531 results about "System usage" patented technology

A computer system implements a unified framework integrating an adaptive elastic funnel (AEF) with a convergent intelligence fabric (CIF) for multi-agent AI collaboration. The system provides a universal multi-modal key-value subsystem for sharing partial computations, implements hybrid placement strategies for dynamic memory management, and incorporates quantum-resistant secure enclaves. The architecture integrates hardware acceleration through GPU-FPGA hybrid caching and neuromorphic processors, applies adaptive energy and thermal management across hardware generations, and implements autonomous flash resource orchestration with multi-dimensional wear management. The system orchestrates tensor workflows using hierarchical scheduling, enables cross-agent collaboration with privacy preservation, and supports continuous learning without catastrophic forgetting. This integration delivers unprecedented computational efficiency and security in high-dimensional decision-making environments while supporting incremental adoption through modular interfaces.

A computing system for adaptive electronic message classification employs a multi-agent architecture comprising a media feature analysis system, a user context refinement system, and a response synthesis system. The media feature analysis system generates pillar scores including message type, intent, and link risk scores with associated confidence values using trained classification models. When pillar scores and confidence values do not satisfy predetermined threshold conditions, the user context refinement system dynamically constructs contextual prompts using the pillar scores and confidence values as input parameters. User responses generate score modification data that refines the pillar scores and contextual response data for recommendation generation. The response synthesis system generates refined classifications and personalized recommendations using the refined pillar scores and contextual response data. An orchestration system coordinates agent interactions using learned uncertainty points and implements asymmetric influence algorithms with variable weighting based on content and URL analysis concordance.

A transformer-based student performance prediction and reasoning intervention is disclosed. The system comprises a data repository coupled to a transformer-based prediction module that processes student data through multi-head attention mechanisms to generate performance predictions and identify potential learning shortfalls. A reasoning-enhanced large language model algorithmically generates personalized corrective action plans by applying structured decomposition of learning challenges, multi-step reasoning, and hypothesis testing. An algorithmic prompt formulation system optimizes inputs using field-specific, level-specific, and shortfall-specific templates. The system implements a workflow including shortfall detection against educational thresholds, causal factor analysis, intervention generation, and adaptive refinement based on outcomes. This approach enables early identification of academic challenges and timely implementation of personalized interventions to improve student learning outcomes.

The present invention discloses a system and method for secure artificial intelligence-based financial technology governance and risk management, designed to provide real-time, autonomous, and verifiable compliance assurance within digital financial ecosystems. The invention integrates a secure artificial intelligence processing unit, a governance control processor, a cryptographically anchored storage unit, a federated learning coordination processor, and a quantum-resistant communication interface enclosed within a tamper-proof hardware structure. The system performs encrypted machine learning computations on financial transaction data using homomorphic encryption and trusted execution environments to preserve confidentiality during analysis. It computes a governance risk index based on probabilistic inference and anomaly detection to identify regulatory deviations, applies adaptive compliance reasoning across multi-jurisdictional frameworks, and automatically enforces governance actions through secure decision logic.

Systems, methods, and devices that relate to intelligent query decomposition and parallel routing for specialized model processing are disclosed. In one example aspect, the system receives a query from a user comprising a request relating to a particular domain. The system determines, using a decomposition model, a set of sub-queries based on semantic boundaries, syntactics, tasks, relationships, and rules relating to particular domains. The system inputs the set of sub-queries into a routing model to determine a set of specialized models. For each sub-query, the system routes the sub-query to a respective specialized model, generates an output, and assigns a confidence score. The system detects conflicts among outputs using a conflict detection model configured to identify discrepancies. The system generates an aggregated output by combining outputs according to a weighted aggregation algorithm prioritizing higher confidence scores and conflict resolution rules, then displays the aggregated output.

Systems and methods disclosed herein automatically authorize, audit, and manage usage of protected digital content via agentic artificial intelligence (AI) models. A data access / usage request is received (e.g., from a graphical user interface) that is associated with digital assets licensed from third parties. The system uses a first AI agent set to identify the digital content and retrieve corresponding access policies from a distributed database. The system uses a second AI agent set (same as or different from the first AI agent set) to evaluate the request against the retrieved policy to generate a permission set and / or settlement instructions. The system uses a third AI agent set (same as or different from the first and / or second AI agent sets) to embed digital watermarks and / or cryptographic signatures into the accessed content, and to record an audit trail of access, authorization, and / or settlement events in a distributed ledger or database.

Systems, methods, and devices that relate to assessing uncertainty associated with entities are disclosed. In one example aspect, the method receives artifacts relating to an entity and categories for assessing uncertainty. For each category, a generative model retrieves and standardizes data points from the artifacts. A rule-based model inputs the standardized data points to output a rating. The generative model then generates an assessment of the rating and data points according to a predefined structure. The method outputs a summary, rating, and standardized data points for each category. These outputs can be used by other systems for assessing the uncertainty of the entity and taking action based on the assessment.

