MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641100707 A) filed by Jeppiaar Institute Of Technology on August 20, 2026, for A Context-Aware Multi-Source Ai Trust Scoring Method For Real-Time Digital Scam Detection.
Inventors include Indhuja. M; Gajalakshmi. R; Dr. D. Paulraj; Seejamini K; Mohanapriya. P; Pramika. V; Brijit Mary. S; Samira Renis. R; Roshini. R; and Adlin Priscilla.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: 6. ABSTRACT OF THE INVENTION (to be given along with complete specification on separate page) The growing reliance on digital platforms for communication, online banking, e-commerce, and social networking has markedly heightened the risk of cyber scams and online fraud. Cybercriminals utilize a variety of attack techniques, including phisning websites, counterfeit QR codes, deceptive emails and messages, scam phone calls, altered images and videos, fraudulent online reviews, and Al-generated misleading content. Most current cybersecurity solutions concentrate on detecting only specific types of threats, necessitating users to employ multiple security tools for comprehensive protection. This disjointed approach frequently results in inconsistent risk assessments, delayed threat detection, and diminished user confidence. The current invention introduces a Context-Aware Multi-Source Al Trust Scoring Framework for Real-Time Digital Scam Detection, which offers a cohesive and intelligent method for identifying cyber threats from various digital sources within a single platform. The framework gathers and analyzes diverse digital inputs such as URLs, OR codes, emails, text messages, voice transcripts, images, videos, online reviews, and social media content. Each input is processed through specialized Artificial Intelligence models that detect suspicious patterns and generate individual confidence scores based on the probability of fraudulent activity.. Additionally, the framework includes an Explainable Decision Module that provides users with transparent justifications for the assigned trust score and offers tailored security recommendations based on the identified risk level.
Disclaimer: Curated by HT Syndication.
