258 results about "Multiple context" patented technology

A method of generating outputs in LLMs including receiving including textual content, defining a context for the documents including identifying a topic or a category, segmenting the textual content into content chunks associated with the topic or category, assigning a tag to each content chunk, identifying selected chunks, adding metadata to the selected chunks indexing the selected chunks, receiving a query, and performing a response generation process including determining if a cache includes information for the query and either retrieving the information from the cache or performing a search of the index to retrieve the information, generating an augmented query by augmenting the query with retrieved information, generating a response from the augmented query, evaluating the response for compliance with criteria, and one of generating a final response and transmitting the final response to the user or performing a fine-tuning process comprising redefining the of the one or more contexts.

A computing system receives a prompt to be provided as input to a large language model. The computing system generates generating an input string to the large language model by appending a plurality of contexts to the prompt. The plurality of contexts defines rules for the large language model to follow when generating the prompt. The plurality of contexts includes a negative context. Based on the prompt and the plurality of contexts, the computing system generates an attention matrix representing relationships between the prompt and the plurality of contexts. The computing system provides the attention matrix to a trained neural network to determine a likelihood that the prompt is malicious. Responsive to determining that the prompt is likely a malicious prompt, the computing system initiates a remedial action.

An adaptive data system (ADS) for cognitive data processing is disclosed. The ADS includes an adaptive semantic preprocessor, a trigger detector, a temporal batching engine, a symbolic encoder, and a dynamic cognitive transformer engine. The adaptive semantic preprocessor is configured to receive input data from one or more databases and identify cognitive data attributes comprising one or more contextual, semantic, and temporal attributes from the received input data. The trigger detector is configured to identify semantic divergence of the identified cognitive data attributes and provide a standardized data. The temporal batching engine is configured to provide a high-dimensional cognitive data from the standardized data. The symbolic encoder compresses the high-dimensional cognitive data. The dynamic cognitive transformer engine is configured to determine decision making rules, analyze the compressed high-dimensional cognitive data based on the decision making rules and provide recommendations based on an outcome of the analysis to a user.

A method is described. The method comprises: extracting one or more contents related to an organization from one or more data sources based on one or more job role names and one or more contextual inputs; determining, using an artificial intelligence engine, at least one of an organizational structure, one or more roles, one or more responsibilities, one or more hierarchical relationships, one or more access levels, one or more service actions, and one or more departments of the organization from the one or more contents; and generating, using the artificial intelligence engine, one or more job descriptions based on at least one of the organizational structure, the one or more roles, the one or more responsibilities, the one or more hierarchical relationships, the one or more access levels, the one or more service actions, and the one or more departments.

An example operation may include one or more of receiving interaction content from a communication session between a source device and a service provider device of a service provider, identifying a plurality of contextual attributes of the communication session based on execution of at least one large language models (LLMs) on the interaction content, converting the interaction content and the plurality of contextual attributes of the communication session into vectorized data based on execution of an additional LLM, labelling the vectorized data with identifiers of the plurality of contextual attributes, and storing the vectorized data within a vector database.

Aspects of the disclosure are directed to automatic prompt generation based on a meeting discussion. A live transcript of a virtual meeting is obtained while a virtual meeting is being conducted. The live transcript includes current content discussed by participants of the virtual meeting. A determination is made based on the live transcript of whether the current content indicates a request of a participant for an operation to be performed with respect to the virtual meeting. Responsive to a determination that the current content indicates the request of the participant for the operation, one or more of a context or a sentiment associated with the request is identified. A prompt for an artificial intelligence (AI) model is generated based on the request and the one or more of the context or the sentiment. The AI model is trained to perform the operation with respect to the virtual meeting.