Techniques include optimizing a base access control policy, resulting in a residual access control policy, which is then indexed. Upon receiving an access request, the system retrieves the residual access control policy from the index using the request's attributes. The access request is then evaluated against the residual policy to make an authorization decision, which is promptly returned. This streamlined process efficiently evaluates and determines authorization, enhancing the speed and accuracy of access control decisions.

A device may provide a prompt to a first machine learning model. The prompt may include at least one instruction to cause the first machine learning model to use at least first natural language input associated with a use of a conversational search system to rank data sources, generate a first search query and reasoning, and use the first search query and the reasoning to generate a second search query. The first search query may include data obtained from at least one of the ranked data sources. The reasoning may include an explanation of how the first machine learning model generated the first search query. A second machine learning model may synthesize a response determined via execution of the second search query. The synthesized response may be provided for presentation via the conversational search system.

An integrated development environment (IDE) for designing, programming, and configuring aspects of an industrial automation system uses a generative artificial intelligence (AI) model and associated neural networks to generate portions of an industrial automation project in accordance with functional requirements provided to the industrial IDE system in intuitive formats, such as spoken or written plain language text. The system uses generative AI to translate plain language requests or functional specifications into industrial control code, human-machine interface (HMI) applications, device configuration settings, or other aspects of an industrial control project.

Systems and methods disclosed herein automatically register, monitor, and authenticate distributed artificial intelligence (AI) agents and their operational contexts using a distributed or federated ledger-based agent knowledge registry. The system obtains a registration or query request (e.g., from an AI agent, orchestrator, or user interface) to identify or store operational context linked to each AI-based agent. The system determines a feature set of agent metadata and operational parameters using a first AI model set, and dynamically generates a cryptographically verifiable registry record set using a second AI model set (same as or different from the first AI model set) based on the operational feature set, to be stored in a distributed ledger database. The system automatically executes registry updating, agent selection, or operational verification workflows using a third AI model set (same as or different from the first and / or second AI model sets) to maintain records that trace inter-agent interactions.

The present invention discloses a system and method for processing trade finance documents and performing automated compliance screening. The system comprises a computing device, and a database for storing trade finance documents. The system processes documents using OCR to extract text and positional data, generating structured document representations via a layout-aware AI model. An AI classifier module categorizes documents based on content, layout, and domain-specific roles, while a semantic verification module aligns document data with master Letter of Credit templates. A rule management module validates compliance against international trade standards, and a financial crime risk control module performs real-time checks against external sanctions, vessel, and dual-use goods databases. The system further determines and reports discrepancies, anomalies, and compliance issues. The system supports heterogeneous layouts, multi-language documents, and integration with banking APIs.

Systems and methods for interfacing with data profilers using a machine learning model. In some aspects, the system receives a data profiler configured to create and interface with a plurality of data profiles. The system trains a profile query model to generate responses to user queries relating to data profiles. The system receives a user query concerning data profiles associated with the data profiler. Using an interpretation model, the system pre-processes the user query and data profile attributes to determine an activation pattern for the profile query model. The system uses the profile query model to process the user query and the data profile attributes of the data profiler and generate a preliminary response. The system post-processes the preliminary response to generate a verified response by applying a corrective program. The system then transmits the verified response in a conversational program related to the user query.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating modifications to an LLM based artificial intelligence assistant based on classifying the severity of errors and focusing the modifications on resolving high-severity errors. In particular, the disclosed systems receive prompts via an artificial intelligence assistant graphical user interface and generate responses to the prompts using the LLM based artificial intelligence assistant. Further, the disclosed systems determine errors in the responses using an annotation tool to generate annotated errors and an error analysis mechanism to generate indications of the errors based on the annotated errors. Additionally, the disclosed systems classify the errors as one of high-severity, mid-severity, or low-severity. Moreover, the disclosed systems generate modifications to components of the LLM based artificial intelligence assistant based on the high-severity errors.

The present invention relates to a cybersecurity system for preventing adversarial reconnaissance in cloud, hybrid, and multi-cloud environments by dynamically modifying user authentication identifiers, hostnames and fully qualified domain names. The system updates login usernames in real time using randomized, non-repeating values generated from a configurable dictionary. Updates occur at scheduled, random, coordinated, or event-triggered intervals to disrupt profiling and targeting attempts. When identifiers are changed, associated sessions and tokens are invalidated to prevent reuse. The system monitors expired credential usage to detect potential reconnaissance, generating alerts with intelligence such as IP addresses, timestamps, and affected resources. By eliminating predictable identity patterns, the system defends against reconnaissance-based attacks, unauthorized access attempts, and other credential-driven threats, thereby improving organizational security across cloud platforms.

