51 results about "Planning" patented technology

The invention discloses a cognitive impairment assessment system and method based on general artificial intelligence, and the method comprises the following steps: S1, collecting multi-modal data through a wearable device, a mobile application and an online tool; s2, carrying out preprocessing on the multi-modal data; s3, comprehensively analyzing the preprocessed data, evaluating cognitive competence, and identifying a cognitive impaired field; s4, a rehabilitation training plan is generated through a neural evolution algorithm, and the training content, sequence, difficulty and type are dynamically adjusted; s5, providing cognitive training and adjusting tasks in real time through virtual reality and augmented reality technologies; s6, predicting cognitive load and psychological state, and dynamically adjusting the intervention plan; and S7, protecting data privacy by using a block chain technology, monitoring a training effect, and generating a progress report. According to the method, the intervention plan and the privacy protection technology are dynamically adjusted through multi-modal data analysis based on general artificial intelligence, and a personalized and real-time cognitive impairment assessment and intervention scheme is provided.

The invention relates to a psychological counseling interaction method and device based on an autonomous psychological planning architecture. The method comprises the following steps: acquiring initial user data of a target user; constructing a dynamic target map of a target user through a three-layer nested target structure, and pre-loading a matched personalized decision tree; after receiving the real-time interaction data, executing real-time path re-evaluation based on the real-time interaction data to obtain a treatment value function of each intervention path; constructing an intervention response prediction model by adopting a Bayesian network and a Monte Carlo tree search algorithm, adjusting the priority and the execution sequence of the intervention paths in the personalized decision-making tree in combination with the treatment value function of each intervention path, and selecting a target intervention path matched with the real-time interaction data from the adjusted intervention paths; the real-time interaction data is mapped into the multi-dimensional psychological state space to serve as the session trajectory, session trajectory planning is conducted based on the target intervention path, real-time interaction with the target user is achieved, and the accuracy of intervention decision making and the session fluency are improved.

The invention discloses a rehabilitation assisting method and device suitable for cognitive disorder treatment and a medium, and relates to medical rehabilitation. A tablet personal computer, a millimeter wave radar and the like are included, artificial intelligence technologies such as signal processing and deep learning are utilized, and rehabilitation training of patients with mild cognitive impairment caused by brain injury and cerebral apoplexy is assisted. Basic information, medical history and current cognitive conditions of the patient are recorded and collected through the tablet computer, and a rehabilitation file is generated; based on the cognitive disorder type and severity of the patient, a rehabilitation training plan is intelligently made; a patient performs rehabilitation training through a gamified training interface, and the physiological state in the training process is monitored in real time in a non-contact mode based on millimeter wave radar; a deep learning model is used for analyzing training data of the patient, and rehabilitation progress is evaluated; training and evaluation data are summarized, and a subsequent rehabilitation scheme is optimized. Full-process automatic management from patient information collection, training plan making, training implementation, rehabilitation evaluation to data analysis report generation can be rapidly and accurately achieved, and the cognitive rehabilitation efficiency and effect are improved.

A neuromodulation system including at least one input module for inputting a planned neuromodulation event or a series of neuromodulation events and at least one analyzing module for analyzing a neuromodulation event or a series of neuromodulation events.The analyzing module and the input module may be connected such that the input module is configured to forward the planned neuromodulation event or a series of neuromodulation events to the analyzing module and the analyzing module is configured to analyze the planned neuromodulation event or a series of neuromodulation events regarding one or more possible neuromodulation conflict(s).

The invention discloses a cognitive disorder early screening and diagnosis system based on multi-modal data, and the system comprises the following modules: (1) a multi-modal data collection module which is used for obtaining image data, gene data, behavior data and clinical narrative information; the clinical narrative information comprises at least two of a patient chief complaint text, an interference factor hooking option and a symptom time axis; the causal purification module is used for filtering pseudo-correlation among the multi-modal data through clinical narrative anchoring and reverse intervention deduction, and comprises a narrative anchoring unit which is used for identifying data conflicts through a structured clinical narrative field based on a preset causal conflict rule base, and triggering freezing or weight descending operation; the intervention deduction unit is used for generating a reverse intervention verification packet containing a low-cost intervention measure and a review plan, and updating a causal rule base based on review data; according to the method, the statistical association limitation of the traditional technology is broken through, and the diagnosis and treatment normal form upgrading from correlation to causality is realized.

