AI-Powered Virtual Fashion: A Patent Analysis of Real-Time Calibration of Virtual Apparel Using Stateful Neural Network Inferences and Interactive Body Measurements (US 12,017,142 B2)

Abstract

Purpose: The purpose of this paper is to systematically examine the patent “System and Method for Real-Time Calibration of Virtual Apparel Using Stateful Neural Network Inferences and Interactive Body Measurements” (US 12,017,142 B2) and to evaluate its technological, strategic, and commercial significance. The study aims to describe the patent's architecture, claims, and innovative elements while situating it among related patents in the AR/AI virtual try-on domain. It further seeks to assess the invention's strengths, limitations, business opportunities, and future value using structured qualitative analysis frameworks.

Methodology: This study adopts an exploratory qualitative research approach to systematically analyze the selected patent. Relevant data were collected from open-access sources, including Google Patents, the USPTO database, and the original patent specification (drawings, claims, and detailed description), supplemented by keyword-driven searches for related prior art and citing patents. The gathered information was then organized and interpreted using the patent-analysis procedure and the SWOC and ABCDEF analytical frameworks proposed by Aithal and Aithal (2018).

Results & Analysis: The analysis reveals that the patent introduces an integrated real-time AR/AI framework in which a retailer (backend) module and a user (frontend) module cooperate through a measurement server, a motion/pose estimator, a query assimilator, and an AR rendering server to fit, and then interactively modify, a virtual apparel on a user's body. A distinctive feature is the stateful interpretation of “sartorial interactions” — such as unbuttoning, folding a collar, or adjusting a waistband — sensed through an optional wearable glove-type apparatus controller and translated by a query-translator/assimilator pipeline into machine-executable apparel-specific actions rendered through Inverse-UV (IUV) texture mapping and GAN-based rendering. The SWOC and ABCDEF analyses indicate strong advantages in personalization, fit accuracy, and return-rate reduction, counterbalanced by dependence on accurate 3D body-measurement infrastructure and computationally intensive real-time neural inference.

Originality & Values: The originality of this patent analysis lies in applying the ABCDEF and SWOC frameworks, for the first time, to a stateful, gesture-and-pressure-driven AR/AI virtual apparel calibration system rather than to a static 2D-overlay try-on solution. The paper's value lies in clarifying how the patent's four-module architecture (Order/Measurement, Pose Estimation, Query Assimilation, and AR Rendering) differs from conventional virtual-fitting-room patents that rely on GAN-based 2D image warping alone. The findings offer researchers, retail-technology firms, and patent strategists a structured reference for evaluating the commercial readiness and IP landscape of interactive AR fashion-technology inventions.

Type of Paper: Case Study-based Exploratory Research.