532 results about "Ai systems" patented technology

A system and method for enabling real-time dispatch of flexibility resources to unlock grid capacity through AI-based orchestration. The invention addresses the challenge of connecting high energy demand users, such as data centers, to constrained electricity grids without requiring infrastructure upgrades. The system establishes a marketplace where flexible asset holders set temporal compensation prices and boundary conditions, enabling true market-based participation. An AI orchestration engine analyzes real-time grid conditions and modifies flexible asset behavior to create inverse consumption profiles that counterbalance new demand loads. The platform integrates hardware and software solutions for remote control and APIs for autonomous systems like electric vehicles. Aggregators and off-takers can establish long-term contracts for flexible capacity at agreed prices. The AI system ensures flexible assets meet user-defined boundary conditions while simultaneously masking high energy demand, making new loads invisible to the grid and enabling immediate connection of data centers essential for industrial deployment.

The invention discloses a control method for a multi-modal information fusion intelligent robot with a body. The control method comprises the following steps: initializing a system, and collecting surrounding physical environment and object state information and a natural language instruction of a user; performing scene understanding and task analysis, processing data through a multi-modal information fusion mechanism and a cross-modal attention module, and generating unified multi-modal data; task planning and priority ranking are carried out, complex tasks are decomposed into subtask sequences, and a priority ranking layer dynamically adjusts the execution sequence; performing action execution and feedback adjustment, and generating a control instruction through a self-adaptive operation control algorithm; continuous learning and strategy verification are carried out, integrated execution is realized by using a hybrid AI system, and the robustness of an operation strategy is verified through a simulation environment; and closed-loop iteration is carried out to realize real-time response of the intelligent robot with the body. According to the method, the perception understanding precision and the task execution efficiency of the intelligent robot with the body are improved, the operation precision adaptability and the system robustness flexibility are guaranteed, and the method is suitable for multiple scenes.

A computerized system for dynamic cybersecurity policy using AI-based contextual adaptive learning includes an AI system that evaluates business contexts, risk tolerance, and productivity impact to generate threat intelligence assessments. The system includes a Contextual Adaptive Learning module that dynamically adjusts cybersecurity policies based on threat assessments to create security workflows. A Cybersecurity Mesh Development module that integrates policies across security frameworks. A Dynamic Scenario Catalog module that updates policy adjustments based on threat intelligence. An Automated Workflow Orchestration module that creates and refines security workflows for optimal efficiency. A Policy Recommendation and Automation module that generates prioritized security recommendations and automates policy changes based on organizational risk profiles and current security controls. This system harmonizes security policies while considering business context, risk, and productivity impacts.

A system and method for querying data sources for cybersecurity analysis is presented. The system and method include: receiving security logs from at least one data source, wherein the security logs lack pre-defined schema; generating a schema of the security logs based on at least a type of data of the security logs, wherein the generated schema includes fields of the security logs and values of the fields; embedding field vectors, wherein each field vector is a vector representation of a value of each respective field; embedding value vectors, wherein each value vector is a vector representation of a natural language description of each value in each respective field; and generating a query in a cyber language query, using an AI system, for execution on at least one target data source based, in part, on the generated schema, the embedded field vectors, and the embedded value vectors.

The invention discloses an AI agent construction system based on a vector knowledge base and large model workflow, and belongs to the technical field of process management. The problem that the whole process of workflow scheduling and quality control cannot be managed smoothly due to the fact that an existing AI system cannot assemble heterogeneous and unstructured domain knowledge and business rules dispersed in internal documents, databases, APIs and expert experience through a standardized and low-code platform in a rapid and high-quality mode is solved. Comprising a project initialization module, a knowledge base configuration module, a model service module, a workflow arrangement module, a test verification module and a deployment monitoring module. According to the method, data, knowledge, reasoning and quality control are communicated through a unified vector knowledge base and a low-code drag workflow, a dynamic hypergraph is driven by using computable information potential energy / cognitive entropy, automatic topology operation cutting circulation and shortcut establishment are performed before deadlock outbreak, and predictable hard constraint management of knowledge fusion, process arrangement and cost process is realized.

