134 results about "Cognitive capability" patented technology

In a described embodiment, a system for information processing is provided including a data acquisition module configured to receive feedback corresponding to one or more outputs generated by a language model. The system further includes a cognitive reasoning module configured to evaluate the reasoning process of the language model, emulate cognitive functions including metacognitive processes, and generate an assessment based on an analysis of the received feedback, wherein the assessment includes classifying the one or more outputs into components, assigning quality scores for each component, and identifying an improvement corresponding to the one or more outputs. Additionally, the system includes a process adjustment module coupled to the cognitive reasoning module for adjusting the reasoning process of the language model based on the assessment is provided. A refinement module coupled to the process adjustment module is provided for iteratively refining the reasoning process based on subsequent updates to the generated assessment until a performance threshold is met.

A computer system for persistent cognitive neural architecture implementing sophisticated state preservation and sleep-state optimization capabilities. The system operates a layered neural network monitored by a hierarchical supervisory system that collects activation data, identifies operation patterns, and implements architectural changes. A meta-supervisory system tracks behavior patterns and extracts generalizable principles. A cognitive neural orchestrator manages operational states and coordinates decision-making across the network. The system maintains persistent neural network state through mechanisms that store and retrieve neural activation patterns and architectural configurations across operational sessions. During designated sleep states, the system executes optimization operations including memory consolidation and insight generation. This innovative architecture enables neural networks to maintain knowledge continuity across system restarts while implementing sophisticated optimization during periods of reduced demand, enhancing long-term performance through persistent cognitive capabilities.

The invention discloses an interdisciplinary PBL teaching plan intelligent generation system based on knowledge graph and multi-Agent cooperation, and the system comprises an interdisciplinary knowledge base construction module which constructs knowledge graphs of mathematics, physics and computer subjects through semantic vectorization and entity alignment; the retrieval enhancement module is used for dynamically supplementing knowledge blind areas based on an RAG mechanism; the teaching demand analysis module is used for analyzing teacher input and generating a teaching target matrix; the structured modeling module is used for decomposing tasks and designing a teaching framework according to a three-dimensional target system (cognition, ability and subject literacy); the teaching plan generation module is used for generating subject contents through multi-Agent collaboration; and the teaching plan optimization module verifies interdisciplinary consistency and outputs a standardized teaching plan. According to the method, the knowledge graph and multi-Agent dynamic weight distribution are creatively fused, deep integration of interdisciplinary knowledge is realized, teaching plan generation efficiency and scientificity are remarkably improved, and intelligent lesson preparation support is provided for teachers.

A scalable expert foundry system enables creation, management, and coordination of multiple specialized expert domains, each developing autonomous cognitive capabilities through geometric manifold formation while maintaining hierarchical oversight and cross-domain knowledge transfer. The system utilizes a Persistent Cognitive Machine architecture with hierarchical supervisory networks that provide multi-layered coordination, conflict resolution, and quality management across distributed expert domains. Cross-domain coordinators orchestrate communication and knowledge sharing between domains through geometric abstraction and manifold projection techniques that preserve semantic integrity while enabling beneficial knowledge propagation. Executive manifold supervisors implement second-order control architectures managing meta-cognitive capabilities and system-wide reasoning strategies. The system supports enterprise deployment across multiple geographic regions with distributed computing resources. Expert domains achieve operational readiness through statistical observables monitoring including cache hit rates, distance distribution shifts, and trajectory coherence measurements that validate manifold maturity. The architecture enables scalable expert-level performance across diverse knowledge domains while maintaining coordination effectiveness and quality standards.

The invention provides a cognitive ability decline detection method and system based on physiological indexes of wearable equipment, and relates to the technical field of feature selection and machine learning. Comprising the following steps of multi-dimensional physiological data acquisition, data preprocessing and time alignment, cognitive ability state label definition, feature engineering and data balance, cognitive ability decline detection model training and optimization, and output of cognitive ability state prediction. Multi-dimensional physiological indexes and time information of a user are collected in real time through a wearable device, and the physiological indexes comprise heart rate fluctuation features, heart rate statistical features, body temperature features, blood oxygen saturation features, motion data, electroencephalogram state features, skin electrical features, near infrared spectrum features and the like. The wearable device is used for integrating multiple types of physiological signal sensors, and continuous and non-inductive collection of multi-dimensional physiological data such as heart rate variability, electrodermal response and oxyhemoglobin saturation is achieved.

