Verbal Scale Recognition System for Data Cleansing
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Solution Overview
Problem
Conventional data cleansing tools struggle with accurately processing verbal scales, which are often inaccurate, incomplete, inconsistent, and non-uniform due to user variability, leading to incorrect conclusions and increased costs in decision-making processes.
Innovation Solution
A method and system for precise numerical interpretation of different verbal judgment sets, involving inputting verbal data, determining word similarity scores, mapping words to predefined scales, and generating confidence scores to transform verbal data into consistent and usable numerical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional data cleansing tools are used to process verbal scales, then data processing can be automated, but the accuracy and consistency of the cleaned data deteriorate due to user variability and non-uniform verbal expressions
Solution Approach 1:
The system transforms verbal scale data from non-numeric to numeric parameters, converting qualitative assessments into quantifiable measurements. This parameter transformation enables automated processing while maintaining accuracy through mathematical relationships that preserve the relative ordering and intensity of verbal judgments.
Solution Approach 2:
The patent introduces an intermediary computational layer that processes verbal scale data through similarity scoring and normalization algorithms. This intermediary system acts as a mediator between raw verbal inputs and final cleaned data, resolving inconsistencies through structured transformation rather than direct conventional cleansing methods.
2Stability of the object's composition
If pre-defined words and numeric values are forced on users, then data uniformity improves, but user experience deteriorates due to time consumption and confusion
Solution Approach 1:
Instead of forcing users to conform to pre-defined options, the system inverts the approach by accepting free-form verbal expressions and automatically transforming them into uniform numeric data. This inversion maintains data uniformity while preserving user freedom, eliminating the need for users to learn or remember specific predefined terms.
Solution Approach 2:
The system enables users to provide data in their own natural language without requiring them to adhere to system-imposed formats. The automated transformation process serves itself by handling the normalization and standardization tasks that would otherwise require user effort, thereby improving ease of operation while maintaining data uniformity.
3Adaptability or versatility
If different users are allowed to use different words to describe the same concepts, then user flexibility improves, but data consistency deteriorates leading to incorrect conclusions
Solution Approach 1:
The system changes the parameter representation from diverse verbal labels to a unified numeric scale. By transforming various user expressions (e.g., 'high', 'hi', 'H', 'good') into consistent numeric values, the system preserves user flexibility in expression while ensuring data consistency through standardized parameter representation.
Solution Approach 2:
The patent replaces manual data standardization processes with automated machine learning algorithms. Instead of requiring users to mechanically adhere to predefined options or requiring manual reconciliation of inconsistencies, the system uses computational methods to automatically detect patterns and transform diverse verbal inputs into consistent numeric data.
4Ease of operation
If verbal scales are used instead of numeric values, then user comprehension improves, but data quality deteriorates due to inaccuracy and incompleteness
Solution Approach 1:
The system segments the data processing into two distinct stages: first collecting verbal expressions for user comprehension, then transforming them into numeric values for data quality. This segmentation allows users to think in familiar verbal terms while ensuring the final dataset meets quality standards through systematic numeric transformation.
Solution Approach 2:
The transformation algorithm serves as an intermediary that bridges verbal expressions and numeric data. It processes the verbal input through similarity scoring and normalization, acting as a mediator that preserves the meaning of user-comprehensible verbal scales while producing reliable, high-quality numeric data suitable for analysis.
Data Source
AI summary
A computer inputs data including different verbal judgment sets. Each different verbal judgment set includes words which are votes that define different rank values and each represents an evaluation of an alternative. The processor determines a word similarity score of each word in the verbal judgment sets to predefined words in a predefined scale. The processor determines a set similarity score between the different verbal judgment set and the predefined scale based on the words included in the different verbal judgment set and the predefined words within the predefined scale. The processor maps the words of the different verbal judgment sets to a numerical scale that corresponds to the predefined scale, based on the set similarity score. The processor interprets the different verbal judgment sets in the universe of known data based on the numerical scale and provides cleansed data which is used by a data-dependent application.