1412 results about "Artificial Intelligence System" 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.

In a described embodiment, a system for information processing is provided including a data acquisition module configured to receive feedback corresponding to one or more outputs generated by a language model. The system further includes a cognitive reasoning module configured to evaluate the reasoning process of the language model, emulate cognitive functions including metacognitive processes, and generate an assessment based on an analysis of the received feedback, wherein the assessment includes classifying the one or more outputs into components, assigning quality scores for each component, and identifying an improvement corresponding to the one or more outputs. Additionally, the system includes a process adjustment module coupled to the cognitive reasoning module for adjusting the reasoning process of the language model based on the assessment is provided. A refinement module coupled to the process adjustment module is provided for iteratively refining the reasoning process based on subsequent updates to the generated assessment until a performance threshold is met.

A method for developing a generative AI system may include constructing a plurality of generative AI systems, wherein constructing the generative AI systems includes executing at least one modeling blueprint; providing a plurality of queries to each of the generative AI systems, the queries being part of an evaluation dataset; during processing of the queries by each generative AI system, monitoring values of one or more quantitative metrics; providing, for display by a user device, data indicating the values of the quantitative metrics for each generative AI system; and providing, for display by the user device, a recommendation regarding use or non-use of at least one generative AI system included in the plurality of generative AI systems.

Systems and methods for generating contextualized data visualizations. A first graphical user interface (GUI) object based on first data is provided, along with a second GUI object based on the second data. A user may select a scenario to be analyzed and, in response to the single selection of the scenario, the system models a first impact of the scenario on the first data to generate predicted first data and separately models a second impact of the scenario on the second data to generate predicted second data. The predicted data may be generated by an artificial intelligence system including a prediction processor. The first and second GUI objects are updated using the predicted first and second data, respectively.

An artificial intelligence driven system of systems may include a layered architecture for providing transaction support to various types of enterprises. A governance layer implements automated governance and policy enforcement through specialized governance modules utilizing generative AI technology. An enterprise layer supports enterprise functions by integrating management and control platforms with digital infrastructure. An offering layer creates and manages system offerings via content generation, personalization, and smart product modules. A transactions layer enables automated transaction orchestration through API integration, execution, and fulfillment modules. An operations layer manages AI systems through generation, training, verification and orchestration modules. A network layer provides adaptive networking capabilities through routing, protocol selection and communication modules. A data layer processes fused data from multiple sources using machine learning and AI systems. A resource layer manages computing, storage, and other resources through specialized resource modules.

A robotic surgical system includes a surgeon consol coupled to a patient consol, and the patient consol coupled to surgical instruments. A surgeon computer is coupled to or at the surgeon consol that is coupled to to one or more surgical instruments. A robotic surgery control system includes an artificial intelligence (AI) system with one or more deep learning algorithms. A feedback loop monitors and collects data from the one or more sensors. One or more cameras provide feedback to the robotic surgical system, and are configured to provide images of an anatomical object in at least a two dimensional (2D) arrangements of pixels / Deep learning algorithms of the AI system distinguish different anatomical objects from the images.

The invention discloses an agent architecture based on a multi-modal large model, which comprises an external support module and an internal core module, and is characterized in that the external support module comprises an input module; an output module; a large model capability module; the internal core module comprises a sensing module; a memory module; a portrait configuration module; a planning module; a tool use module; and an action module. According to the intelligent agent architecture based on the multi-modal large model, the efficient, flexible and extensible intelligent agent architecture is innovatively realized, and the problem that most of existing intelligent agents are single-function artificial intelligence systems and are difficult to meet the requirement for multi-capability combination in a complex application scene is effectively solved.

A system may include an Al convergence system of systems for a value chain network environment.

