Text Data Standardization for Skill Comparison
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Solution Overview
Problem
Processing content items for identifying related skills across different formats, authors, and perspectives is challenging due to variations in text data, making it difficult to compare and analyze skills expressed in skill phrases.
Innovation Solution
A system standardizes text data by replacing verbs with nouns based on frequency in a data corpus, restructuring phrases to create a standard format, allowing for comparison of identical or similar skills across content items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text data from different content items is processed as-is, then the original format and author perspective are preserved, but comparing and analyzing skills across content items becomes difficult
Solution Approach 1:
The patent transforms text data by changing linguistic parameters - converting verb phrases to noun phrases (e.g., 'manage projects' to 'project management'), standardizing tense and voice, and normalizing grammatical structures. This parameter transformation enables consistent skill comparison across diverse content items while maintaining the essential meaning of the original text.
Solution Approach 2:
The patent segments text data into discrete skill phrases that can be individually processed and standardized. By breaking down complex sentences into identifiable skill components, the system can apply consistent transformation rules to each segment, facilitating accurate comparison without requiring complete restructuring of the entire text.
2Productivity
If skill phrases are standardized to a common format, then comparison and analysis efficiency improves, but the diversity of expression and perspective across content items is lost
Solution Approach 1:
The patent applies controlled parameter changes that standardize grammatical and structural aspects of skill phrases while preserving the core semantic meaning. Transformations such as converting 'analyzed data' to 'data analysis' maintain the skill identity while enabling efficient comparison, without eliminating the substantive diversity of actual skill expressions across different content items.
Solution Approach 2:
The patent creates a universal standardized format that can represent multiple different skill expressions. The standardization process establishes a common language for skill description that can accommodate various original expressions, allowing the same skill to be identified across different content items regardless of the author's original wording or perspective.
3Measurement precision
If manual processing of skill phrases is performed, then accuracy in identifying related skills is maintained, but the time and resources required for processing increase significantly
Solution Approach 1:
The patent implements automated text processing systems that perform standardization transformations without human intervention. The system self-applies consistent transformation rules to convert skill phrases from various formats into a standardized form, eliminating the need for manual processing while maintaining accuracy through rule-based consistency.
Solution Approach 2:
The patent replaces manual mechanical processing with automated computational processing. Instead of human reviewers manually analyzing and standardizing skill phrases, the system uses algorithmic transformations to automatically convert text data, dramatically reducing processing time while maintaining or improving consistency through systematic application of transformation rules.
Data Source
AI summary
Techniques for standardizing text data are disclosed. The system may identify, within a content item, a target phrase that is to be standardized. A subset of characters of a verb in the target phrase may be selected for comparison to a list of nouns. The subset of characters may be compared to a list of nouns identified in a data corpus. A noun in the list of nouns may be added to a candidate subset of nouns to replace the verb if the noun includes a sequence of characters that matches the subset of characters. A particular noun to replace the verb may be selected from the candidate subset of nouns based on a frequency associated with the particular noun occurring within the data corpus. The system may convert the target phrase to generate a standard phrase at least by replacing the verb with the particular noun.