MUMBAI, India, July 13 -- Intellectual Property India has published a patent application (202641066146 A) filed by The Principal, Sns College Of Technology on May 26, 2026, for System And Method For Ai Powered Adaptive Landscape Design And Optimization Platform.
Inventors include Prabhu V; Devadharsni. N; Elakkiya. T; Gopikrishnan. B; and Vijaya Sankar. S.
The application for the patent was published on July 10, 2026, under issue no. 28/2026.
Abstract: An automated, intelligent landscape design platform is disclosed for managing sustainable land utilization through data-driven analysis and machine learning. The invention addresses the persistent bottleneck in traditional landscape planning where users often lack the technical expertise required to analyze soil characteristics, climate beginning with a User Input System (102) to collect raw site details, which are verified by an Input Processing and Validation System ( 1 04) to ensure data integrity before analytical processing. An Environmental Analysis Engine (1 06) integrates spatial datasets and climate records to evaluate temperature, rainfall, humidity, and sunlight exposure specific to the user's location. The platform employs an AI/ML Decision Engine (1 08) that matches validated environmental parameters with optimal plant characteristics to generate personalized recommendations and layout designs. To ensure economic and ecological feasibility, a Cost and Budgeting Engine (112) provides resource-optimized estimates for acquisition and maintenance. The system supports distinct user roles, featuring advanced project management for Landscaping Businesses (B2B) and simplified guidance for Homeowners (B2C). By prioritizing native, climate resilient species and efficient resource usage, the platform promotes long-term sustainability and biodiversity conservation. All interactions are recorded by a ยท Q) Consultation Records Management System ( 114) and stored in a Persistence Layer C) m (110) for historical tracking and trend analysis. The invention provides a salable, accessible alternative to manual expert analysis, reducing plant failure rates while t:.. optimizing land productivity.
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