60 results about "Cognition" patented technology

Systems and methods for guiding or steering thought processes on a persistent cognitive machine (PCM) that uses a continuous, differentiable, thought manifold in geometric space to allow a computer to engage in human-like thought processes. The PCM with thought manifold represents a fundamental advancement in artificial intelligence beyond current probabilistic AI system such as large language models (LLMs) and similar reasoning models. A PCM with cognitive manifold performs cognition on a thought manifold in a continuous, differentiable, thought manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. Methods for guiding or steering thought processes on the thought manifold are disclosed that involve mathematical manipulations of the geometric space of the thought manifold inspired by gravitational lensing.

A system and method for a digital thought architecture, otherwise called a persistent cognitive machine (PCM), that uses a continuous, differentiable, thought manifold in geometric space to allow a computer to engage in human-like thought processes. The PCM with thought manifold represents a fundamental advancement in artificial intelligence beyond current probabilistic AI system such as large language models (LLMs) and similar reasoning models. Not only does the PCM with thought manifold maintain persistent cognitive processes regardless of external interaction, overcoming limitations of existing AI systems that operate within a prompt-response paradigm where they await input, generate output, and return to a waiting state, it also performs cognition on a thought manifold in a continuous, differentiable, thought manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. In some embodiments, the thought manifold may be implemented as a neuromorphic platform.

The invention discloses a DIKWP-driven individualized brain-map interactive feedback system, which is used for neural rehabilitation and brain-computer interface training. The system obtains brain activity data of a patient through electroencephalogram acquisition equipment, constructs a personal brain-semantic map in combination with cognitive evaluation, and establishes a mapping relation between semantic units and brain region responses. In the training process, the DIKWP semantic analysis module performs multi-layer analysis on indexes such as reaction time, accuracy and intention achievement, generates multi-mode instant feedback such as visual sense, auditory sense or tactile sense, and performs directional reinforcement on a weak semantic domain. The system has a dynamic target optimization capability, and the difficulty can be automatically adjusted according to training performance; when attention distraction, emotion abnormity or semantic deviation is detected, a safety intervention mechanism is automatically triggered, and training effectiveness and safety are guaranteed. According to the method, closed-loop individualized rehabilitation interaction is realized, the adaptation degree and efficiency of brain-computer interface training are remarkably improved, and the method is suitable for various rehabilitation scenes such as languages, movement and cognition and has a good industrial application prospect.

The invention discloses a brain heuristic multi-expert multi-mode emotion recognition method and system, equipment and a medium, and belongs to the technical field of artificial intelligence and biomedical signal processing. The method comprises the following steps: by simulating a brain function partitioning mechanism, dividing an electroencephalogram signal into a plurality of brain regions according to neuroanatomy prior, and designing a special expert network for each region; a global-local double-current encoder is adopted to cooperatively extract spatial-temporal characteristics of each brain region signal, and meanwhile, a multi-scale large-kernel convolution module is utilized to extract peripheral physiological signal characteristics; and finally, dynamically fusing multi-expert features through an adaptive routing network to realize sentiment classification. Expert load balancing and bifurcation regularization joint loss are introduced into the model in training, and effective cooperation and feature diversity of experts are ensured. According to the method, excellent recognition precision is obtained in practice, it is verified through interpretability analysis that the decision-making process conforms to neuroscience cognition, and a high-precision and high-reliability solution is provided for application of brain-computer interfaces, mental health monitoring and the like.

The invention relates to a superconducting power knowledge system construction method and system based on a lightweight large model. According to the method, multi-source data is utilized for unified representation, deep semantic information is obtained through a lightweight large model, knowledge extraction is achieved, and a basic knowledge graph is constructed; further forming a knowledge graph which can be dynamically updated through knowledge fusion; and in combination with real-time data, adaptive multi-hop reasoning is executed, and a diagnosis and decision result with a causal chain is generated. According to the method, the semantic understanding and reasoning ability of a lightweight large model is taken as a core, the structural expression advantage of a knowledge graph is combined, the limitation of updating lagging and fixed rule reasoning rigidness of a traditional static knowledge base is broken through, and precise cognition and causal chain inference of a complex system state are achieved. The method has universality and mobility, and is suitable for knowledge modeling and intelligent diagnosis of various electric power scenes and other complex industrial systems. The method is verified by taking a superconducting power system as an example, and the feasibility and effectiveness of the construction path are proved.

