Vector-Based Partiality Refinement for Personalized Product Selection
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Solution Overview
Problem
Existing shopping paradigms face inefficiencies in presenting consumers with personalized purchasing options based on their preferences, as existing approaches are limited in scope and do not effectively ensure that products best suited to individual consumers are available for purchase at the time of their visit.
Innovation Solution
A vector-based characterization system that represents individuals' partialities as vectors with magnitude and angle, allowing for the identification of products that align with their values, affinities, and aspirations, thereby facilitating the presentation of products that reduce the effort required to achieve their desired order.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a preferences-based approach is used to present personalized purchasing options, then consumer satisfaction may be improved, but the scope and effectiveness of personalization is limited due to product-specific information having little value apart from very specific products or product categories
Solution Approach 1:
The patent transitions from traditional product-specific preference data to a vector-based dimensional representation where consumer partialities and product characteristics are mapped across multiple dimensions (values, affinities, aspirations). This dimensional transformation allows preferences to be generalized across product categories while maintaining personalization effectiveness, resolving the contradiction between personalization scope and information value.
Solution Approach 2:
The patent changes the parameters used to represent consumer preferences from discrete product-specific attributes to continuous vector parameters with magnitude and direction. These vector parameters can be applied across diverse product categories, expanding personalization scope while preserving information value through mathematical operations on the vectors.
2Reliability
If traditional shopping paradigms are used where consumers view products at shared physical locations, then product availability is ensured, but logistical and temporal inefficiencies occur and products best suited to consumers may not be available at the time of visit
Solution Approach 1:
The patent performs preliminary action by pre-identifying and preparing personalized product recommendations based on consumer partiality vectors before the consumer arrives at the shopping location. The system calculates optimal product matches in advance using vector operations, ensuring that personalized products are ready for immediate presentation, thus eliminating waiting time while maintaining product availability.
Solution Approach 2:
The patent creates a virtual representation (copy) of the shopping experience where personalized product recommendations are generated and presented digitally before the physical shopping visit. This virtual catalog of personalized products allows consumers to review options in advance, reducing the time needed during the actual shopping visit while ensuring product availability through pre-identification.
Data Source
AI summary
Some embodiments provide customer partiality vectorization refinement systems, comprising: a customer database storing a set of multiple customer partiality vectors for each of multiple different customers; a product database storing a set of multiple product partiality vectors for each of multiple different products; and a vectorized refinement control circuit configured to: identify, for a first customer, a multi-dimensional partiality vector target area defined by a limited range of partiality magnitudes and limited range of representative partiality directions; select a first product having at least a product partiality vector that is within a threshold alignment with the partiality vector target area, and cause the first product to be presented to the first customer; receive feedback associated with the first customer and corresponding to the first product; and adjust the partiality vector target area based on the feedback.