The present invention relates to a cognitive computing-assisted clinical decision support system designed to enable personalized neurological rehabilitation. The system acquires structured user data across clinical, anatomical, radiological, etiological, pathological, and rehabilitation domains to create individualized profiles. These profiles are mapped against a repository of historical cases using analog matching and similarity scoring to generate stratified, evidence-based rehabilitation recommendations. Real-time monitoring of rehabilitation progress is performed using global recovery and function outcome indicators, allowing for dynamic adjustment of treatment plans. Clinician intervention modules ensure safety, interpretability, and context-aware customization. The system incorporates a continuous feedback mechanism to refine future predictions and recommendations, making it increasingly adaptive over time. The invention improves rehabilitation outcome prediction accuracy, reduces recovery variability, and optimizes functional outcomes by transforming static rehabilitation models into intelligent, responsive, and personalized care pathways.

The application discloses a kind of cognitive impairment early screening and diagnosis system based on multi-modal data, including following module: (1) multi-modal data acquisition module, for obtaining image data, gene data, behavior data and clinical narrative information;The clinical narrative information includes at least two kinds of patient complaint text, interference factor hook item, symptom time axis;(2) causal purification module, for filtering the pseudo-correlation between multi-modal data by clinical narrative anchoring and reverse intervention deduction, including: narrative anchoring unit: based on preset causal conflict rule base, through structured clinical narrative field identification data conflict, trigger freezing or weight reduction operation;Intervention deduction unit: generate the reverse intervention verification package containing low-cost intervention measures and review plan, and update causal rule base based on review data;The application breaks through the statistical correlation limitation of traditional technology, realizes the upgrade of diagnosis and treatment paradigm from "correlation" to "causality".

The invention relates to the technical field of child cognitive function evaluation, and particularly discloses an intelligent child cognitive function evaluation method and device and a medium, and the method comprises the steps: collecting real-time behavior physiological coupling response data of a tested child under the stimulation of color and digital dual tasks through a multi-modal stimulation collection device, and obtaining a cognitive response chain dynamic model; according to the cognitive reaction chain dynamic model, performing performance quantification on the joint distribution of the reaction accuracy and the reaction time of the child under the stimulation of color and digital dual tasks to obtain a cognitive performance quantitative index; meanwhile, self-adaptive calibration of the individual cognitive baseline is carried out to obtain individual cognitive baseline data; and finally obtaining a personalized course recommendation matrix. The method not only is helpful for evaluating the current cognitive ability level of children, but also can provide reference for self-adaptive calibration of individual cognitive baselines. The method is crucial for recognizing cognitive deviation and formulating a personalized training plan. Adjustment can be carried out according to the specific cognitive requirements of children, and the effectiveness and pertinence of training can be improved.

The embodiment of the invention relates to the technical field of fatigue driving intervention, and particularly discloses a method and a system for identifying the influence of music intervention on a brain network under driving fatigue. According to the embodiment of the invention, through grouping experiment planning, dynamic simulation is carried out according to experiment planning data, and fatigue monitoring data is collected; eEG collection and fNIRS collection are carried out, and EEG collection data and fNIRS collection data are obtained; constructing a weighted brain network diagram, and performing time sequence synchronization on the weighted brain network diagram and the fatigue monitoring data to generate time sequence synchronization data; and performing grouping and intra-group comparative analysis on the time sequence synchronization data, identifying music intervention influence, and performing scene application recommendation. Grouping experiment planning and dynamic simulation can be carried out, a weighted brain network graph is constructed through EEG collection and fNIRS collection, time sequence synchronization is carried out on fatigue monitoring data, grouping and intra-group comparative analysis are carried out, the music intervention influence is recognized, and therefore the influence of music intervention on the brain network under driving fatigue is recognized.

The invention discloses an Alzheimer's disease rehabilitation assistance system based on artificial intelligence. The Alzheimer's disease rehabilitation assistance system comprises a data collection module, a cognitive evaluation module, a patient classification module, a rehabilitation planning module and a comprehensive assistance module. The invention belongs to the technical field of Alzheimer's disease rehabilitation management, and particularly relates to an Alzheimer's disease rehabilitation auxiliary system based on artificial intelligence, which realizes cognitive risk level judgment by acquiring multi-modal data of voice, writing, behavior and nursing evaluation, extracting structural features and constructing a cognitive evaluation method fusing rules and a lightweight model; performing multi-dimensional symptom grading in combination with patient behaviors and living abilities, screening personalized intervention tasks through an adaptive scoring mechanism, and generating a dynamic rehabilitation path and plan; closed-loop linkage among cognitive assessment, hierarchical classification and rehabilitation intervention is realized, and the accuracy and practicability of rehabilitation assistance are improved.