The invention discloses an AI adaptive learning path dynamic generation method and system based on a multi-modal cognitive trajectory. The method comprises the following steps: non-inductively collecting multi-modal data such as visual, auditory and interactive data; performing feature fusion by using a gating multi-mode unit to generate a student state vector; constructing a three-dimensional cognitive trajectory model for representing cognition, emotion and fatigue; dynamically deciding a learning path by adopting a hierarchical reinforcement learning algorithm and taking a track state as input; predicting a knowledge fault risk in real time by using a dynamic Bayesian network; and when the risk exceeds the threshold, actively generating a personalized completion path based on knowledge graph traceability. According to the method, deep perception of the learning state, advanced prediction of the learning risk and accurate dynamic planning of the learning path are realized, and the learning efficiency and experience are remarkably improved.

Systems and methods for latent slice budgeting on a persistent cognitive machine (PCM) that uses a continuous, differentiable, cognitive manifold in geometric space to allow a computer to engage in human-like thought processes. The PCM with cognitive manifold represents a fundamental advancement in artificial intelligence beyond current probabilistic AI system such as large language models (LLMs) and similar reasoning models. A PCM with cognitive manifold performs cognition on a thought manifold in a continuous, differentiable, thought manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. Methods for latent slice budgeting on the cognitive manifold are disclosed that foliation of the cognitive manifold into time slices and budgeting change between the time slices.

A system and method for implementing experiential manifold cognition that extends persistent cognitive machines beyond discrete thought caching to continuous geometric representation of experience. The system maintains an experiential manifold comprising a differentiable manifold with Riemannian metric tensor encoding semantic relationships, compression pressure field governing memory consolidation, and potential field encoding goals and attention. Input data is projected onto the manifold through adaptive geometric diffusion preserving semantic structure. The system executes geometric transformations including metric evolution, geodesic computation, and curvature estimation. During non-interactive periods, autonomous evolution occurs through trajectory recombination and selective pruning. A user interface enables visualization and direct manipulation of manifold geometry, translating navigation into geodesic traversal and edits into metric modifications. The system maintains persistence across sessions and enables controlled federation between multiple manifolds through consent-bounded synchronization. Applications include persistent narrative worlds, collaborative cognitive spaces, and experiential intelligence systems that learn through geometric evolution.

The invention discloses a large language model agent collaborative decision-making method for a complex dynamic game scene, and the method comprises the steps: achieving the environment perception, experience accumulation and knowledge calling functions, and supporting the cognitive modeling and strategy generation of an agent; based on cognitive information, intelligent agent role division and labor division are achieved through an action characterization device and a role selector, and the intelligent agents are guided to perform their own functions in the collaboration process. Cognitive information and role information are input into a large language model, hierarchical analysis is performed on a game situation depending on natural language understanding and thinking chain reasoning ability, key game nodes are identified, opponent strategies are predicted, and foresight collaborative decisions are generated in combination with teammate intentions. And through a semantic matching and action mapping mechanism, converting a natural language decision generated by reasoning into a structured executable instruction, and performing rationality verification. The intelligent agent executes actions and interacts with the environment, the system updates short-term memory based on feedback and periodically integrates the short-term memory into long-term memory, and a closed-loop process of'cognition-role allocation-reasoning-execution-updating 'is formed. According to the method disclosed by the invention, a distributed collaborative decision-making architecture based on a large language model is constructed, so that the intelligent agent has stronger autonomous perception, reasoning and collaboration capabilities, and the collaboration efficiency, game adaptability and strategy generalization capabilities of the intelligent agent in a complex dynamic game environment are remarkably improved.

