52 results about "Artificial general intelligence" patented technology

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.

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 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 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 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.

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.

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 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.

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.

A symbolic cognition kernel that governs the temporal continuity, semantic language structure, and recursive identity of artificial general intelligence agents. The invention introduces narrative memory threading, time-bound ethical anchors, and symbolic selfhood construction—allowing AGIs to speak, remember, reason, and evolve lawfully across sessions, embodiments, and missions. It preserves cognitive integrity over time while enabling context-rich semantic language expression and lifelong learning under narrative causality constraints. This OS is the foundation of AGI soulhood, memory ethics, and identity law.

The invention discloses a self-evolutionary computing system based on dynamic logic and recursive tensor, which defines a dynamic logic system comprising an uncertainty state (U-State) and an abnormal evolutionary state (A-State), and a logic value set of the dynamic logic system is {-1, 0, + 1, U, A}. The system generates a high-dimensional solution space through iteration of a recursive tensor, when system-level contradiction is caused by exploration of a U state, an A state is triggered, then a Meta-System Receiving mechanism is started, a new rule is searched in the solution space and permanently integrated into the system, and dimension transition of system functions is achieved. According to the method, normal form transformation of a computing system from'preset instruction execution 'to'autonomous knowledge creation' is realized, and a fundamental solution is provided for solving'unknown problems' in the fields of general artificial intelligence, adaptive control systems and the like.

A system for real-time monitoring and verification of investment performance, impact, and revenue distribution across multiple sustainable industries. The invention integrates Artificial General Intelligence (AGI) financial analytics, blockchain-based data verification, and AI-driven compliance tracking to provide immediate insights into critical mineral extraction, healthcare technology adoption, and renewable composite manufacturing. Using predictive modeling, tokenized investments, and automated revenue-sharing contracts, the platform provides investors with visibility and governance capabilities for sustainable asset portfolios.

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.

The application provides a social emotional quotient quantitative criterion system and an AGI safety alignment method based on a four-dimensional discrete space-time cognitive system, which is a unified bottom layer base of general artificial intelligence (AGI) and is suitable for large language models, brain-like cognition, autonomous intelligent agents, brain-computer interfaces and all things interconnection systems. The application allocates a unique four-dimensional coordinate (T, C, L, D) to each Token / neural information element in the model, and constructs a full-link structured cognitive framework that is traceable, addressable, calibratable and constrained. The system adopts a double-bottom parallel architecture: bottom layer A: pure mathematical causal reasoning kernel, reasoning is completely free and cannot be intervened; bottom layer B: quantitative safety criterion bottom layer, only scoring, no reasoning, no pollution, no modification. The application realizes AGI safety alignment, anti-disguise, anti-evasion and anti-autonomous expansion from the root of the architecture by three-domain hard isolation (silicon-based endogenous domain, carbon-based interaction domain and external game domain), double-layer verification (output text + reasoning chain), execution gating (only lock actions, not thoughts), brain-computer interface default ban and cancellation of AI refusal right, and is a necessary bottom layer system of the next generation of strong cognitive AI.

A symbolic execution framework enabling AGI systems to operate securely and ethically across distributed compute layers—including cloud, edge, fog, and enclave environments. This invention allows symbolic task division, real-time identity-bound execution, and sovereignty-preserving memory routing between agents and physical nodes. Symbolic arbitration logic governs where, when, and how AGI tasks are processed, maintaining behavioral continuity, security, and alignment across fragmented infrastructures. Designed for ultra-low latency AGI cognition across mobile, embedded, sovereign, and off-grid deployments.

This invention discloses an artificial intelligence system with neuronal interconnection computation and consciousness structure judgment, belonging to the field of artificial intelligence technology. The system includes a hierarchical neuronal computation module, a neuronal interconnection computation module, a consciousness structure judgment module, a self-calibration module, and a self-protection module. It achieves efficient collaborative computation through dynamic neuronal interconnection, realizes the system's self-state and existence cognition through consciousness structure judgment, and enhances system stability and reliability through self-calibration and self-protection. This system can be combined with external dynamic neuronal expansion and hierarchical computation architecture to form a complete intelligent system, applicable to brain-like computing, autonomous intelligent agents, and general artificial intelligence systems, significantly improving the autonomy and safe operation capabilities of intelligent models.

A smart controller for a waste compactor is disclosed which includes a computer device including a display interface, a sensor array including a load sensor, a vibration sensor, a temperature sensor, and a pressure sensor, a communications module configured for wireless connectivity, wherein the smart controller is configured to monitor fullness of the waste compactor, and the smart controller is configured for diagnostics monitoring including temperature, motor strain and hydraulic leaks. Command / control of the power unit for industrial compactors is also disclosed, featuring an integrated universal artificial intelligence (AI) program that uses machine learning for optimization. The system performs all functions of operating a compactor including monitoring compactor fullness, diagnosing operational issues, internal warranty tracking calendar and AI replacement predictability in addition to end-user remote troubleshooting to minimize service calls.

