4776results about "Mental therapies" patented technology

The invention discloses a multi-modal interview automatic quality analysis and evaluation method and system based on a large model, and the method comprises the steps: collecting and storing multi-modal data, such as texts, audios, videos and behavior interaction, and carrying out the preprocessing of the multi-modal data to form a standardized data set; utilizing a preset interview structure and a large model to dynamically guide the process, adjusting the topic rhythm according to real-time feedback, and recording stage conversion information to form logic trajectory data for process coherence management; automatically coding text data through a large language model, extracting features such as keywords and performing topic clustering, performing cross validation and semantic fusion in combination with data analysis results of each modal, and generating deep analysis results such as psychological states; and generating a comprehensive assessment report containing qualitative description, quantitative score and psychological abnormality or cognitive disorder risk prompts based on a deep analysis result, thereby providing a basis for psychological health assessment and cognitive competence evaluation. According to the method, automatic analysis of multi-modal data is realized, and evaluation scientificity and efficiency are improved.

The invention discloses an intelligent psychological intervention system based on multi-modal fusion, which is characterized in that a three-dimensional evaluation system is constructed by integrating speech sentiment analysis, keyboard dynamics monitoring and physiological signal acquisition, and time sequence alignment and feature weighted fusion of multi-source data are realized by adopting a cross-modal Transform model. The core of the system comprises an adaptive intervention engine which defines a multi-dimensional state space based on a hierarchical reinforcement learning architecture, optimizes an intervention strategy through a PPO algorithm, and realizes dynamic emotion interaction in AR and VR scenes in combination with a digital twin training module; according to the clinical decision support system, physiological behavior characteristics and psychological assessment trends are integrated by using a multi-time scale risk prediction model, and a personalized early warning threshold system is constructed, so that the psychological state recognition accuracy is improved, the intervention intensity self-adaptive adjustment response time is shortened, and the high-risk signal early warning timeliness reaches the minute level; and the problems of evaluation hysteresis and strategy stiffness of traditional psychological intervention are obviously improved.

An apparatus and method for real-time assessment and mapping of quality of life indicators includes: selecting a set of quality of life dimensions, receiving objective quality of life indicators based on the quality of life dimensions, receiving subjective quality of life indicators based on the quality of life dimensions, performing a statistical analysis on the objective quality of life indicators and the subjective quality of life indicators, storing the objective quality of life indicators, the subjective quality of life indicators, and the results of the statistical analysis in a database, and outputting the objective quality of life indicators, the subjective quality of life indicators, and the results of the statistical analysis.

The invention discloses a screening and evaluating system and method for cognitive impairment of old people, and belongs to the technical field of intelligent medical treatment. According to the system, electroencephalogram, VR / AR interaction, physiology and gait data are collected through the multi-modal acquisition module, the data processing module carries out data processing, dynamically selects a scale and carries out fusion analysis, the man-machine interaction module realizes natural interaction, and the output module generates a visual report and carries out early warning. The core algorithm comprises dynamic scale selection, multi-modal fusion evaluation and the like. The evaluation method comprises the steps of data acquisition, scale selection, data fusion, confidence verification, output and the like. Compared with a traditional method, the method overcomes the defects that subjectivity is high, and static evaluation, data splitting and base layer application are limited. The cognitive impairment assessment method can accurately and dynamically assess cognitive impairment, improves assessment accuracy, realizes full utilization of data, is simple and convenient to operate, is suitable for various scenes, provides efficient and reliable support for diagnosis, monitoring and intervention of cognitive impairment, and promotes development of intelligent medical treatment.

The invention discloses a cognitive function rehabilitation training system and method based on virtual reality, and the system comprises a cognitive function test unit which is used for carrying out the cognitive impairment test evaluation of a patient based on a virtual reality module, and generating a cognitive function evaluation report of the patient; the cognitive training unit is used for generating a personalized cognitive training scheme based on the cognitive function evaluation report, carrying out targeted cognitive training in a virtual reality environment, and collecting physiological data and training performance data of the patient in the cognitive training process; the feedback unit is used for comprehensively analyzing the physiological data and the training performance data and generating a staged feedback report; and the rehabilitation effect tracking unit is used for analyzing rehabilitation effects of the patient in different time periods based on the periodic feedback report and optimizing a subsequent cognitive training scheme based on an analysis result. And the interestingness and interactivity of rehabilitation training are improved.

