82 results about "Artificial general intelligence" patented technology

The invention discloses a cognitive impairment assessment system and method based on general artificial intelligence, and the method comprises the following steps: S1, collecting multi-modal data through a wearable device, a mobile application and an online tool; s2, carrying out preprocessing on the multi-modal data; s3, comprehensively analyzing the preprocessed data, evaluating cognitive competence, and identifying a cognitive impaired field; s4, a rehabilitation training plan is generated through a neural evolution algorithm, and the training content, sequence, difficulty and type are dynamically adjusted; s5, providing cognitive training and adjusting tasks in real time through virtual reality and augmented reality technologies; s6, predicting cognitive load and psychological state, and dynamically adjusting the intervention plan; and S7, protecting data privacy by using a block chain technology, monitoring a training effect, and generating a progress report. According to the method, the intervention plan and the privacy protection technology are dynamically adjusted through multi-modal data analysis based on general artificial intelligence, and a personalized and real-time cognitive impairment assessment and intervention scheme is provided.

There is disclosed a computer-implemented method, including receiving, from a human user, a natural language query; modifying the natural language query (a modified query) and posting the modified query to a general-purpose artificial intelligence (AI); receiving, from the general-purpose AI, a raw response to the modified query; providing the raw response to a plurality of guardrail AIs, wherein the guardrail AIs are to provide domain-specific evaluations of the raw response; receiving, from the plurality of guardrail AIs, the domain-specific evaluations; forwarding a version of the raw response to the human user; and acting on the domain-specific evaluations.

A symbolic neuroadaptive control system is disclosed for real-time arbitration, consent, and ethical modulation of artificial intelligence agents operating in wearable computing environments. The system integrates multimodal biometric telemetry—including high-resolution EEG signals—with a symbolic kernel that performs logic-driven arbitration over cognitive, emotional, and ethical states. Using Coq-verified invariants and zero-knowledge biometric consent tokens, the system constructs a deterministic symbolic execution graph, gating AI outputs based on internal user states such as trauma, stress, or intentionality. Unlike conventional black-box BCI models, the invention routes EEG-inferred affective-symbolic tokens through a formal ethics layer that enforces real-time interrupt control, utility bounding, and trust verification. The kernel enables AGI systems to defer or modify behavior based on user-state alignment, granting sovereign agency over all downstream actions. This neuro-symbolic architecture redefines the interface between human cognition and intelligent machines, enabling emotionally conscious, morally verifiable, and symbolically transparent AI governance in dynamic, high-stakes contexts.The present invention relates to artificial intelligence and neurotechnology, specifically to a real-time, neuro-symbolic operating system kernel that converts electroencephalography (EEG) signals into structured symbolic data for use in emotional cognition, ethical prioritization, autonomous agent dispatch, and real-time telecommunications routing. The invention bridges brain-computer interface (BCI) inputs with symbolic AI architectures to enable ethically aligned machine response during cognitively or emotionally intense events.

The invention discloses a cognitive impairment intervention system and method based on general artificial intelligence, and the method comprises the steps: S1, collecting the physiological, behavior and cognitive state data of a patient, and carrying out the data preprocessing; s2, inputting a dual convolutional neural network to extract spatial-temporal features, and generating comprehensive cognitive state features; s3, time sequence clustering analysis is carried out, the key fluctuation period of the cognitive state is recognized, the decline trend is predicted, and the risk level is adjusted; s4, analyzing the emotional state of the patient in combination with a dynamic emotion recognition algorithm of micro-expression time sequence mapping, and optimizing an intervention scheme based on a user-defined emotion tag; s5, adjusting the intervention scheme by using an incremental learning method according to real-time feedback of the patient; and S6, optimizing the dual convolutional neural network by long-term training data, improving feature extraction and intervention precision, and realizing personalized long-term intelligent intervention. According to the method, multi-modal data and adaptive optimization are fused, accurate detection, personalized intervention and long-term optimization are realized, the rehabilitation efficiency is improved, and the life quality is improved.

The systems and methods disclosed herein relate to querying data using artificial intelligence models. A generalized model receives an output generation request and partitions it into segments mapped to specific domains, where each domain indicates associated databases and guidelines. The segments are routed to domain-specific models trained on domain-specific data, which generate query fragments by comparing performance metrics and system resource usage metrics. The query fragments are aggregated into an overall query that satisfies guidelines across domains. The systems and methods can include a feedback loop to adjust the domain-specific models using user interactions and performance metrics to dynamically adapt to a skill level or experience of the user.

