MUMBAI, India, Sept. 28 -- Intellectual Property India has published a patent application (202541024545 A) filed by Dhanasekar R; Thanapranesh Kumar K; Sudharsan S R; and Dr. Sundara Murthy S on March 19, 2025, for Adaptive Mental Health Ecosystem: Ai-Driven Emotional Intelligence And Predictive Mental Wellness.
Inventors include Dhanasekar R; Thanapranesh Kumar K; Sudharsan S R; and Dr. Sundara Murthy S.
The application for the patent was published on September 25, 2026, under issue no. 39/2026.
Abstract: ABSTRACT OF THE INVENTION: The Adaptive Mental Health Ecosystem represents a transformative Al-driven platform engineered to address global mental health challenges through real-time emotional intelligence and predictive analytics. By synthesizing multi-modal data streams-inc.luding physiological signals from wearable devices, linguistic patterns from social media, self-reported mood logs, and electronic health records (EHRs)-the system delivers proactive, personalized mental health interventions. At its core, AMHE leverages machine learning (ML) algorithms such as convolutional neural networks (CNNs) for signal processing, recurrent neural networks (RNNs) for temporal behavioral analysis, and transformer-based natural language processing (NLP) models to decode emotional states from text. This integration enables the platform to detect subtle indicators of stress, anxiety. and depression, often before users consciously recognize symptoms. The ecosystem's emotional intelligence module employs sentiment analysis, voice tone recognition, and facial expression decoding (via integrated smartphone cameras) to create a holistic emotional profile. Coupled with predictive analytics, the system forecasts mental health risks using ensemble models that combine logistic regression, gradient-boosted decision trees, and deep learning architectures. These models analyze historical trends, real-time biometrics (e.g., heart rate variability, sleep patterns), and contextual stressors (e.g., workload, social interactions) to generate risk scores with configurable thresholds. Early warnings trigger tailored interventions, such as mindfulness exercises, cognitive-behavioral therapy (CBT)based activities, or referrals to licensed professionals, all curated by a dynamic personalization engine that adapts to user feedback and evolving needs. A secure, intuitive user interface (UI) facilitates engagement through features like mood-tracking dashboards, Al-guided journaling, and virtual reality (VR)-enabled relaxation sessions. The Ul prioritizes accessibility. offering multilingual support and adaptive designs for users with disabilities. Crucially, the platform reduces stigma by enabling discreet access to resources, bypassing traditional clinical settings. Underpinning the system is a privacy- preserving framework that anonymizes data via differential privacy techniques, encrypts communications using AES-256 standards, and adheres to GDPR, HIPAA, and other regulatory mandates. Data sovereignty is maintained through federated learning, allowing model training across decentralized datasets without raw data exchange. AM HE's interoperability is achieved via RESTful A Pis, enabling seamless integration with electronic medical record (EMR) systems, telehealth platforms, and loT devices. This ensures clinicians receive actionable insights-such as risk stratification reports and intervention efficacy metrics-enhancing diagnostic accuracy and treatment planning. For users, the platform supports longitudinal self-management through adaptive goal-setting, gamified well ness challenges, and community support forums moderated by AI to ensure safe interactions. The system's continuous learning mechanism employs reinforcement learning (RL) to refine its emotional intelligence algorithms based on user interactions. For instance, if a user consistently dismisses anxiety alerts but engages with stress-reduction tools, the Rl:.. agent adjusts recommendation strategies to align with observed preferences. Over time, this results in hyper-personalized care pathways that improve user adherence and outcomes. By bridging gaps in traditional mental health care-such as delayed diagnoses, fragmented data. and one-size-fits-all therapies-AMHE establishes a paradigm shift toward preventive, data-driven mental wellness. Its modular architecture allows scalability across diverse populations, from corporate wellness programs to clinical depression management. Pilot studies demonstrate a JO% improvement in early intervention efficacy and a 25% reduction in emergency psychiatric referrals, underscoring its potential to alleviate healthcare burdens. In summary, the Adaptive Mental Health Ecosystem harnesses cutting-edge AI to democratize mental health support. offering scalable, stigma-free, and ethically compliant solutions. By merging technological innovation with psychological science, it empowers individuals and professionals alike to achieve sustainable mental well-being in an increasingly complex world.
Disclaimer: Curated by HT Syndication.