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.

A computer-implemented method includes receiving patient responses over a network from a recorded screening interview, utilizing a custom Large Language Model (LLM) to generate transcriptions and insights from the audio, performing video sentiment analysis to assess emotional states, and based on these data, generating an AI model to predict risk levels for various mental health conditions. The method further comprises presenting personalized questions based on previous screening insights, creating longitudinal summaries of patient histories, continuously improving the AI models through reinforcement learning, and integrating a recommendation engine to suggest targeted interventions. Additionally, the method includes tracking outcomes over time and enabling causal inference across multiple screenings, thereby providing a comprehensive platform for mental health assessment, intervention, and outcomes tracking.

The invention provides a system platform for psychological assessment and emotion feedback. The system platform comprises a multi-source data acquisition module, an emotion data analysis module, a psychological state assessment module, an intervention scheme generation module, an emotion change feedback module, a scheme optimization module and a data security protection module. The emotion recognition accuracy is improved by synchronously acquiring physiological-behavior-environment data and combining with a dynamic feature fusion method, a personalized intervention strategy is generated based on a reinforcement learning algorithm, real-time feedback is realized, and a data security protection system is constructed by adopting federal learning and homomorphic encryption technologies, so that the data security is ensured.

The invention provides a psychological crisis multi-stage joint control method and system based on a psychological large model, and aims to realize real-time monitoring, accurate evaluation and intelligent intervention of psychological states through a multi-modal data fusion and deep learning technology. The system collects multi-source information such as texts, voices, videos, physiological signals and behavior data, performs cross-modal analysis by using models such as Transform, LSTM and CNN, constructs personalized psychological portraits, and analyzes and predicts the psychological state change trend in combination with a time sequence. According to the method, a psychological crisis dynamic grading model is adopted, the psychological state of a user is divided into a normal grade, a mild grade, a moderate grade and a severe grade, multi-grade intelligent intervention is provided based on different risk grades, and the multi-grade intelligent intervention comprises AI self-service adjustment, psychological counseling matching, social support enhancement, emergency medical intervention and the like. The psychological intervention strategy is optimized in combination with reinforcement learning, the intervention mode is dynamically adjusted according to user feedback, and individuation and adaptability are improved.

A computer implemented method for governing risk actions by an artificial intelligence (AI) browser, by classifying a proposed action by the AI browser based on a large language model (LLM) as safe or risky based on AI weights or based on policy rules; initiating a step up authentication flow for a risk action; presenting an action summary and required capabilities to the user for approval; and enforcing user configured spend or scope limits on the risk action.

The invention relates to the technical field of psychological health monitoring, in particular to a self-injury behavior early warning and intervention method and system based on multi-modal physiological data and AI.The method comprises the steps that physiological signals, behavior data, text data and environment parameters of a user are collected in real time based on a contact sensor and non-contact sensing equipment, and the physiological signals, the behavior data, the text data and the environment parameters of the user are obtained; generating a multi-source original data set; performing time synchronization and spatial correlation analysis on the multi-source original data set to form a fusion feature vector; inputting the fusion feature vector into a preset multi-modal deep learning model, obtaining a self-injury behavior risk score value, and dividing risk levels based on the self-injury behavior risk score value; and triggering a hierarchical intervention strategy of AI psychological counseling, emergency contact notification and medical resource linkage according to the risk level. The method has the effect of improving the precision and quality of teenager mental health assessment and self-injury behavior early warning results.

The invention belongs to the field of multi-modal data processing, and provides a multi-modal mixed expert psychological health assessment system, method, medium and equipment in order to solve the problem of low feature fusion efficiency caused by insufficient dynamic interaction between modals of an existing mixed expert model in a multi-modal scene. The multi-modal mixed expert psychological health assessment system comprises a multi-modal data acquisition module, and an adaptive grouping module. The cross attention fusion module is used for extracting features of corresponding modal data in the basic group and the auxiliary group, performing intra-group cross attention fusion on the features of each group, and generating intra-group features; performing global information fusion on all the intra-group features through a cross-group cross attention mechanism to obtain cross-group features; and after the cross-group features, the intra-group features and the original features of the multi-modal data are spliced, a dynamic gating weight is obtained through a gating network. The method can provide more accurate guidance for psychologists.