An approach is disclosed for enabling contextually relevant conversational interaction. Environment data is received by an AI System which detects a plurality of physical objects in a physical environment and forms a contextual understanding of the plurality of physical objects and the physical environment and identifies a user relevant to the contextual understanding. A most relevant contextual information to the user is predicted by the AI system and transformed into a textual form. A set of intents and objectives is predicted by the AI system for user-centered interaction. The AI system and the user interact iteratively through the user-centered interaction to determine an understanding of a most relevant intent and a most relevant objective which is validated by the AI system with the user until the user agrees. The validated most relevant intent and the most relevant objective is utilized to facilitate the user-centered and contextually relevant conversational interaction.

Techniques for creating and updating natural language summaries representing personalized user knowledge (e.g., user interests, user affinities, user preferences, family structure, routines, and other insights) based on conversational interactions with and other natural language content available to an AI system are described. In some embodiments, to provide a more personalized service, a system can use a generative model to summarize learnings about a user and determine helpful nuanced insights about the user such as “the user is learning how to play guitar.” This “user knowledge” can be updated based on further (later) conversations with the user, where updating can involve negating or deleting stored information, adding to or modifying stored information, etc.

A composite AI system and method for advanced reasoning and automation that integrates symbolic knowledge graphs and algorithms with non-symbolic, or connectionist, models such as neural embeddings. A hierarchical architecture enables dynamically distributed, cooperative reasoning through layperson and expert-led challenge-based verification, model blending, model fitness and retraining and selection, comprehensive feedback loops at individual model or model blend or process flow with or without supervision, and specialized routing of processing to account for various operational risk, regulatory, legal, privacy, or economic considerations. Models, datasets, knowledge bases, simulations and simulation components, and embeddings are iteratively refined using knowledge graph elements and model, process, simulation or flow / process optimal hyperparameters which are recorded and tracked. Extraction of symbolic representations from connectionist models links them to curated ontologies of facts and principles.

The invention relates to a large model energy consumption optimization method and device, computer equipment, a computer readable storage medium and a computer program product. Comprising the steps of collecting hardware state data and task data of a large model; performing stage identification according to the task data, and determining a current stage; performing state prediction according to the hardware state data and the task data through a prediction model corresponding to the current stage to obtain target state information; generating optimization parameters of the current stage through a joint optimizer according to the target state information; and adjusting the resources of the large model according to the optimization parameters. In combination with stage perception and dynamic resource adjustment, a differentiated resource adjustment strategy based on different stages is realized, specific energy consumption pain points in an AI scene are solved, high power consumption of large model training, instantaneous fluctuation of reasoning requests and the like are reduced, sustainable and efficient operation of an AI system is ensured, the computing power demand and resource consumption of a large model are balanced, and the system performance is improved. And wide landing and development of a large model in various scenes are facilitated.

Systems and methods of processing domain-specific content in a generative AI system including receiving a prompt, tokenizing the prompt, identifying an identified domain of the tokenized prompt identifying domain-specific functions within the identified domain, generating domain-specific sub-functions from the domain-specific functions according to a hierarchical mapping, generating H-Tokens, each encapsulating one of a domain-specific function or a domain-specific sub-function and relationships domain-specific functions and the domain-specific sub-functions, implementing the of H-Tokens, assembling a response using the implemented of H-Tokens, and transmitting the response to the user.

A model verification system and associated method for employing a multi-party verification technique to verify machine learning models and generative AI systems. The models and associated systems can be deployed in an enterprise and require verification to ensure that cohorts are properly verifying the models and systems and evaluation to ensure that the models and systems operate responsibly and achieve intended outcomes. A dynamic, multi-stakeholder blinded verification process can be employed for the continuous verification and evaluation of machine learning models and the systems that use them. This helps promote unbiased, reproducible verification, evaluation and assessments by preventing potential biases from cohorts form part of the verification process.