The present disclosure relates to a system and method for monitoring air quality and providing recommendation for managing air quality. The system comprises one or more environment monitors, a user device and a server in communication with the environment monitor. The system enables the user to input a contextual information to specify a space and / or use of space in which the environment monitor is placed. The system is configured to provide recommendation based on the designated contextual information. The system offers flexibility in selecting sensors and provide specific, actionable advice tailored to individual environments. The system integrates seamlessly with other smart home devices for automated air quality management. Furthermore, the system allows users to select sensors based on the materials they work with, the processes they apply, and the pollutants of concern within their workspace.

Disclosed embodiments provide techniques for computerized moderation, authorship recording, and distribution of social media content. Moderator-supplied tags are associated with content and supplied to a machine learning system as training data. Using blockchain, authorship is authenticated and can be converted from anonymous to non-anonymous. Collaboration among authors on content is supported with authorship lists that can contain a mix of anonymous and non-anonymous authors. Contribution limits are established to determine royalty payments for sale and rent of content.

Carbon emission reduction using machine learning is provided. A system receives data for profiles linked with locations of an entity. The system determines, based at least in part on data from a utility service provider system and the data indicative of energy consumption associated with the locations, a first value of a metric indicative of carbon emissions associated with the locations. The system determines, based on a comparison of the first value of the metric with a threshold, to invoke an automated process via the payroll processing system to reduce the metric. The system generates, generate, using one or more models trained with machine learning, an action to execute via the automated process. The system selects a first profile from that is compatible with the action, and commands the payroll processing system to execute the generated action to cause a reduction in the first value of the metric.

A contextual natural language query response system (contextual system) leverages contextual attribute-identifier mappings to improve large language model (LLM) responses to natural language queries. The contextual system replaces identifiers in natural language queries with attributes according to a contextual mapping table between identifiers and attributes to generate attribute-based natural language queries. The contextual system then uses retrieval-augmented generation with the attributes-based natural language queries to prompt an LLM to generate attribute-based database queries. The contextual system uses the mappings from the contextual mapping table to convert the attribute-based database queries to identifier-based database queries and queries a database with the identifier-based database queries. The contextual system responses to the natural language queries using results from querying the database.

Disclosed herein are systems and methods for cross-domain training of sensing-system-model instances. In an embodiment, a system receives, via a first application programming interface (API), an input-dataset selection identifying an input dataset, which includes a plurality of dataframes that are in a first dataframe format and that have annotations corresponding to one or more sensing tasks performed with respect to the dataframes. The system executes a plurality of dataframe-transformation functions to convert the plurality of dataframes of the input dataset into a predetermined dataframe format. The system trains an instance of a first machine-learning model using the converted dataframes of the input dataset to perform at least a subset of the one or more sensing tasks. The system outputs, via the first API, one or more model-validation metrics pertaining to the training of the instance of the first machine-learning model.

In some aspects, systems and methods are described herein for determining rightsizing adjustments to a cluster of cloud servers using a multi-dimensional operating ratio. The system collects usage data from a cloud server, comprising multiple dimensions of cloud computation usage. The system then processes the usage data using a cleansing process to generate processed usage data, wherein the cleansing process comprises outlier removal and seasonality adjustment. The system compares the processed usage data against a benchmark to generate a workload metric. The workload metric corresponds to values in the multiple dimensions of the cloud computation usage, indicating a distance from the expected usage data. Based on the workload metric and using a predictive model, generating expected capacity needs. Based on the expected capacity needs, determining a set of rightsizing changes, wherein the set of rightsizing changes comprises changes to capacities of the cloud server.

The invention provides a lightweight configurable cue word labeling method and system for a Web system, and relates to the technical field of Web front-end development and man-machine interaction, and the system comprises a front-end labeling plug-in module, a cue word content service module, a cue word management module and an intelligent recommendation module. The front-end labeling plug-in captures text content selected by the user by monitoring a global shortcut key, and allows the user to add prompt words of a text, a rich text or a link type; the prompt word content service module provides an API (Application Program Interface) to realize persistent storage of data; the prompt word management module provides a unified management interface for an administrator; and the intelligent recommendation module automatically judges the prompt word display priority through a candidate recall and sorting algorithm based on the user click behavior data, and continuously optimizes the recommendation result by using an online learning mechanism. The method is integrated to an existing Web system in a non-intrusive mode, business core codes do not need to be modified, the use threshold of the system is remarkably lowered, and precipitation and sharing of business knowledge are promoted.