The invention discloses an elderly cognitive function adaptive training method and system based on multi-modal interaction feedback, and relates to the technical field of smart medical treatment. The method comprises the steps that basic information of a user is collected for initial cognitive ability evaluation, a user cognitive portrait is constructed according to an evaluation result, and an initial training task with the corresponding difficulty is allocated; collecting multi-modal interaction data in real time according to the initial training task; carrying out fusion analysis on the multi-modal interaction data by utilizing a machine learning model to obtain a quantized real-time state index; based on the real-time state index and the performance data of the current task, dynamically adjusting a subsequent training task through an adaptive decision rule engine; all-dimensional data of each training task is recorded, a visual cognitive competence development trend report is generated through longitudinal comparative analysis, and a machine learning model and a self-adaptive decision rule engine are continuously optimized and trained by utilizing accumulated user data to form an optimized training closed loop. The cognitive function training effect of the old people can be improved.

The invention provides a system and a method for evaluating the independent living ability of old people. The method comprises the following steps: acquiring multi-modal original data (including physiology, behavior and interaction data) of target old people; performing modal coding, query vector interactive fusion (including missing information completion) and feature compression on the multi-modal original data to obtain a standardized feature vector; a first-layer model evaluates behavior and cognitive ability indexes, and a second-layer model combines individual background information to predict a living ability grade and a degradation risk; optimizing the first-layer model through a feature feedback mechanism; and finally, outputting and visualizing a structured evaluation result. The method breaks through the limitation of traditional manual evaluation, achieves the deep fusion of multi-modal data, improves the objectivity, robustness and interpretability of evaluation, and can meet the precise evaluation demands of home care, community monitoring and smart medical scenes.

The invention relates to the technical field of intelligent education management, in particular to a self-adaptive learning progress informatization teaching test system, which comprises the following contents: a knowledge node construction module, a learning state identification module, a test task generation module, a path scheduling adjustment module and a feedback data backtracking module. According to the method, refined numbering management of knowledge points is realized by constructing a directed edge numbering set, a dependency direction is set in combination with a label propagation algorithm, the semantic expression ability of a node relationship is enhanced, mastering weights and stability coefficients are matched based on answering behaviors, a state weight sequence is formed, and the stability of classification and recognition is improved; cognitive ability coding and time-consuming median value linkage state weight are introduced, questions are screened, a priority queue is constructed, task pushing precision is improved, numbering and sorting weight are fused to construct a path chain, dependency intensity is calculated to expand path branches, and individual suitability and task rhythm coordination ability of path scheduling are enhanced.

The invention provides a multi-mode content review method and device, a medium and a computer program, and relates to the technical field of content review. The multi-modal content review method comprises the following steps: calling a special recognition model to recognize subtitles and voice in target review content to obtain a character recognition result and a voice recognition result; the special identification model is a model obtained based on learning of a content sample and a subtitle label and a voice label of the content sample; and calling an artificial intelligence large model, performing fusion analysis and reasoning on the character recognition result, the voice recognition result and the video picture of the target review content according to the violation review rule, and outputting a violation behavior recognition result. The universality of the content review method is improved based on the good cognitive ability of the artificial intelligence large model; the artificial intelligence large model and the special small model are organically combined, the understanding cognitive ability of the artificial intelligence large model and the perception ability of the special small model are fully exerted, and accurate and effective examination of the content is realized.