The invention discloses an AI-based vulnerability repair rule generation method and related equipment, and relates to the field of electric digital data processing. A computer device performs double analysis on a vulnerability code snippet and a vulnerability description text through a large language model, constructs double representation of an abstract syntax tree and a semantic graph, and performs cross-modal alignment on the semantic graph and the abstract syntax tree to obtain a vulnerability context relationship matrix. And the computer device traverses the abstract syntax tree, extracts vulnerability code feature vectors, and determines a plurality of repair modes according to vulnerability type identifiers. And the computer equipment calculates the similarity between the vulnerability code feature vector and a plurality of context constraint conditions, screens a target repair mode, performs feasibility verification on the target repair mode based on the vulnerability context relation matrix, and determines an optimal repair mode, thereby generating a repair rule. The double analysis and cross-modal alignment mode greatly improves the accuracy and reliability of vulnerability repair rule generation.

The present disclosure relates to cybersecurity architectures and systems for assessing and quantifying security threats and risks associated with machine-readable codes, such as quick response codes, barcodes, data matrix codes, and other types of codes. A security application comprises a multi-context threat assessment system configured to analyze a broad spectrum of risk assessment attributes across multiple contexts. These contexts relate to the machine-readable code itself, target network resources identified by the code, entities affiliated with the code, end-users interacting with the code, and enterprise systems policies. The system can evaluate various risk assessment attributes for each of these contexts to more accurately quantify potential security risks associated with the machine-readable codes. The security application further includes an API for extending its threat assessment capabilities to various digital ecosystems and an AI-powered learning network comprising language models and computer vision systems to enhance threat detection and risk quantification capabilities.

A multi-channel ticket creation and service management system that includes: a graphical interface controller configured to provide a graphical user interface (GUI) accessible through a plurality of predefined Uniform Resource Locators (URLs), the GUI including interactive service request forms associated with a plurality of hierarchical service and subservice classifications; a form processing circuit operatively coupled to the graphical interface controller and configured to receive a selected service-subservice combination from a user and dynamically populate subsequent input fields based on a predefined service taxonomy, the input fields including contact information, email address, subject, location identifier, area of ​​responsibility field, and a description field; a rule execution logical processing unit coupled to the form processing circuitry, the rule execution logical processing unit configured to enable or disable one or more context-sensitive fields based on logical rules stored in a service-subservice dependency tree, the logical rules governing the enablement of date and time fields exclusively when the selected service is journalism and the selected subservice is newspaper reporting; an authentication mapping processor comprising a session parser and a user-to-department resolver, wherein the session parser is configured to extract user credentials and associated metadata from session cookies or authentication tokens, and the user-to-department resolver is configured to auto-populate an activity-driven scope field based on the extracted metadata and an organizational mapping scheme; and a validation and transmission control unit configured to check the completeness and data type conformity of all enabled fields, generate a structured ticket record, and transmit it to a central ticket management platform.

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and / or the like for retrieving a subgraph that is used to generate one or more answer outputs responsive to an input query by: (i) generating one or more context embeddings that are associated with an input query, (ii) identifying one or more candidate node paths and one or more node relations based on a knowledge graph, (iii) identifying, using a predictive machine learning model, one or more context-relationship rankings based on the one or more candidate node paths, the one or more node relations, and the one or more context embeddings, and (iv) generating one or more subgraph data objects based on the one or more context-relationship rankings.

The invention discloses a power industry dynamic knowledge base construction method and system based on a large language model, and relates to the field of electric digital data processing, and the method comprises the steps: receiving a power field document imported by a user, and converting the power field document into an electronic document; calling a large language model to analyze the electronic document, extracting a terminology definition, chart position information and a document logic level in the electronic document, and generating a knowledge unit containing a plurality of contexts; connecting the knowledge units into directed knowledge links, calculating semantic similarity between adjacent knowledge units in the directed knowledge links, and segmenting the directed knowledge links to obtain a plurality of knowledge topics; receiving a knowledge retrieval instruction, and determining a target knowledge theme corresponding to the knowledge retrieval instruction; and extracting a corresponding target knowledge unit in the target knowledge theme, and generating a knowledge retrieval result based on a target context of the target knowledge unit. By implementing the method, knowledge base construction can be optimized, and the integrity of knowledge base data is ensured.

