1745 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 operation may include one or more of storing a portfolio of assets of a user in memory, receiving contextual data of the user from a user device of the user, identifying an asset of interest that is not included in the portfolio of assets of the user based on execution of a generative artificial intelligence (GenAI) model on the portfolio of assets of the user and the received contextual data of the user, generating a different portfolio of assets based on the asset of interest that is not included in the portfolio of assets of the user, and displaying the different portfolio of assets via a user interface.

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.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for enabling artificial intelligence to generate new images based on contextual data and to generate digital components based on the images. In one aspect, a method includes receiving one or more queries from a client device of a user. A digital component is selected based on the one or more queries. A customized digital component is generated by obtaining an image of an object corresponding to the selected digital component and generating, using a language model, an image editing prompt for editing the image based on digital component data related to the digital component and query data including the one or more queries and contextual data. The image and the image editing prompt are provided to an image editing model. An edited image is received and used to generate the customized digital component.

A property management software system (PMSS) receives, from a client device connected to the PMSS, a request associated with a property management task, identifies a workflow corresponding to the property management task, the workflow comprising a sequence of operations, generates a prompt based on the request, the workflow, and additional contextual data from the PMSS, and provides the prompt as an input to a generative AI model agent, the generative AI model agent to execute the sequence of operations to perform the property management task.

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.

The invention provides a multi-agent cooperative task planning method, system and device and a storage medium, and the method comprises the steps: constructing an asynchronous communication framework, responding to a task event through an event-driven model, and decoupling the production and consumption of the task event through a message buffering queue; performing condition matching on the task event based on a predefined behavior rule, if matching succeeds, commanding the intelligent agent to execute an action corresponding to the rule, otherwise, marking the task event as an unmatched task, and transmitting the unmatched task to an LLM language model; loading the historical process data of the unmatched task into the historical message context of the LLM language model, generating a task allocation decision in combination with the guide template, and optimizing the task allocation decision through an abnormal re-decision mechanism to obtain a decision result; and distributing a decision result to a corresponding agent for execution, and updating context data of the task event and a predefined behavior rule in real time. The method can significantly improve the reliability, efficiency and flexibility of task execution, and is suitable for various complex business 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.

The present disclosure relates to using a large language model (LLM), provided with user context information and user high-level goal information to generate contextualized action recommendations that can help the user achieve the high-level goal(s). In one exemplary embodiment, a user system, and an AI action recommendation system that is associated with an LLM, are communicatively coupled and cooperatively implement a contextualized action recommendation virtual assistant. Input data comprising personal information data of the user that includes at least one high-level goal of the user, and user context data obtained from the user system can be collected and used to generate a prompt that is input to the LLM. The LLM can then generate a contextualized action recommendation for the user based on the prompt, and the contextualized action recommendation can be presented to the user via a virtual assistant user interface on a display of the user system.

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.

An application may receive a prompt from a user related to an input for a text field of digital content. An application may generate context data about the digital content. An application may provide the prompt and the context data to a generative language model. An application may receive a response generated by the generative language model and provide the response as a suggestion for the input for the text field.

Embodiments of the disclosed technologies include, responsive to a first use of a first application by a first user, configuring, in a first prompt, at least one instruction based on first application context data and first user context data. The first prompt is stored in a memory that is accessible to the first application and a second application. Via the second application, first output of a generative artificial intelligence (GAI) model is presented to the first user. Based on the first output of the GAI model, at least one second use of the first application by the first user, or at least one first use of a third application by the first user, is configured.

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.

Systems and methods for generating geolocation-based images for a target object are provided. A geolocation module receives a set of geolocations associated with a geographic region of interest. Each geolocation of the set of geolocations is mapped to context data associated with the geolocation. A prompt generation module generates multiple prompts based on the set of geolocations and the context data. The prompt generation module comprises a first generative artificial intelligence (AI) model. An image generation module generates multiple synthetic images based on the multiple prompts. The image generation module comprises a second generative AI model. Each synthetic image depicts the target object in a background generated based on a prompt.

A system and method for AI-assisted conversation utilize a biosensor to receive biosignals, which are analyzed by the perception engine. The engine generates context tags and text descriptions from multimodal sensory inputs and biosignal analysis. This information is then used by the conversation engine, along with memory engine data (conversation history, biographical background, keywords), to generate prompts for a language model. These prompts are displayed to a user through a computing device that presents various conversation features like communication history, context data, and selected content. The system enables interactive AI-assisted conversations between humans and machines or other humans.

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.

An artificial intelligence (Al) assisted generative digital task fulfillment process within digital model platforms is provided. Disclosed are methods and systems for carrying out digital tasks through generative Al, including tasks related to the streamlined design, validation, verification, certification, assembly, operations, and maintenance processes of complex systems. Hie method includes receiving access to a context Al model trained on Internet-scale data, receiving a user prompt indicating the digital task, and generating contextual data based on the user prompt using the context Al model, where the contextual data identifies a syntax Al model. The method includes training the syntax Al model to generate a template script having a placeholder variable for a parameter related to the digital task. The method also includes using a parameter substitution process to generate the orchestration script by substituting the variable with a parameter value.

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.

A method, a device, and a non-transitory storage medium are described in which a network slice selection service is provided. The service may include using route selection policies that includes radio frequency criteria for selection of a network slice. The service may include establishing an application session based on the route selection policies. The service may further include determining that a radio frequency of the network slice cannot be sustained. The service may include executing an application session release procedure and an application session establishment procedure. The procedures may include using cause data and context data pertaining to the initial application session to re-establish the application session via a different network slice and a different radio frequency based on the route selection policies.

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.