759 results about "Ai systems" patented technology

A system and method for extending AI-enhanced decision platforms with deontic and normative reasoning capabilities that enhance adjustably autonomous decision-making through a novel integration of symbolic and neural approaches. The invention uses hierarchical and fuzzy deontic logic implementations alongside connectionist AI / ML to manage obligations, permissions, and prohibitions while maintaining observer awareness to achieve goals while incorporating knowledge across multiple expert domains. The system employs dynamic event and spatio-temporal knowledge graphs along with debate mechanisms, enabling high-assurance automated reasoning while preserving explainability through neuro-symbolic integration. In at least one embodiment, the invention operates through a federated distributed computational graph architecture that allows for arbitrary scaling while maintaining coherence, consistency and supporting compound workflows. The invention provides a framework for AI systems to make logically consistent, ethically-aware decisions by combining deontic reasoning with multi-agent coordination, token space communications and knowledge, including on intermediate results, enabling automated decision-making for a variety of applications.

An advanced model management platform for optimizing and securing generative artificial intelligence systems such as large language models (LLMs) and diffusion models. The platform incorporates various techniques to address the limitations of current generative AI systems, such as hallucination, lack of validation, security vulnerabilities, and inadequate model management. The system employs reinforcement learning algorithms for model optimization, retrieval augmented generation (RAG) for hallucination mitigation, domain-specific validation against expert knowledge, model distillation and similarity scoring for security, adversarial training for robustness, and attention mechanism search and model blending for advanced management and neuro symbolic AI routine combinations. By integrating these techniques, the platform significantly improves the performance, reliability, and security of generative AI across a wide range of tasks and domains leveraging the best elements of symbolic and connectionist techniques alongside automated planning and modeling simulation.

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.

A robotic surgical system. a surgeon console operatively coupled to a patient console and one or more surgical instruments. A surgeon computer is coupled to or integrated with the surgeon console, the surgeon computer further operatively connected to the one or more surgical instruments; A surgical robot is coupled to a robotic surgery control system and a feedback loop. The robotic surgery control system includes or is coupled to an artificial intelligence (AI) system. A feedback loop is further configured to receive performance-related data from the one or more sensors, the data analyzed by the robotic surgery control system or the AI system to dynamically adjust the robotic system's operation as needed. A data extraction module retrieves, from the robotic surgery control system or the AI system. one or more programmed steps executed by the surgeon for positioning at least one of the surgical instruments during the surgical procedure.

In various examples, systems and methods are disclosed relating to language model jailbreak detection using length-perplexity metrics. A system can identify a prompt for a language model—such as an LLM, VLM, etc.—and generate a perplexity score for the prompt. The system can determine, based at least on the perplexity score and a length of the prompt, that the prompt is indicative of a jailbreak attempt for the large language model. The system can restrict the prompt from input to the large language model—or block an output generated based on the prompt from being shared—responsive to determining that the prompt is indicative of the jailbreak attempt.

A platform for dynamically generating application experiences. The platform comprises a design management system, an agent orchestration system, an analytics system, a model management system, a user management system, and databases for storing design elements and templates. The design management system provides a portal for application owners / designers to create UX / UI designs, allowing them to select design elements from a set of categories or templates. The platform gathers existing websites / applications to identify common design patterns, stored in a design catalogue database, and suggests historical interfaces for design exploration. It enables the generation of templated applications that integrate with legacy systems. The agent orchestration system parses user specifications, selects generative AI systems, and generates UX / UI content based on the specifications. The analytics system collects and analyzes data to provide insights for improving UX / UI design and optimizing website performance. The model management system trains and maintains generative AI models used for content generation.

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 system and method for automatically generating a change history of source code using a generative artificial intelligence (“AI”) system. In examples, a generative AI system receives a request inquiring about one or more changes made to software code of a software service or application. In response to receiving the request, the generative AI system navigates one or more information sources to collect code change context relevant to history of the code change(s). The generative AI system generates an instruction corresponding to the received request, where the instruction and the code change context are provided as input to a language model (LM) (e.g., a generative AI model). Based on the inquiry of the request, the LM processes the input, generates, and provides a corresponding output. The generative AI system then uses the output to generates and provide an explanation about the code change(s) to a requestor of the request.

A method and apparatus are disclosed for generating permission-based large language model responses by using a query received from a user to identify a plurality of documents that are semantically similar to the query, using an access token received from the user to identify user accessible documents from the plurality of documents that the user is permitted to access, processing the user accessible documents to define a context of user accessible documents that is associated with the query, and then submitting the query and the context of user accessible documents to a large language model (AI system) to generate an AI system response to the query.