The invention discloses an intelligent feedback type encephalopathy rehabilitation guidance training system which collects physiological data of a patient in real time through a wearable device or a brain-computer interface, carries out accurate evaluation and generates a personalized training plan in combination with individual differences and rehabilitation progress of the patient. In the training process, the system provides immediate feedback and guidance, and correctness and effectiveness of training are ensured. Meanwhile, the system also integrates comprehensive rehabilitation guidance functions, including cognition, language and motion function recovery, psychological support, life skill training and the like; through the cloud data processing center, medical personnel can remotely monitor the rehabilitation condition of a patient and adjust a training plan in time. The system not only improves the pertinence and effectiveness of rehabilitation training, but also enhances the rehabilitation confidence and participation degree of the patient. In a word, the intelligent feedback type encephalopathy rehabilitation guidance training system provides all-around and personalized rehabilitation services for encephalopathy patients, and the rehabilitation effect and the satisfaction degree of the patients are remarkably improved.

The invention, which relates to the technical field of child development assessment, discloses a child neural development state assessment method for pediatric neurology department, comprising the following steps: collecting basic information and current state information of a child, and analyzing the current emotional state and physical condition of the child; and according to the current emotional state and physical condition of the child, planning a test environment to evaluate the development level of the child. According to the child neural development state evaluation method for the pediatric neurology department, a comprehensive and objective neural development state portrait is provided for the child by integrating data of development level, muscular tension and consciousness expression, the specific expression of the child in a certain aspect is concerned, the relevance and coordination among various abilities are also concerned, and the child neural development state evaluation method is suitable for the pediatric neurology department. The method can comprehensively reflect the neural development condition of the child, helps parents and professionals to timely discover the problems of the child in the aspect of neural development, provides a scientific basis for making a personalized intervention plan, and ensures that intervention measures are more accurate and effective.

The invention relates to the technical field of psychological rehabilitation, and discloses a CBT therapy-driven AI personalized psychological rehabilitation path generation system, which comprises a data acquisition and evaluation module, a knowledge graph construction module, an AI path planning module, a dynamic adjustment module and an interactive feedback module, the data acquisition and evaluation module is used for acquiring basic information, psychological state evaluation data and cognitive behavior characteristic data of a user, and generating an initial psychological problem diagnosis result based on a CBT evaluation scale; the mapping knowledge domain construction module is used for constructing a CBT therapy mapping knowledge domain. A preliminary personalized psychological rehabilitation path is generated through a reinforcement learning algorithm, a CBT therapy is systematized and structured through a knowledge graph construction module, it is ensured that the generated rehabilitation path meets evidence-based practice standards, and the problem that a traditional AI system lacks theoretical support is solved; on the basis of multi-dimensional user data and a reinforcement learning algorithm, a rehabilitation path adaptive to individual cognitive features and problem types is generated, and the limitation of a standardized scheme is avoided.

Intelligent systems and methods for facilitating insomnia therapy are disclosed. Sensor data (e.g., non-contact sensor data) can be used to determine physiological parameter(s), such as sleep-related physiological parameter(s), which can be used to generate a sleep disorder prediction. The sleep disorder prediction can be used, along with an identified sleep therapy plan, to generate and facilitate application of (e.g., present to a user or automatically apply) a sleep therapy recommendation. When sleep apnea is predicted in concert with an identified cognitive behavioral therapy for insomnia (CBTi) plan, a warning can be presented to the user to not engage in certain CBTi therapies. Sensor data can also be used to automatically update therapy parameter(s) of an ongoing sleep therapy plan, such as in realtime.

