1048 results about "Artificial Intelligence System" patented technology

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 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.

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 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.

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.

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.

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.

The invention provides a privacy protection federated distillation and backdoor defense method for large model fine tuning, and belongs to the technical field of artificial intelligence security and federated learning, and the method comprises the steps: 1, carrying out the distillation and core representation extraction of a data set based on contribution degree weighted federated pre-training and local neural feature function matching; step 2, self-adaptive noise back door defense processing based on multi-feature fusion; according to the method, a dataset distillation mechanism based on neural feature function matching and a self-adaptive noise defense strategy are adopted, so that effective balance of the large model among data simplification, privacy protection and backdoor defense robustness is realized; the method is of great significance in improving the safety and reliability of an artificial intelligence system in a distributed environment.

Systems and methods described herein enable adaptive, threshold-based modification of node maps representing ontologies, knowledge graphs, or code development pipelines using generative artificial intelligence. The disclosed platform can retrieve a node map and generate one or more candidate perturbations that modify nodes or relationships within the node map. The disclosed platform can evaluate the effect of the perturbations by comparing respective outputs against ground-truth data. Perturbations can be automatically determined based on changes in external datasets, compliance policies, or operational requirements. The perturbations can be implemented when a computed perturbation quality value satisfies a threshold quality criterion. As such, the system enables efficient, policy-compliant evolution of relational system architectures in dynamic environments.

A training data set which includes event sequences representing actions of respective users of an application, properties of the users, and dynamic attributes associated with events such as the elapsed time between successive events, is prepared. A machine learning model which provides probabilistic predictions of next events of input event sequences is trained using the training data set. A trained version of the model is stored.

The embodiments disclose a cryptographically governed system for contextual memory management in artificial intelligence including a Contextual Orchestration and Scoped Memory Protocol (COSMP) and a Decentralized Memory Wallet (DMW), which enforce secure, auditable, and policy-driven access to AI memory. Traditional token-based memory tracking is replaced by memory capsules secure data units with embedded access policies and tamper-evident logs 5400 organized via a distributed ledger. Access control is managed through decentralized identifiers (DIDs) and private cryptographic keys, ensuring that all memory interactions are signed, verifiable transactions. This architecture establishes a universal trust layer for AI, enabling post-hoc compliance checks and privacy-preserving audits. Applicable across domains such as national security, healthcare, and autonomous systems, the invention enforces rigorous behavioral constraints and access governance within AI memory and decision-making processes.

An artificial intelligence system based on spatial-temporal information pairs is provided by the present disclosure. By integrally deploying paired vision, auditory and olfactory acquisition devices, a device that can collect the data within a 720-degree area is constructed, and the multi-dimensional continuous spatial-temporal information pairs such as positions, morphologies, motion states, sounds and odors from the ambient environment of the acquisition device or a same spatial object in the environment are recorded in real time. These information pairs can not only contain spatial relationships and a clock attribute, but also contain rich label attributes, for example, identifiers of acquisition devices, and a name, a category and a behavior pattern of the spatial object.

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.

A generative artificial intelligence system includes a retrieval augmented generation (RAG) assistant that utilizes function calling to facilitate multi-source data retrieval to enhance user queries transmitted to a large language model (LLM). The RAG assistant Transmits, to the LLM, a function selection instruction prompt that includes conversation history data, a function list including function definitions that each correspond to a data source, and instructions directing the LLM to return a function call to at least one function defined on the function list identified as relevant to the conversation history data based on a corresponding function descriptor. In response to receiving a function selection response from the LLM that includes the function call, the RAG assistant selects and executes a conditional operation based on a name of the at least one function.