The invention belongs to the technical field of artificial intelligence, and discloses a novel large model autonomous reasoning system based on fast and slow thinking, which comprises a system 1 fast integration information module, a system 2 logical reasoning and knowledge extension module and a knowledge induction module, wherein the system takes meta-cognitive competence as a core, and realizes autonomous reasoning in combination with a multi-level reasoning and knowledge induction module of a language model. According to the novel large model autonomous reasoning system based on fast and slow thinking, the fast and slow thinking theory is used for reference, the fast intuition of the system 1 and the logical reasoning of the system 2 are combined, the meta-cognitive ability is introduced, the novel autonomous reasoning system is constructed, the reasoning efficiency and accuracy of the model can be improved, and the reasoning efficiency and accuracy of the model can be improved. And the capability of processing complex tasks can be enhanced, and a new possibility is provided for application of artificial intelligence in a complex open environment.

The invention belongs to the technical field of visual training, and particularly relates to a multi-dimensional visual training feedback method for improving binocular fusion visual and stereoscopic visual functions, and the method comprises the following steps: carrying out medical baseline detection, generating an initial ability portrait through visual function evaluation and cognitive ability testing, matching training difficulty parameters, and generating a personalized scheme; multi-modal game training is carried out, training types comprise gamification training, animation scene training and offline family task training, and the training difficulty is dynamically adjusted; a tutor performs remote one-to-one guidance, and dynamically adjusts a training scheme according to a change report by tracking the training progress in real time; and the system performs multi-dimensional effect evaluation and self-optimizes a difficulty prediction algorithm. According to the method, the problems of dull training and passive participation, scheme stiffness and execution deviation, single stimulation and cognitive load, scene limitation and continuous interruption are solved.

The invention discloses a data government method and system based on a large government affair model, and belongs to the technical field of deep learning, and the method comprises the steps: government affair data collection: obtaining basic data from a data source for implementing data government, the basic data comprising historical data and current data; government affair data treatment: preprocessing the collected basic data, and integrating the data to obtain a data sequence; a platform adaptively selects a preset deep learning framework according to the government affair scene features, and a staged parameter optimization strategy is combined to train and generate a government affair decision model with a domain cognitive ability; and intelligent analysis: deploying a semantic understanding engine, converting the unstructured government affair text into data vector representation which can be analyzed by a machine, and outputting a data analysis result through a multi-modal feature fusion technology. According to the invention, the data management difficulty in a government affair scene can be reduced, and deep transformation of intelligent government affairs and establishment of an intelligent decision support system are promoted.

The application discloses a robot cognitive development method based on ontology semantics, and comprises the following steps: constructing a robot article identification professional knowledge base based on attribute function and ontology information representation of article definition; information determination based on attribute discrimination and semantic search; and robot cognitive development based on attribute information addition. The application simulates the process of human memory, learning and cognition of articles based on an ontology semantic knowledge base, and through machine learning and sensor attribute information, the robot can actively cognize, learn, expand and accumulate learned knowledge and experience according to information data; the robot can continuously develop its cognitive ability through learning, automatically construct a robot article identification professional knowledge base of unknown articles, and based on a semantic structure of triplets, share knowledge and exchange operation logic between man and machine, so that the cognitive level of the robot is improved and the operation experience of an operator is improved.

The invention discloses a picture book live broadcast personalized book recommendation method and system based on artificial intelligence, and relates to the technical field of live broadcast book recommendation, and the method comprises the following steps: obtaining multi-modal interaction data of a user in picture book live broadcast microphone connection, and generating a three-dimensional ability portrait vector and a psychological portrait vector representing the cognitive ability and psychological tendency of the user based on the multi-modal interaction data; performing time sequence analysis, and calculating an evolution direction vector and an evolution rate scalar; when the evolution direction is positive, according to a comparison result of an evolution rate scalar and an adaptive threshold range, updating a preset cooperation coefficient in the threshold range, if the evolution rate scalar exceeds the threshold range, adjusting an ability integrating degree weight and an emotion resonance degree weight, and dynamically updating parameters based on weight adjustment counting; and calculating a matching total score through a matching decision formula by using the dynamically adjusted parameters, and outputting picture book recommendation. According to the method and the device, the user state is adapted in real time in live broadcast interaction, the recommendation strategy is adaptively optimized, and the accuracy of the instant individuation degree of recommendation is remarkably improved.