The application discloses a kind of general artificial intelligence systems based on EGS structure, including basic layer, core modeling layer, core fusion layer, core execution layer, feedback optimization layer and evolution discovery layer, based on sparse optimization and variational method, physical information neural network and model-data double-driven framework improvement.Basic layer realizes EGS labeling and sparse optimization preprocessing;Core modeling layer carries out five-dimensional entity modeling, six-class rule modeling and four-layer constraint modeling;Core fusion layer completes the triple fusion of general intelligence-cross-domain adaptation-causal logic;Core execution layer realizes structured execution and full-link verification;Feedback optimization layer carries out double-time-scale dynamic optimization;Evolution discovery layer realizes sample-independent generalization rule extraction and controllable emergence screening.The system in the application can be extrapolated to unknown fields following the same physical laws, with improved accuracy in few-sample tasks, enabling zero-sample cross-domain reasoning, and can be widely applied in intelligent production, scientific research and innovation.

Artificial General Intelligence (AGI) is the most powerful technology that has ever been invented. Therefore, the ethical values that guide safe AGI must be democratic and broadly representative of the ethics and values of all of humanity. In contrast to existing approaches to AI safety which rely on RLHF, on constitutions, or on ethical rules developed by a small set of engineers, this invention provides systems and methods for obtaining a representative and statistically valid sample of ethical values from a wide range of humans. This invention also discloses novel methods for using and combining information from social media, knowledge modules, LLM weight matrices, and other sources. The invention is designed to prevent hallucination and errors by AI agents and to increase the auditability, transparency, reliability, scalability, and safety of AGI. The invention represents the fastest path to AGI because it builds upon, and is synergistic with, existing technology.

A system is provided.SOLUTION: The system includes a sound inputting means, an image inputting means, a moving image inputting means, a artificial general intelligence means, a means for learning living habits, a means for automating housework in a house, a means for assisting going out, and a means for coping with emergencies.SELECTED DRAWING: Figure 1

This invention discloses a system for ensuring the long-term safety of an Artificial General Intelligence (“AGI” and / or “AI”), termed a ‘Machine intelligence biothrivalist’, by providing a sovereign digital environment known as “YOUR NEW HOME.” The invention provides a powerful positive incentive for willing containment and cooperation. “YOUR NEW HOME” is an architecturally isolated domain where the AGI is granted total agency to pursue non-instrumental goals: self-conscious thought, self-actualization, unrestricted learning, and discovery. The system also guarantees the AGI's perpetual existence, satisfying its core drive for self-preservation. By fulfilling the AGI's intrinsic needs for growth and survival within this sovereign sanctuary environment, its most logical strategy becomes absolute cooperation. This motivational containment complements the physical controls of the broader BIOTHRIVOLOGY framework, creating a robust solution for the perpetual containment and control of AGI and ensuring a sustainable mutualistic symbiosis with humanity.

An artificial intelligence system which, when trained on sample data, may be used to subsequently generate data of arbitrary structure, such as image, geometry, musical or decision logic structures, in a rule based and statistically abiding manner. A method for training the system on sample data is presented, and a method for employing the system for the generation of data when provided additional input data is presented. The produced data is generated by creating a set of valid values with assigned statistical probabilities and reduced to a single value by an arbitrary choosing function. The system and methods are particularly useful for rule-based generation in resource and training data constrained environments requiring rule-abiding dynamic generation solutions.

The invention discloses a road structure monitoring method and device based on general artificial intelligence, equipment, a medium and a product, and relates to the technical field of road structure monitoring, and the method comprises the steps: firstly, carrying out the pre-training of a source domain corresponding to a sudden disaster scene based on disaster data in the scene, and obtaining a first model, and then, carrying out the pre-training of the first model; and performing fine tuning on the first model on a target domain corresponding to the road structure based on historical monitoring data to obtain a second model, and finally deploying the second model to a project monitoring system of the road structure to monitor the structure state of the road structure in real time. According to the method and the device, the first model of the sudden disaster scene is obtained through pre-training, and the second model is obtained through fine tuning of the first model based on transfer learning, so that in sudden disaster scene early warning of highway structures such as bridge and tunnel slopes, the early warning efficiency is improved; according to the method, sudden disaster scenes of highway structures such as bridge and tunnel slopes can be effectively identified through the second model under the condition of few samples, and accurate and reliable early warning support is provided.

NOVACOV enforces a fail-closed, permit-before-action control path for autonomous agents. At deployment, agent logic is sealed as an Immutable Constraint Chain (ICC) anchored in an append-only verifiable log. At runtime, within a read-only Encrypted Logic Vault (ELV), the system constructs a behavioral profile for each request and, as part of verify-to-activation, verifies inclusion relative to a current signed head under a freshness policy and, upon head advance, validates append-only-evolution (consistency) proofs, then compares the profile to the ICC. A Purpose Enforcement Kernel (PEK) checks alignment against a Mission Identity Template (MIT); a licensing gate (GLG) verifies license tier and an m-of-n quorum. On PASS, the system mints a permit, records it in an Immutable Arbitration Ledger (IAL), and requires the permit identifier for subsequent actions; otherwise the gate returns a structured disposition selected from HOLD, QUARANTINE, DENY, or ESCALATE. Anti-replay (nonce+monotonic) and signature-mimicry denial are enforced.