Personalized Sizing Engine Using Machine Learning Profiles
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Solution Overview
Problem
Existing sizing systems for consumer items are inflexible and fail to provide personalized sizing codes tailored to individual users, as they rely on rigid pre-determined correlations between item entities, which do not account for user-specific preferences and dimensions.
Innovation Solution
The use of machine learning models to generate personalized size codes by creating size profiles based on cohort data, where size nodes from multiple users are matched to the most closely associated size profile, allowing for user-specific size recommendations across different item entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pre-determined correlations between item entities are used for sizing, then the system is simple to operate, but the sizing accuracy for individual users deteriorates
Solution Approach 1:
The system automatically generates personalized size codes by analyzing user purchase history and feedback without requiring manual user input or configuration. The machine learning model autonomously learns user-specific sizing patterns from historical data, eliminating the need for users to manually adjust or configure sizing parameters while achieving high accuracy for individual users.
Solution Approach 2:
The system transitions from using fixed, pre-determined size correlations to dynamically generated size codes that are personalized for each user based on their unique purchase history and feedback. The machine learning model adjusts sizing parameters individually for each user, transforming the static sizing approach into a dynamic, adaptive system that improves measurement precision while maintaining ease of operation.
2Device complexity
If rigid pre-determined sizing correlations are used, then the system complexity is low, but the adaptability to user-specific preferences deteriorates
Solution Approach 1:
The system incorporates user feedback from purchase history and size feedback into the machine learning model to continuously refine and improve size recommendations. This feedback mechanism enables the system to adapt to user-specific preferences and sizing patterns over time, transforming a rigid static system into an adaptive learning system that improves versatility while managing complexity through automated feedback loops.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based sizing systems with a machine learning-based system that uses algorithms to analyze user data and generate personalized size codes. This substitution eliminates the need for complex manual configuration while enabling high adaptability to user-specific preferences through automated machine learning processes.
3Ease of manufacture
If generic sizing schemas are applied across all item entities, then the system is easy to implement, but the personalization for individual users deteriorates
Solution Approach 1:
The system segments the generic sizing schema into user-specific personalized size codes by analyzing individual purchase history and feedback patterns. The machine learning model divides the general sizing problem into user-specific sub-problems, generating customized size recommendations for each user while maintaining the overall framework of standardized item entity sizing. This segmentation enables high precision for individual users without requiring complete redesign of the implementation framework.
Data Source
AI summary
Systems and methods for outputting a size code include receiving a plurality of size nodes for a plurality of users, the plurality of size nodes comprising incoming size nodes or outgoing size nodes for one or more items, generating a plurality of size profiles based on the plurality of size nodes, wherein the plurality of size profiles each correlate a size for each item entity to each other item entity of the respective item entities, receiving first user size nodes for a first user, matching the first user to a first size profile of the plurality of size profiles based on the first user size nodes, receiving a first item corresponding to a first item entity of the item entities, identifying, at the size engine, a size code for the first item based on the first size profile, and outputting the size code.