The invention provides a cognitive function evaluation method based on neurovascular coupling analysis. The method comprises a signal preprocessing step, a signal processing step, a feature extraction step and a coupling coefficient calculation step. A new normal form of cognitive evaluation is constructed from nerve-blood vessel coupling and cross-system cooperation dimensions, and accurate description of a cognitive state is realized through real-time quantification of a dynamic cooperation effect of brain nerve activity (EEG) and blood flow regulation (CBP variability) and establishment of an association relationship between a dynamic cooperation index and cognitive ability (alertness, reaction speed and memory). And a new objective basis is provided for the fields of plateau medicine, intelligent health, neurological and mental disease diagnosis and treatment and the like.

The invention particularly relates to a closed-loop neural feedback training method and device based on tactile-motion electroencephalogram signals. The method comprises the steps that the current finger somatosensory time discrimination threshold and electroencephalogram data of a tested individual are collected; performing neural feedback training on the tested individual by using a closed-loop neural feedback system and an electroencephalogram amplifier; and collecting the current finger somatosensory time discrimination threshold and the electroencephalogram data of the tested individual again, and processing the behavioral data of the finger somatosensory time discrimination threshold of the tested individual and the electroencephalogram characteristic data of the tested individual to obtain an effect after neural feedback training. According to the method, the primary somatosensory cortex damage is utilized, fewer index tactile electroencephalogram characteristic indexes constrained by cognitive competence are extracted, a patient can observe signals related to the electroencephalogram characteristic indexes of the brain in real time in the closed-loop neural feedback training process, self-adjustment is carried out, and the accuracy of the patient is improved. Therefore, the basic nerve mechanism of the human body is more effectively changed, and the early Alzheimer's disease is controlled and intervened.

The invention belongs to the technical field of cognitive power, and relates to a cognitive competence assessment method, training method, system, equipment and medium, the system comprises a data acquisition unit used for acquiring electrophysiological signal data and behavior data of a user; the evaluation unit is connected with the data acquisition unit and is used for executing a cognitive evaluation scale task and synchronously acquiring the electrophysiological signal data, the behavior data and the scale score of the user; the fusion analysis unit is connected with the data acquisition unit and the evaluation unit and is used for integrating the cognitive evaluation scale data, the electrophysiological signals and the behavior data and inputting the data into a multi-modal fusion evaluation model so as to output a cognitive load index and an emotional stability score; and the training unit is connected with the fusion analysis unit and is used for adjusting the VR training difficulty according to the cognitive load index and the emotion stability score. According to the scheme of the invention, the accuracy of cognitive ability evaluation and the applicability of cognitive training are effectively improved.

In described embodiments, a system for information processing is provided that includes a data acquisition module configured to receive feedback corresponding to one or more outputs generated by a language model. The system also includes a cognitive inference module configured to evaluate an inference process of the language model, simulate a cognitive function including a meta-cognitive process, and generate an evaluation based on an analysis of the received feedback, where the evaluation includes classifying one or more outputs into components, assigning a quality score to each component, and outputting the one or more components based on the quality score. And identifying improvements corresponding to the one or more outputs. Additionally, the system includes a process adjustment module coupled to the cognitive inference module for adjusting an inference process of the language model based on the evaluation. A refinement module coupled to the process adjustment module is provided for iteratively refining the reasoning process based on subsequent updates to the generated evaluation until a performance threshold is met.

The application provides a device for evaluating the sentiment cognitive ability of a large language model, which has the following characteristics: an evaluation data generation module for generating reply statements corresponding to each test statement for the large language model to be evaluated; a key event recognition evaluation module for evaluating each key event reply statement to obtain a key event evaluation score; a mixed event recognition evaluation module for evaluating each mixed event reply statement to obtain a mixed event evaluation score; an implied sentiment recognition evaluation module for evaluating each implied sentiment reply statement to obtain an implied sentiment evaluation score; an intent recognition evaluation module for evaluating each intent reply statement to obtain an intent evaluation score; and a comprehensive evaluation calculation module for calculating a comprehensive evaluation score. In summary, the device can obtain a comprehensive and objective quantitative result of the sentiment cognitive ability of the large language model.

