SYSTEM FOR VISUALIZING NUTRITIONAL INFORMATION AND PROVIDING RECOMMENDATIONS BASED ON MULTIMODAL USER INPUTS AND INTERACTIVE VISUALIZATIONS

Smart overview of the Invention

The system is designed to process multimodal inputs related to meals, such as images, text, video, and audio, to enhance meal logging accuracy and provide personalized nutritional recommendations. By integrating various data types, the system creates dynamic and interactive graphical user interfaces that visualize meal components and their potential health impacts. This approach addresses the limitations of traditional meal tracking by minimizing manual input and offering more comprehensive insights into dietary choices.

Technological Context

The system aims to tackle challenges associated with current meal tracking applications, particularly for individuals with conditions like Type-Two diabetes. Traditional systems often require manual entry, leading to incomplete data and less effective health insights. This invention leverages continuous glucose monitoring and other health data to provide more accurate and meaningful feedback on dietary impacts, addressing issues like inaccurate glycemic response tracking and the lack of automated meal detection.

System Components

The meal visualization system includes a model that processes inputs from various sources, such as medical devices and user-generated content. It combines this data to generate personalized glucose impact predictions and interactive visual elements. The system can integrate with continuous analyte sensors and electronic health records, facilitating secure communication and real-time physiological data acquisition. These capabilities enable the system to offer tailored dietary recommendations based on individual health profiles.

Personalization and Interaction

The system supports a range of user inputs, including dietary preferences and restrictions, to further personalize recommendations. By using a generative artificial intelligence model, the system can predict potential glucose spikes and suggest dietary adjustments. The interactive visualizations allow users to modify meal components and provide feedback, which the model uses to refine its recommendations and improve its accuracy over time.

Feedback and Adaptation

Interactive visualizations generated by the system enable users to adjust meal attributes and submit queries, which are processed using natural language processing. This feedback loop allows the model to enhance its meal detection capabilities and become more attuned to the user's unique physiological responses. Over time, this iterative process enhances the system's ability to deliver relevant and personalized nutritional insights, improving user engagement and health outcomes.