The invention relates to the technical field of psychological health management, and comprises an AI-based college psychological health comprehensive service system and method, and the system comprises a psychological behavior scoring module, an interactive behavior analysis module, a psychological health assessment module, a social intervention matching module, and a dynamic adaptation optimization module. According to the method, based on behavior data of learning, work and rest, social contact and the like, the time matching degree of behavior categories is calculated, the comprehensiveness of individual psychological health scoring is improved, social interaction data is combined, the social behavior resonance degree is analyzed, the psychological integrating degree between individuals is measured, semantic emotion polarity change is combined, and psychological state fluctuation is quantified; based on psychological health state change analysis, individuals with high social mode similarity are screened, pertinence of intervention matching is enhanced, fluctuation trends of individual social states are synchronously identified, psychological intervention strategies are adjusted according to changes of psychological health scores, personalized adaptability is improved, and fine adjustment of psychological health management of college students can be achieved.

Systems and methods for generating a patient-specific clinical assessment are disclosed. A processor receives patient health data corresponding to a patient. The patient health data is analyzed using a trained large language model (LLM) configured to process the patient health data. Clinical insights are identified based on the analysis of the patient health data using the trained LLM. A set of interactive prompts is generated for a patient interface based on the identified clinical insights. The set of interactive prompts being configured to obtain additional information associated with the patient. A set of patient responses responsive to the generated set of interactive prompts are received. The clinical insights and the set of patient responses are displayed on a clinician interface.

The invention discloses a psychological accompanying method based on multi-modal emotion recognition, and belongs to the technical field of psychological health services, and the method comprises the steps: S1, synchronously collecting physiological signals, voice features, facial expressions and interactive behavior data of a user through a multi-modal sensor; s2, performing fusion analysis on the multi-modal data by using a deep learning model, and identifying a current emotional state and an emotional intensity level of the user; and S3, dynamically generating an adaptive psychological accompanying intervention scheme based on a preset emotion-intervention strategy mapping rule in combination with historical emotion data and personalized preferences of the user. According to the method, the physiological signals, the voice features, the facial expressions and the interactive behavior data are synchronously collected through the multi-modal sensor, the deep learning model is used for fusion analysis, and compared with single-modal recognition, the emotion state and the intensity level of the user can be judged more comprehensively and accurately, the emotion misjudgment risk is reduced, and a reliable basis is provided for subsequent intervention.

A programmatically generated AI avatar includes a customizable personality module, acting as the embodied interface for a powerful AI “mind” that delivers personalized coaching to improve user health, well-being, and longevity. The system uses machine learning, large language models, and biometric modeling to synthesize real-time, multi-modal health data—including sleep, nutrition, glucose, mood, and activity—and generate forward-prescribed KHAs. Unlike human coaches, it continuously adapts based on context and behavior, targeting the root cause: metabolic dysfunction—namely by restoring healthy, sustainable body composition through the preservation or building of lean muscle mass and reduction of excess fat. KHAs can also be shared with friends or programmatically generated AI avatars, allowing for coordinated action, emotional support, and accountability through social connection—further reinforcing positive behavior and adherence. The system's reinforcement learning engine incorporates both individual response data and anonymized population-level insights to optimize recommendations over time, learning which interventions are most effective for users with similar physiological and behavioral profiles. First validated with Olympic athletes—resulting in measurable improvements and medal-winning outcomes—this system offers a scalable, emotionally intelligent coaching engine that exceeds human capability, designed for the ultimate purpose of supporting sustainable health, resilience, and human thriving.

A computer-implemented method (200) is disclosed for performing a clinical assessment, the method comprising: providing a first input (206) to a machine learning model (208), the first input comprising template data encoding a template for carrying out a part of the clinical assessment; providing a second input (210) to the machine learning model, the second input comprising assessment data recorded during the clinical assessment; wherein the first input is provided to the machine learning model to condition the machine learning model to provide an output (212) based on the second input for use in the clinical assessment; and using the output from the machine learning model to perform the clinical assessment.