The invention discloses an active real-time interaction system based on LLM. The active real-time interaction system comprises a self-cognition module, a real-time perception module, an active planning module, a situation decision module, a dynamic execution module, an emotion engine module, an adaptive interaction module and a safety management and control module. The system adopts a multi-dimensional vector to represent role attributes, and constructs a hierarchical memory storage structure to record interaction experience; generating an environment state vector through a multi-modal data processing technology; executing the target task decomposition algorithm to generate a multi-path execution plan; making a real-time decision based on the multi-dimensional decision factor; mapping the abstract decision into an instruction sequence and monitoring an execution process; simulating a system emotional state and influencing decision expression; dynamically adjusting an interaction strategy according to the user characteristics; evaluating decision rationality and starting a corresponding intervention mechanism. All the modules form a closed-loop workflow through a standardized data interface, the problems that a traditional AI system is passive in response, lacks self-cognition, is limited in environmental perception, is single in planning capability, lacks flexibility in decision making and the like are solved, and active service, self-evolution and safety controllability of the system are achieved.

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.

The invention discloses a multi-modal deep neural network model, system and method based on continuous learning. The multi-modal deep neural network model comprises a data acquisition and preprocessing module used for acquiring multi-modal data of a crop growth environment; the feature extraction module is used for extracting key agricultural features; the multi-modal information fusion module is used for effectively fusing the extracted key agricultural features; the knowledge continuous learning module is used for memorizing and storing the crop growth mode to a crop growth mode library and applying a model parameter self-adaptive updating strategy; the intelligent decision-making module is used for performing crop management decision-making based on the fusion features; and the effect evaluation and feedback module is used for evaluating the decision effect. According to the invention, the problem of knowledge forgetting of the existing AI system is solved, and the adaptability and decision accuracy of the intelligent agricultural system are improved.

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 relates to the technical field of medical information, in particular to a method and a system for processing medical record documents by utilizing artificial intelligence, and particularly relates to a method and a system which can receive and process multi-modal input information including images, videos and voices, can work cooperatively through a multi-agent system and can process the medical record documents. The invention discloses a system for automatically generating, controlling quality and safely inputting medical records in combination with a medical knowledge base and an implementation method thereof. The invention relates to an automatic medical record writing system based on multi-modal input and multi-agent driving. The automatic medical record writing system comprises a multi-modal input module, a multi-agent processing platform and a safety intranet input module. According to the method, establishment of the data channel between the medical intranet and the external AI system is proposed for the first time, the problem of medical intranet isolation is solved, industrial pain points are solved in a breakthrough mode, and a new medical AI landing path is developed.

A robotic surgical system. a surgeon console operatively coupled to a patient console and one or more surgical instruments. A surgeon computer is coupled to or integrated with the surgeon console, the surgeon computer further operatively connected to the one or more surgical instruments; A surgical robot is coupled to a robotic surgery control system and a feedback loop. The robotic surgery control system includes or is coupled to an artificial intelligence (AI) system. A feedback loop is further configured to receive performance-related data from the one or more sensors, the data analyzed by the robotic surgery control system or the AI system to dynamically adjust the robotic system's operation as needed. A data extraction module retrieves, from the robotic surgery control system or the AI system. one or more programmed steps executed by the surgeon for positioning at least one of the surgical instruments during the surgical procedure.

A provider computing system includes a processing circuit having at least one processor coupled to at least one memory device and at least one artificial intelligence (AI) system. The processing circuit performs operations including receiving a first request for a first transaction having one or more first parameters; analyzing a transaction history comprising one or more previous transactions having at least one of the one or more first parameters; determining a response to the first request; and transmitting the response to the first request. The at least one AI system is configured to perform operations including: simulating one or more transactions; identifying one or more second parameters of the one or more simulated transactions; comparing the one or more second parameters to the one or more first parameters; and determining a legitimacy value associated with the first transaction based on the comparison.

A system and method for improving computer functionality by retrieving answers / responses to questions / input from a cache such as those used with chatbots and generative AI systems. Disclosed is a multi-layered caching strategy that focuses on the relevance of a cache hit by improving the quality of the answer. The approach demonstrates that response latency is significantly reduced when using caching and how a caching strategy could be applied in various layers of increasing relevance for a simple Question-and-Answer system with the possibility of extending to more complex generative AI interactions.

