Adaptive digital therapy system with real-time emotional state analysis and symbolic language communication

A system for providing adaptive digital therapy, comprising:

• a hardware access module comprising at least one of sensors, microphones, cameras, and EEG devices, configured to receive user data comprising at least one item selected from the group consisting of text, speech, vision, symbols, and brain wave inputs;
• a processor configured to process the received user data using multi-modal deep learning methods to determine a user's emotional expression and valence;
• a non-transitory computer-readable medium configured to store a dynamic user representation mapped from the processed user data;
• a comparison module comprising a processor and software configured to compare the dynamic user representation to established psychological therapeutic frameworks;
• wherein the processor and software of the comparison module are configured to:
• execute a hierarchical multimodal self-organizing map (HSOM) to analyze the user data, including the text, the speech, the vision, the symbols, and the brain wave inputs,
• represent and process various levels of sensory-motor and symbolic information in a hierarchical structure that mimics neural processing of the human brain, and
• organize and interpret the user data to generate the dynamic user representation, and
• a communication module comprising wireless interfaces and protocols configured to transmit generative communications to the user based on the comparison.

A method for monitoring a client user's emotional states, comprising:

• transmitting user data via an access module, the user data including information about one or more labeled compounds administered to the client user;
• transcribing and processing the user data via a computer processor and multi-modal models to determine a user's emotional expression and valence, wherein the processing comprises:
• executing a hierarchical multimodal self-organizing map (HSOM) on the computer processor to analyze text, speech, vision, symbols, and brain wave inputs from the user data,
• representing and processing various levels of sensory-motor and symbolic information in a hierarchical structure that mimics neural processing of a human brain, and
• generating a dynamic user representation based on the analyzed user data;
• calculating a valence score quantifying the user's emotional state on a continuum from highly negative to highly positive, wherein the calculating comprises
• analyzing physiological data comprising heart rate variability, skin conductance, and brainwave patterns captured by EEG devices,
• analyzing linguistic content of the user's speech and text inputs using natural language processing algorithms,
• applying a weighted sentiment analysis algorithm that processes the physiological data and linguistic content to calculate a composite valence score,
• comparing the composite valence score against predetermined thresholds to categorize the emotional state using a machine learning classifier;
• transferring the processed user data and the composite valence score to a data visualization interface for display;
• mapping language inputs from the user to a neural library, wherein the mapping comprises:
• identifying neural patterns in the user's brain activity that correlate with specific semantic meanings, and
• updating the neural library with the identified correlations between the neural patterns and the semantic meanings;
• and
• transmitting generative communications back to the user to create a closed-loop communication system, wherein the generative communications are dynamically adjusted based on the calculated composite valence score and mapped neural patterns to guide the user's emotional state towards an optimal pattern.

A method for training a machine learning model to interpret neural responses as outputs, comprising:

• collecting a dataset comprising a wide range of neural responses and corresponding outputs, wherein the neural responses include electrical patterns captured from user brain activity via neural monitoring devices selected from the group consisting of EEG, MEG, or fMRI;
• exposing the machine learning model to various neural patterns and words or phrases from the dataset, wherein the exposure involves processing the neural patterns through the model to identify and learn associations between specific brain activities and language elements;
• employing supervised learning techniques to train the machine learning model, wherein the trained machine learning model adjusts its parameters based on a comparison of its output to linguistic outputs in the dataset;
• validating the trained machine learning model by testing its ability to accurately convert new, unseen neural patterns into linguistic outputs;
• integrating the trained machine learning model into a therapeutic system, wherein the trained machine learning model is utilized to interpret real-time neural responses of users into linguistic outputs that facilitate communication or therapeutic feedback;
• and
• continuously updating the trained machine learning model by incorporating new neural responses and linguistic outputs into the dataset, thereby refining the trained machine learning model's accuracy and adaptability over time.