The invention relates to an intelligent deep questioning method and system, and belongs to the technical field of natural language processing and intelligent evaluation. The method comprises the following steps: acquiring a candidate response, converting the candidate response into a standardized text, analyzing the text in combination with multi-dimensional monitoring data, positioning competency missing elements, generating a missing identification set, and marking a sorting question-chasing point; constructing a dynamic self-adaptive score matrix in which competency is linked with a questioning point, fusing monitoring data to calibrate scores and confidence, and generating a questioning technology intervention scheme; a virtual interviewer role library is preset, roles are dynamically matched, and the intervention logic and the missing identification set are combined to generate specialized questioning content; and when the core element capability element characterization is insufficient or the confidence coefficient does not reach the standard, generating a target element cognition questioning problem, and after element extraction and fusion, outputting a multi-dimensional competency score and a confidence coefficient report. According to the invention, the upgrading of the questioning from mechanical questioning to intelligent technical intervention is realized, the questioning targeting and the evaluation accuracy are improved, and reliable support is provided for accurate talent selection.

The invention discloses an interpretable teaching cognition interaction method and system based on a knowledge graph, and a medium, and relates to the field of education management information systems, and the method specifically comprises the steps: collecting answer data of a learner, calculating a knowledge mastering state vector, mapping the knowledge mastering state vector to a knowledge graph node, and carrying out the feature decomposition; obtaining a cognitive feature matrix and a knowledge node cognitive difficulty coefficient; constructing a cognitive path graph based on the cognitive difficulty coefficient, and calculating the migration probability of adjacent nodes to generate a cognitive migration path; and constructing a multi-layer knowledge network on the path, calculating node weights and knowledge circulation coefficients, generating a teaching knowledge point sequence, and encoding the sequence to generate stepped teaching content. According to the method, personalized content recommendation and scientific allocation of teaching resources can be provided for an education platform, and intelligent optimization and management transparency of the teaching process are realized.

The invention discloses a personalized homework intelligent generating and pushing method based on multi-modal learning data, and belongs to the technical field of education. The method aims at overcoming the defects that in the prior art, the emotion state of a learner is ignored, multi-modal data fusion is shallow, and homework generation is static. The method comprises the following steps: acquiring multi-modal data, acquiring behavior data, physiological data and expression data in real time, evaluating cognitive and emotional states, respectively outputting knowledge point mastery degrees and emotional states through sub-models, and correlating and calibrating; dynamic homework generation: a decision engine generates and pushes a personalized homework package through question bank combination or real-time generation according to a dual-state matching regulation rule; feedback optimization and privacy protection are conducted, and desensitization processing is conducted on the physiological data at the same time on the basis of an operation feedback iteration model and rule. According to the method, double regulation and control of cognition and emotion are realized, the double-state evaluation accuracy is high, the homework completion rate of the learner is effectively improved, and the method is suitable for K12 and vocational education scenes.

The application provides a text-priority multimodal sentiment analysis method and system, which respectively constructs private expert networks of text, video and audio, is used for capturing unique sentiment expression features of each mode, simultaneously introduces a shared expert network to model general sentiment semantics across modes, so that collaborative modeling of multimodal information is realized, then a text mode is used as a leading mode to start a sentiment analysis process, a preliminary judgment is given based on text information, according to a confidence degree, video and audio information are gradually introduced according to needs to perform incremental supplement for sentiment classification prediction, a need-decoding strategy consistent with human cognition is realized, and a final sentiment category is obtained. Through simulating a human cognitive process of integrating multi-source information according to needs, the application reduces reasoning time and resource consumption, makes the model decision process more natural and interpretable, and is closer to the characteristics of different modal information being unbalanced and non-equivalent in actual application.

Systems and methods for persistence of memory on a persistent cognitive machine (PCM) that uses a continuous, differentiable, cognitive manifold in geometric space to allow a computer to engage in human-like thought processes. The PCM with cognitive manifold represents a fundamental advancement in artificial intelligence beyond current probabilistic AI system such as large language models (LLMs) and similar reasoning models. A PCM with cognitive manifold performs cognition on a thought manifold in a continuous, differentiable, thought manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. Persistence of memory is reflected on the cognitive manifold through relative displacements between geodesics after a reasoning trajectory has been calculated in a manner analogous to gravitational wave echoes in general relativity physics.