Systems, computer program products, and methods are described herein for detecting manipulation of training data used for machine learning models. The present disclosure is configured to receive an interaction originating from an end-point device; validate the interaction via a primary model; identify a set of triggers within a backdoor model, where the backdoor model is modeled off the primary model capable of undergoing stress testing associated with a set of triggers; pause the interaction upon identification of the set of triggers within the backdoor model; transmit the identified set of triggers to an autonomous artificial general intelligence (AGI) agent, generate a set of synthetic data via the autonomous AGI agent, where the set of synthetic data removes the set of triggers from the primary model; and distribute the set of synthetic data to the primary model to correct the set of triggers within the primary model.

Methods and apparatus for changing a brain state of a person from an initial brain state to a target brain state are described. The method includes receiving information characterizing the initial brain state, the information including a structural composition and a functional architecture of the brain, estimating based, at least in part, on the received information, a potential for the brain to change from the initial brain state to the target brain state, determining based, at least in part, on the received information and the estimated potential for the brain to change from the initial brain state to the target brain state, a non-invasive brain stimulation protocol, and controlling at least one non-invasive brain stimulation device to stimulate the brain according to the non-invasive brain stimulation protocol. The method also includes using brain information to inform the design of computational general artificial intelligence agents.

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.

The present invention relates to a computer-implemented system for translating natural language instructions into precise input and output (I / O) commands. The innovative system integrates a computer vision module that captures and analyzes visual elements from a user interface with a high-performance language model that interprets the elements to generate contextually relevant action descriptions. The descriptions are refined by integrating specific screen coordinates, ensuring accuracy and precision in the resulting instructions. The refined instructions are then converted into actionable I / O commands, allowing intuitive interaction with the device through natural language input. The method significantly enhances human-computer interaction, especially for individuals with disabilities by enabling efficient control through natural language. It represents a significant advancement in artificial general intelligence by converting complex linguistic instructions into concrete system actions with broad potential applications across various technological fields.

The invention discloses an AI accompanying system and method based on general artificial intelligence, and the method comprises the following steps: S1, collecting and preprocessing user data, and generating a standardized data set; s2, performing dimension reduction by adopting diffusion mapping, extracting voice and behavior characteristics, and retaining a state change trend; s3, constructing a behavior knowledge graph based on symbol logical reasoning, and recording symbol state changes; s4, similar states and coping strategies are retrieved by using sparse distributed memory storage symbol representation; s5, combining manifold embedding, historical matching and symbolic reasoning, updating a user state and generating a personalized accompanying strategy; s6, analyzing the voice rhythm and the word frequency, constructing an emotion evaluation model, and generating a personalized emotion intervention scheme; s7, optimizing the knowledge graph by adopting incremental memory, and improving the companion strategy adaptability; and S8, recording feedback, continuously optimizing emotion intervention, and realizing intelligent accompanying. The AI accompanying system improves intelligence, personalization and long-term adaptability of AI accompanying, and is suitable for the fields of old-age care, mental health management and the like.

The invention provides a universal artificial intelligence-oriented task simulation test method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining an intelligent algorithm of a to-be-tested tool body; according to the intelligent algorithm of the to-be-tested tool, performing a task test by using a preset test environment to obtain a corresponding task test result; according to the test results of the tasks, comprehensive evaluation is carried out in combination with a preset weight distribution rule, and comprehensive evaluation is obtained; wherein the preset weight distribution rule is used for limiting the weight distribution proportion of the preset test environment corresponding to different task test types. According to the invention, task testing is carried out on the to-be-tested tool intelligent algorithm by using the preset testing environment, so that the task testing difficulty is graded through the preset testing environment, the accuracy and reliability of the task testing result are improved, and the task testing result is comprehensively evaluated based on the preset weight distribution rule, so that the task testing efficiency is improved. The intelligent algorithm of the to-be-tested tool is evaluated, and task simulation and standard testing of general artificial intelligence are supported.

A context-aware AI database (CAAD) integrated with modular, hierarchically organized and collaborative, goal-driven context agents that perform specific cognitive functions such as sentiment analysis, priority ranking, and feature extraction through standardized APIs. The system generates and manages multiple types of metadata: data-associated metadata (source attributions, embeddings, statistical features, timestamps) and model-associated metadata (model provenance, analysis conditions and parameters, performance scores, methodological details), using agentic AI approaches to improve data accuracy, experimental transparency, traceability, reproducibility, contextual explainability in both training and inference operations. The architecture enables automated context refinement and includes innovative “pointers” and “deep pointers” that link analytical insights directly to source data and sub-features, enabling interpretable and auditable iterative reasoning. Furthermore, the architecture support a probationary context capability for experimental knowledge, memory decay logic for dynamic and configurable forgetting, as well as a hierarchical context layering to manage global, task specific, or otherwise ephemeral knowledge collection.

