40972 results about "Artificial intelligence" patented technology

According to some embodiments, a method is performed by a wireless device for fallback operation of a machine learning (ML) model. The method comprises: transmitting a message indicating a capability of the wireless device for supporting a combination of at least one ML-based feature for a functionality and at least one fallback feature for the functionality to a network node; operating the at least one ML-based feature for the functionality; and operating the at least one fallback feature for the functionality.

The invention relates to the field of battery fault prediction, in particular to a lithium ion battery fault prediction method and system based on a BMS. The method comprises the following steps: extracting multi-dimensional operation monitoring parameters of a battery through a BMS (Battery Management System), carrying out multi-state evolution perception and label mapping processing, and constructing a global multi-state perception map of the battery; short-term abnormal sudden change detection is carried out according to the multi-dimensional operation monitoring parameters of the battery, and normal characteristic deviation trend analysis is carried out, so that an abnormal fluctuation deviation evolution trajectory is constructed; and performing deep topological correlation learning on the global multi-state sensing map of the battery based on the abnormal fluctuation deviation evolution trajectory, performing heterogeneous node global sensing, performing abnormal behavior causal relationship mining on heterogeneous deviation nodes in the battery, and performing multi-causal fission simulation to generate a battery behavior deterioration chain under an abnormal trend. According to the method, accurate and efficient fault prediction is realized, transfer learning is carried out, and the perspectiveness of subsequent BMS fault prediction is improved.

Systems and methods for adaptive edge computing using artificial intelligence (AI) include monitoring real-time accuracy of a neural network by using a feedback loop configured to detect changes in inference accuracy and dynamically adjusting the structure of the neural network by adding or removing hidden layers based on monitored error rates and predetermined computational constraints. A Kalman gain computation determines neural network weight adjustments based on monitored error rates. Weight matrices undergo incremental updates derived from these adjustments. Incremental weight adjustments remain stored in memory to enable low-bandwidth model updates. The neural network stores inference results and refined weights in an inference result database. Pre-trained models periodically receive incremental updates based on stored adjustments. Predictive holistic inference logic (PHIL) applied to stored inference results improves the accuracy of the inference results.

A system and method for generating multimedia artifacts with managed scene continuity in visual and multimedia using an AI-based and scene continuity aware media generation platform. The system receives a user or AI agent specification or simulation result(s), selects or trains generative models based on the specification, preprocesses relevant data, and generates scene narrative or frame-specific, sequence specific or broader continuity aware content using the selected or trained model(s). The generated content may be further enhanced using frame interpolation and view synthesis techniques to create smooth transitions or novel viewpoints or to aid in more efficient transmission or viewing or persistence of resultant content. The system enables efficient and customizable generation of high-quality scene continuity aware content for various applications in visual and multimedia production using neuro-symbolic and simulation enhanced compression, representation and generation processes.

The invention relates to the technical field of radar signal processing, in particular to a radar target analytical calculation method based on multi-dimensional data fusion and a radar device. Comprising the following steps: deploying a multi-band radar sensor array comprising an X band, a C band and a Ku band in a radar monitoring area; performing pulse compression and Doppler processing on the time domain echo signal, and extracting a time domain feature; spectral analysis is carried out on the frequency domain signals, and frequency domain features are extracted; performing angle estimation on the spatial signals, and extracting spatial features; a dynamic weight distribution model is constructed, a fusion weight is calculated through an adaptive algorithm based on three-dimensional quality indexes of a real-time signal-to-noise ratio (SNR), feature stability (SI) and data integrity (CI), and a joint representation vector containing time domain, frequency domain and space multi-dimensional information is generated. According to the invention, by deploying the multi-band radar sensor array, the recognition capability of the subtle feature difference of the target is improved.

The invention relates to a high-altitude large steel structure corridor safety risk monitoring method and system and a medium, and belongs to the technical field of civil engineering structure health monitoring, and the monitoring method comprises the steps: collecting original monitoring data through a multi-source sensor group disposed at each node of a steel structure corridor; performing space-time alignment processing on the original monitoring data to generate a sensor data matrix; performing dynamic noise suppression on the sensor data matrix based on an environmental noise transfer function model, and outputting a pure signal matrix and a damage characteristic frequency band identifier; calculating a thermal stress sensitive factor, extracting a multi-physical field coupling feature associated with the damage feature frequency band identifier in the pure signal matrix, fusing to generate a high-dimensional damage feature tensor, inputting the high-dimensional damage feature tensor into a pre-constructed digital twinborn risk assessment model, and outputting an assessment result including a risk level label and a damage position coordinate; and matching the regulation and control strategy according to the risk level label, and generating a corresponding equipment control instruction. According to the invention, the scientificity and accuracy of risk decision can be improved.

