Diamond Inventory Selection Using N-Dimensional Vector Projection
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Solution Overview
Problem
The manual evaluation of large inventories of diamonds with complex attributes and grades is time-consuming and prone to missing suitable diamonds, as consumers and vendors struggle to identify matching diamonds based on selection criteria.
Innovation Solution
A computer-implemented method that generates data vectors based on selection criteria, projects them into an N-dimensional inventory space, and identifies matching diamonds with similar attributes, allowing for automatic analysis and presentation of suitable diamonds through a graphical user interface, reducing human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation of large diamond inventories is performed, then selection accuracy can be maintained, but time consumption and labor resources increase significantly
Solution Approach 1:
The patent introduces an automated computer-based system as an intermediary between the diamond inventory and the consumer/vendor. This system uses algorithms to automatically evaluate diamonds based on selected attributes, generating recommendations without requiring manual examination of each stone. The intermediary process maintains selection accuracy by systematically applying evaluation criteria while dramatically reducing the time required to analyze large inventories.
Solution Approach 2:
The patent replaces the manual mechanical evaluation process with an automated computational system. Instead of vendors or consumers physically examining diamonds and comparing attributes, the system uses software to automatically retrieve diamond data from databases, compare attributes against selection criteria, and generate ranked recommendations. This substitution eliminates the time-consuming manual labor while preserving evaluation accuracy through systematic algorithmic processing.
2Reliability
If manual evaluation of complex multidimensional diamond data is performed, then comprehensive analysis can be achieved, but the volume of data makes it difficult to identify all suitable diamonds
Solution Approach 1:
The patent segments the complex diamond evaluation process into distinct automated components: attribute selection, data retrieval, comparison logic, and recommendation generation. The system allows users to select specific attributes of interest, then automatically segments the inventory analysis by retrieving and evaluating diamonds based on those selected criteria. This segmentation enables comprehensive analysis of relevant data while maintaining high productivity through automated processing of each segment.
Solution Approach 2:
The system enables self-service automated evaluation where the computer system independently performs the complete evaluation process without requiring manual intervention for each diamond. Once selection criteria are established, the system automatically retrieves diamond data, performs comprehensive comparisons across all inventory items, and generates recommendations. This self-service capability ensures thorough analysis of the entire inventory while maintaining high evaluation efficiency through autonomous processing.
3Ease of operation
If the inventory space is reduced to a manageable subset for consumer review, then ease of selection is improved, but good candidate diamonds may be missed during the filtering process
Solution Approach 1:
The patent performs preliminary automated evaluation of the entire diamond inventory against selection criteria before presenting results to the consumer. The system pre-retrieves and pre-evaluates all diamonds that meet the selected attributes, ranking them according to how well they match the criteria. This preliminary action ensures that the manageable subset presented to the consumer contains all suitable candidates, eliminating the risk of missing good diamonds while maintaining ease of selection through organized presentation.
Solution Approach 2:
The system incorporates feedback mechanisms where consumer preferences and selections are used to refine and adjust the evaluation process. As consumers interact with the presented diamonds, the system learns from their selections and adjusts future recommendations to better match their preferences. This feedback loop ensures comprehensive coverage of suitable diamonds while improving the ease of selection over time by adapting to consumer behavior patterns.
Data Source
AI summary
An inventory application automatically explores a high-dimensionality inventory dataset associated with an inventory of diamonds to identify one or more diamonds that meet one or more selection criteria associated with a user. The inventory application determines a user profile that reflects one or more selection criteria associated with a user and maps the user profile to one or more templates. A given template is an abstract representation of a diamond that meets the selection criteria to some degree. The inventory application projects the one or more templates to one or more corresponding locations in an N-dimensional space representing all diamonds included in the inventory of diamonds. The inventory application identifies one or more diamonds within the inventory space that reside proximate to each location associated with the one or more templates and returns data associated with these diamonds to the user.