Techniques are described with respect to a system, method, and computer program product for dynamically creating virtual learning environments. An associated method includes analyzing plurality of users associated with a virtual environment; determining a plurality of contextual information based on respective geographic locations of the plurality of users; and adjusting the virtual environment based on the determination; wherein adjusting comprises monitoring a plurality of interactions of the plurality of users with the virtual environment based on the plurality of contextual information.

Media, methods, and systems are provided for contextual automation within a group-based communication system using buttons persisted within a virtual space of the group-based communication system. The buttons are configured to initiate one or more workflows that perform one or more external or internal operations contextually based on one or more contextual parameters associated with the buttons. When actuated, the buttons call a specific workflow and pass the one or more contextual parameters to the workflow.

A system and method for adapting one or more cybersecurity microservices to accelerate cybersecurity threat mitigation includes constructing a subscriber-specific data corpus comprising a plurality of distinct pieces of computing environment-informative data of a target subscriber; adapting a subscriber-agnostic microservice of the cybersecurity service to a subscriber-specific microservice, wherein: the subscriber-agnostic microservice includes a plurality of subscriber-agnostic cybersecurity event handling instructions, and adapting the subscriber-agnostic microservice to the subscriber-specific microservice includes generating a plurality of context-informed cybersecurity event handling instructions; augmenting the subscriber-agnostic microservice to include the plurality of context-informed cybersecurity event handling instructions; computing for a target cybersecurity event a subscriber-specific threat severity level based on one or more of the plurality of context-informed cybersecurity event handling instructions; executing, by one or more computers, a threat mitigation action or threat disposal action based on the computing of the subscriber-specific threat severity level for the target cybersecurity event.

The invention discloses a context poisoning attack defense method oriented to an RAG application, and relates to the technical field of RAG. the method comprises the following steps: inputting a target query statement, and retrieving the target query statement to obtain multiple pieces of context information; taking representative sentences in the retrieved context information, and identifying and filtering potential malicious template clusters; the big language model gives all candidate answers according to existing context information, the logarithmic probability of all contexts to different candidate answers is calculated, and after the influence of parameter knowledge of the big language model is removed from the logarithmic probability, the support degree of all contexts to different candidate answers is obtained; the whole logarithmic probability vector is used as a support degree distribution condition of the context to the candidate answers; identifying a single piece of harmful information from the support degree distribution condition of the context to the candidate answers through a logistic regression model so as to filter wrong answers; according to the attack defense method provided by the invention, centralized injection of multiple malicious texts and sparse injection of a small number of malicious texts can be defended.

An example method performed by a Wireless Transmit / Receive Unit (WTRU) is described. The method comprises determining context information for execution of an edge-assisted task associated with a machine-type-communications (MTC) application, sending the context information, receiving one or more context-aware parameters and information related to usage of the one or more context-aware parameters, determining a configuration for at least one of an application layer or an upper network (NW) layer based on the received information, and sending a first uplink (UL) transmission using the one or more context-aware parameters for the application layer or the upper NW layer based on the determined configuration. The context information may indicate one or more of performance of the MTC application or performance of the edge-assisted task.

The invention discloses a visual Token compression method and device based on double-path fusion, and the method comprises the steps: obtaining an initial visual Token through employing a visual encoder; obtaining a learnable query vector, and adopting an attention mechanism to enable the learnable query vector to interact with the initial visual Token to obtain a plurality of context Token; analyzing a self-attention map of the initial visual Token in the visual encoder, so as to screen out a plurality of detail Token according to the self-attention map; obtaining a text prompt Token, and calculating a task correlation score of each detail Token by taking the text prompt Token and the plurality of context Token as a query; dynamically filtering or weighting the plurality of details Token according to the task correlation score to obtain a plurality of filtered details Token; the plurality of filtered detail Tokens are spliced with the plurality of context Tokens, so that a compressed visual Token can be obtained; therefore, the key global and local visual information can be intelligently reserved while the high compression ratio is realized and the calculation cost is reduced.

