Inventory Item Normalization System for Price Comparison
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Solution Overview
Problem
Consumers face challenges in identifying and comparing prices of items similar to their desired but unavailable or unaffordable preferred items, as these similar items often have different features that complicate apples-to-apples pricing comparisons.
Innovation Solution
An inventory normalization system that allows users to input their preferred item's features, normalizes them to generic terms, generates a vectorized list, and uses machine learning to identify similar items, then facilitates an online auction where vendors offer prices adjusted for important and missing features, with pricing normalized to reflect user preferences and location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If consumers compare prices of identical items from different vendors, then they can easily find the best price, but this approach fails when the desired item is not widely available or when consumers want cost-effective alternatives
Solution Approach 1:
The system transforms the comparison criterion from exact item identity to feature-based similarity. By normalizing item features into standardized parameters and calculating similarity scores based on these parameters, the system enables price comparison across different item variants while maintaining adaptability to consumer preferences.
Solution Approach 2:
The system introduces a feature normalization layer as an intermediary between the consumer's desired item and available inventory items. This normalization process converts diverse item features into a common framework, enabling meaningful comparison between the preferred item and alternative items from different vendors.
2Adaptability or versatility
If consumers purchase similar items with different features, then they can find available alternatives, but they lose the ability to do straightforward apples-to-apples pricing comparison
Solution Approach 1:
The system changes the measurement parameters from item identity to feature similarity scores. By normalizing features and calculating weighted similarity metrics, the system provides a precise quantitative basis for comparing prices of substitute items, restoring measurement precision to the pricing comparison process.
Solution Approach 2:
The system segments item features into distinct normalized parameters that can be independently evaluated. This segmentation allows the system to calculate similarity scores by comparing individual feature parameters, enabling precise pricing comparison that accounts for differences in specific features while maintaining overall comparability.
3Measurement precision
If the system normalizes features to generic terms and generates vectorized lists, then it can identify relevantly similar items, but the system complexity increases
Solution Approach 1:
The system applies parameter transformation by converting diverse item features into standardized normalized values and vector representations. This parameter change enables precise similarity identification through mathematical operations on vectors, achieving high measurement precision while managing complexity through systematic transformation rules.
Solution Approach 2:
The system creates simplified copies of item features in the form of normalized parameter sets and vector representations. These copied representations retain the essential information needed for similarity comparison while being much easier to process and compare mathematically, reducing the effective complexity of the comparison operation.
Data Source
AI summary
An inventory normalization system is provided. The system may normalize and vectorize features associated with a preferred item to be purchased by a user. The normalized and vectorized features may be used with one or more user-defined importance weightings to identify, using various machine learning techniques, items similar to the preferred item. Vendors having in inventory the preferred item or one or more of the similar items may be identified. An online auction may be opened and the vendors may be requested to submit offers for the preferred item or one or more of the similar items. Received offer prices may be normalized, based on the importance weightings, to discount for missing features or unwanted features. The normalized offer prices may be compared and a best offer identified. The system may initiate purchase of the item corresponding to the selected offer.