The invention discloses an artificial intelligence-based advertisement effect evaluation method and system, and the method comprises the steps: synchronously obtaining multi-source data containing a user behavior data flow and an advertisement putting index flow through a distributed collection engine, and generating a time-space synchronous multi-dimensional data cube; performing feature decoupling on the multi-dimensional data cube, and outputting a dynamic feature topology network with a weight; inputting the dynamic feature topology network into an adversarial training framework, and finally outputting an advertisement conversion probability space-time distribution diagram; based on the advertisement conversion probability space-time distribution map, deploying an attribution calculation unit for real-time feedback, and generating an incremental attribution map with a confidence interval; and inputting the incremental attribution atlas into a strategy generation adversarial network, and outputting an adversarial optimization advertisement putting strategy set meeting Pareto optimum. According to the embodiment of the invention, the accuracy and real-time performance of advertisement effect evaluation can be improved.

A device, system and method comprising computer vision techniques for fire prevention / detection and risk assessment, as well as for determining deviations from an ideal operational state. The present invention includes for example systems and methods which leverage data collected by camera systems composed of infrared and visible light sensors to detect and / or prevent a fire from starting, and additionally, use this data to determine a risk assessment for the building. The present invention also provides for example a system and method for monitoring and controlling safety risks in indoor industrial environments by determining deviations from an ideal operational state using computer vision techniques and game-theoretic competitive ranking frameworks.

The invention relates to the technical field of integration of artificial intelligence security and network security, and discloses an AI dynamic security transmission system based on an SASE framework, which realizes security access control based on AI dynamic identity verification through an SASE integration access module, acquires and predicts network performance change in real time by using a network state sensing module, and transmits the network performance change to a network server. Equipment, environment and data content are subjected to multi-dimensional analysis by means of a security risk assessment module, a quantitative risk score is generated, and a transmission protocol, parameters and encryption strength are dynamically adjusted according to a network state and the risk score by means of a dynamic transmission optimization module and a self-adaptive encryption module; the problem that safety protection and transmission efficiency are difficult to cooperate in a traditional architecture is effectively solved, low-delay and high-reliability data transmission service can be provided for AI application in a complex network environment, meanwhile, self-adaptive dynamic protection of the whole data transmission process is achieved, and data safety is comprehensively guaranteed.

The invention relates to an intelligent early warning method and system for urban ground collapse based on multi-source factor fusion, and belongs to the technical field of urban disaster early warning. A PS-InSAR and an SBAS-InSAR are adopted to process and calculate deformation values respectively, deformation time sequences extracted through the two processing methods are verified and analyzed, and a settlement graph is generated through vector results which are verified to be qualified; setting a settlement rate threshold value, and carrying out preliminary ground collapse early warning identification according to deformation; dividing a dynamic factor and a static factor for the deformation time sequence and the collected multi-source data, constructing a multi-channel weighted space-time diagram structure taking a monitoring area grid unit as a node, and performing multi-source data fusion and predicting a comprehensive risk probability by using a space-time diagram neural differential attention network; and carrying out dual-channel fusion study and judgment. According to the method, the nonlinear coupling and space-time dynamic relation between disaster-inducing factors is comprehensively described in the fusion process, and high-precision, low-false-alarm and strong-generalization prediction of the urban ground collapse risk can be achieved.

Methods, devices, and systems for machine learning (ML)-assisted position determination are disclosed. Information is received which indicates artificial intelligence / machine learning (AI / ML) models for determining position. Information is received which indicates transmission reference points (TRPs) (502, 504) associated with corners. Information is received which indicates a reference signal received power (RSRP). The TRPs associated with corners include a first TRP. It is determined that the WTRU is located in a corner based on an RSRP of a positioning reference signal (PRS) (506) received from the first TRP being above an RSRP threshold. Position information is determined based on an AI / ML position model and the determination that the WTRU is located in the corner. Information indicating the position of the WTRU is transmitted.