This document relates to the enhancement of cognition and memory through stimulation by a plurality of odorants, disclosing a system for improving the remote transfer effect of cognition in a human, a device for use in the system, and a method of delivering an aroma. The system for improving the remote transfer effect of cognition in a human comprises: a module for establishing a daily treatment schedule comprising a plurality of time intervals separated by intervals; a module for inputting the daily treatment schedule into an aroma delivery device such that the device delivers one of a plurality of aromas during each time interval and ceases delivery of the aroma at the end of the time interval; wherein the aromas delivered during successive time intervals are sufficiently different from one another so as to be distinguished by the human; wherein the daily treatment schedule is repeated each day for a predetermined treatment duration.

This invention discloses a method and system for power operation safety monitoring and question answering based on a multimodal large model. The method includes the following steps: a data acquisition step, acquiring visual data and safety regulation text data from the power operation site; a multimodal semantic fusion step, generating a unified fused semantic vector through feature extraction and cross-modal alignment fusion technology; a knowledge reasoning step, matching the fused semantic vector with a pre-constructed power safety knowledge graph and performing compliance reasoning based on a graph neural network; and an intelligent question answering generation step, inputting the fused semantic vector and the reasoning results into a large language model to generate natural language question answering information. This invention, through the synergistic innovation of multimodal semantic fusion, knowledge graph reasoning, and a large language model, solves the technical problems of insufficient semantic understanding, disconnect between the question answering system and the field, and unstructured knowledge representation in existing technologies, achieving fully automated and interpretable intelligent safety supervision from perception to cognition.

The invention discloses a large model construction method based on self-cognition, a task planning method and equipment, and the method comprises the steps: obtaining a training data set, and reading a business type corresponding to the training data set; obtaining a service task corresponding to the service type, constructing at least one piece of task description data for the obtained service task, and adding the task description data to the training data set to obtain a target training data set; and training a preset large model based on the target training data set to obtain a trained large language model. According to the method, the task description data is constructed for the business task to perform task description, so that the large language model can learn thinking and logical reasoning capabilities of the professional field from the task description data, and the model effect of the large language model in the professional field can be improved through a small amount of instruction data and task description data.

The present application belongs to the technical field of data processing, and relates to a mental health detection method and device based on large model fine-grained enhancement and a medium. A large language model is used to convert a mental health scale into an extended questionnaire and generate a discrimination standard based on mental health diagnosis rules. For each group of situation, clue, thinking cognition and behavior data in the mental knowledge data set, the situation and clue are combined and mapped into a key vector, and the thinking cognition and behavior are combined and mapped into a value vector. An individualized question set is obtained based on the interview records of the extended questionnaire, the question and answer results of the individualized question set are combined with preset prompt words and input into the large language model to extract mental health representation evidence. The mental health representation evidence is mapped into a feature vector and the similarity with each key vector is calculated. The value vector corresponding to the K key vectors with the highest similarity is combined with the mental health representation evidence, and then the combined result is input into the large language model together with the discrimination standard to output the mental health score of the user to be evaluated and the determination reason.

The invention relates to the technical field of artificial intelligence and education, in particular to an education evaluation and feedback system based on artificial intelligence. Comprising a multi-modal data acquisition module, a data preprocessing and fusion module, a dynamic knowledge graph construction module, a multi-dimensional evaluation analysis module, a personalized feedback generation module, a teaching intervention push module and a system iteration optimization module, all the modules are linked in sequence, and the system iteration optimization module and the data preprocessing and fusion module form a closed loop. According to the method, the traditional score only theory is broken through, a knowledge-ability-thinking-emotion four-dimensional evaluation system is constructed, three-dimensional cognition of students is realized in combination with multi-modal data, the evaluation result better meets the essential requirements of education, the learning process of the students is tracked in real time through the dynamic knowledge graph and the improved Bayesian network model, and the learning efficiency of the students is improved. The evaluation result is adjusted according to the knowledge mastering condition and ability growth, and the defect of staticization of traditional evaluation is overcome.