The invention relates to the technical field of cognitive training, in particular to a cognitive ability training method and system based on virtual reality. The method comprises the steps that a training mode selected by a user is obtained, a corresponding virtual scene is loaded according to the training mode, and the training mode comprises a grabbing training mode, a balance training mode and a racing training mode; acquiring training data when a user performs training in the virtual scene; determining an error rate, response time and continuous success times of the user based on the training data, and matching an adjustment rule according to the error rate, the response time and the continuous success times; and adjusting and outputting the corresponding training difficulty according to the adjustment rule, so that the user performs training under the adjusted training difficulty. The cognitive ability training effect can be improved.

The invention provides a neural feedback training scheme automatic screening and effect suggestion method. The method comprises the following steps: acquiring biological data of a testee by using a brain wave collection device; wherein the brain wave collecting device comprises a household brain wave collecting device; transmitting the biological data to a brainwave database of a remote cloud system through a network; the brain wave database converts the biological data into a corresponding training parameter suggestion; and remotely feeding back the training parameter suggestions to a neural feedback training module for the testee to perform heart and brain training. Therefore, suggested training parameters are provided through a software interface, and superior and inferior brain area networks, digital therapy suggested training schemes and related health education are arranged, so that the effects of long-distance feedback and improvement of cognitive competence by brain training at home are achieved.

The application provides a cross-scene cognitive ability evaluation method based on fine-grained migration, which comprises the following steps: taking cognitive data of a user in a first scene as source domain data and taking cognitive data of the user in a second scene as target domain data; taking a source domain data set as a training set, training a random forest classifier, and generating a source domain model; obtaining a test accuracy rate of the individual classifier on a target domain data set and an information gain difference from source domain features to target domain features; clustering all the individual classifiers into multiple clusters according to the test accuracy rate and the information gain difference; updating the individual classifiers in each cluster by using a corresponding growth mechanism to obtain a target domain model; and evaluating the cognitive ability of the user in the second scene through the target domain model. The application also provides a cross-scene cognitive ability evaluation system based on fine-grained migration and a data processing device.

An object manipulation system comprising a storage device and a plurality of objects, which are configured to be placed within the storage device and separately taken from the storage device. The object manipulation system helps stimulate cognitive abilities of a user and provide quality of life moments without the need for supervision by allowing the user to arrange the items according to one or more arranging cues. The object manipulation system also helps stimulate blood flow of the hands and arms of the user and provides the added stimulating benefits of exercise and flexibility by enabling the user to bend and manipulate one or more manipulative devices of the system. Particularly, in some embodiments, the plurality of objects are bendable and are able to be bent and manipulated as the objects are taken from the storage device and arranged according to the one or more arranging cues.

The invention discloses an image-text associated cognitive training system and a method thereof, and relates to the technical field of cognitive training and artificial intelligence. According to the method, image memorability calculation, image-text semantic matching and user behavior driven personalized recommendation and cognitive ability evaluation mechanisms are fused, the problems that in the prior art, image selection is random, image-text pairing is extensive, training lacks personalized adaptability and feedback analysis and the like are solved, and personalized, self-adaptive, efficient and intelligent cognitive training is achieved. The system comprises the following core modules: an image memorability database module; an image automatic matching module; a personalized recommendation module; and a cognitive training module.