The invention discloses an oral and maxillofacial head and neck tumor rehabilitation nursing system based on artificial intelligence, which belongs to the technical field of medical auxiliary systems and comprises a special data acquisition and monitoring module, a deep reinforcement learning analysis module, a biological feedback analysis module, a personalized rehabilitation plan generation module and the like. The system can monitor physiological parameters and biological feedback signals of a patient in real time, generate a personalized rehabilitation plan and provide real-time monitoring and early warning functions, in addition, the system further comprises a plurality of auxiliary modules such as a local database, a patient interaction module and an intelligent early warning and emergency intervention module, the reliability and practicability of the system are further improved, and the system is suitable for popularization and application. According to the system, the defects of a traditional rehabilitation nursing method can be overcome, the rehabilitation precision and effectiveness are improved, the participation degree and confidence of a patient are enhanced, and more comprehensive and efficient rehabilitation nursing service is provided for the oral and maxillofacial head and neck tumor patient.

The invention provides a motion fear intervention device based on virtual reality. The motion fear intervention device comprises a VR scene generation module; the subjective evaluation module presents the TSK scale to the user to obtain a TSK scale score of the user, and obtains a TSK grade corresponding to the TSK scale score according to the TSK scale score; the biological signal acquisition module is used for acquiring biological feedback of a user and calculating a comprehensive exercise fear grade according to the following formula: the comprehensive exercise fear grade = alpha * TSK grade + beta * HRV score + gamma * GSR score + delta * pupil change score + belonging to * EMG score; the dynamic intervention module is used for generating scene parameters in real time according to the comprehensive exercise fear level and inputting the scene parameters into the VR scene generation module to generate a corresponding scene; the dynamic intervention module is also used for providing forward excitation. According to the method and the device, the currently displayed VR hierarchical scene is adjusted through biological feedback, and closed-loop feedback adjustment of motion scene stimulation is realized.

A method for providing mental health advice, a method for training a deep-learning network and a deep-learning based mental health advisory system using Acceptance and Commitment Therapy (ACT). The method for providing mental health advice comprises the steps of: receiving textual input from a user in a consultation session; processing the textual input by applying a mental health condition relationship to the textual input to identify the mental health status of the user; and providing an output associated with the mental health status of the user.

Embodiments described herein relate to systems and methods for an individualized intervention to generate and provide wellness pattern aware interventions. The system generates digital instructions and output for an online web application, device hosted application, smart device, or similar system. In one embodiment, the system provides the individualized intervention that matches device capacity, user context, and the best intervention type for a multi feel state longitudinal journey over a multi-day duration.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

The invention relates to the technical field of mental health early warning, in particular to a student mental health early warning method and early warning system based on AI enabling. Collecting metadata of psychological health activities of students in real time by constructing an event monitor and an event trigger; converting the psychological activity state when the student executes the structured psychological health activity into a difference equation, and solving and processing the psychological activity state difference equation by using the metadata to obtain a psychological activity fluctuation state matrix of the student when each piece of metadata is generated; training a rating model by using a clustering fluctuation state matrix of the associated crisis rating thermodynamic diagram of the psychological health of the student, and generating a psychological health crisis rating model; and mapping a standardized psychological health rating model and a psychological health crisis rating model when the psychology of the student is in a health standard state, and analyzing the distribution distance offset so as to carry out monitoring and early warning. The system can continuously monitor and evaluate psychological states of school students and send out early warning signals, so that corresponding intervention measures are taken.

The invention discloses an apoplexy patient management method and system based on a multi-mode RAG and LLM model, and the method comprises the steps: obtaining the standardized electronic medical record data of a patient based on an FHIR standard electronic medical record; performing feature extraction on the electronic medical record data to obtain multi-modal data; integrating and representing a time-space relationship among the multi-modal data, and obtaining a dynamically updated dynamic knowledge graph; analyzing and integrating the electronic medical record data to obtain integrated information; constructing a stroke risk causal network based on an LLM model in combination with a causal reasoning mechanism; obtaining a personalized health report based on the dynamic knowledge graph and a stroke risk causal network; and based on a personalized report result and feedback data of the patient, a treatment scheme and a management strategy of the patient are optimized in real time. According to the application, the causal reasoning mechanism is embedded into the Transform architecture of the LLM, so that the modeling and prediction capabilities of the stroke risk causal relationship are enhanced.