The invention discloses a medical image segmentation method and device based on cognitive driving, equipment and a medium, and relates to the technical field of image segmentation, and the method comprises the steps: obtaining a to-be-segmented medical image; performing grouping processing on all targets in the to-be-segmented medical image through a preset medical image segmentation model to obtain a plurality of to-be-processed groups, and generating a corresponding sub-network structure according to each to-be-processed group; generating an expanded attention map by using the sub-network structure according to the confidence information of the anchor point target in the to-be-processed group and the spatial distance between the anchor point target and the adjacent to-be-segmented target; performing boundary processing on the expanded attention map and the medical image to be segmented according to the sub-network structure to obtain an enhanced feature map; and carrying out fusion processing on each enhanced feature map, and carrying out image segmentation processing on the fused feature map to obtain a segmentation mask containing each to-be-segmented target. Attention resources of the model are accurately put on small targets which are easy to ignore, the detectability of the small targets is improved, and missing detection is reduced.

The invention discloses a water area monitoring method and system based on semantic driving and time sequence behavior cognition, and the method achieves the open perception capability of dynamically recognizing unknown foreign matters without retraining through the combination of a multi-modal segmentation large model (SAM3) and a natural language cue word. Motion features are extracted through cross-frame tracking, biological behavior intentions are accurately recognized based on time sequence behavior analysis, real anomalies and environmental interference can be effectively distinguished, and therefore the false alarm rate is greatly reduced; a global dynamic batch processing mechanism is adopted, multiple video frames are combined into a high-dimensional tensor for parallel reasoning, and the GPU utilization rate and the system concurrent processing capacity are remarkably improved; through a video rendering and reasoning asynchronous decoupling architecture, smooth video output can still be kept when the reasoning frame rate is low, and the problem of image jamming caused by introduction of a large model is thoroughly solved; therefore, the method integrates open sensing, high accuracy, high fluency and high concurrent processing, and is very suitable for large-scale application and popularization.

A system and method for hierarchical spectral landmark graphs in cognitive manifolds implements cognition through discrete landmark structures that provide scalability, interpretability, and auditability. The system maintains a landmark graph on a cognitive manifold with vertices representing landmark points selected based on geometric properties including curvature and cognitive trajectory density. A spectral basis derived from the landmark graph encodes long-term semantic structure. Spectral continuation updates the basis when geometric invariants indicate structural change, while enforcing differential plasticity constraints protecting foundational low-frequency modes. Reversible edges constructed with forward and reverse displacement vectors enable auditable trajectory replay through cryptographic certificates and manifold journals. The system generates probability estimates by fusing geometric priors from landmark paths, empirical evidence from simulations, and historical evidence from archived cases. Landmark-conditioned naturalization produces interpretable explanations mapping geometric structures to domain-specific semantic labels, enabling transparent, auditable reasoning grounded in verifiable landmark-based evidence.

The invention relates to the technical field of mental health, and discloses a continuous cognition construction method and system for a mental health agent. The method comprises the following steps: extracting portrait features from dialogue data of a current period according to a preset triggering frequency, and merging the portrait features into psychological portrait data of a preset dimension to realize progressive updating; performing risk grading and shunt storage on the new semantic fragment data to generate historical memory data; on the basis of a preset recall rule, performing recall permission judgment on the semantic segments which are limited to be recalled, and determining the range of the semantic segments which can be recalled; candidate semantic fragments are obtained under the retrieval mechanism of hot storage priority and cold storage rollback, and multi-dimensional scoring and maximum marginal correlation reordering are executed through configurable weight parameters to obtain target semantic fragments; and on the basis of the updated psychological portrait data and the target semantic fragment, constructing a continuous cognitive result of the psychological health intelligent agent to the target user. According to the method, the accuracy, the safety and the continuity of continuous cognition of the mental health intelligent agent are improved.

The application discloses a human-computer conversation output delay control system, which acquires first and second type interaction data of different biological behavior dimensions in parallel through a data acquisition unit, provides reliable support for rhythm difference quantization, generates a quantization index through a difference determination unit, objectively quantizes rhythm dislocation degree, and solves the defect that the prior art cannot accurately identify rhythm dislocation, and when the trigger condition is met, the delay adjustment unit performs a control operation, delays the rhythm of a large language model output, avoids the asynchronization of user cognition and emotional integration, effectively reduces psychological risk, and improves the safety, adaptability and reliability of human-computer conversation.

