Self-Calibrating Attribute Matrix for Dynamic Product-User Fit
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Solution Overview
Problem
Existing beauty and fashion product recommendation systems fail to account for dynamic user feedback and expert input, leading to static and linear recommendations that do not adapt to ever-increasing parameters, resulting in frustrating product searches, post-purchase dissonance, and low purchase motivation.
Innovation Solution
A self-calibrating, ever-expanding n-dimensional array/matrix system that correlates 'product-user-fit' and 'user-persona-fit' in a non-linear fashion, using multi-modal engines to integrate user profiles, expert feedback, and product attributes, intelligently mapping and locating data items based on spatial extrapolation and threshold values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If linear product recommendation systems are used, then implementation is simple, but they cannot adapt to dynamic user feedback and expert input
Solution Approach 1:
The patent transforms static linear recommendation systems into dynamic n-dimensional arrays that continuously adapt to user feedback and expert input. The system evolves by incorporating new parameters and recalculating correlations in real-time, making the recommendation engine responsive to changing user preferences and market conditions while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent transitions from traditional linear recommendation approaches to an n-dimensional array structure that incorporates multiple parameters simultaneously (user attributes, product attributes, feedback data, expert opinions). This dimensional expansion enables the system to process complex, multi-factor recommendations while organizing data in a structured grid format that balances comprehensiveness with computational efficiency.
2Adaptability or versatility
If static recommendation parameters are used, then system complexity is low, but they do not account for ever-increasing parameters
Solution Approach 1:
The patent segments the recommendation system into distinct n-dimensional arrays, with each array representing specific parameter categories (user profiles, product attributes, feedback data). This segmentation allows the system to handle increasing numbers of parameters by organizing them into manageable dimensional groups, where each array can be independently updated and processed without overwhelming system complexity.
Solution Approach 2:
The n-dimensional array structure serves multiple functions simultaneously: it stores user data, product information, feedback metrics, and expert opinions in a unified framework. This universal data structure can accommodate any combination of parameters by adjusting array dimensions and indices, providing scalability without requiring separate systems for different data types.
3Measurement precision
If non-linear data correlation is implemented, then product-user-fit accuracy improves, but computational complexity increases
Solution Approach 1:
The patent employs parameter changes by transforming user and product attributes into standardized numerical values that can be processed through mathematical operations on the n-dimensional arrays. By converting qualitative attributes into quantifiable parameters with defined ranges and weights, the system achieves non-linear correlation calculations that improve matching accuracy while maintaining computational tractability through consistent parameter transformation rules.
Data Source
AI summary
This invention discloses a system configured with an ever expanding, self-calibrating, array of one or more types of attributes, said system comprising: creating a first n-dimensional attribute matrix having a plurality of a first set of blocks, each block, from the first set of blocks, having a product data item resident; creating a second n-dimensional identity matrix having a plurality of second set of blocks, each block, from the second set of blocks, having a user data item resident; determining spatial extrapolation between vectors; receiving a new product data item; intelligently mapping each of said new product data items; intelligently locating each of said new product data items.