Disclosed are apparatuses, systems, and techniques that evaluate suitability of prompts for language model (LM) processing for improved quality and security of LM outputs. The techniques include determining prompt verification score(s) that include a first subset of tokens and a second subset of tokens, and obtaining, using an LM, the individual prompt verification score characterizing a likelihood that the second subset of tokens occurs, in the prompt, together with the first subset of tokens. The techniques further include determining, using the prompt verification score(s), whether the prompt is to be provided to the LM.

Systems and methods for translating natural language to SQL on a custom enterprise data warehouse powered by Generative AI. With an embodiment of the present invention, a natural language question may be converted to a meaningful and accurate database query, e.g., SQL query, relevant to tables existing in an enterprise data warehouse. An embodiment of the present invention is directed to a comprehensive approach of transforming a natural language query to a focused SQL query using domain specific data models across firmwide metadata systems and data systems. In response to a user query, an embodiment of the present invention performs metadata analysis, targeted data retrieval and then SQL generation. An embodiment of the present invention may apply data warehousing standards and guidelines followed in the enterprise and provide a plug-and-play type architecture and solution that is scalable to large warehousing and other systems.

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.

Provided are a conversational artificial intelligence (AI) system and method based on real-time multimodal emotion recognition. The system includes a model server configured to provide a machine learning-based conversational model, a terminal configured to perform a conversation with the machine learning-based conversational model through the model server, display a virtual human responding to a user during a conversation with the user, and capture a facial image of the user during the conversation, and a multimodal empathetic conversation-generation system configured to access the model server and receive a response to a question of the user from the terminal, and assess an emotion of the user from the facial image of the user and control, based on the assessed emotion, an expression of the virtual human displayed on the terminal.

In some embodiments, a configured artificial intelligence system includes a plurality of intelligence models; a scoring system configured to generate know-your-model scores that quantify suitability for specific tasks of each model; a model execution system configured to provide standardized execution environment for the plurality of intelligence models; a training and reinforcement system configured to monitor outcomes relating to decisions or predictions made by the plurality of intelligence models and use outcome data as feedback to reinforce model performance; and a governance and analysis system configured to ensure model operations comply with governance standards. The intelligence controller may be configured to receive task requests, analyze task complexity, decompose tasks into manageable subtasks, and dynamically select appropriate models from the plurality of intelligence models to execute each subtask based on model suitability and performance characteristics.

The present disclosure relates to configured artificial intelligence methods and systems and related transportation systems and methods, including software-defined vehicles, for transportation systems using sensor and other data, and the integration of a transportation system with an AI convergence system of systems, providing a multi-layered system for intelligent automation and data-driven decision making across operational aspects of a transportation system.

The invention belongs to the technical field of natural language processing, and particularly relates to a conflict-aware legal case judgment prediction method and a conflict-aware legal case judgment prediction system. The method comprises the following steps: constructing a dynamically updated structured law knowledge base, and fusing laws and regulations, judicial interpretation, case data and the like; a multi-stage intelligent processing flow is designed; law elements in key cases are extracted through case element identification and preprocessing; constructing an output candidate set through generation of crime names / causes; processing time, level and application range conflicts through conflict perception analysis, and dynamically selecting eligible law specifications according to law application principles; and finally, through judgment prediction, interpretable judgment suggestions are generated. According to the method, the defect that the existing legal artificial intelligence system neglects longitudinal and transverse conflicts in legal specification retrieval can be effectively overcome; and the legal applicable conflict and reasoning reliability is greatly improved.

An information technology system for a distributed manufacturing network includes an additive manufacturing management platform configured to manage process and production workflows for a set of distributed manufacturing network entities through design, modeling, printing, and supply chain stages. The information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities of the distributed manufacturing network to optimize digital production processes and workflows. The information technology system includes a distributed ledger system integrated with a digital thread configured to provide unified views of workflow and transaction information to entities in the distributed manufacturing network.

A computer system may have one or computers and one or more data storage devices storing instructions, which when executed by the one or more computers implements a characterization manager, comprising: a test data database storing a plurality of test data sets; the characterization manager configured for selecting one or more test data sets from the test data database and apply the selected test data sets to a Generative AI system to derive from the Generative AI system an output; an output analyzer for processing the output to generate characterization data describing one or more facets of the output.