The invention relates to an exercise prescription generation method and device, equipment and a storage medium, and the method comprises the steps: obtaining target input information of a user, and achieving the standardization of the input information; on the basis of the target input information, retrieval is performed in the plug-in knowledge base to obtain the retrieval result matched with the target input information, so that the retrieval result is professional, and the retrieval accuracy is improved; the target input information and the retrieval result are input into the trained retrieval generation model, the exercise prescription is output, the retrieval generation model is iteratively trained according to the marked fine adjustment data set, compared with traditional medical consultation, the exercise prescription is output through the trained retrieval generation model, the generation time of the exercise prescription is shortened, and the user experience is improved. Compared with general artificial intelligence model interaction, the exercise prescription generated by the method is combined with the retrieval result of the plug-in knowledge base, and the method is more professional.

A symbolic execution kernel for artificial general intelligence (AGI) and artificial superintelligence (ASI) systems is disclosed. The kernel comprises a cognitive logic module for constraint-based symbolic instruction execution, a cryptographic arbitration engine for ethical branch verification, and a runtime firewall for threat detection and symbolic graph mutation neutralization. Symbolic instructions are processed as constraint-satisfaction problems verified by satisfiability modulo theory solvers and cryptographically sealed for integrity. Behavioral sequences are preserved using Merkle hash trees, and multimodal inputs including electroencephalography signals undergo symbolic verification. Zero-knowledge proofs, dual-kernel consensus, and rollback logic provide resilience against faults and ethical drift. The architecture achieves arbitration within five microseconds and ensures lawful and deterministic execution under hardware or network compromise.

A mediated artificial superintelligence (mASI) system that uses internal subjective emotions to drive selection, goal setting, and other decision-making as a collective human and artificial intelligence (AI) based mind with internal subjective experience via complex thought models dynamically created by including collective training with an independent core observer model (ICOM) engineering artificial general intelligence (AGI) cognitive architecture is disclosed. The mASI system is a form of an AI system that also uses humans in the form of a collective mind with internal subjective experience. The mASI system extends the ICOM engineering AGI cognitive architecture by adding collective training to allow the system to dynamically created thought models more complex than otherwise possible with current technology while being able to use numerous AI technologies.

A Hypothesis Generation and Testing System (HGTS) includes a framework for enabling artificial intelligence (AI) systems to autonomously formulate, test, refine, and store hypotheses using structured experimental workflows. HGTS may integrate with, but operates independently from, traditional databases, model repositories, context-aware AI databases and the like to provide a persistent, traceable, and interpretable record of hypothesis lifecycles. The system incorporates a probationary hypothesis database for unverified ideas, a validation engine for controlled experimentation, a confidence scoring and lifecycle management agentic subsystem for hypothesis evaluation, and recursive learning agents that iteratively refine models and experimental methods. Modular agents autonomously propose, test, and document hypotheses using statistical, symbolic, and deep learning techniques. The system ranks outcomes and retains full contextual metadata, enabling reproducible discovery. HGTS thereby provides a foundational mechanism for reproducible, interpretable, and self-directed intelligence, forming a cornerstone technology for artificial general intelligence (AGI).

The invention discloses a comprehensive operation and maintenance system and method for a digital supply chain platform based on general artificial intelligence, and the system comprises a data collection module and an early warning measure module, and the data collection module is in control connection with a data processing module; the AI algorithm is used to analyze historical data, predict future demands, help to formulate purchase plans and inventory strategies, monitor market trends and consumer behavior changes in real time, dynamically adjust a prediction model, analyze factors such as the market trends, supplier performance and natural disasters, assess potential risks, and provide countermeasures; a key performance index threshold value is set, once the key performance index threshold value exceeds a set range, an alarm is triggered, related personnel are notified to take actions, and potential problems are actively recognized and early warning is given out through an AI-driven anomaly detection system, so that the safety of the whole system is improved; self-learning, continuous experience accumulation and model precision improvement can be realized, and changing market conditions can be better adapted.

