene types, the first model comprises a dynamic correlation function of environment indexes and abnormal probabilities, and the second model comprises a nonlinear mapping rule of behavior characteristics and risk levels; and based on the real-time context deviation degree and the characteristic fluctuation coefficient, a target model is triggered to generate a dynamic early warning instruction, and the dynamic early warning instruction is pushed to an execution module to adjust a trigger threshold of an abnormal response strategy or a priority of a risk disposal process. According to the method, multi-dimensional data is combined, the adaptability and accuracy of anomaly detection are improved through dynamic model triggering and response strategy adjustment, and the method is suitable for various monitoring scenes.

Techniques for using a model to generate a response to a user input, where the response is associated with a personality determined to be relevant to the user input, are described. The system receives a user input and context data associated with the user input. Using the user input data and / or the context data, the system determines a personality (e.g., including a personality type and / or personality characteristics) relevant to the user input. The system generates a prompt instructing a model to generate a response to the user input that corresponds to the personality. The model processes the prompt to generate a response to the user input that corresponds to the personality. In some embodiments, the model generates a request for another component of the system to generate information responsive to the user input. The model may transform the responsive information into the personality-associated response.

Systems and methods for generating a workflow data structure are provided. The system includes one or more processors; and one or more transitory or non-transitory computer-readable media storing instructions that are executable to cause the one or more processors to perform operations, the operations comprising: receiving input data comprising a corpus of documents, a user query, and query context data; processing the corpus of documents to generate training data; training a large language model (LLM) using the training data; classifying, using the LLM, the user query to at least one content cluster of a plurality of content clusters based on the query context data; constructing, using the LLM, a workflow data structure as a function of the classifying; and generating, using the LLM, a query response as a function of the user query, the query context data, and the workflow data structure.

The invention provides an intelligent recommendation method and system for an e-commerce platform, and the method comprises the steps: collecting user interaction behaviors and time-space context data in real time, and constructing a user behavior multi-modal feature matrix; extracting commodity multi-level features, and generating a commodity comprehensive feature matrix; identifying and predicting a user intention based on the user behavior feature matrix, and generating an intention distribution vector; a recommendation candidate set is obtained by combining the commodity feature matrix and utilizing a context awareness collaborative filtering enhancement technology; a multi-objective optimization function is constructed, and after the user intention vector is input, a personalized recommendation sequence is generated in combination with an optimization result and the candidate set; and user feedback is monitored in real time, online learning and reinforcement learning algorithms are adopted, and a recommendation strategy is continuously optimized based on user instant feedback and long-term satisfaction. According to the scheme, the recommendation accuracy, the diversity of recommendation results and the user experience can be improved.

The invention discloses an AI interactive data protection method and system based on a security context protocol. According to the method, a client application sends a user request carrying a session identifier to an application gateway; the gateway queries a required context range and a security processing strategy according to the request type, and transmits a strategy instruction; after the SCS verifies the gateway permission, the context data is retrieved and processed according to the strategy instruction, and a security context data block is generated; the gateway assembles the user input and the security context data block into a final request according to an SCP protocol, and sends the final request to an AI model; the AI model processes the request and returns a response; after receiving the response, the gateway updates the session context and updates the stored context data; and finally, the gateway returns the response of the AI model to the client application to complete interaction. According to the method, the exposure of the sensitive context data in the system can be reduced to the greatest extent while the AI interaction effect is ensured, so that the data security and compliance of the AI application are improved.

The invention provides a knowledge-driven underground space information retrieval method, system and equipment, and the method comprises the steps: carrying out the intention analysis of an instruction of a user, and recognizing key entity information; key entity information is retrieved in the underground space entity knowledge graph, and the feature attributes, the association relation and the relation with other entity nodes of the key entity information are traversed based on the graph relation to obtain an entity information retrieval result; retrieving related entities from the domain knowledge graph according to the key entity information, and traversing the related entities and the association relationship along the graph relationship to obtain a graph side local retrieval result; matching a related community summary as context data based on a domain knowledge graph according to the key entity information, and calling a large language model to generate a graph side global retrieval result based on the context data; matching vector representation in a semantic knowledge base according to the atlas retrieval result to obtain a semantic side retrieval result; and fusing the retrieval results, and inputting the fused retrieval result into the large language model to generate a final retrieval result.

The efficient generative task reasoning acceleration method based on the hybrid expert network comprises the steps that context information of related generative tasks is obtained, context data is preprocessed, and it is ensured that the data is suitable for model input; constructing an efficient reasoning model based on the hybrid expert network, and determining a reasoning process of the efficient reasoning model based on the hybrid expert network; training an efficient reasoning model of the hybrid expert network, optimizing model parameters, and storing an optimal model structure; and generating a reasoning result by using the optimal model structure. In a model improved by the method, an expert network is composed of a plurality of independent experts, and each expert is responsible for processing different input characteristics. The gating network dynamically selects which experts participate in the calculation according to input characteristics, and assigns an expert to each input by calculating a probability distribution. In this way, the gating network and the expert network are closely matched, it is ensured that only the most suitable expert is used, and therefore the calculation efficiency and the model performance are improved.

