3D-Scanning Automated Shopping Assistants for Personalized Shoe Fitting

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Solution Overview

Problem

Conventional shoe shopping, both online and in-store, is inefficient and user-unfriendly due to the reliance on manual assistance and the need for repeated fitting processes, leading to frustration and inefficiency.

Innovation Solution

An automated shopping assistant system that utilizes 3D scanning and machine learning to create personalized shopping avatars, allowing for accurate product recommendations and customization based on user anatomical and preference data, with virtual try-on features and social feedback integration.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If automated shopping assistant system with 3D scanning and machine learning is implemented, then measurement precision and manufacturing precision are improved, but device complexity increases

Engineering Contradiction:

Improveanatomical data accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system enables self-service through automated 3D scanning and machine learning algorithms that automatically process anatomical data and generate product recommendations without requiring manual measurement or expert intervention. The automated shopping assistant independently performs data collection, processing, and matching functions.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Traditional manual measurement methods are replaced with optical 3D scanning technology and machine learning systems. The mechanical process of manual fitting and measurement is substituted with automated digital scanning and computational analysis, significantly improving precision while reducing operational complexity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If personalized product recommendations based on user history and preference data are provided, then adaptability is improved, but loss of information increases due to extensive data processing requirements

Engineering Contradiction:

Improvepersonalization capabilityVSAvoiddata processing overhead

Core Design Contradiction:

Adaptability or versatilityVSLoss of information

Solution Approach 1:

The system performs preliminary actions by collecting and processing user anatomical data, shopping history, and preferences in advance to create comprehensive user profiles. This pre-processing enables rapid and accurate product recommendations without requiring extensive data processing at the point of purchase.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates digital copies of user anatomical characteristics through 3D scanning and generates virtual avatars that represent the user's physical attributes. These digital copies are used for virtual try-on simulations and product matching, eliminating the need to repeatedly access and process raw anatomical data.

Inventive Principle:

Principle #26Copying

3Ease of operation

If virtual try-on features and simulation of anatomical characteristics are implemented, then ease of operation is improved, but device complexity increases

Engineering Contradiction:

Improveuser interface simplicityVSAvoidsimulation system complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system introduces a virtual avatar as an intermediary between the user's actual anatomy and the product visualization. The virtual try-on feature uses this digital representation to simulate how products will fit and look on the user, simplifying the user interface while managing complexity through abstraction.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Adaptability or versatility

If the system integrates multiple data sources including user history, preference data, and anatomical data, then adaptability is improved, but loss of time increases due to comprehensive data matching requirements

Engineering Contradiction:

Improverecommendation accuracyVSAvoiddata matching time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary data processing and profile creation in advance, consolidating user anatomical data, shopping history, and preferences into pre-processed user profiles. This reduces the time required for data matching during actual shopping interactions.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system maintains continuous operation by processing and updating user profiles in real-time as new data becomes available. The matchmaking system continuously refines recommendations based on accumulating user data without requiring repeated full-data processing, thereby reducing time loss while maintaining adaptability.

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS20250315871A1System, Platform and Method for Personalized Shopping Using an Automated Shopping Assistant

Publication Date: 2025.10.09 NIKE INC

AI summary

There is provided an apparatus, system, and method to provide personalized online product fitting. A method for personalized shopping may include the steps of an automated shopping assistant system accessing product data, a matchmaking system accessing user history data, the matchmaking system accessing user preference data, the matchmaking system accessing user anatomical data acquired from an automated shopping assistant apparatus, and the automated shopping assistant system matching user history, preference and anatomical data with data to generate a personalized matching system.