33269 results about "Artificial intelligence" patented technology

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 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.

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.

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.

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.

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.

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.

A system and method for real-time team intent modeling using persistent cognitive machines with federated human profiles which processes individual team member behavioral signals through geometric intent analyzers that generate high-dimensional vector representations of individual objectives and preferences. A team intent orchestrator aggregates individual vectors into collective representations within a dynamic geometric manifold that evolves based on team coordination patterns. Federated human profiles enable privacy-preserving knowledge sharing across teams through geometric abstraction techniques that preserve coordination utility while protecting individual privacy. The system implements proactive conflict detection through trajectory analysis that identifies potential coordination issues before performance impact, and provides real-time synchronization mechanisms that maintain team coordination coherence despite individual behavioral changes. Cross-team learning capabilities enable organizational intelligence development through pattern abstraction and context-aware adaptation of successful coordination strategies. The persistent cognitive architecture maintains coordination patterns across sessions and team composition changes, enabling continuous improvement through accumulated team experience.

The invention discloses a structure perception graph neural network physical field prediction method based on a geometric gating attention mechanism, and belongs to the technical field of artificial intelligence and computational physics crossing. The method comprises the following steps: extracting geometric features for standardization processing and organizing in a graph data structure; inputting the edge-level geometric features into a geometric gating function, generating a dynamic adjustment factor through a multi-layer perceptron, obtaining an attention weight after geometric modulation, and performing normalization processing to obtain output features of nodes; splicing the output features of the plurality of attention heads as final output features of the model; training the graph neural network model to obtain a physical field prediction model; and inputting the geometric features of the three-dimensional structure model to be predicted into the physical field prediction model. According to the method, the precision and physical consistency of complex physical field prediction are improved, and the problems of boundary blur, field intensity sudden change and the like caused by neglecting geometric dynamic change in a traditional static coding method are reduced.

Systems and methods for generating industry-specific solutions using collaborative Artificial Intelligence (AI) agents are disclosed. In an aspect, input data corresponding to an industry-specific problem is received. A goal context for the industry-specific problem is then identified. Further, an industry-specific process workflow corresponding to the industry-specific problem is selected based on the goal context. Furthermore, an agentic context, historical intelligence data, group dynamics data for agent compatibility, appropriate agent character data, and historical user feedback data corresponding to the industry-specific workflow are retrieved. Moreover, AI agents and agent compatibility rules to execute user goals are selected and the rules are assigned to each AI agent. An agentic process workflow for the industry-specific problem is then generated. A candidate solution is then generated by executing the generated agentic process workflow. The candidate solution, agentic process workflow and agent compatibility rules are then outputted on a user interface of a user device.

The invention relates to a quick river flood forecasting method based on a physical information neural network, and belongs to the field of river flood forecasting. The method comprises the following steps: on the basis of a traditional physical information neural network (PINN), introducing a boundary condition parameter as an input variable, and enabling the PINN to learn a flood wave propagation rule under different boundary conditions. Furthermore, on this basis, a physical information neural network flood fast forecasting framework (RFF-PINN) integrated with a hydrodynamic method is provided, a numerical solution based on grid discretization is reconstructed into a continuous function in a time-space domain through a piecewise polynomial interpolation method, residual error loss between network output and a hydrodynamic model simulation value is constructed, and therefore, a flood fast forecasting result is obtained. The network parameters are optimized in cooperation with the PDE loss, and the problem that the network parameter optimization effect is reduced due to the fact that the PDE loss of the complex flow state area is difficult to converge is solved. The method has the beneficial effect that the water depth change process of each section of the river channel under any boundary condition can be accurately and quickly predicted.

The invention belongs to the field of aero-engine bearings, and provides a method and system for predicting the remaining service life of an aero-engine bearing, and the method comprises the steps: carrying out the processing of an original vibration signal collected in the operation process of the engine bearing through a Pearson correlation analysis method, carrying out feature extraction to obtain a plurality of feature sequences of common bearing RUL prediction statistics; smooth processing and cumulative transformation are carried out on the statistical feature sequence, monotonicity and tendency values are calculated, screening is carried out, and a screened cumulative feature sequence is obtained; and inputting the screened accumulated feature sequence into an attention full convolutional network (AFCN) model based on physical information to predict a life ratio, and calculating a residual service life prediction value through the life ratio. According to the method, through the full convolutional network fusing the bearing physical degradation mechanism and the attention mechanism, the key feature capturing capability and prediction precision in the long-time-sequence degradation process are improved, and technical support is provided for safe operation and maintenance of an aero-engine.