The invention discloses a personalized content recommendation method and platform based on a user behavior track, and relates to the technical field of personalized content recommendation, and the method comprises the steps: constructing a user behavior sequence through multi-source data; extracting a short-term interest vector and a long-term interest vector from the user behavior sequence, and weighting to obtain a user interest vector; performing dynamic fusion according to context data and the user interest vector, and performing recommendation calculation to obtain context-aware recommendation; recalling the context awareness recommendation according to the real-time interest vector, and obtaining a context recall awareness recommendation through online reasoning; and performing personalized content recommendation through the context recall perception recommendation. The technical problems that in the prior art, personalized content recommendation is difficult to give consideration to short-term interest changes and long-term preferences of users, contextual adaptability is poor, and recommendation accuracy is insufficient are solved, and the technical effect of improving accuracy, real-time performance and contextual adaptability of personalized content recommendation is achieved.

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.

Embodiments of the present invention provide computer-implemented methods, computer program product, and computer systems. One or more processors analyze user prompts using one or more natural language understanding techniques. One or more processors then enrich the user prompts by integrating contextual data from user interaction history and adapt the enriched user prompts to align with characteristics of Large Language Models (LLMs) and Application Programming Interfaces (API) requirements of the LLMs.

Techniques for processing with respect to a user input as contextual information is available are described. A system generates a first task prediction using first context data that is available when a user input is received. The system generates a second task prediction (e.g., updated first task prediction) when second context data is received, and then further generates a third task prediction when third context data is received. Example first context data may include device type information, time information, location, etc. Example second context data may include automatic speech recognition (ASR) data. Example third context data may include natural language understanding (NLU) data. Using the third task prediction, the system generates an output responsive to the user input.

The invention relates to the technical field of ocean data interpolation filling, in particular to a space-time deficiency filling method and system based on context association and physical guidance. The method comprises the following steps: acquiring seawater dissolved oxygen data and context data; multivariable space-time dependence extraction is carried out based on the obtained seawater dissolved oxygen data and context data; gaussian noise diffusion is carried out based on the obtained seawater dissolved oxygen data; noise prediction is carried out based on double-view space-time correlation; and the prediction error is constrained based on the joint loss function. According to the method, a physical consistency constraint mechanism based on a partial differential equation is introduced in a model training process, so that model output better conforms to a physical coupling rule among variables in a marine environment. The constraint effectively inhibits non-physical fluctuation possibly occurring in the interpolation result, enhances the physical credibility and interpretability of the result, and provides a more reliable data basis for subsequent scientific analysis and process modeling.

The invention discloses a nursing pressure real-time monitoring system and method based on multi-modal data. The system comprises a data acquisition module, an intelligent analysis module, an analysis module and a real-time feedback module. The data acquisition module synchronously acquires physiological data, behavior activity data and environment situation data of nursing personnel and forms a nursing signal; the analysis module receives the nursing signal from the data acquisition module, receives the dynamic situation information from the intelligent analysis module, and generates a pressure state signal containing pressure event evaluation; the real-time feedback module receives the pressure state signal containing the pressure event evaluation from the analysis module, and generates and outputs a real-time feedback signal based on the pressure state signal. According to the nursing pressure real-time monitoring system and method based on the multi-modal data, the problem that traditional wearable equipment only collects physiological behavior data and cannot be associated with specific nursing situations such as drug administration operation and patient first aid can be solved.

A computer-implemented method for managing a telecommunications network based on a large language model is disclosed, the large language model being fine-tuned with technical documentation for the telecommunications network and historical data originating from the telecommunications network. The method comprises receiving one or more key-performance indicators and determining whether the one or more key-performance indicators indicate an anomaly. The method further comprises, in response to detecting an anomaly, determining contextual data associated with the real-time data and feeding a prompt to identify a root cause to the large language model, the prompt containing the contextual data and a task description for root cause analysis for the anomaly. The method also comprises performing one or more responses to address the root cause.

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.

A computer-implemented method is provided for generating a trained TTS-GAN which may be used to generate synthetic longitudinal data for use in survival analysis, a clinical trial or clinical research. The TTS-GAN is configured to generate synthetic time-series data based on synthetic context data generated using a machine-learning model by virtue of being trained using training data comprising real context data and added noise data. A technique for executing survival analysis is also provided, which relies on the synthetic longitudinal data.

An apparatus includes at least one processing device including a processor coupled to a memory, wherein the at least one processing device is configured to modify first data obtained from one or more sources, wherein the modifying includes adding user context data to the first data to generate second data, the second data representing the first data supplemented with a per-user context and, in response to receipt of a query, generate a response to the query using at least one generative language model supplemented by a retrieval augmented generation process based on at least a portion of the second data.

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.

The invention provides an information resource matching recommendation method and system based on context awareness, and the method comprises the steps: firstly obtaining a context data set which is generated by real-time interaction of a target user and comprises user operation behavior data and scene awareness parameter data, and then carrying out the demand feature extraction of the context data set, the method comprises the following steps: acquiring a dynamic demand feature and a context association feature, matching the dynamic demand feature and the context association feature with a pre-stored information resource library to generate a real-time information resource matching result set, and optimizing a matching strategy of a target user matching model in real time according to a dynamic feedback parameter of the real-time information resource matching result set. According to the method, a resource matching strategy is optimized to obtain an optimized resource matching strategy, and finally, a target information resource set related to the dynamic demand characteristics is pushed to a target user terminal device based on the optimized resource matching strategy, so that more accurate and personalized information resource recommendation can be realized, and the recommendation quality and the efficiency of obtaining effective information by a user are improved.

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.