Methods, systems, and non-transitory computer readable storage media are disclosed for controlling access to artificial intelligence systems based on determined intent of queries. The disclosed system utilizes one or more digital content analysis models to determine an intent of one or more queries to an artificial intelligence system. The disclosed system utilizes the one or more digital content analysis models to determine an intended use of the artificial intelligence system. Additionally, the disclosed system determines whether the intent of the one or more queries aligns with the intended use of the artificial intelligence system by generating a similarity score and comparing the similarity score to a similarity threshold. Based on whether the intent aligns with the intended use, the disclosed system executes computing instructions to enable or block the one or more queries from being processed by the artificial intelligence system.

Systems, methods, and software are disclosed herein for a system of agents for managing tasks of software applications which is guided by generative AI. In an implementation, a computing apparatus determines that a task has been assigned to an application assistant of an application. The application assistant includes multiple agents which interact with a generative AI model. The computing apparatus orchestrates the multiple agents in their interactions with the generative AI model in furtherance of completing the task and updates the contextual information of the task based on the interactions.

An agentic artificial intelligence system processes insurance claims, medical claims, financial transactions, and sales leads by receiving and preprocessing claimant, patient, transaction, and prospect data to standardize formats, remove sensitive identifiers, and enrich records. It uses machine learning, deep learning, natural language processing, and computer vision to analyze both structured and unstructured data, identify errors, inconsistencies, or fraudulent patterns, verify eligibility and compliance, and assign relevant codes based on historical and contextual information. The system calculates expected payouts or reimbursements, assesses transaction feasibility, and generates risk scores while adapting its predictions to market conditions, contractual factors, or clinical guidelines. A multi-agent framework coordinates specialized agents for eligibility verification, coding, pricing, fraud detection, and sales outreach, supporting multi-channel communication, lead prioritization, and natural language generation of outreach messages and decision-making explanations. Continuous learning is achieved via retraining, feedback loops, federated learning, and blockchain-based recordkeeping, ensuring secure, transparent, and compliant operations across multiple domains.

The invention relates to the technical field of network operation and maintenance, in particular to an automatic operation and maintenance method and system based on artificial intelligence, and the method comprises the steps: collecting detailed log information containing a security event in real time through a security information and event management system configured at a target endpoint; performing cleaning, labeling and structured processing on the log data by utilizing a proxy artificial intelligence system to generate standardized metadata, and performing MITRE ATTamp with the standardized metadata; mapping the CK knowledge base, identifying technical features and behavior patterns of attacks, comparing enriched log data with an external threat intelligence source, analyzing TTP of a known attack group, generating a threat intelligence association report, generating a targeted response plan by using a large language model, generating an executable command sequence according to the response plan, and generating a threat intelligence association report; the proxy executor is connected with the server through a WebSocket protocol, executes a command in a POSIX shell environment, captures and returns an execution result, verifies the execution result, carries out necessary command optimization, records an optimized response to a vector database, and automatically matches a historical event and triggers a predefined response process through a vector retrieval mechanism. And continuous threat monitoring and adaptive response are realized. According to the method, the problem that a closed loop aiming at a terminal executor, data enrichment and historical event recall cannot be formed by security operation and maintenance in the prior art can be solved.

Provided herein are systems and methods that improve the performance and accuracy of artificial intelligence (AI) systems and enhance real-world uses thereof. For example, provided herein are incentivization systems and methods for expert curation systems that prevent or reduce the frequency of AI hallucinations; allow for rapid identification of errors, misinformation, and out of date information; enable faster and easier corrections; and provide accurate and actionable results.

