Self-Calibrating N-Dimensional Attribute Array for Cosmetics
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Solution Overview
Problem
The cosmetics market faces challenges such as overwhelming product choices, post-purchase dissonance, and low purchase motivation due to fragmented industry structures and over-the-top marketing. Consumers struggle to find products that match their individual needs and preferences.
Innovation Solution
A system and method for creating an ever-expanding, self-calibrating array of attributes that predicts and fulfills the gap between user perceptions and product attributes. This is achieved by defining n-dimensional attribute arrays, determining spatial extrapolation between vectors, and intelligently mapping and locating new data items within the arrays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If linear product recommendations are used, then the system is simple and easy to implement, but the recommendations are static and cannot adapt to user feedback
Solution Approach 1:
The patent transforms static linear recommendations into dynamic multi-dimensional arrays that continuously adapt to user feedback and expert input. The system evolves from fixed parameters to flexible n-dimensional structures that can expand and recalibrate based on new data, enabling real-time adaptation while maintaining operational simplicity through automated processes.
Solution Approach 2:
The patent introduces n-dimensional arrays to move beyond linear one-dimensional recommendations. By adding multiple dimensions (user attributes, product attributes, feedback parameters, expert opinions), the system creates a multi-faceted recommendation space that captures complex relationships while preserving ease of use through systematic data organization and automated matching algorithms.
2Adaptability or versatility
If an n-dimensional array with ever-increasing parameters is created, then the system can account for user feedback and expert opinions, but the device complexity increases
Solution Approach 1:
The patent segments the complex n-dimensional data structure into manageable blocks, where each block represents a specific dimension or parameter category. This segmentation allows the system to handle vast amounts of data from multiple sources (users, experts, products) by organizing them into discrete, processable units that can be independently managed and combined.
Solution Approach 2:
The patent creates a universal n-dimensional array framework that can accommodate diverse data types and sources through a single unified structure. This multi-functional data model handles user feedback, expert opinions, product attributes, and market data within the same systematic framework, reducing overall system complexity despite the versatility it provides.
3Measurement precision
If the array is made non-linear to account for complex relationships, then the system can capture nuanced user-product fits, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent implements feedback mechanisms where user responses and expert evaluations continuously refine the non-linear relationships within the n-dimensional array. This feedback loop enables the system to learn from actual user-product interactions, automatically adjusting weights and correlations to improve measurement precision while managing the complexity of non-linear analysis through iterative optimization.
Solution Approach 2:
The patent replaces traditional mechanical or linear analytical methods with computational algorithms capable of handling non-linear relationships. By using computer-based processing and automated calculations, the system can detect and measure complex non-linear patterns in user-product fits that would be intractable through manual or conventional methods.
4Adaptability or versatility
If the system expands to include more attributes and dimensions, then it can provide better personalization, but the loss of time for data processing increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing and organizing data into the n-dimensional array structure before actual recommendation queries. User profiles, product attributes, and relationship matrices are prepared and stored in advance, allowing the system to quickly retrieve and process recommendations without performing extensive real-time calculations, thus reducing processing time while maintaining high personalization levels.
Solution Approach 2:
The patent maintains continuous useful action by continuously updating and refining the n-dimensional array as new data becomes available. Rather than periodic batch processing, the system continuously learns from new user feedback and expert inputs, keeping the recommendation engine current and accurate without requiring lengthy reprocessing cycles, thereby balancing personalization quality with processing efficiency.
Data Source
AI summary
This invention discloses a system configured with an ever expanding, self-calibrating, array of one or more types of attributes, comprising: creating a first n-dimensional attribute matrix having a plurality of a 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 having a user data item resident; polling one or more products, product experts, product users, users, in order to obtain first bias (first dimension), second bias (second dimension), third bias (third dimension), first fixed user attribute, and second variable user attribute; inputting a new product; fixing a base truth value; aligning said products basis said fixed base truth values in said n-dimensional attribute matrix; receiving feedbacks; correcting said truth value; interspersing said new product in said n-dimensional attribute matrix, basis said corrected truth value, using spatial data extrapolation.