A computer implemented method for managing and remediating security drift in a public cloud network is disclosed. A security drift event may be received at a contextual impact classification engine of a server. An impact tier for the received security drift event may be assigned at the contextual impact classification engine. A queue shaping orchestrator at the server may reorder a queue with entries that include the received security drift event based on the assigned impact tier. A remediation engine of the server may determine a remediation for the received security drift event based on the assigned impact tier, and / or one or more contextual inputs received by the server.

The invention discloses an image super-resolution reconstruction method and device, equipment, a medium and a program product, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a to-be-reconstructed image; inputting an image to be reconstructed into the image super-resolution model to obtain a reconstructed image output by the image super-resolution model; the image super-resolution model is based on a residual network model and comprises a plurality of context attention modules and a plurality of wavelet fusion modules; the plurality of context attention modules are used for extracting global features corresponding to the to-be-reconstructed image; each wavelet fusion module is used for fusing global contour information and local detail texture information corresponding to a plurality of feature maps associated with the to-be-reconstructed image; the reconstructed image is obtained by fusing the image super-resolution model based on the to-be-reconstructed image, the global features, the global contour information and the local detail texture information. According to the method, the problems that the to-be-reconstructed image is blurred, unclear and low in resolution are solved.

A system and method for managing queries pertaining to a product are provided. The method includes receiving, by a processing unit, a user query from a user device. Further, one or more contexts are identified from a plurality of contexts by comparing the user query with each context among the plurality of contexts. Each of the contexts includes values of one or more attributes characteristic to at least a part of the product. Further, a prompt is generated by updating the user query based on the one or more contexts identified. The prompt is provided as input to a Large Language Model to generate a natural language output, on an output device.

Provided are a communication method and a communication device, the method comprising: a first AP MLD receiving a first frame, the first frame being used for requesting to transfer one or more contexts of a non-AP MLD to a second AP MLD, the non-AP MLD being associated with the first AP MLD; the first AP MLD transfers a data path of the non-AP MLD to the second AP MLD according to the first frame; and after the data path of the non-AP MLD is transferred to the second AP MLD, the first AP MLD transfers one or more contexts of the non-AP MLD to the second AP MLD according to the first frame. Through the method, the data path of the non-AP MLD can be switched to the target AP MLD before the context transfer is completed, so that the data volume of data transmission is reduced, and the transmission overhead is reduced.

A method of generating outputs in LLMs including receiving including textual content, defining a context for the documents including identifying a topic or a category, segmenting the textual content into content chunks associated with the topic or category, assigning a tag to each content chunk, identifying selected chunks, adding metadata to the selected chunks indexing the selected chunks, receiving a query, and performing a response generation process including determining if a cache includes information for the query and either retrieving the information from the cache or performing a search of the index to retrieve the information, generating an augmented query by augmenting the query with retrieved information, generating a response from the augmented query, evaluating the response for compliance with criteria, and one of generating a final response and transmitting the final response to the user or performing a fine-tuning process comprising redefining the of the one or more contexts.

A context analysis system receives a query from a user. The context analysis system generates one or multiple context profiles and generates a prompt for a foundation model for each of the context profiles. The context analysis system analyzes each of the context profiles and generates a relevancy score. The context analysis system selects one of the context profiles based on the relevancy score. In some examples, the context analysis system iteratively determines predicted latencies and relevancies of processing a query in conjunction with a generated context and, based on the predicted latencies and / or relevancies, processes the query using a foundation model, such as a large language model (LLM).