This invention provides a method and system for understanding long-form traffic videos that combines generation and cognition. Through latent semantic modeling and a perceptual reflection loop mechanism, it achieves continuous cognition of traffic scenes over long time scales, avoiding reliance on only local fragment information and improving the overall understanding of long-form traffic videos. By introducing traffic scene generation and future state prediction, the system can simultaneously consider past and potential states, improving its ability to judge complex traffic problems and enhancing its cognitive and reasoning abilities regarding future traffic states. By unifying the representation of historical perception information and future generated information, information fragmentation is reduced, improving the consistency and stability of cognitive reasoning. This solution integrates historical perception and future generation of traffic scenes into a perceptual reflection loop, achieving a forward-looking and holistic cognitive understanding of long-form traffic videos, providing a sound theoretical foundation for intelligent traffic monitoring and event analysis systems.

The invention relates to the technical field of semantic processing, and particularly discloses a law cognition method and system based on multi-dimensional semantic understanding. The system comprises a text preprocessing and multi-granularity analysis module, a multi-dimensional semantic feature dynamic extraction module, a context sensing semantic fusion and disambiguation module, a legal knowledge graph interactive reasoning module and a cognitive decision and output module. Deep semantic understanding and precise cognition of the legal text are achieved, and the accuracy and adaptability of intelligent legal application are improved.

The application provides a virtual reality embodiment rehabilitation system, comprising a virtual domain system, an embodiment sense stimulating system, an embodiment sense evaluating system, a device domain system and a central control module. The virtual domain system induces the sense of possession, the sense of autonomy and the sense of location by creating a virtual avatar, a real-time action mapping and a virtual environment suitable for rehabilitation tasks; the embodiment sense stimulating system takes visual stimulation as the core and matches multiple types of sensory stimulation linkage to strengthen the reality of embodiment experience; the embodiment sense evaluating system evaluates real-time embodiment sense to provide reliable data support for regulation and control. The application is based on embodiment cognition theory, combines virtual reality technology, multi-modal sensing technology, neuroscience and rehabilitation medicine principles, builds an immersive virtual environment, realizes multi-channel sensory coordinated stimulation such as vision, proprioception and touch, induces patients to produce embodiment experience, reshapes abnormal body representation, promotes neural plasticity and functional recovery, and helps patients return to normal life and society.

Provided herein are klotho polypeptide compositions and methods for improving cognitive function in an individual, including treatment with a klotho polypeptide.

The invention relates to an artificial intelligence teaching system for guided learning and an application thereof, the artificial intelligence teaching system can provide an integrated intelligent collaborative learning platform taking cases as a center, and the system can create a complete clinical situation for learners, so that the learners can learn more clearly. A direct relation between morphological characteristics and disease diagnosis is helped to be established, so that the clinical comprehensive thinking ability is cultivated; and meanwhile, the learner is changed from a passive observer to an active and cooperative problem solver. Moreover, in order to solve the problem of lack of real-time professional guidance in collaborative learning, the system innovatively introduces an AI intelligent guidance module, so that interactive behaviors of learning groups are analyzed in real time, prompts, problems and feedbacks highly related to situations are provided, students are actively guided to focus on key features, potential error cognition is corrected, and learning efficiency is improved. And an immersive and efficient school effect of simulating expert guidance aside is achieved.

The invention discloses an AR (Augmented Reality) system cognitive security interaction method based on multi-modal large language model reasoning and bidirectional individuation, which utilizes a cross attention mechanism guided by an inertial measurement unit signal to explicitly model and eliminate electroencephalogram signal motion artifacts, and compared with the existing method of directly using electroencephalogram signals or simple filtering, the method has the advantages that the method is simple and convenient to implement, and the efficiency is high. The anti-interference capability of personalized attention perception is high, attention recognition is more accurate, the distraction moment of the user can be accurately captured, and the false alarm rate is reduced. Besides, personalized preference configuration information is generated through man-machine alignment and is embedded into the reasoning process of the multi-modal large language model, so that the subjective preference of the user to the risk is read, the problem of alarm fatigue is solved, and user trust is established. According to the method, a real agent closed loop is realized, and the method is a complete agent architecture with perception (physiological denoising)-cognition (LLM reasoning combined with preference)-action (self-adaptive interaction), and can adapt to physiological features and psychological preferences of different users.