Product Similarity Score System Using ML Analysis
Solution Overview
Problem
Market researchers face challenges in efficiently identifying similar products in crowded marketplaces, relying heavily on human discretion which can lead to errors and inefficiencies, especially when introducing new products.
Innovation Solution
A similarity score system utilizing machine learning algorithms to analyze product characteristics, calculate similarity scores, and reduce human error by selecting focus items, generating primary and secondary characteristic sets, and determining relative uniqueness and similarity metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human discretion is used to identify similar products, then flexibility and adaptability are maintained, but errors and inefficiencies increase
Solution Approach 1:
The patent replaces the mechanical system of human manual product comparison with an automated computational system that uses machine learning models and algorithms to identify similar products. This substitution eliminates human error while maintaining high processing speeds, directly resolving the contradiction between reliability and productivity in product similarity identification.
Solution Approach 2:
The system enables products to be automatically compared and matched without human intervention by using self-learning algorithms that continuously improve their ability to identify product similarities. The automated system serves itself by automatically selecting focus items, generating characteristic sets, and determining similarity metrics without requiring manual oversight, thereby improving both accuracy and efficiency.
2Measurement precision
If comprehensive product characteristics are analyzed, then measurement precision improves, but computational burden increases
Solution Approach 1:
The patent segments the comprehensive product analysis into distinct hierarchical levels: primary characteristics (basic product attributes), secondary characteristics (detailed product features), and tertiary characteristics (specific product variations). This segmentation allows the system to analyze comprehensive product data with improved precision while managing computational complexity through structured, modular processing of different characteristic types.
Solution Approach 2:
The system dynamically changes analysis parameters by adjusting the depth and breadth of characteristic examination based on the specific analysis context. The machine learning models adaptively select which parameters to prioritize and how deeply to analyze each characteristic type, enabling precise measurements while optimizing computational resource allocation to prevent excessive complexity.
Data Source
AI summary
Methods, systems, articles of manufacture and apparatus to determine product similarity scores are disclosed. An example apparatus includes calculation set generating circuitry to identify a set of candidate comparison items based on primary characteristics corresponding to the a focus item, and generate a calculation set of items from the set of candidate comparison items based on secondary characteristics corresponding to market performance, and weight calculating circuitry to calculate primary characteristic scores corresponding to the focus item, the primary characteristic scores based on a uniqueness between the primary characteristics corresponding to the focus item and primary characteristics corresponding to the calculation set of items.