The invention discloses a zero-trust network dynamic access control method based on an AI behavior portrait, and the method comprises the following steps: 1, collecting multi-source real-time behavior data during an access request; 2, constructing an AI behavior portrait engine based on historical data, inputting the integrated multi-source behavior data, calculating a behavior deviation degree through the AI behavior portrait engine, and outputting a risk score; 3, dynamic strategy decision making, wherein a decision making engine executes hierarchical control according to the risk score; 4, continuous session monitoring and real-time adjustment are carried out; step 5, when risk upgrading is detected in the session, degrading the session authority, limiting high-risk operation, terminating the session, and retaining evidence obtaining data; step 6, audit event generation and portrait updating; and step 7, strategy optimization closed loop. According to the method, the risk score is calculated in real time based on the AI behavior portrait, transition from static authorization to dynamic permission adjustment is realized, and internal threats such as voucher stealing and the like are effectively blocked.

The present invention introduces a universal AI-powered pet management platform that establishes an entirely new category of technology, transcending conventional pet training systems. This comprehensive system integrates a modular wearable pet device with interchangeable sensors, sophisticated AI processing capabilities, and diverse output modules to create a unified ecosystem for holistic pet care. Unlike traditional training devices focused solely on behavior modification, this platform simultaneously manages multiple domains including real-time health monitoring, environmental safety assessment, emotional well-being analysis, autonomous training, emergency response, and seamless integration with external systems. The platform's universal architecture enables dynamic adaptation across diverse applications from companion animals to service animals, wildlife monitoring, and specialized deployments. By leveraging advanced artificial intelligence models, multimodal communication pathways, and a universal API for third-party integration, the system creates an interconnected technological framework that fundamentally transforms the relationship between pets, technology, and human interaction, rendering isolated pet devices obsolete.

The systems and methods disclosed herein obtain a set of alphanumeric characters defining constraints for agents and the agents' operational data. Each agent uses an output from a first set of artificial intelligence (AI) models and predefined objectives to autonomously generate proposed actions for execution on software application(s). For each agent, a second set of AI models evaluates the agent by identifying gaps in the proposed actions by comparing them with the expected actions. Using a third set of AI models and the identified gaps, the systems modify the proposed actions by adding, altering, or removing actions from the proposed actions.

Methods and systems are presented for using blockchain technologies to manage the life cycle and authentication of artificial intelligence (AI) agents. When an AI agent is created, the AI agent registers itself with an authentication system. The authentication system creates identity information for the AI agent and store on a blockchain. The identity information is used to track the changes of the AI agent through its life cycle, including upgrading of the binary code, transfer of ownership, change of a delegator, and others. When the AI agent requests for access of one or more resources, the authentication system uses the information stored on the blockchain to authenticate the AI agent before granting the AI agent access to the one or more resources.

The invention relates to the technical field of flight training evaluation, and discloses an electroencephalogram feature and physiological index fused flight training evaluation system. The system comprises a physiological signal acquisition module which synchronously captures multichannel electroencephalogram original signals and body surface physiological index data, and the body surface physiological index data comprises an electrocardiograph R-R interval sequence, respiratory wave frequency amplitude and galvanic skin response amplitude; the multi-modal fusion module is used for analyzing an electrocardiograph R-R interval sequence to generate a heart rate variability feature vector and establishing dynamic association mapping of an electroencephalogram entropy value and a physiological feature vector; the cognitive state modeling module is used for generating a cognitive load index according to the dynamic association mapping and constructing a cognitive stability quantization matrix; the self-adaptive feedback module is used for receiving related data and dynamically adjusting simulated flight scene parameters; and the evaluation output module is used for integrating the data to generate a comprehensive training evaluation report containing a neurophysiological coordination degree score and an operation accuracy rating. According to the system, comprehensive evaluation and dynamic training adjustment of the cognitive state of the pilot are realized.

A neural network system is provided. The system includes an autoencoder configured to encode input data into a latent space representation; a generator neural network configured to receive a noise vector and the latent space representation and output a set of routing coefficients; a discriminator neural network configured to evaluate the effectiveness of the routing coefficients by measuring the performance of a capsule network utilizing said routing coefficients; and a capsule network comprising a first capsule layer and a second capsule layer, wherein the routing coefficients are used to dynamically route outputs from the first capsule layer to the second capsule layer.

Techniques for generating AI-powered responses tailored to a specific entity's communication style. The techniques involve selecting a particular AI agent associated with an entity, receiving a user query, and generating an embedding from it. This embedding is used to retrieve relevant content from the entity's knowledge database. The entity's communication type is then determined. A large language model (LLM) prompt is created, combining the retrieved content and instructions to apply the entity's communication style. This prompt is submitted to an LLM service, which generates an output. A response based on this output is returned to the user. The techniques enable the creation of AI-generated responses that are both informative and aligned with the entity's preferred communication style, enhancing the consistency and effectiveness of AI-powered customer interactions or information dissemination for the entity.