The invention belongs to the field of student psychological health management, and particularly relates to a student psychological evaluation system based on AI reinforcement learning optimization. According to the system, the psychological state of the student is dynamically evaluated through multi-modal data acquisition, deep data processing and reinforcement learning optimization, and the problems of singleness and hysteresis in a traditional psychological evaluation method are solved. The system comprises a data acquisition layer, a data processing layer, a reinforcement learning optimization layer, a gamification evaluation module, a feedback and intervention layer and a user interface layer. The data acquisition layer acquires behavior, physiological and social data in multiple channels; the data processing layer performs cleaning, feature extraction and fusion on the data; the reinforcement learning optimization layer adjusts the psychological evaluation strategy in real time; the gamification evaluation module enhances the student participation degree through a task and reward mechanism; and the feedback and intervention layer provides real-time feedback and personalized intervention suggestions. The psychological evaluation accuracy, participation and intervention effect can be improved, and the method is suitable for psychological health management scenes of schools and related educational institutions.

The invention relates to the field of healing dialogues, and particularly discloses a psychotherapy-healing-oriented large-model dialogue agent which realizes deep analysis of core emotions, cognitive states and potential demands of a user by integrating real-time input, historical dialogue abstracts and emotional memory and adopting a structured coding and feature fusion technology. Based on the emotion signal and the semantic analysis result, matching a predefined emotional strategy label, and ensuring that a response strategy accords with psychological intervention logic. The psychological theory is converted into an executable dialogue framework through key element recognition and instruction embedding, and the integrating degree and safety are improved in cooperation with multi-candidate generation and an emotional quality evaluation mechanism based on semantic embedding. According to the scheme, by continuously optimizing emotion representation and strategy matching, the defects of a traditional model in the aspects of emotion tracking, professional fusion and safety control are effectively overcome, and the depth and reliability of psychological support are remarkably improved.

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 subject matter described herein includes methods, systems, and computer readable media for early detection of a neurodevelopmental or psychiatric disorder using scalable computational behavioral phenotyping. According to one method for early detection of a neurodevelopmental or psychiatric disorder using scalable computational behavioral phenotyping includes obtaining user related information, wherein the user related information includes metrics derived from a user interacting with one or more applications executing on at least one user device; generating, using the user related information and a machine learning based model, a user assessment report including a prediction value indicating a likelihood that the user has a neurodevelopmental or psychiatric (neurodevelopmental / psychiatric) disorder and a prediction confidence value computed using relative contributions of the metrics to the prediction value generated using the machine learning based model; and providing the user assessment report to a display or a data store.

The invention relates to the technical field of child cognitive training, and discloses an ADHD child cognitive training system. The system comprises a multi-modal sensing module, an attention evaluation module, a dynamic task generation module, a behavior feedback module and a self-adaptive adjustment module. The multi-modal perception module collects multi-dimensional behavior data, the attention evaluation module extracts attention fluctuation features, the dynamic task generation module generates a cognitive training task sequence, the behavior feedback module performs emotion state classification processing to generate feedback signals, and the self-adaptive adjustment module generates personalized training difficulty steps. According to the system and the method, through comprehensive data acquisition, precise attention assessment, dynamic task generation, real-time emotion feedback and difficulty adaptive adjustment, personalized and precise cognitive training of ADHD children is realized, the training effect is effectively improved, and the problems of lack of personalization, insufficient state monitoring, unscientific difficulty adjustment and the like of an existing training method are solved.

The invention relates to the technical field of natural language processing, and provides a psychological medical map retrieval method based on an enhanced large model, which comprises the following steps of: aiming at a user input problem, extracting a subject entity by adopting a large language model; obtaining a user intention label by adopting a light-weight large model; based on a user intention label, controlling a relationship screening and path expansion process of the knowledge graph, and constructing a semantic path set; and judging whether the semantic path set has enough semantic support to answer the user question, and if so, outputting an answer according to the question input by the user and the semantic path set. On the basis of an existing reasoning framework, a fine-tuning lightweight large language model is introduced to perform multi-level semantic intention recognition on user questions, and a relation path selection process in a knowledge graph is controlled accordingly, so that a mechanism of scoring and sequencing paths hop by hop depending on the large language model in an original framework is replaced; therefore, the user can obtain scientific and complete psychological health knowledge, and the reliability of psychological health service is improved.