Embodiments of the present disclosure may include a method of training an electro-mechanical system that includes local trainable AI, the method including training a local AI for a controller of the electro-mechanical surgical device or system with simulated data. Embodiments may also include performing a task or set of tasks or operations with the device or system as controlled, instructed, influenced, or the like by the controller having the local AI system. Embodiments may also include collecting real-life data from the step of performing. Embodiments may also include further training the local AI system and improving the AI simulated data generator with real-life data. Another embodiment relates to a system for providing a user with suggestions during social interactions. Embodiments of the invention may extend across many types of devices and systems, control systems, sensors, types of AI, and so forth, and combinations of the foregoing.

Data is the “fuel” that powers the machine learning “engine” for Artificial Intelligence. However, identifying high quality data that can catalyze smarter AI, AGI, and SuperIntelligent systems is becoming an increasingly challenging bottleneck for machine learning. This invention not only describes novel methods for identifying the most valuable data, but it also presents an entirely new framework for understanding the information content of AI-relevant datasets. The methods can be used by intelligent systems autonomously or in collaboration with humans. Novel methods for accelerating AI learning, and for updating the knowledge of AI systems in real-time, are also disclosed. Consistent with the view that human survival may depend on the fastest path to AGI also being the safest path, the invention describes catalysts which help maximize alignment between the values of AGI and humans. These innovative catalysts increase not only the intelligence, but also the safety, of AI systems.

Systems and methods for guiding or steering thought processes on a persistent cognitive machine (PCM) that uses a continuous, differentiable, thought manifold in geometric space to allow a computer to engage in human-like thought processes. The PCM with thought manifold represents a fundamental advancement in artificial intelligence beyond current probabilistic AI system such as large language models (LLMs) and similar reasoning models. A PCM with cognitive manifold performs cognition on a thought manifold in a continuous, differentiable, thought manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. Methods for guiding or steering thought processes on the thought manifold are disclosed that involve mathematical manipulations of the geometric space of the thought manifold inspired by gravitational lensing.

A system and method for a digital thought architecture, otherwise called a persistent cognitive machine (PCM), that uses a continuous, differentiable, thought manifold in geometric space to allow a computer to engage in human-like thought processes. The PCM with thought manifold represents a fundamental advancement in artificial intelligence beyond current probabilistic AI system such as large language models (LLMs) and similar reasoning models. Not only does the PCM with thought manifold maintain persistent cognitive processes regardless of external interaction, overcoming limitations of existing AI systems that operate within a prompt-response paradigm where they await input, generate output, and return to a waiting state, it also performs cognition on a thought manifold in a continuous, differentiable, thought manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. In some embodiments, the thought manifold may be implemented as a neuromorphic platform.

A genetic algorithm is implemented to generate prompts that evade content filters of generative artificial intelligence (AI) systems. The genetic algorithm applies grammar operations to mutate candidate prompts, communicates the candidate prompts to generative AI systems, and selects candidate prompts that successfully evade content filters according to corresponding responses. A disambiguation model that corrects grammar in prompts is tested on the selected prompts to determine if grammar is properly corrected. Once tested, the disambiguation model is deployed in an ensemble with a classifier that outputs verdicts for prompts with grammar corrected by the disambiguation model.

A node-edge symbolic consent kernel (NESCK) provides a computing architecture in which every instruction is gated by a verifiable human-intent signal and an ethical-predicate chain prior to execution. The system integrates a biometric-sensing front-end (EEG / GSR / facial micro-affect), a symbolic arbitration engine that transforms bio-intent data into consent tokens, and a cryptographically bonded node-edge ledger that records execution lineage, revocation, and audit proofs. Each node represents an executable state bound to a human consent fingerprint, while each edge encodes the ethical transition rules authorizing propagation through the network. At runtime, the kernel evaluates symbolic predicates, verifies zero-knowledge proofs of consent, and allows or halts instruction dispatch. The framework operates across devices, edge nodes, and cloud layers, enabling real-time lawful AI behavior, revocable autonomy, and tamper-proof moral audit trails. Embodiments span neuroadaptive wearables, autonomous vehicles, robotics controllers, and sovereign AI systems requiring continuous consent and transparent accountability.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating enhanced prompts and digital components using the enhanced prompts. In one aspect, a method includes generating, by an AI system, a first prompt that includes a first request for one or more models to generate a structured description for a subject of a new digital component to be generated and initial data for use by the one or more models to generate the structured description. The system generates a second prompt that includes a second request for the one or more models to generate a content output. The system generates a third prompt that includes a third request for the one or more models to generate the new digital component based on the content output, and obtains the digital component output by the one or more models.

