MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641103383 A) filed by Ms. Bershy P; Ms. Shalu Rakshana J F; Ms. Shajeena. R; Ms. Sivakami R; Ms. Rency R Oliver; Ms. Asmitha Shani Michae; Ms. Priya Dharshini S R; Ms. Thanya S; and Ms. Varshini B R on August 27, 2026, for Ai-Based Adaptive Mentrual Cycle Prediction And Health Support System.
Inventors include Ms. Bershy P; Ms. Shajeena. R; Ms. Varshini B R; Ms. Thanya S; Ms. Priya Dharshini S R; Ms. Asmitha Shani Michael; Ms. Rency R Oliver; and Ms. Sivakami R.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: 5. CLAIMS: 1. An Al-based menstrual cycle prediction and health support system that analyzes menstrual history and lifestyle behavior to generate adaptive cycle predictions. 2. The system of claim I, wherein lifestyle factors including sleep, physical activity, stress, hydration, and nutrition are integrated to improve prediction accuracy. 3. The system of claim I, wherein a self-learning optimization module continuously updates the prediction model using previous prediction outcomes and user feedback. 4. The system of claim I, wherein the AT engine identifies menstrual cycle phases and provides personalized health recommendations based on the predicted phase. 5. The system of claim I, wherein abnormal menstrual patterns are detected by analyzing recurring deviations in cycle duration, frequency, or user-reported symptoms. 6. The system of claim I, wherein notifications and reminders are generated to provide timely health guidance and encourage appropriate medical consultation when persistent irregularities are identified. 7. The system of claim I, wherein the integrated prediction, continuous learning, and personalized support framework provides an adaptive and user-centric menstrual health management solution. 6. ABSTRACT OF THE INVENTION The present invention relates to an AI- Based Adaptive Menstrual Cycle Prediction and Health Support System that improves menstrual cycle prediction by analyzing menstrual history along with lifestyle factors such as sleep, physical activity, stress, hydration, nutrition, and user-reported symptoms. The collected data is processed using an artificial intelligence engine that predicts the upcoming menstrual cycle and continuously improves its accuracy through a self-learning prediction mechanism. Based on the predicted menstrual phase, the system provides personalized recommendations related to nutrition, hydration, exercise, sleep, and overall well-being. It also detects recurring irregularities and generates timely notifications to encourage appropriate medical consultation. The proposed system offers an adaptive, reliable, and user-centric approach to menstrual health management by combining intelligent prediction, continuous learning, and personalized health support in a single platform.
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