The invention provides an efficient remodeling production scheduling method, medium and system based on AI multi-agent dynamic negotiation, and belongs to the technical field of agents. A residual connection mechanism is used for processing time sequence correlation characteristics under a nonlinear working condition to establish a remodeling time prediction basis, a dynamic layered negotiation architecture is established to realize information interaction and decision transmission, and an equipment agent predicts remodeling time based on a multi-scale time sequence perception model and generates a bidding scheme. A coordination agent processes a bidding scheme by adopting a Pareto leading edge multi-objective optimization algorithm to balance multiple objectives, dynamically adjusts model parameters through a hybrid similarity evaluation mechanism to guarantee prediction stability, and automatically identifies an affected order subset to trigger an incremental re-negotiation process when production disturbance occurs. The technical problem that the production scheduling plan is frequently adjusted due to inaccurate remodeling time prediction is solved.

The invention discloses an intelligent contract vulnerability detection and repair system based on a heterogeneous graph neural network, and belongs to the technical field of block chain security, and the system comprises a contract analysis module, a multilayer graph construction module, a heterogeneous graph neural network module, a vulnerability feature library, a vulnerability recognition engine, an automatic repair module and a visual interface. After the source code of the intelligent contract is input, code analysis and standardization are completed by a contract analysis module; the multi-layer graph construction module constructs a contract internal heterogeneous graph, an inter-contract interaction graph and an ecosystem relation graph based on a graph theory; the heterogeneous graph neural network module learns a vulnerability feature mode; the vulnerability recognition engine combines the vulnerability feature library to realize vulnerability classification and risk assessment; the automatic repairing module generates a repairing scheme; and the visual interface realizes detection progress monitoring, result display and encrypted report export. The intelligent contract vulnerability detection and restoration system based on the heterogeneous graph neural network provided by the invention provides technical support for block chain digital asset security and ecological stability.

The invention discloses a concrete mix proportion optimization method and system based on machine learning, and particularly relates to the technical field of intelligent proportioning of building materials, and the method comprises the following steps: constructing a mapping model of target performance indexes by using raw material performance parameters and historical trial matching data, cross-station mix proportion optimization is carried out based on real-time sensing data of a plurality of mixing stations, the resonance relation between the moisture content of raw materials and the iteration frequency of a model is monitored in the dynamic construction process, and a parameter smoothing and disturbance suppression mechanism is triggered to correct a mix proportion scheme. The corrected mix proportion adjustment scheme is applied to raw material controlled feeding of multiple mixing stations; according to the method, the quality of training samples is improved through unified data processing, a multi-dimensional mapping model is constructed to realize cross-mixing-station performance consistency optimization, and a resonance detection and disturbance suppression mechanism is introduced to ensure the stability and anti-interference capability of mix proportion adjustment under a dynamic construction condition; therefore, high-performance, low-cost and high-robustness concrete intelligent optimization control is realized.

The invention discloses a multi-terminal mixed intelligent inspection and task scheduling method and system based on air-ground-person cooperation. The method comprises the following steps: constructing dynamic unknown environment active cognition; updating dynamic unknown environment active cognition; the candidate path segments are evaluated through a path cost evaluation function, path reachability judgment is performed based on a cost value, and an exploration guide item is introduced to actively guide an inspection carrier to enter an unknown area to realize synchronization of task execution and map expansion; in combination with semantic task description, real-time states of multiple inspection carriers and an updated global environment model, task dynamic allocation and path collaborative optimization are performed, and the emergency, execution efficiency and resource utilization rate of inspection tasks of multiple terminals are balanced; the operation state and environment cognitive quality of each inspection carrier are monitored in real time, and an MR teleoperation intervention mechanism is automatically triggered when it is detected that continuous planning fails, sensing confidence is lowered or a key unknown area is entered; projecting a global environment model, real-time sensing data and a planned path to an operator MR device in an immersive manner, guiding by an operator through gestures, gazing or voice, and finishing path correction or task redistribution by adopting a progressive sharing control strategy and fusing with a manual instruction and autonomous planning; a successful MR intervention process of an operator and a context environment thereof are recorded in a context memory library, and when a similar task or environment scene is encountered subsequently, a verified guide strategy or control parameter is actively recommended.