The invention discloses an AI software system defect repairing method, device and equipment of a government affair platform and a medium, and relates to the technical field of computers, and the method comprises the steps that target data of a current platform AI software system in the running process is obtained through an eBPF probe loaded by a K8s cluster of the government affair platform, and defect fingerprints are determined based on the target data; determining a target defect classifier based on the graph neural network, the teacher model, the student model, a preset distillation mechanism and a KL divergence loss function, determining a target strategy composer by using reinforcement learning and a preset strategy library, and determining a target defect repair strategy based on the defect fingerprint, the target defect classifier, the business data and the target strategy composer; and performing defect repair on the current platform AI software system by using the target defect repair strategy, verifying the repaired AI software system, and if the verification is passed, completing the defect repair operation. And the AI system defect automatic detection and repair efficiency is improved.

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.

The invention belongs to the technical field of cipher machines, and discloses an MCP protocol-based intelligent security service calling method, which comprises the following steps of: defining a standardized MCP message structure, and creating a mapping relation table of an MCP instruction and a cipher machine API (Application Program Interface); constructing an MCP server interface module, and receiving an MCP format request sent by the AI agent; analyzing the request to determine a password operation type; converting the operation type into a corresponding cipher machine API call based on the mapping relation table; aPI calling and transmitting parameters are sent to the server cipher machine; and receiving an operation result, packaging the operation result into a response message according to the MCP message structure, and returning the response message to the AI agent. Through a standardized message structure and a mapping mechanism, the calling process of the cipher machine is simplified, and the technical threshold is reduced; and meanwhile, seamless connection between the AI application and the cipher machine is realized through an interface module and a session management mechanism, so that the AI system can call the hardware-level cipher service on the premise of ensuring the safety.

Multi-stage hybrid network integrates relationship regularization links and explainable elements to improve alignment with human values, explainability, robustness, and efficiency. The network comprises neural components, event prediction elements, and probability models across multiple stages, with relationship constraints enforcing structured knowledge representation. Explainable elements provide interpretable rationales for decisions, enhancing transparency. Training incorporates supervised learning, human-guided refinement, semi-automated knowledge engineering, and adversarial robustness techniques. A Socratic reasoning module detects contradictions and refines outputs for logical consistency. Indexed model elements enable dynamic memory optimization for improved efficiency. Candidate outputs may be scored, verified, or selected using neural and symbolic criteria. The invention supports retry loops and configurable subsystem pipelines to improve output quality. Applications include text generation, speech recognition, translation, and decision support. By combining structured constraints, human oversight, and modular architectures, the system improves the trustworthiness, safety, and adaptability of AI systems across diverse modalities and tasks.

The invention relates to the technical field of medical literature translation, in particular to a medical literature translation method based on a large language model and a term library. Comprising the following steps: S1, reading a to-be-translated document to obtain a document object model (DOM); s2, screening out a special format in the document object model; s3, a translation database is constructed, the translation database comprises a term library, a memory library and an abbreviation library, an Aho-Corasick automaton and a vector index technology are used, the document to be translated is matched with the translation database, and a corresponding matching result is obtained for each translation database; s4, using an AI system to provide a matching result in the step S3 for the AI system as a reference, and completing translation of the to-be-translated document to obtain a translated text; s5, in the translated text translated in the step S4, the special format screened out in the step S2 is restored, and the accuracy and specialty of medical literature translation can be improved.