The invention relates to the technical field of mining industry, and discloses an intelligent prediction method for gold ore beneficiation process parameters based on cloud and edge fusion, which realizes space-time correlation modeling of beneficiation process parameters and accurately depicts dynamic interaction influence among equipment. The cloud edge collaborative architecture considers global optimization and real-time response requirements, and the prediction stability under complex working conditions is effectively improved. The introduction of physical constraints enhances the applicability of the model in an actual production environment, a bidirectional feedback mechanism ensures the adaptive ability of the system in a dynamic change environment, and through the joint reasoning of a knowledge graph and a neural network, the consistency of a prediction result and a process principle is enhanced, and the risk of misjudgment under an abnormal working condition is reduced; the man-machine cooperation mechanism significantly improves the labeling efficiency of high-value samples, shortens the model iteration period, and ensures the continuous optimization capability of the prediction system in the actual production environment.

The invention discloses an educational scene-oriented knowledge graph enhanced large model personalized learning recommendation method and system, and the core thought of the method is that through the coupling of a knowledge dominant structure and a large model semantic capability, personalized learning resource pushing of triple driving of structure + semantics + behavior is realized. And deep, multi-dimensional and dynamic feedback-driven learning resource personalized intelligent recommendation can be realized in combination with student individual differences, learning paths, cognitive levels and teaching resource semantic structures.

Methods, systems, and devices may assist in artificial intelligence (AI) or machine learning (ML) communications in 5G system, AI or ML traffic differentiation, AI or ML slice type, AI or ML triggering rules, AI or ML policy, AI or ML operations, or user equipment communication interface exposure.

The invention relates to a low-rank fine-tuning transformer fault diagnosis method based on adaptive attention guidance, and belongs to the technical field of artificial intelligence. An attention scoring mechanism, an adaptive attention scoring mechanism, a dynamic rank allocation strategy and a hierarchical learning rate adjustment mechanism are introduced, and a context-aware dynamic updating strategy is further fused, so that the low-rank fine-tuning transformer fault diagnosis method based on adaptive attention guidance is realized. According to updating of real-time performance, loss and gradient dynamic intelligent triggering key parameters in the model training process, a large-model lightweight adaptation frame suitable for a transformer fault diagnosis task is constructed, and on the premise that diagnosis accuracy is ensured, model fine adjustment and deployment cost is remarkably reduced.

An autonomous environmental regulation and behavioral monitoring system is disclosed, configured to adapt temperature, lighting, and acoustic conditions based on real-time emotional and physiological data. The system includes a dual-redundant central processor, hierarchical communication networks, multi-angle visual acquisition units, infrared thermometers, and modular environmental subsystems. It detects posture, gestures, facial expressions, and thermal signals to classify user states and apply individualized airflow, light, and sound modulation without relying on external internet connectivity. The system also monitors connected appliances using voltage-based pressure analysis to forecast device degradation. With integrated gesture recognition, privacy-preserving data handling, and predictive adaptation, the invention enables multi-user personalization, long-term learning, and uninterrupted operation within residential, administrative, or healthcare infrastructures.

Some examples of the disclosure are directed to object manipulators and associated processes for manipulating an object representation in a three-dimensional environment. The object representation may correspond to a scan of a real-world object in a real-world environment. The object manipulators may include an object cleanup manipulator and a text annotation manipulator. The object cleanup manipulator may be selectable to display one or more control affordances providing functionality for selectively removing portions of the object representation in the three-dimensional environment and / or selectively adjusting one or more parameters of the object representation in the three-dimensional environment. The text annotation manipulator may be selectable to display one or more control affordances providing functionality for selectively generating one or more text labels in the three-dimensional environment. The one or more text labels may be associated with the object representation in the three-dimensional environment.

Systems and methods disclosed herein automatically evaluate, select, and coordinate artificial intelligence (AI)-based agents for collaborative distributed task execution based on dynamic, multi-attribute scoring and resource allocation models. The system obtains a task specification request defining a computational requirement set, a performance metric set, and an available resource set for one or more tasks to be executed by a network of AI-based agents. A first AI model set generates domain-specific test datasets and validates prospective agents by comparing agent-generated fingerprints against predetermined hash values stored on a distributed or federated ledger. A second AI model set constructs a multi-dimensional scoring data structure for each agent by using historical performance metrics to compute weighted composite scores. The system selects a subset of AI-based agents, ranks the agents, and allocates resources proportional to each agent's composite score. A third AI model set coordinates and executes distributed computer-executable workflows across the selected agents.