The application relates to the technical field of sales support, and discloses a medicine sales data analysis method, which comprises the following steps: constructing a cognitive map model; performing clinical path complexity grading; applying a clinical path complexity grading algorithm to analyze clinical decision process data of a medical institution, and calculating a clinical path complexity index; implementing cognitive-clinical path matching; adopting a cognitive-clinical path matching algorithm to process sales representative cognitive state data and clinical path complexity data, and calculating a matching degree score; performing hierarchical progressive information presentation; based on a hierarchical progressive information presentation algorithm, processing the matching degree score and the clinical path data, dividing information into different priority levels, and outputting an optimized information presentation sequence; and providing personalized clinical knowledge navigation; the application significantly improves the accuracy and effectiveness of professional information transmission by intelligently adapting the cognitive ability of a sales representative to the complexity of a clinical path.

According to the invention, the cognitive ability or exercise ability of the user is effectively improved. The information processing system includes: a blocking unit that makes all or parts of a user's body invisible; a request unit that requests the user to perform a body action in which a part of the body of the user or an object operated by the user overlaps with a target object; and a control unit that controls the difficulty of the body movement in accordance with the movement of a joint part different from the body part on which the body movement is performed, in an environment in which the abnormal movement of the joint generated by the shielding unit and the requesting unit appears.

The invention relates to a cognitive impairment detection method, device, equipment, medium and product, and the method comprises the following steps: an obtaining step: obtaining information data about the cognitive state of a user through a plurality of voice interaction tasks; an extraction step of performing feature extraction on the obtained information data based on a machine learning mode to obtain key features, namely feature vectors, of nodes corresponding to each voice interaction task; a processing step: processing the nodes through the two graph attention networks, updating the feature representation of the key feature of each node, and obtaining a new feature vector of each node; and a detection step: through the new feature vector of each node, calculating to obtain a global feature representation reflecting the characteristics of the whole graph, and evaluating the cognitive ability according to the global feature representation so as to detect the cognitive impairment. According to the invention, the accuracy and adaptability of detection in various environments can be significantly improved.

The invention discloses a space factor function principal component method for brain function structure identification. The method comprises the following steps: S1, inputting original data; s2, extracting function data by utilizing a factor process; s3, decomposing the factor load into a smooth function of a space coordinate and an additionally determined piecewise constant matrix; s4, applying the principal component analysis of the function to the potential process to process the correlation on the variables, and obtaining the final form of the function; determining a block structure of the brain; a regression model about the cognitive function is established as a covariable and is used for analyzing the influence of the ROI volume on the cognitive function. According to the method, the internal correlation, the spatial correlation and the segmentation smoothness of the ROI volume curve can be effectively captured, and the low-dimensional scalar features easy to operate are extracted from the ROI volume curve for subsequent regression analysis, so that the prediction precision of cognitive competence is improved.

The invention belongs to the technical field of multi-modal image-text alignment, and particularly relates to an urban scene image-text alignment method and system based on spatial topology constraint, and the method comprises the following steps: firstly, respectively extracting corresponding features of text data and image data by utilizing Transform, and encoding, so as to solve the problem of representation difference between different modal data types; the method comprises the following steps of: firstly, carrying out image-text alignment on an image, then describing a relationship and constraint between the image and text features by utilizing spatial topology constraint, minimizing differences among different modes, keeping geometric consistency and relationship consistency among entities, and finally, inputting the constrained features into a CLIP alignment module to finish an image-text alignment task. According to the method, by fusing image and text information, the cognitive ability of the model to a complex scene is enhanced, and the method has an important promotion effect in the fields of multi-modal data processing and deep learning.

This invention provides a method and apparatus for automatically annotating analogical corpora using the computing power of an intelligent computing center. The method includes: determining prompt words, which contain analogical corpus pairs as examples and their analogy relationship analysis process; calling a large language model running in the intelligent computing center to analyze the analogy relationship analysis process of the analogical corpus pairs as examples in the prompt words; analyzing the analogy relationships of unannotated analogical corpus pairs; obtaining the analogy relationship analysis results of unannotated analogical corpus pairs; and annotating the analogy relationship types of the analogical corpus pairs based on the analogy relationship analysis results. High-quality analogical corpus pairs can be selected, thereby improving the cognitive ability and analogical reasoning ability of the large language model trained with these high-quality analogical corpus pairs.