Numeric Estimation Model for Child Clothing Size
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Solution Overview
Problem
Children's clothing often fails to fit properly due to varying growth rates and differing sizes and styles, leading to wasted money on ill-fitting garments and unreturned items, as existing solutions lack accurate and efficient methods for determining the right clothing size and style.
Innovation Solution
A system utilizing a numeric estimation model and machine learning to estimate a child's clothing size and recommend appropriate articles of clothing based on image data, physical measurements, and other inputs, which can be updated and refined through user feedback and new data, facilitating communication between devices and retailers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sizing methods are used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent replaces traditional mechanical measurement systems (physical measuring tapes, manual sizing charts) with a machine learning-based numeric estimation model that processes image data and physical data to predict clothing sizes automatically, achieving higher precision without requiring complex physical measurement devices
Solution Approach 2:
The system creates a digital copy of the child's physical characteristics through image data and physical measurements, then uses this digital representation to estimate clothing sizes without needing physical trial fittings, thereby improving measurement precision while reducing the complexity of physical measurement processes
2Reliability
If multiple data sources are integrated, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple data sources including image data, physical data (height, weight, age), and clothing size data into a unified numeric estimation model, combining these diverse inputs to improve recommendation reliability while managing complexity through integrated processing
Solution Approach 2:
The numeric estimation model serves multiple functions simultaneously: it processes different types of input data (images, physical measurements), performs size estimation, predicts future sizes, and generates clothing recommendations, thereby improving reliability through multi-functional integration without requiring separate systems for each function
3Measurement precision
If manual measurement methods are used, then ease of operation is maintained, but measurement precision deteriorates
Solution Approach 1:
The system performs automatic size estimation and clothing recommendations without requiring users to manually measure children or consult sizing charts, with the machine learning model autonomously processing image and physical data to generate accurate size predictions, thereby improving precision while maintaining ease of operation
Solution Approach 2:
The patent replaces manual measurement operations with automated image-based and data-based estimation, substituting physical measuring actions with computational analysis that achieves higher precision while requiring minimal user effort to operate
Data Source
AI summary
Methods and non-transitory machine-readable media associated with clothing recommendations are described. Clothing recommendations can include identifying, using a model built based on input data previously received in association with an article of clothing associated with a child, physical data associated with the child, and image data of the child with a reference object, output data representative of a clothing size recommendation for the child and sending, in response to a user request or a data refresh, the clothing size recommendation, a different article of clothing recommendation for the child based at least in part on the output data, or both, to a user device.