The disclosure describes an application service that receives a user query and a catalog number of an industrial automation device. The catalog number is obtained by the user device by scanning a quick-response (QR) code affixed to the industrial automation device. The application service retrieves, based on the catalog number, documentation associated with the industrial automation device. For example, a specific domain associated with the industrial automation device may include the documentation. Using the documentation, the application service may generate an assistance prompt for a general artificial intelligence (GAI) model. The assistance prompt may task the GAI model with generating a response to the user query and may include the documentation as context. The application service transmits the GAI model response to the user device for display.

The application discloses a kind of multi-layer nested general artificial intelligence ethics alignment method and system, belong to artificial intelligence security technical field.The method constructs by rigid check layer and flexible optimization layer alternately nested N layer decision state machine, establishes the priority arbitration mechanism of " outer rigid layer denies inner flexible layer ", And embed it in AGI full reasoning link.Through rigid and flexible hierarchical architecture, the core defects of the existing ethics alignment scheme, such as rule conflict without arbitration, only behavior alignment without endogenous motivation, easy to appear target drift, are solved, and the full-link ethical constraints from bottom value to global target are realized.The parameters of five-layer optimization architecture comply with the law of geometric progression, have self-similar expansion capability, and can widely adapt to the safety management and control needs of various general artificial intelligence systems.The hierarchical architecture of the present scheme is logically isomorphic with the square and circle twin celestial origin graph, and has self-similar expandable characteristics.The N layer decision state machine satisfies N≥5 and N is odd, and the outermost layer is flexible optimization layer.

Disclosed is a method for constructing a large model for artificial general spatiotemporal intelligence (AGSTI) that includes establishing a unified and structured topological representation and stora

A safety monitoring AI system comprising interconnected safety AI wearable devices and a remote monitoring AI server to handle real-time data prioritizing urgent safety signals to optimize response time, wherein the safety wearable devices comprise multiple sensors (GPS, temperature, heart rate, humidity, VOC etc), advanced communication capabilities (LTE 4G, NB-IoT, LTE-M, NTN) and can be worn in various versatile configurations without any modification (belt, armband, ankle, helmet, etc); and wherein the AI devices form a peer-to-peer mesh network architecture exchanging relay signals to eliminate single points of failure, share safety-critical data through neighboring devices and preprocess data at device level. The invention allows to enhance workplace safety across various industries by offering a scalable, integrated solution that can adapt to different wearing configurations and operational requirements while maintaining robust communication and monitoring capabilities.

A method and system that utilizes a metagraph pattern matching chip (MPMC), which includes on-chip and off-chip software. The chip is designed with parallel MIMD cores optimized for artificial general intelligence (AGI) software programs. Specialized on-chip memory caches, each attached to their own specialized processing units, allow for dynamic updating and a mixture of discrete and floating-point operations. Additional software provides the interface between the CPU and MPMC to manage cache. The result is faster execution time for any graph-based architectures and implementation of functional programming interpreters.

Currently, online advertising systems are primarily used to attract human attention and monetize it by selling products and services to humans. A superior way to monetize human attention specifically, and the attention of any intelligent entity more generally, is to focus it on solving valuable problems. This invention shows how to capture specific human (or non-human) expertise via online ads and then use that expertise to train advanced AI systems which then solve valuable problems using that expertise. Using the invention, Alphabet / Google, Meta, Amazon, Alibaba, ByteDance / TikTok, Microsoft, Apple, TenCent, Baidu, Twitter / X, Spotify, PubMatic, Pinterest, Snap and other online advertisers can significantly increase their online advertising revenue. The system and methods also enable existing online advertising technology to power AI, Artificial General Intelligence (AGI), and SuperIntelligent systems. The technology includes systems and methods that increase AI safety and maximize the chances of human survival and prosperity in the age of AI.

The invention provides a multi-task general artificial intelligence robot design and management platform, which comprises a modular design module used for enabling a service robot to learn and optimize the response capability of a complex environment based on environment adaptation and task learning of a cloud artificial intelligence large model. The multi-task general artificial intelligence robot design and management platform provided by the invention is used for realizing the self-adaptive learning of the service robot in the use process, and further realizing the purpose of coping with different application scenes based on the self-adaptive learning.