A method and system for obtaining a measurement of cognitive performance in an individual, provide for obtaining a measure of at least one of the following activity parameters for the individual: 1) spatial memory accuracy; 2) planning accuracy; 3) ability to carry out dual-task interactions while navigating to a goal, wherein omission of the dual-task interactions is measured; 4) perseverations of incorrect dual-task interactions while navigating to a goal; 5) total time required by the individual to complete a navigation route; 6) upper extremity neuro-motor parameters; 7) reaction time of dual-task interactions; and 8) reaction time of the idle state of the individual; receiving the measures obtained into an algorithm; and computing a functional impairment score for the individual on the basis of baseline measurements obtained from a population of healthy individuals. The measurements are obtained using an app on an electronic portable device. The system and method may include an application executable by a user device to generate a gamified environment, to receive a first user input or set of user inputs at a first time and to transform said first user input or set of user inputs into a first measurement or set of measurements, and to receive a second user input or set of user inputs at a second time and to transform said second user input or set of user inputs into a second measurement or set of measurements; configured to receive said first measurement or set of measurements and compute a first functional impairment score and to receive said second measurement or set of measurements and compute a second functional impairment score; and configured to compare said second functional impairment score with said first functional impairment score and determine a magnitude and / or a speed of change in said functional impairment scores.

The application provides a man-machine cooperative cognitive decision space construction and on-the-spot decision method, that is, in the action planning process, on the basis of the main line action plan designed based on the static task purpose, a person estimates possible variables in action execution in advance, refines situation cognitive characteristics of key decision points, and proposes countermeasures, the system explores diversified possible action processes, analyzes and excavates key variables which have greater influence on action results, explores the best action options under the guidance of countermeasures and trains a dynamic planning intelligent agent, finally, the cognitive characteristics and countermeasure options, the planning intelligent agent are added to the main line action plan, and a cognitive-decision space is constructed; in the action execution process, the cognitive-decision space is used to guide the real-time monitoring of the change of situation characteristics and the dynamic triggering of key decision points, the online optimization of the best countermeasure and the dynamic generation of subsequent action planning.

The invention discloses a precise neurology department nursing thousand-pre-nursing method. The method comprises the following steps of 1, collecting and analyzing patient data; 2, making a personalized nursing plan; 3, implementing dynamic monitoring and feedback; 4, implementing virtual reality psychological intervention; and 5, effect evaluation and continuous improvement are carried out. According to the technical scheme, by effectively integrating the multi-dimensional patient data, the personalized level of the nursing plan can be remarkably improved. The change not only enables the nursing scheme to be more targeted, but also can perform dynamic adjustment according to the real-time feedback and physiological change of the patient to improve the treatment effect, and under the support of precise nursing, introduction of the virtual reality technology provides a brand new solution for psychological intervention. Patients are subjected to psychological training in an immersive environment, anxiety and pressure can be effectively reduced, and the sense of participation and interactivity of treatment are enhanced. By means of the mode, the limitation of time and space is broken through, and the patient can conduct self-adjustment in a more relaxed state.

The invention relates to a technical development for project management for improving the efficiency of an action plan in terms of expected impact on the cognitive abilities of a target audience. The technical development includes a method and system for automating the optimisation of the expected impact. The method consists in evaluating the expected impact, qualitatively and quantitatively, on the basis of a scientific database which is updated according to the specific features of the project, in order to optimise the expected impact when planning and following the action plan. The system automates the analysis of the data in order to evaluate impact indicators and indices and automates the optimisation recommendations from the evaluated indicators and indices. An example of better usage shows how to optimise the expected impact of a communication plan on the cognitive abilities of multiple groups of a target audience. Figures 1, 2 and 3 represent the method, the analysis system and the recommendation system, respectively.

The invention relates to the technical field of education management, and discloses a learning cognitive path planning system based on a cognitive map. Comprising a cognitive map construction module for recognizing subject knowledge points, extracting a dependency relationship and constructing a cognitive map; the evaluation portrait construction module is used for constructing a learning evaluation portrait; the difference atlas drawing module is used for identifying a difference atlas in the cognitive atlas; the path calculation and screening module is used for calculating the path value of the feasible path and screening out a learning cognitive path; according to the method, the cognitive dynamic change condition of the learner can be regularly monitored and analyzed, the continuous cognitive updating effect of the learner along the timeline is realized, and the limitation and hysteresis existing when a learning evaluation portrait is constructed by single-dimensional static data are avoided; and the phenomenon that part of associated subject knowledge points are missed and lost due to direct comparison of the real-time cognitive level and the planned cognitive level is avoided, and the planning accuracy of the learning cognitive path is improved.

