Context-Aware AI: Personalized Healthcare Recommendations Driven by Physiological Sensors
This invention is a novel AI system that seamlessly integrates Large Language Models (LLMs) with real-time physiological and environmental sensor data to create a truly personalized and responsive agent. Its primary advantage is transforming generic AI advice into empathetic and context-aware recommendations that reflect the user's current physical and emotional state, overcoming a key limitation of existing models in healthcare.
Description
The innovative technology functions as a Sensor-in-the-Loop AI Agent, designed to dynamically inject a user's current physical and emotional context into their conversational interaction with a large language model. The system continuously obtains and processes data from various health sensors, such as those monitoring activity level, stress level, and sleep quality. This raw information is then processed into a sensor aggregation file that assigns a severity score to each category, effectively summarizing the user's current state (e.g., "high fatigue" due to poor sleep). The core innovation lies in the dynamic prompt refinement process. When a user enters a query, the system first calls the LLM with the user's prompt and the sensor aggregation file to determine a query refinement goal based on their immediate physiological needs. For example, the system may automatically adjust a generic fitness query to prioritize rest and recovery if the user's poor sleep severity score is high. A second call to the LLM then uses this newly refined prompt to generate a final, empathetic, and highly personalized response that ensures the advice is suitable for the user's current condition.Applications
- Digital Health and Wellness Coaching Platforms: Integration into mobile applications for personalized fitness, nutrition, and stress management guidance.- Wearable Technology Ecosystems: Licensing to smart device manufacturers to power next-generation, context-aware AI features in smartwatches and fitness trackers.
- Telemedicine and Remote Patient Monitoring (RPM): Enhancing patient-facing conversational AI to provide more empathetic and context-aware triage and support for chronic condition management.
- Corporate Wellness Programs: Offering hyper-personalized wellness feedback and interventions to employees based on monitored data.
- Mental Health and Sleep Quality Apps: Providing personalized conversational AI for users tracking sleep and stress to manage fatigue, anxiety, and general well-being.
Advantages
- Achieve True Personalization: Overcomes the key limitation of generic AI advice by incorporating real-time physiological and environmental context into every LLM response.- Deliver Empathetic and Context-Aware Recommendations: Ensures all suggestions are highly relevant and suitable for the user's immediate physical state, preventing generic advice from leading to unsuitable or even harmful actions.
- Enhance Efficacy of LLMs in Healthcare: Leverages sensor data to actively refine user queries, making the AI a more effective partner in preventative and personalized wellness, shifting the focus beyond mere health prediction.
- Mitigate Risk of Unsuitable Advice: Provides an automated, data-driven check that minimizes the risk of the AI recommending activities or actions that are inappropriate given a user's current health status (e.g., high stress or poor sleep).
- Broad Compatibility: Operable with various data sources, including wearable devices (like smartwatches) and mobile device onboard sensors.