The invention discloses a multi-industry power load data prediction method and device, a medium and a product. The method comprises the following steps: preprocessing power load data of multiple industries in a power table to obtain complete power table data; through a TabTransform model, category type data in the complete power table data are converted into context embedding features, and numerical data in the complete power load data are converted into a high-dimensional continuous feature sequence; a dependency relationship among category embedding features is learned through a plurality of context modeling layers in a TabTransform model, context embedding and high-dimensional continuous features are fused, a target feature sequence used for prediction is constructed, each TabTransform layer comprises a multi-head self-attention mechanism, and the multi-head self-attention mechanism comprises a multi-head self-attention mechanism; and splicing the context-enhanced embedded features and the numerical features, inputting the spliced features into a multi-layer perceptron, and outputting a prediction result of the multi-industry power load data. According to the method, the context modeling capability is enhanced through a multi-head self-attention mechanism, and the prediction accuracy of the model on the power load data is effectively improved.

Various examples are directed to systems and methods for upgrading a cloud-implemented application. A cloud service may receive a request from a user group to access the application. The cloud service may access consumer context data comprising a plurality of context properties of the user group and may access a rollout strategy map comprising a first rollout record. The cloud service may compare the plurality of context properties of the first user group to first selector data indicated by a first rollout record. Based on the comparing, the cloud service may add the first version of the application to a list of permissible versions for the first user group.

Embodiments include methods performed by a trust management system associated with a communication network. Such methods include, for each of a plurality of contexts of a network function, NF, of the communication network, determining (1810) one or more metrics representative of trustworthiness of the context and, for each context, determining (1820) a context trust score based on the one or more metrics representative of trustworthiness of the context. Such methods include determining (1840) a current NF trust score based on the plurality of context trust scores and determining (1860) a context-specific action for at least one context of the NF, based on the current NF trust score and a current value of a configurable trust threshold for the NF. Other embodiments include trust management systems configured to perform such methods.

The invention relates to the technical field of industrial document management, in particular to a document management method and system and a computer readable storage medium. The method comprises the following steps: constructing a structure tree according to a plurality of documents and a plurality of entries; according to a preset standard relationship type, establishing an association relationship between the entries; according to user requirements, target operation is executed on the items in the structure tree, and the target operation comprises any one of item reference, item copying and item distribution. The method not only solves the problems of cross-document multiplexing of contents, consistency keeping and circulation and use of entries in a plurality of contexts, but also realizes fine management control of the entry level.

Techniques for generating recommendations using a recommendation model include receiving one or more user inputs, determining one or more context features based on one or more context inputs, determining one or more user features based on the one or more user inputs, determining one or more user embeddings based on the one or more user features, determining one or more context embeddings based on the one or more context features, merging the one or more user embeddings and the one or more context embeddings to generate one or more merged embeddings; and generating recommendations based on the one or more merged embeddings.

The invention discloses a training data quality inspection method and a related device, and relates to the technical field of data processing, and the method comprises the steps: marking instruction data to obtain a label set of the instruction data, the label set comprising labels of multiple granularities, labels of multiple visual angles and labels of at least two marking dimensions in labels associated with multiple contexts; clustering the labels in each label set to obtain a plurality of label clusters, and performing quality evaluation from at least two evaluation dimensions of semantic accuracy, logic consistency, field correlation and user demand matching degree corresponding to each label cluster to obtain a comprehensive quality score based on the at least two evaluation dimensions corresponding to each label cluster; determining a diversity score of the instruction data set according to the distribution uniformity of the plurality of instruction data clusters, the distribution condition of the instruction length and the label distribution condition; the complexity score of the instruction data set is determined at least according to the density of the instruction data cluster and the density of the semantic association matrix, and the accuracy of training data quality inspection is improved.

Methods, systems, and apparatus for generating trajectory predictions for one or more target agents. In one aspect, a system comprises one or more computers configured to obtain scene context data characterizing a scene in an environment at a current time point, where the scene includes multiple agents that include a target agent and one or more context agents, and the scene context data includes respective context data for each of multiple different modalities of context data. The one or more computers then generate an encoded representation of the scene in the environment that includes one or more embeddings and process the encoded representation of the scene context data using a decoder neural network to generate a trajectory prediction output for the target agent that predicts a future trajectory of the target after the current time point.