The invention relates to the field of environment monitoring, discloses an ecological protection red line holographic dynamic monitoring and early warning method and system, and aims to solve the defects of an existing monitoring and early warning mechanism in the aspects of timeliness, data integration and intelligent analysis. According to the method and the system, multi-source heterogeneous data are acquired and fused, a space bottom line digital model is constructed, space-time situation awareness analysis and change abnormity identification are performed, risk assessment early warning triggering is performed, and early warning issuing and processing feedback are managed. According to the invention, high-precision holographic acquisition and fusion of multi-source data can be realized, the accuracy and timeliness of change abnormity identification are improved, passive early warning is changed into active early warning, a monitoring disposal closed loop is formed, supervision intelligence and treatment efficiency are improved, and ecological safety is guaranteed.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a task planning and correction method, device, equipment and medium, and the method comprises the steps: obtaining a task instruction and environment perception data, and generating instruction features and environment features; retrieving a reference case from an experience case library according to the instruction features and the environment features; decomposing the task instruction into a subtask sequence containing a target object, and generating a task plan based on the reference case; executing the current subtask and detecting whether the target object is missing or not; if the target object is missing, acquiring an environment object set, and determining an alternative object semantically associated with the target object; and updating the current subtask by using the replacement object and executing the correction subtask. According to the method, the task plan is generated by combining the environment perception and the reference case, and the correction sub-task is dynamically replaced and generated when the target is missing, so that the task is executed without interruption, and the robustness and the environment adaptability are improved.

A system detects changes in model context protocol (“MCP”) processes, and performs a remedial action. A server-sent events (“SSE”) bridge sends a request to an MCP server. A first resource profile is received from the MCP server. This is stored and compared against a second updated version of the resource profile. When a difference is detected, the SSE bridge determines whether to block a resource command from reaching the SSE bridge. The decision is based on comparing the difference to security rules, which can be defined as part of a management profile.

The invention relates to the technical field of cultivated land supervision, in particular to a big data-based cultivated land utilization supervision system, which comprises a pattern spot remote sensing interpretation module, a pixel state aggregation module, a boundary mutation recognition module, a time sequence cultivation fluctuation analysis module and a response priority pattern output module. According to the method, a time sequence label grid set is established through spatial boundary analysis and pixel index extraction of remote sensing image spots, state labels are introduced to classify pixel tillage evolution, an image spot state evolution chain and path structure is constructed on the basis of spatial aggregation, and a continuous fluctuation area is extracted through sliding window analysis of planting behavior factors in the image spots. A co-occurrence block of boundary disturbance and behavior fluctuation is positioned in combination with a space overlapping relation, a response urgency index is constructed after multiple indexes such as state density, disturbance range and duration period are quantified, multi-dimensional judgment and intervention priority ranking of the farmland utilization abnormal area are achieved, and the identification precision and response efficiency of farmland utilization changes are effectively improved.

The invention relates to a multi-scale remote sensing image spatial-temporal difference cooperative water quality dynamic monitoring method, and belongs to the technical field of water quality monitoring. The method comprises the steps that a multi-scale time sequence remote sensing image is acquired, a key monitoring domain is delimited through pollution risk and function partition coupling, and time-space registration and spectrum calibration are completed in combination with hydrological parameters; the method comprises the following steps: directionally extracting water quality parameter correlation difference characteristics, quantifying hierarchical characteristic correlation intensity and filtering non-pollution interference signals; a feature-oriented inversion framework is built, a pollution diffusion boundary is delimited, a pollution source is traced, and a water quality parameter space-time dynamic distribution diagram is generated through multi-feature adaptation fusion; verifying the adaptive deviation of the region and the local dimension, dynamically correcting the weight of the model, and constructing a two-factor early warning rule to form a whole-process monitoring link. According to the method, multi-scale image space-time difference characteristics are fully utilized, the accuracy and dynamic response capability of water quality monitoring are improved, and reliable support is provided for pollution source tracing and risk early warning.

The invention discloses a method for carrying out data cleaning decision making and automatic tuning by using AI workflow, and relates to the technical field of data processing, the method comprises the following steps: obtaining original data from various heterogeneous data sources, and generating a correlation table and a result through field extraction, structure analysis and language attribute recognition; based on this, storage and task generation are performed, and standardized data and task queue information are output; identifying a quality problem through feature analysis, defining a target and constructing a cleaning task atlas, and generating cleaning metadata through preliminary screening and marking; and an AI cleaning workflow containing multiple strategy paths is constructed and optimized, after execution, a downstream feedback evaluation effect is combined and adjustment is carried out, and finally a multi-version cleaning result is integrated and output. According to the method, the adaptation capability to different types of data sources is effectively improved, the dependence on a static preset strategy is reduced, the manual intervention requirement is reduced, the tuning efficiency is improved, and the systematicness and normalization of data cleaning are enhanced.