Artificially intelligent systems and methods for financial coaching provide personalized, fiduciary-compliant financial guidance through advanced machine learning architectures with measurable performance criteria. The systems implement privacy-preserving processing pipelines that detect personally identifiable information using multi-layered pattern recognition including regular expressions for formatted data sequences, named entity recognition with confidence thresholds above 0.85, and contextual analysis algorithms. A multi-step artificial intelligence processing workflow includes automated language detection, emotional tone classification with confidence scoring, financial profile transformation using predefined templates, context-aware question rephrasing, and semantic similarity matching employing vector embeddings with financial domain vocabulary weighting applying multiplier values between 1.3-2.0. Specialized training methodologies expand datasets through mathematical transformation functions utilizing statistical standard deviations with incremental variations between 0.5-2.0. Mood-based escalation logic automatically transfers users to human advisors when emotional indicators exceed confidence thresholds above 0.8. The systems maintain response times below 5 seconds while providing regulatory compliance through curated content sources and predefined fiduciary instruction parameters.

Provided herein are systems and methods for improving healthspan. For example, provided herein are systems and methods that generate actionable interventions, based on expert curated artificial intelligence (AI) systems, to increase healthspan.

A computer implemented method is described, which may be applied to controlling bias in generative artificial intelligence models. The method includes determining that a text prompt is silent in at least one respect that may be connected to bias or a risk of bias. One or more transformed prompts are generated, to include text providing details of the silent aspect of the text prompt. The one or more transformed text prompts may then be passed to a generative artificial intelligence system.

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.

Certain aspects of the disclosure provide artificial intelligence (AI) methods and systems for generating personalized social media content with trend integration. A method generally includes retrieving data from data sources that includes customer interactions with a business, and inventory data of the business, determining trending-product pairs that increase engagement of the customers with products recorded in the inventory data of the business based on the retrieved data. A generative artificial intelligence (AI) model is used to generate one or more of a caption, a hashtag, and a promotional image that are personalized to each of the customers in response to receiving prompts that contain information about the customers, information about trending-product pairs, and social media platforms of the customers. The method sends one or more of the captions, the hashtags, and the promotional images that are personalized to the customers to social media platforms of the customers.

The disclosed technology includes a computer-implemented technique for generating images of physically producible products in response to user input describing a conceptual product. The system receives user input—such as natural language text, speech, or images—via a user interface, configures a prompt for a generative artificial intelligence (AI) system, and generates an image representing a version of the conceptual product with distinct physical attributes. Each version is associated with a unique identifier, enabling selection, modification, and purchase of the conceptual product. The system supports multiple product categories, including jewelry, home décor, and fashion, and extracts physical attributes to determine manufacturability and pricing. User feedback is incorporated to improve AI performance. The invention enables presentation of selectable product versions and initiates manufacturing processes based on user selections, supporting unstructured user input and multiple data modalities.

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.

A modular open system architecture for common intelligence picture generation is disclosed. The system receives intelligence requirements through a multimodal artificial intelligence system and calculates collection feasibility across multiple intelligence sources based on physical and temporal conditions. The system develops integrated collection plans through the containerized analytics workbench using containerized analytics modules and processes intelligence through GPU-accelerated deep learning models for automated target recognition. Satellite collection is orchestrated through satellite data acquisition optimization platform by evaluating weather conditions, orbital parameters, and sensor capabilities, while space domain awareness is maintained through space domain awareness system for real-time collection asset management. Multi-source intelligence data is fused through multi-source intelligence fusion system to populate a common intelligence picture. The system implements automated workflows for intelligence analysis and dissemination while maintaining security controls, with the containerized analytics workbench providing pattern of life analysis and dynamic exploitation through containerized microservices.

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.

An information technology system for a distributed manufacturing network includes an additive manufacturing platform configured to manage workflows for a set of distributed manufacturing network entities associated with the distributed manufacturing network. The information technology system includes a set of digital twins generated by the additive manufacturing platform. The information technology system includes an artificial intelligence system configured to be executed by a data processing system in communication with the additive manufacturing platform. The artificial intelligence system is trained to generate process parameters for the workflows managed by the additive manufacturing platform using data collected from the set of distributed manufacturing network entities. The information technology system includes a control system configured to adjust the process parameters during an additive manufacturing process performed by at least one of the set of distributed manufacturing network entities.