AI Article Design System for Dynamic Customer Needs
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Solution Overview
Problem
The retail industry faces challenges in efficiently identifying and designing products that are in demand, as existing systems rely heavily on manual inputs and are inefficient in adapting to changing customer interests, leading to unsold inventory and losses.
Innovation Solution
An article designing system that analyzes image, margin, and sales data to identify popular article attributes, generates new designs using AI and Deep Neural Networks, and assigns a design score for popularity and sellability, allowing for dynamic product design that meets customer demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inputs and existing assessment systems are used to identify customer interests and design products, then product design can be created, but the process is tedious, time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical design processes with an automated AI-based system that uses machine learning models to analyze customer data, generate design parameters, and create product designs automatically, eliminating the need for tedious manual inputs and significantly reducing design time
Solution Approach 2:
The system enables self-service product design by automatically analyzing customer preferences and market data to generate optimized design parameters without requiring manual intervention, allowing the system to autonomously create product designs based on real-time customer insights
2Measurement precision
If existing systems rely on personalization and segmentation to assess customer interests, then some product recommendations can be made, but they are inefficient in identifying customer interests and designing products popular for a large number of customers
Solution Approach 1:
The patent moves beyond traditional personalization and segmentation approaches by introducing a multi-dimensional analysis framework that processes diverse data types (customer feedback, market trends, sales data) simultaneously through AI models, enabling more accurate identification of broad customer interests while maintaining design efficiency
Solution Approach 2:
The AI-based system serves multiple functions: it analyzes customer preferences, generates design parameters, creates product designs, and predicts market success, replacing multiple separate manual processes with a single unified system that improves both accuracy and efficiency
3Adaptability or versatility
If the retail industry continuously adapts to changing customer interests and requirements, then profitable sales can be achieved, but it is cumbersome and not always plausible to effectively adapt
Solution Approach 1:
The patent implements a dynamic system that continuously adapts to changing customer preferences by processing real-time data and automatically adjusting design parameters through AI models, enabling the system to remain agile and responsive to market changes without manual intervention
Solution Approach 2:
The system incorporates feedback loops where customer data, market trends, and sales performance are continuously analyzed by AI models to refine and update design parameters, enabling automatic adaptation to changing customer interests while managing complexity through automated control mechanisms
Data Source
AI summary
Examples of article designing are described herein. In an example, image data, margin data, and sales data corresponding to a plurality of articles may be obtained. The obtained data may be analyzed to identify a first article image of a first article and a second article image of a second article. The first article image is integrated with the second article image, based on an article attribute to generate a transformed article image. The article attribute may be an attribute having a maximum likelihood of making the article popular. The transformed article image may be filtered based on predefined filtering rules to obtain a curated article design image. The curated article design image is assessed to generate a design score indicative of a popularity and/or a sellability of an article, and a design of the article may be selected for a post design selection process, based on the design score.