USPTO Patent Application: Electronic Devices Precluding User Interaction

ChangeBridge: Patent Apps - Business Methods (G06Q)

Published September 20th, 2024

Detected March 27th, 2026

Summary

The USPTO has published a patent application (US20260087542A1) detailing electronic devices and systems designed to preclude user interaction events in e-commerce environments. The application describes a method for detecting potential fraud by analyzing device activity and, if a fraud propensity score exceeds a threshold, blocking certain user interactions like shopping cart modifications.

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What changed

This document is a patent application, not a regulatory rule or guidance. It describes a technical system for an electronic device operating an e-commerce environment. The system aims to mitigate fraudulent returns by analyzing device-level activities (e.g., SIM card swaps, factory resets) to calculate a 'fraudulent return propensity score.' If this score surpasses a predefined threshold, the system automatically prevents user interactions such as modifying a shopping cart or initiating a product return. The application suggests using machine learning for enhanced accuracy.

While this is a patent application and not a direct regulatory mandate, it signals potential future technological developments in e-commerce fraud prevention. Retailers and technology companies involved in e-commerce should be aware of such innovations as they may influence industry standards or become integrated into platforms, impacting user experience and operational security. Compliance officers should monitor trends in AI-driven fraud detection and their implications for customer interaction policies.

Source document (simplified)

← USPTO Patent Applications

Electronic Devices, Methods, and Corresponding Systems for Precluding User Interaction Events in an Interactive Computing Environment

Application US20260087542A1 Kind: A1 Mar 26, 2026

Inventors

Krishnan Raghavan, Vijayprakash Idlur, Prasad A G

Abstract

An electronic device includes one or more processors, a user interface, and a memory. The device operates an electronic shopping interactive computing environment. Upon initiation of an interactive session, the processors determine a fraudulent return propensity score based on various input parameters, including device-level activities such as subscriber identification module card swaps, factory resets, and application reinstalls. When the propensity score exceeds a predefined threshold, the processors preclude one or more user interaction events, such as shopping cart interactions or product returns, from occurring in the shopping environment. The system can employ a machine learning algorithm to generate a normalized propensity score, enhancing fraud detection accuracy. This proactive approach mitigates the risk of fraudulent returns, protecting retailers from financial losses and maintaining the integrity of the e-commerce platform.