MUMBAI, India, Oct. 12 -- Intellectual Property India has published a patent application (202641118912 A) filed by Mlr Institute Of Technology; and Marri Laxman Reddy Institute Of Technology And Management on October 05, 2026, for An Artificial Intelligence-Based Chatbot System And Method For Water Quality Monitoring And Analysis.
Inventors include Mrs. V. Divya; Ms. P. Varshini; Ms. Maheza Nabeela; and Mr. R. Dinesh.
The application for the patent was published on October 09, 2026, under issue no. 41/2026.
Abstract: This project presents an AI-based chatbot system designed for efficient monitoring and analysis of groundwater quality parameters. Groundwater is a crucial resource for drinking, agriculture, and industrial use; however, it is increasingly threatened by contamination due to industrial discharge, agricultural runoff, and urbanization. Ensuring the safety and quality of groundwater is therefore essential for public health and environmental sustainability. The proposed system focuses on evaluating key water quality indicators such as pH, Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), turbidity, and temperature. These parameters are critical in determining the level of contamination and overall suitability of water for consumption and other uses. The system employs a rule-based analysis mechanism implemented in Python, where user-provided data is compared against standard permissible limits defined by environmental authorities. The chatbot interface serves as the primary point of interaction, allowing users to input water quality parameters in a simple and user-friendly manner. Using Natural Language Processing techniques, the chatbot interprets user queries and provides instant, easy-to-understand feedback regarding the condition of groundwater. Based on the analysis, the system classifies water quality into categories such as safe, moderately polluted, or highly contaminated. To ensure reliability and performance, the model is tested using sample datasets that simulate real-world monitoring scenarios. The results demonstrate that the system can quickly process and analyse water quality data, reducing the need for complex manual interpretation. The chatbot enhances accessibility by presenting technical information in a simplified conversational format, making it suitable for both general users and environmental authorities. Furthermore, the system can be extended to integrate real-time sensor data and advanced machine learning techniques for predictive analysis and continuous monitoring. This would enable early detection of pollution trends and support proactive decision-making. Overall, the proposed AI chatbot system provides a cost-effective, efficient, and user-friendly solution for groundwater quality monitoring. It bridges the gap between complex environmental data and user understanding, promotes awareness about water safety, and supports effective environmental management and decision-making processes.
Disclaimer: Curated by HT Syndication.