The invention relates to the technical field of knowledge graph construction and maintenance, and discloses an AI knowledge graph construction and maintenance method based on ontology engineering, and the method comprises the steps: obtaining multi-source heterogeneous data to construct an initial graph ontology model; loading the constraint rules and the entity alignment parameters to carry out cross-modal knowledge fusion, and generating a dynamic knowledge evaluation model; executing ontology topology reasoning based on the model, and constructing time sequence knowledge evolution simulation data; training the semantic conflict resolution model to generate a knowledge fusion optimization model, and outputting map maintenance parameters; knowledge increment simulation information is generated, a multi-dimensional optimization model is constructed to iteratively adjust the ontology structure, and optimal knowledge fusion path parameters are generated; and dynamic ontology matching is realized by combining real-time reasoning and historical track updating maintenance strategies. According to the method, the construction efficiency, the fusion quality and the self-adaptive maintenance capability of the knowledge graph are improved, the method is suitable for an intelligent knowledge management scene of multi-source heterogeneous data, and the defects of a traditional method in the aspects of semantic conflict resolution, ontology dynamic evolution and the like are overcome.

A computer-implemented method is disclosed for transforming academic test performance into personalized feedback and learning recommendations. The method involves presenting an academic test to a user via a user interface of an online learning platform and receiving the user's submitted answers. The system accesses input parameters including historical user-performance data, correct answers, and coaching session data. The user's responses are compared with the correct answers to identify incorrect responses. A prompt generator creates a prompt to guide and constrain an AI engine in analyzing the test responses. The AI engine correlates the incorrect responses with historical performance data and coaching session information to detect learning patterns or recurring errors. Based on the identified patterns, the system generates personalized feedback and targeted learning recommendations to address specific learning gaps. The method enables adaptive, AI-assisted post-assessment guidance, improving learning outcomes through individualized support.

The invention discloses a lithium battery temperature state estimation method based on a physical information neural network, and relates to the technical field of lithium ion battery temperature state estimation, and the method comprises the following steps: obtaining multi-working-condition lithium battery charging and discharging data, and carrying out sliding window denoising and abnormal data elimination preprocessing; a battery thermal model containing total irreversible heat, reversible heat and heat dissipation is constructed by combining heat production and heat dissipation mechanisms, and a temperature change thermodynamic equation is obtained; establishing a physical information neural network, based on a residual network, embedding a time-varying internal resistance module and a time sequence feature extraction module based on the Arrhenius law, constructing a multi-component total loss function, and optimizing parameters through adaptive weight adjustment and an Adam algorithm; temperature state estimation is realized in training and prediction stages, the model is optimized and parameters are stored in the training stage, and a temperature result is output and verified in the prediction stage. The method gives consideration to both physical consistency and data fitting precision, and can support thermal management of the battery.

The invention belongs to the technical field of multi-source heterogeneous data processing and intelligent strategy self-adaption, and discloses a real-time data stream classification and security strategy self-adaption system based on an AI model, which comprises the following steps: acquiring a multi-source heterogeneous data stream, injecting five-dimensional semantic tags, complementing an implicit relationship among the tags through association reasoning, and generating a standardized enhanced data stream; the optimal AI model is matched to execute real-time data flow classification, credibility grading is carried out, and a candidate strategy set is generated through matching; matching an association rule through a rule relation graph, and combining system real-time state mirror image execution strategy influence chain rehearsal to generate a rehearsal verification strategy set; cross-domain transactions are packaged according to cross-domain transaction description specifications, and transaction execution states and physical feedback data are collected in real time through four-stage submission protocol execution; life cycle management is implemented through rule efficiency evaluation, and a rule evolution instruction and sample enhancement data are obtained by combining full-link auditing and are fed back to a preorder link to form a closed loop.

The invention relates to the technical field of intelligent war game deduction, in particular to an intelligent war game deduction simulation system based on a digital twin fusion large model. Comprising a digital twin modeling unit which adopts a dynamic precision adaptive modeling mechanism; a large model decision unit; a deduction execution unit; and a dynamic interaction unit. A dynamic precision self-adaptive modeling mechanism is adopted by the digital twin modeling unit, the model precision can be dynamically switched on demand, the detail simulation demand and the computing resource consumption are balanced, and meanwhile, the sub-second-level dynamic synchronization of a virtual model and a war game deduction scene is realized through a time sequence calibration algorithm; and space confrontation, time sequence decision and rule triggering features are fused through the multi-modal feature analysis module, and the Wargame rule verification module is combined to embed entity performance boundaries and scene rule constraints, so that compliance confrontation decision parameters conforming to Wargame deduction logic can be generated, and the problem of insufficient model precision adaptation and decision constraints is solved.