Systems and methods for generating system architecture representation by implementing machine learning (ML) techniques are disclosed. The system receives a request for generating system architecture representation from at least one user and generates an architecture summary for the received request. Further, the system determines an architecture pattern relevant to the received request based on a context of the generated architecture summary using the large language models and machine learning (ML) models. The system further generates an architecture code corresponding to the received request. The system validates the generated architecture code for determining errors in the generated architecture code based on compliance-based rules, legal-based rules, and security-based rules specific to an organization using the machine learning (ML) models. The system further generates the at least one system architecture representation based on successful validation and outputs the generated architecture code and the at least one system architecture representation on a user interface.

In accordance with the described techniques, a system receives a plurality of facets describing language-agnostic aspects of language translation, a translation document describing language-specific rules for translating from a source language to a target language, and a source text in the source language. Using one or more machine learning models, a plurality of guidelines are extracted from the translation document and assigned to respective facets of the plurality of facets. The system translates the source text to a translated text in the target language using one or more machine learning models conditioned on the plurality of guidelines assigned to the respective facets.

A system generates data modification operators that reduce bias or distortions in artificial intelligence (AI) models. The system uses a first artificial intelligence (AI) model to generate outputs based on a set of corresponding inputs to the first AI model. First measurement values of one or more model output metrics in the outputs generated by the first AI model are received. Based on the first measurement values, the system generates a set of data modification operators that specifies one or more operations for modifying inputs to a second AI model. Inputs to the second AI model can be modified using the set of data modification operators to generate a modified set of corresponding inputs. The second AI model can then be applied to the modified set of corresponding inputs to the second AI model.

A system and method for generating structured software application specifications through multi-phase conversational dialogue is disclosed. The system implements multiple specialized AI agents including a framework generation agent, an interactive coaching agent, specialized capsule agents for analyzing different concern categories, and a specification coaching agent. During Phase 1, the system conducts exploratory dialogue using a tailored question framework while detecting and storing user concerns in structured capsule entries. Concern injection into the dialogue is strategically timed based on algorithmic evaluation of cooldown periods and user sentiment analysis. During Phase 2, the system conducts comprehensive concern resolution dialogue and generates a final specification in structured JSON format with explicit traceability linking requirements to source conversations and concern resolutions. The system solves technical problems of preserving concern context across conversation phases, optimizing injection timing to avoid overwhelming users, and generating machine-readable specifications suitable for automated downstream processing.

An artificial intelligence-based system and method facilitate enhanced interaction between users and mechanical devices such as vehicles. One system uses an application equipped with AI algorithms to process user inputs concerning specific vehicle information and issues, providing tailored recommendations and feedback. This includes automated troubleshooting, parts recommendations, and maintenance advice, aiming to improve user experience and vehicle upkeep efficiency.

Computerized methods and systems obtain threat data generated from activity data using unsupervised learning. The activity data is collected from enterprises and describes activities performed on the enterprises. The threat data indicates likelihood that sequences of activities performed on the enterprises are indicative of malicious intent. A supervised ML model that processes sequential data is trained by providing a training set of sequential data to the supervised ML model. The training set includes at least some of the obtained threat data, and data derived from activity data collected from at least some of the enterprises. The trained supervised ML receives new data that describes a sequence of activities performed on an enterprise, and processes the received new data to produce a prediction of whether the sequence of activities performed on the enterprise will lead to a malicious action on the enterprise. In some embodiments, multiple supervised ML models are used.

Systems and methods are disclosed for optimizing a first machine learning (ML) model using a second ML model. In some examples, a system generates modifications to the first ML model. Each of the modifications is associated with a respective node of the first ML model. The system tracks a processing characteristic corresponding to modified variants of the first ML model (corresponding to the modifications) processing a test dataset to generate respective results. In some examples, the system trains the second ML model based on context (the modifications and the respective changes). The system identifies, using the second ML model and based on the context (e.g., the training), a modification to the first ML model that adjusts the processing characteristic of the first ML model in a predetermined direction. The system modifies the first ML model according to the modification to generate a modified first ML model.

A system and method are disclosed for interfacing with one or more databases using natural-language queries. The system translates a natural-language input into an intermediate formal representation, such as a Concept Query Language (CQL), which references domain concepts rather than database-specific structures. A data access subsystem maps these domain concepts to database-specific queries using a domain dictionary, enabling seamless translation across heterogeneous databases and database management systems (DBMSs). The system supports distributed data retrieval, error recovery, and dynamic query planning. A presentation subsystem formats the results into user-friendly outputs such as charts or tables. The architecture allows for modular grammar and dictionary configuration, enabling rapid adaptation to new domains, schemas, or user roles without procedural code changes. This approach improves accessibility, maintainability, and scalability of database interactions by abstracting technical complexity from end users.