The invention discloses a multi-region collaborative power grid planning system and method based on an improved multi-target particle swarm optimization algorithm, relates to the technical field of power system planning, and solves the problems of multi-target coupling and cross-region coordination in traditional power grid planning by constructing an economical, environment-friendly and reliable multi-dimensional target function and introducing a game theory method to quantify a multi-target constraint relation. The system comprises a data acquisition module, a multi-objective optimization model construction module, an improved particle swarm algorithm execution module, a collaborative decision module and a result output module, the improved particle swarm algorithm adopts dynamic adaptive inertia weight, time-varying acceleration coefficient and differential mutation operation, and the convergence speed and Pareto frontier distribution quality are remarkably improved; and the collaborative decision-making module realizes cross-regional parameter interaction and scheme optimization through a hierarchical collaborative mechanism and a fuzzy entropy theory. According to the method, collaborative optimization of calculation efficiency and scheme balance is realized in multi-regional power grid collaborative planning, and technical support is provided for scientific planning of a complex power grid system.

Described herein are techniques for secure orchestration and publication control among agents (e.g., distributed agents), such as non-human identities (NHI), using cryptographic certificates, trust rules, and / or an Information-Centric Networking (ICN) architecture. In an example, a framework establishes identity for human and non-human identities-such as AI agents, services, and autonomous workloads—via cryptographically signed publications and / or collections. Trust policies can be defined and enforced through signed, verifiable trust rules, enabling access control, provenance validation, and / or policy delegation across federated domains. The disclosed techniques can enable multi-agent systems (MAS), zero-trust enforcement, and / or secure cross-domain communication using ICN-named role-based certificates and programmable trust shims. The disclosed techniques can also enable decentralized validation and selective replication of data while maintaining traceability and fine-grained control of agent behavior.

Various systems and methods are directed to an electro-mechanical dice shaker gaming system designed to enhance fairness, transparency, and security in wager-based gaming environments. The system includes an electro-mechanical random number generator (RNG) assembly that physically isolates the dice shaking mechanism from the player terminal, preventing external interference from affecting game outcomes. The dice shaker mechanism is housed in a transparent enclosure, allowing players and casino operators to visually verify each roll. Integrated sensors, including tilt and vibration detectors, monitor environmental conditions to detect and prevent tampering. A camera-based monitoring system captures images of each dice roll, facilitating automated outcome verification and compliance auditing. The system supports modular configurations for multi-player gaming, real-time streaming for remote participation, and AI-driven fraud detection. Additionally, an adjustable mirror enhances dice visibility, and automated mechanisms allow dynamic control over the number of dice in play, offering a scalable and secure gaming solution.

The invention discloses a speech recognition and authentication method and system based on multi-modal features and dynamic evaluation in the technical field of speech recognition and authentication, and the method comprises the steps: collecting an original speech signal of a user through a microphone, and carrying out the preprocessing of the original speech signal, and obtaining the preprocessing speech data; and extracting feature data of the preprocessed voice data by adopting a multi-dimensional feature hierarchical extraction technology, and injecting a multi-source noise sample into an acoustic feature space of a voiceprint feature model based on an initial training stage established by the voiceprint feature model to construct an anti-noise mixed voiceprint map. Through integrating voiceprint, semantics, behavior characteristics and an environment adaptation mechanism, an authentication threshold is adjusted in real time according to dynamic risk assessment, meanwhile, a risk scoring model is utilized to calculate a comprehensive risk value, authentication modes of different levels are started according to risk scenes of different degrees, and two-factor authentication is forcibly implemented for high-risk scenes. And the authentication security and reliability can be obviously enhanced.

The invention discloses a data text desensitization method and system for economic big data, and particularly relates to the field of text desensitization. In a data receiving stage, heterogeneous data sources are accessed through multiple channels, structured data are subjected to field-level standardization processing, and unstructured texts are subjected to OCR conversion and semantic segmentation; the method comprises the following steps: constructing a data consanguinity graph, identifying sensitive entities based on a pre-trained NER model, realizing three-layer hierarchical management in combination with dynamic weight calculation, and dividing the entities into a core layer, an association layer and a non-sensitive layer; an entity association network is constructed through a knowledge graph technology, a privacy association strength coefficient is quantified, and cross-entity desensitization consistency control is realized; a privacy risk assessment model is adopted to calculate a residual risk value, an adaptive enhancement mechanism is triggered when the residual risk value exceeds a threshold value, data availability is maintained through a utility assessment unit while privacy is protected, and dynamic balance between risk and utility is formed.