The invention relates to the field of man-machine interaction, and particularly discloses a multi-mode man-machine interaction application system for sky-eye insight based on a pathologist visual angle, which is characterized in that firstly, a candidate focus thermodynamic diagram is generated through rapid scanning of a full-slice image, low-power lens global browsing of a doctor is simulated, and the doctor is guided to lock a key area interactively through professional judgment; therefore, the processing efficiency of the oversized image is greatly improved. And then, the system only performs high-resolution deep analysis on the focus confirmed by the doctor, and performs multi-modal fusion on the extracted microscopic visual features and the patient text information to generate a preliminary report with an interpretable basis, so that the problem that the multi-modal function deviates from a clinical core task is solved. Finally, the doctor can check and finalize the report through visual interaction, and the dominant position and the final decision making right of the doctor in the diagnosis process are ensured, so that the bottlenecks of black box operation and low clinical acceptability of a traditional AI system are overcome.

The present invention comprises a novel social media video debating web and mobile application. The platform will provide a space for users to debate uninterrupted by both the audience and the opponent whereby each participant is given a set time to express their thoughts on a subject matter. The online debate platform provides a controlled setting for the participants to have their debates viewed, voted on and subsequently ranked by the other users of the platform. The online debate platform is also monitored by a unique AI system that updates debate “winners,” flags offensive content, and moderates each debate on the platform in real time. The disclosed platform and following figures will provide a space for individuals to debate subjects in a uniformed structure and have real-time results from active user viewership. The online debate platform aims to provide an established place for constructive debating.

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.

The invention discloses an AI system for generating customized contents based on user preferences, which belongs to the technical field of artificial intelligence, and comprises a user preference modeling module for constructing and continuously updating a user feature vector library and realizing personalized demand modeling and classification; the interactive input and early warning module is used for receiving user input, implementing risk control and providing a visual interactive correction tool; an intelligent parameter conversion module; a video preprocessing and enhancing module; a cross-modal semantic generation module; the dynamic training and optimizing module is used for controlling a model training process and balancing repair quality and style migration; the multi-version generation and evaluation module is used for outputting a differentiated repair result and quantitatively evaluating the performance; the feedback learning and iteration module is used for collecting user preference data and driving the system to continuously optimize; and a distributed task scheduling module. According to the method, the real-time performance and accuracy of the user portrait are ensured through the layered modeling mode, accurate matching of personalized content generation is supported, and user experience and content recommendation are effectively improved.

A generative artificial intelligence (AI) system includes a generative AI model configured to generate outputs based on a training data set. The AI system additionally includes a secure data vault system. The secure data vault system additionally includes a sandbox system storing the generative AI model and operatively coupled to the generative AI model to send inputs to generate the outputs from the generative AI model, wherein the sandbox system comprises an execution environment configured to restrict execution of the generative AI model to a predefined memory address range. The secure data vault system further includes a secure network service communicatively coupled to the sandbox system and configured to authenticate a connection to an external system and to download from the external system an update package for the generative AI model when the connection is authenticated.

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.

Systems and methods for machine-learned generation of data insight summaries are provided. A computing system can obtain numerical time series data comprising a plurality of numerical values associated with a plurality of times. The computing system can identify, based on the numerical time series data, one or more first mathematical relationships in the numerical time series data. The computing system can generate, based at least in part on the mathematical relationships, a first input context comprising first natural language data indicative of the mathematical relationships. The computing system can provide the first input context to a first machine-learned sequence processing model. The first machine-learned sequence processing model can generate, based at least in part on the first input context, one or more outputs describing the one or more first mathematical relationships. The computing system can output the one or more outputs.

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.

Systems and methods for directing behavior of a generative artificial intelligence (AI) system are provided. In particular, a computing device may obtain an input prompt associated with a requested task for one or more generative artificial intelligence (AI) systems, obtain one or more attributes based on the input prompt, modify the input prompt based on the one or more embedded attributes, and provide the modified input prompt to the one or more generative AI systems.

An advanced AI system, known as Bayesian Graph-Based Retrieval-Augmented Generation with Synthetic Feedback Loop (BG-RAG-SFL), combines Bayesian evaluation, graph-based retrieval, and synthetic data feedback to create a continuously improving AI platform. The present invention integrates multiple LLMs, optimizing their performance while managing complexities across different models. Key features include a knowledge graph-based RAG system, a Bayesian evaluation network, a secondary ground-truth graph for verification, synthetic data generation for ongoing improvement, and a multi-agent verification system. The system also functions as an AI operating system capable of acting as a virtual user with screen I / O control and managing multiple computers as an intelligent process automation system.

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.