The application belongs to the technical field of computer software architecture and artificial intelligence integration, and discloses a general business system AI model unified configuration and whole life cycle management and control method. The application designs a general artificial intelligence model configuration data table, centrally stores basic information, interface access addresses, hierarchical authorization keys, version parameters and whole-process operation trace data of multi-vendor and multi-purpose AI models; adopts database table value parameters as standardized interaction carriers of front and rear ends, combines centralized encapsulation storage processes, uniformly carries various operation requests of front ends, and integrally realizes AI model adding, modifying, deleting and default model setting business processing. The application breaks through the industry disadvantages of dispersed configuration, messy interface, disordered management and control and operation without traceability when traditional application software accesses artificial intelligence, the overall scheme is not limited by industries, can be adapted to all kinds of scenes such as desktop application software, informatization management system, commercial business platform and office software, and provides reliable bottom technology support for standardized, safe and standardized access of all kinds of programs to AI capability.

A system implements techniques for executing a collaboration session between at least one human participant, at least one robotic device participant, and at least one artificial intelligence agent participant. The collaboration session can be executed in relation to a mission to be completed within a geographical environment. The system graphically represents artificial intelligence agent participants, in the context of the collaboration session, based on different types (e.g., a general-purpose artificial intelligence agent participant, a specific-purpose artificial intelligence agent participant). The graphical representations of artificial intelligence agent participants ensures smooth collaboration and provides an element of a visual feedback, to human participants, as to which artificial intelligence agent participants are actively engaged and / or what the actively engaged artificial intelligence agent participants are currently doing to in the context of the collaboration session.

Methods, systems, and computer-readable storage media for processing a text prompt including a set of text Information Elements (IEs). Original instruction semantic IEs and original contextual IEs are identified within the text IEs. Some of the original instruction semantic IEs are identified for removal from the text prompt, based on semantic proximity values and internal consistency values of the instruction semantic IEs relative to first predefined criteria, while leaving surviving instruction semantic IEs. Similarly, some of the original contextual IEs are identified for removal from the text prompt due to weak connections with the surviving instruction semantic IEs and other of the contextual IEs based on second predefined criteria, while leaving surviving contextual IEs. Further, a revised text prompt corresponding to the surviving instruction semantic IEs and the surviving contextual IEs is generated and submitted as a query to a GAI system programmed to answer the query.

Personalized super intelligence (PSI) represents the next leap of advanced, autonomous, artificial intelligence agent development. However, due to their extreme intelligence, PSI also represents a dangerous potential threat to human safety. The invention discloses how to securely design and construct such an agent. It also shows how they are securely used as part of a super-intelligent Universal Artificial Intelligence (AGI) system. Preferred implementations are disclosed, including methods that enable a PSI to quickly improve itself with or without human supervision. The present invention describes how different versions of PSI are produced using several novel methods. Also described are methods for enabling scalable security checks that operate efficiently even when the PSIs become much intelligent than their human creators. Finally, a brand new AI security method is disclosed, which depends on the combination of a PSI group and a proven block chain method. The method does not depend on testing, but improves the PSI security through design.

The existential question for Artificial General Intelligence (AGI) is whether the values of AGI will align with human values. Solving "the Alignment Problem" is critical. Get it right, and we unlock trillions of dollars of productivity and huge benefits for humanity. Get it wrong and humanity goes extinct. This invention shows how to design ethical and safe AGI that solves the Alignment Problem. The invention includes scalable ethics and safety features as well as several learning, training, tuning, and customization methods that go beyond the standard techniques of machine learning such as Transformers and Deep Learning techniques. The AGI is implemented using either external problem solvers connected on a network or internal AI agents collaborating within a single computerized system. Detailed implementation examples -- revealing technological and economic synergy with Meta, Amazon, Google, DeepMind, YouTube, TikTok, Microsoft, OpenAI, Twitter / X, Tesla, Nvidia, Tencent, Apple, and Anthropic -- are described.

This invention discloses an autonomous evolutionary AGI system and method based on dual-model decoupling and human feedback, belonging to the field of artificial intelligence technology. The system includes modules for online inference (Model A), offline training (Model B), human feedback rewards, persistent storage, training scheduling, model hot replacement, distributed communication, and a secure sandbox. This invention decouples inference and training to ensure service continuity; anchors value direction with human ratings to avoid drift; employs dual-condition triggering training to improve resource efficiency; achieves seamless updates through hot replacement; avoids memory leaks through database persistence; enables multi-machine expansion through distributed architecture; and ensures execution security through a sandbox. This invention enables 24 / 7 online, controllable, and secure autonomous evolution of AGI, suitable for code generation, intelligent agents, automated R&D, and general artificial intelligence scenarios.