A computer-implemented method for supporting bias mitigation in an artificial intelligence (AI) system includes determining a set of sensitive attributes and providing a dataset including a number of data elements. Each data element is labelled with sensitive attributes. The AI system runs on the dataset and determines whether a prediction for an element is correct. Upon checking whether a bias with regard to a sensitive attribute is present, for each sensitive attribute that exhibits a bias, a model is trained for an attribute-based global explanation for each class of correct and incorrect predictions. For each incorrectly predicted data element based on the trained model for the at least one attribute-based global explanation, a counterfactual data element is generated that leads to a correct classification. The method has applications including, but not limited to, use cases in facial recognition and medical / healthcare for optimizing machine learning and supporting decision making.

The invention discloses an intelligent document generation system and method based on generative artificial intelligence, and relates to the technical field of generative artificial intelligence and document processing, the system comprises an intelligent document processing service layer and a data resource layer, when the intelligent document processing service layer executes a task, required data is read from the data resource layer; the intelligent document processing service layer comprises a content generation module, a verification module and a coordination management module, and the content generation module is used for generating preliminary content; the verification module is used for verifying the preliminary content, generating structured feedback information when the verification is not passed, and driving the content generation module to perform iterative correction according to the feedback information; and the coordination management module is used for receiving tasks and performing overall scheduling of the system. According to the method, the AI system is endowed with self-checking and self-correcting capabilities, the problems of reliability and structure maintenance of content generation can be systematically solved, and the quality and credibility of automatic document generation are remarkably improved.

An information processing apparatus identifies a first path on the basis of relationship information indicative of data transfer relationships between two of functions in an AI system, management target data, and relevant persons. The first path indicates a route which follows a data transfer relationship from a start point to an end point with the start point set to one of the functions, the management target data, and the relevant persons and the end point set to another one. The information processing apparatus selects a first check item related to a first data transfer relationship on the route indicated by the first path from among a plurality of check items associated with any of the data transfer relationships indicated by the relationship information. Furthermore, the information processing apparatus displays information on the first check item.

An adaptive artificial intelligence (AI) control system receives a prompt for an AI system from a user interface component of a software application on a mobile device. The adaptive AI control system determines a current battery level of the mobile device using a battery level monitoring component. The adaptive AI control system then selects a generative AI model of the AI system to use to generate a response for the prompt based on the current battery level using a model selection component. The generative AI model that is selected is one of a plurality of different generative AI models of the AI system which are capable of processing the prompt, each of the plurality of generative AI models having a different level of complexity.

In modern industrial environments, there may be numerous independent applications deployed throughout a facility, all performing different tasks, gathering different data, and communicating data and control information among one another. For example, this may include industrial control, quality control, work instructions, training, oversight, and so forth. Against this backdrop, an AI system is trained with a large language model to assist in characterization of new applications, in order to support administration and management of the software infrastructure for a facility.

The invention discloses a generative AI adopting logical reasoning. Systems and methods related to generative AIs utilizing logical reasoning are disclosed herein. For example, the LLM may be used to convert statements, such as natural language statements or software code lines, to logical statements in a logical specification language, the logical reasoning engine may be used to evaluate the logical statements, and the LLM may be used to interpret the output of the logical reasoning engine in a natural language (e.g., using a system that employs retrieval enhanced generation (RAG) and / or trim the LLM). Thus, the present technology can be used to provide a generative AI system that can logically infer a set of statements (e.g., natural language statements or software code), infer new facts therefrom, calculate their logical outcomes, and / or check their consistency, and provide a logical interpretation of how to compute.

The invention provides an intention-driven semantic evolution mechanism based on a tensor semantic field and an application system thereof, and relates to the technical field of artificial intelligence, semantic networks and cognitive computing. According to the method, five types of semantic primitives including data, information, knowledge, intelligence and intention are expressed as high-order tensor nodes, a semantic tensor field network is constructed, and dynamic evolution and intention driving of semantics are achieved. The system comprises a multi-scale semantic aggregation mechanism, an intention weight diffusion algorithm, a semantic tensor evolution operator and a white box interpretation interface, supports full-link semantic processing from original data to high-level wisdom to intention constraint, and overcomes the defects in semantic representation and evolution, intention fusion and system interpretability in the prior art. And the generative AI system has stronger intention perception, semantic self-optimization and process transparency capabilities, and is suitable for applications such as a semantic perception large model platform, an AI cognitive map system and an interpretable language generator.