Automated Object Classification via Affinity Scoring
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Solution Overview
Problem
Manual sorting and classification of objects into folders is laborious and time-consuming, especially when dealing with numerous folders containing similar information, as it requires extensive navigation and analysis to determine the appropriate folder for an unsorted object.
Innovation Solution
A method and system that infer rules to classify objects by computing degrees of affinity between the object's properties and those in target folders, using a first degree of affinity for uniqueness and a second degree for statistical significance, to determine the most suitable target folder for classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual sorting and classification of objects into folders is performed, then objects can be organized, but the process is laborious and time-consuming
Solution Approach 1:
The system performs automated classification by computing affinity scores between objects and folders, eliminating the need for manual sorting. The classification process serves itself by using object properties and folder contents to automatically determine placements, thus resolving the contradiction between ease of operation and time loss.
Solution Approach 2:
The patent replaces manual mechanical sorting actions with an automated computational system that calculates affinity scores based on object properties and folder contents. This substitution of mechanical manual operations with automated information processing resolves the time-consuming nature of manual classification.
2Measurement precision
If manual navigation between multiple folders is performed to analyze and classify objects, then classification accuracy can be maintained, but the process becomes time-consuming
Solution Approach 1:
The system pre-computes affinity scores between all objects and folders before classification is needed. By performing this analysis in advance and storing the results, the system eliminates the need for real-time manual navigation and analysis, thus maintaining accuracy while reducing time loss.
Solution Approach 2:
The patent introduces an intermediary computational layer that calculates affinity scores as intermediaries between objects and folders. This intermediary mechanism provides classification recommendations without requiring direct manual navigation through folders, thus maintaining accuracy while reducing time consumption.
3Productivity
If users manually analyze folder contents to determine appropriate folders for unsorted objects, then classification can be performed, but user confusion increases when many folders contain similar information
Solution Approach 1:
The system performs self-service classification by automatically computing affinity scores and generating placement recommendations, eliminating the need for users to manually analyze folders. This automated approach increases productivity while preventing user confusion by providing clear, objective classification recommendations based on calculated affinities.
Solution Approach 2:
The system provides feedback in the form of affinity score calculations and recommended folder placements. This feedback mechanism guides users through the classification process by presenting objective data-driven recommendations, thus increasing productivity while reducing user confusion about which folders are most appropriate.
Data Source
AI summary
Described are methods and systems related to inferring rules to classify an object to one of one or more target folders. One or more properties of the object to be classified are determined. A first degree of affinity between the object to be classified and the objects of the target folders, having a property identical to the object to be classified, is computed. A second degree of affinity between the object to be classified and the objects within each target folder, having a property identical to the object to be classified, is computed. A total degree of affinity between the object to be classified and each target folder is calculated. A normalized total degree of affinity is calculated by averaging the total degree of affinity across all target folders. The object is moved to a target folder having a highest value of the normalized total degree of affinity.