MUMBAI, India, Feb. 6 -- Intellectual Property India has published a patent application (202541122493 A) filed by New Prince Shri Bhavani College Of Engineering And Technology; Sheneka K; Sakthisri D; Priyadharshini S; and Subburam S, Chennai, Tamil Nadu, on Dec. 5, 2025, for 'intelligent expense tracker with sms-based transaction monitoring.'
Inventor(s) include Sheneka K; Sakthisri D; Priyadharshini S; and Subburam S.
The application for the patent was published on Feb. 6, under issue no. 06/2026.
According to the abstract released by the Intellectual Property India: "The rapid growth of digital payment systems such as Google Pay, PhonePe, and Paytm has significantly increased the number of electronic financial transactions conducted on a daily basis. Every transaction generates an SMS notification from banks or payment gateways, which contains essential details such as the amount, time, and transaction description. However, manually tracking these SMS messages and classifying them into meaningful financial records is both time-consuming and error-prone. To address this issue, this project proposes the development of an /!-powered Expense Tracker Application that automatically reads transaction-related SMS messages, categorises them intelligently, and presents a clear financial overview to the user. The proposed system is designed to function entirely offline, thereby ensuring data privacy and security by keeping all user information within the device itself. The application employs a Naive Bayes Machine Learning Algorithm (implemented in Dart language) for text classification. The received SMS messages are pre-processed by tokenising and extracting keywords, which are then fed into the machine learning model to predict the most appropriate expense category, such as Food, Travel, Bills, or Shopping. In cases where the model prediction is uncertain, the user can manually assign a category, and this correction is used to incrementally retrain the model, allowing the system to continuously learn and tmprove accuracy over time. The system architecture is divided into three layers: the Data Access Layer (responsible for receiving transaction SMS and UPI app data), the Business Logic Layer (handling SMS reading, keyword extraction, machine learning classification, and categorisation), and the Presentation Layer (responsible for displaying insights through dashboards, reports, and charts). The application also provides features such as budget setting, dark/light mode themes, income and expense summaries, and notifications when expenses exceed the predefined budget. This ensures that the user has not only an automated categorisation system but also a Lt; personalised financial management assistant."
Disclaimer: Curated by HT Syndication.