An embodiment may involve obtaining a prompt for a large-language model (LLM), generating, using the LLM, an output of an artificial intelligence system, obtaining a validation model configured to detect a property in the output, the property indicating a fault in the output, generating, using the validation model on the output, a metric indicating likelihood of the property in the output, determining that the metric satisfies a fault threshold, and in response to determining that the metric satisfies the fault threshold, labeling the output as untrustworthy.

Technology embodied in a method that includes receiving, as an input to a machine-learning model, data indicative of user-interaction of a particular user with a generative artificial intelligence (AI) system. The machine learning model is trained to identify one or more topics associated with inputs provided to the machine-learning model. The method also includes identifying a first context associated with the data indicative of the user-interaction with the generative AI system, and parsing a file system to determine that one or more folders within the file system correspond to the first context. The file system includes multiple folders each corresponding to a separate topic as identified from historical interactions of the particular user with the generative AI system. The data indicative of the interaction is augmented and provided to the generative AI system for generation of a response to the interaction.

The invention discloses an intelligent management and control system based on a large model and integrated multi-dimensional spatio-temporal data fusion, and belongs to the field of artificial intelligence and engineering application. The system comprises a space-time large model base, an engineering object three-dimensional or four-dimensional integrated model, an intelligent agent cooperation module and an autonomous analysis decision and control module. The method comprises the following steps: embedding engineering field knowledge type public domain data based on a large model, forming a space-time large model base, fusing three-dimensional or four-dimensional geometry and attribute data of an engineering object and dynamic business data such as monitoring and equipment automation, constructing a private domain data source which is integrally expressed, stored and managed, and autonomously analyzing and deciding by utilizing the space-time large model reasoning capability. And the scheduling agent collaboratively completes task planning and arrangement, safety production analysis and decision support and control, and automatically generates various engineering image-text reports. The intelligent management and control problem in the engineering fields of mines, factories, traffic, water conservancy, buildings, machinery and the like can be solved, and the practicability of an artificial intelligence system in the engineering field is remarkably improved.

In various examples, systems and methods are disclosed relating to engaging users through a personalized interface. One system includes at least one processor configured to determine first conversation history with a user. The at least one processor further configured to determine a response by applying the first conversation history with the user to a machine learning model, wherein the machine learning model is updated using user input indicative of an interest level of the user for each of a plurality of candidate responses to a question, a content of the question is determined by the at least one processor, and the plurality of candidate responses are determined using the machine learning model, and the machine learning model is updated using the user input as a reward signal.

Provided herein are systems and methods that improve the performance and accuracy of artificial intelligence (AI) systems and enhance real-world uses thereof. For example, provided herein are expert curation systems and methods that prevent or reduce the frequency of AI hallucinations; allow for rapid identification of errors, misinformation, and out of date information; enable faster and easier corrections; and provide accurate and actionable results.

Techniques disclosed integrate generative AI assistant plugins with a large language model (LLM) to enhance conversational interactions. The techniques include receiving a user's input and retrieving relevant text passages based on a query derived from this input. A complex LLM prompt is generated, including these passages, descriptions of candidate plugins, and the user's input. This prompt is sent to an LLM service, which selects the most suitable plugin for the user's needs. Following this, a query is sent to the chosen plugin, and its response is used to craft the agent's reply to the user. The techniques emphasize dynamic selection and integration of specialized plugins based on real-time user input, leveraging LLM capabilities to interpret and recommend the best plugin response. This approach ensures tailored, informed interactions by providing responses that are both relevant and enriched with specialized plugin knowledge or functionality.

Agentic AI, Contextual AI, and Generative AI systems and methods are provided for quantitative or qualitative patient analytics, blood work analytics, medical image analytics (x-ray, ultrasound, CT Scan, MRI and the like), brain scan analytics, present or historical genetic analytics and the like, to be used to guide, assist or even replace some of these physician functions with improved patient outcomes or with an improved understanding of the source of ailments, the analysis of treatment efficacy or treatment side-effects for individuals or populations of individuals. Genetic information is used to create individualized or population-based analysis, diagnostics, or treatment plans with even better outcomes.