MUMBAI, India, Aug. 31 -- Intellectual Property India has published a patent application (202641099812 A) filed by Dr Shashikala Cs; Ms. Lakshmi Devi Chikkasasalaiah; Ms. Abigail Sathish; Dr Rajini K S; Dr. Francis Xavier Virgin Fraga; Ms. Gokila Selvaraju; Ms. Deepavathi N; Mr. Chandrashekar Nagaraj; Mr. Avinash Lobo; Ms. Fousia Jaleel; Ms. Sai Pranathi Pissey Venkata; and Ms. Raksha Sadhashiva on August 18, 2026, for Ai-Based Startup Success Prediction And Adaptive Business Strategy Recommendation System.
Inventors include Dr Shashikala Cs; Ms. Lakshmi Devi Chikkasasalaiah; Ms. Abigail Sathish; Dr Rajini K S; Dr. Francis Xavier Virgin Fraga; Ms. Gokila Selvaraju; Ms. Deepavathi N; Mr. Chandrashekar Nagaraj; Mr. Avinash Lobo; Ms. Fousia Jaleel; Ms. Sai Pranathi Pissey Venkata; and Ms. Raksha Sadhashiva.
The application for the patent was published on August 28, 2026, under issue no. 35/2026.
Abstract: ABSTRACT AI-Based Startup Success Prediction and Adaptive Business Strategy Recommendation System The proposed invention relates to an Artificial Intelligence (AI)-based Startup Success Prediction and Adaptive Business Strategy Recommendation System configured to evaluate, predict, and continuously improve the success probability of startup ventures through multimodal business data analysis and adaptive strategic recommendations. The system integrates startup-specific parameters including founder capabilities, business model characteristics, market size, customer demand, competitive intensity, financial resources, revenue projections, operational performance, funding status, product development progress, customer acquisition metrics, industry trends, economic indicators, and historical startup outcomes. An AI prediction engine processes the collected data using machine learning, deep learning, ensemble learning, and explainable artificial intelligence techniques to generate a dynamic startup success score and identify significant success and failure factors. A strategy recommendation engine maps identified risks, opportunities, and performance gaps to adaptive business strategies relating to pricing, marketing, customer acquisition, product development, financial management, funding, staffing, market expansion, and operational optimization. The system continuously receives updated startup performance data and external market information, thereby enabling periodic or real-time recalculation of the predicted success probability. A feedback mechanism evaluates the effectiveness of previously recommended strategies and modifies subsequent recommendations according to observed outcomes. An explainability module provides interpretable reasons for predictions and recommendations, enabling founders, investors, incubators, accelerators, and business advisors to understand decision factors. The proposed invention thereby provides a data-driven, personalized, continuously adaptive decision-support platform capable of reducing uncertainty associated with startup planning and improving strategic decision-making throughout different stages of startup development.
Disclaimer: Curated by HT Syndication.