The application discloses a brain surgery path intelligent planning method based on reinforcement learning and relates to the technical field of brain surgery. A standardized surgery environment data set is formed; a three-dimensional brain structure topology model is obtained; a dynamic three-dimensional constraint tensor is generated; a surgery timing decision Token sequence is formed; the surgery timing decision Token sequence is input into a Decision Mamba path decision network based on a selective scanning state space model to perform strategy optimization, and an underlying obstacle avoidance candidate path decision vector sequence is output; a safe candidate path sequence meeting an anatomical safety constraint is obtained; an executable surgery trajectory instruction set is generated; the dynamic three-dimensional constraint tensor and the surgery timing decision Token sequence are updated and recalled, and path decision and safety correction are re-executed. The application improves the response speed and precision of intraoperative path adaptive re-planning in a strong dynamic environment of brain tissue stress deformation.

The present invention provides a method for classifying mental illness based on the local coupling pattern of the structural-functional brain network, which belongs to the field of medical information intelligent diagnosis technology; its technical solution is: frequent subgraph mining is performed on the connection matrix of the structural brain network to obtain all frequently connected subgraphs; the coupling coefficient between the structural brain network and the functional brain network is calculated through all the frequently connected subgraphs mined; then the coupling coefficient obtained is feature selected using the lasso model through five-fold cross validation; the coupling coefficient obtained after attribute simplification is classified using random forests to evaluate the accuracy. The beneficial effects of the present invention are: the present invention can better capture the key features of the structural-brain network, significantly improve the accuracy and efficiency of mental illness classification, and help doctors formulate more effective treatment plans for patients.

The invention relates to the technical field of data analysis, in particular to a cognitive impairment grade evaluation method for a cognitive impairment user group, which comprises the following steps: setting MMSE and MoCA detection periods of cognitive impairment users, and performing MMSE and MoCA detection on the cognitive impairment users in real time based on the set detection periods; mMSE and MoCA detection results of the cognitive impairment user are obtained, and a database is constructed to store the obtained MMSE and MoCA detection results of the cognitive impairment user; according to the method, the MMSE and MoCA detection results of the cognitive impairment user accumulated and stored in the database are traversed, the MMSE and MoCA detection is taken as contact, the completion degree of the MMSE and MoCA detection is analyzed, and the MMSE and MoCA detection results of the cognitive impairment user are continuously observed in a continuous specific detection period, so that the cognitive impairment level evaluation is finally performed on the cognitive impairment user, and the accuracy of the cognitive impairment level evaluation is improved. Rehabilitation treatment planning reference is provided for medical staff with higher reference value, and meanwhile a certain monitoring effect is brought to cognitive disorder user groups.

A method for predicting a patient or patient cohort's cognition score on the Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-Cog). The method comprises obtaining data relating to a plurality of patients or patient cohorts, the data including information relating to the longitudinal trajectories of the plurality of patients', or patient cohorts', ADAS-Cog score over time, each patient or patient cohort having undergone a treatment plan selected from a plurality of treatment plans; encoding the data into a tensor across patients or patient cohorts, time and treatment plan; generating a synthetic model of a target patient or target patient cohort using a machine learning process and the tensor; and predicting an ADAS-Cog score of the target patient or target patient cohort under a target treatment plan selected from the plurality of treatment plans, using the synthetic model.

The application belongs to the field of intelligent teaching, and particularly relates to an English oral pronunciation correction system based on artificial intelligence, which comprises a multi-modal perception module, a deep pronunciation feature extraction module, a cognitive compensation fusion module and an intelligent correction and training planning module; the abstract pronunciation is converted into a tactile experience through a multi-modal learning process, the tactile information and the auditory information are synchronously input, the memory retention rate of the visually impaired students is improved, and the students are guided to deeply remember the acoustic characteristics of the pronunciation; the application simulates the auditory-tactile joint representation ability of the visually impaired brain through the learnable attention weight and the cognitive compensation prior mask, realizes the real sense of "touch instead of vision", simultaneously identifies the error reasons through multi-task learning, provides scientific basis for targeted training, designs a special tactile generation network, maps the acoustic characteristics and the pronunciation error types into tactile patterns, provides instant tactile correction feedback for the students, and forms a closed-loop multi-modal learning experience.

The invention discloses a multi-agent cooperation decision-making method and system based on dynamic cognitive state modeling, and aims to overcome the defects of fuzzy intention reasoning, communication redundancy and cooperation deadlock due to the fact that existing multi-agent cooperation based on a large language model depends on unstructured text splicing. According to the method, an explicit cognitive space containing an own intention vector, a social cognitive matrix and a cognitive uncertain covariance is initialized for an intelligent agent, an enhanced observation vector for a cooperative partner is generated by pre-training a large language model encoder in combination with multi-head attention, and a cognitive state is dynamically updated by adopting adaptive Kalman filtering; a second-order belief model is introduced to detect implicit cognitive dislocation, pruning-quantization type hierarchical cooperation is executed based on Shannon entropy, and finally natural language interaction is triggered only during cognitive ambiguity or dislocation. According to the method, the robustness of cognitive updating is improved, the communication storm can be effectively inhibited, the method can be applied to scenes such as complex games and distributed task planning, and the collaborative decision-making efficiency and quality are greatly improved.

The application discloses an end-to-end automatic driving decision planning method and equipment combined with meta-learning multi-task optimization, adopts an end-to-end learning mode to overcome error transmission, simultaneously optimizes the convergence of a model by using a multi-task constraint method, and further increases the explainability of the model by multi-branch output results of the multi-task. Eight different sub-task branches are designed, including a perception task for an environment and a cognition for a self state, and a model of the sub-task is used to convert original sensor features to a bird's-eye view space, thereby improving the accuracy of decision planning prediction. Further, in order to optimize different multi-task combinations, firstly, a multi-task closeness is used to evaluate different task combinations to obtain initial task weights, and then a multi-task optimization method based on the combination of meta-learning is used to dynamically adjust the weights between different tasks in the training process, so that the optimization of the main task prediction is realized.

The invention relates to a technical development for project management for improving the efficiency of an action plan in terms of expected impact on the cognitive abilities of a target audience. The technical development includes a method and system for automating the optimisation of the expected impact. The method consists in evaluating the expected impact, qualitatively and quantitatively, on the basis of a scientific database which is updated according to the specific features of the project, in order to optimise the expected impact when planning and following the action plan. The system automates the analysis of the data in order to evaluate impact indicators and indices and automates the optimisation recommendations from the evaluated indicators and indices. An example of better usage shows how to optimise the expected impact of a communication plan on the cognitive abilities of multiple groups of a target audience. Figures 1, 2 and 3 represent the method, the analysis system and the recommendation system, respectively.

The invention discloses a large-model multi-hop reasoning optimization method fusing a psychological cognitive mechanism. According to the method, a'meta-cognition 'theoretical framework in cognitive psychology is used for reference, a reasoning process is divided into three cognition stages of'planning-monitoring-evaluation', and firstly, a large model is guided to generate initial reasoning steps and sub-questions for user questions; secondly, K text blocks most related to the sub-problem are retrieved; then screening related text fragments by the large model and outputting a reference reason; evaluating whether the current information is sufficient to answer the question by using a large model; if not, outputting missing information and correcting the reasoning step; and finally, on the basis of a knowledge external cognition strategy, inputting the missing information and the corrected reasoning step into the model again, generating a new retrieval target sub-problem, and circulating until an answer condition is met or the maximum number of iterations is reached. According to the method, by simulating a human cognitive mechanism, the capability of processing redundant information and the multi-hop reasoning performance of the model can be effectively improved under the condition of limited computing resources.

Presented herein are systems and methods of identifying locations to place stereo-electroencephalography (SEEG) electrodes on brain tissues of subjects for detection of epileptogenic zones (EZs). A computing system may receive, for a subject at risk of or diagnosed with epilepsy, a dataset comprising a neuroimage of the brain of the subject and a metric indicating a degree of neuropsychological function of the subject. The computing system may apply the dataset to a machine learning (ML) model. The computing system may generate a plurality of coordinates for a corresponding plurality of locations along a brain tissue of the subject, each of the plurality of coordinates identifying a plurality of locations at which to place a plurality of SEEG electrodes on the brain tissue to detect an EZ within the subject.

To provide effective means for utilizing information on prevention and improvement of dementia.SOLUTION: A dementia prevention and improvement information utilization system includes: means for evaluating user's cognitive state; means for providing individualized dementia prevention and improvement information; and means for providing customized dementia prevention and improvement programs in accordance with the user's cognitive state.SELECTED DRAWING: Figure 1