Occupation Classification System Using Semantic Mapping
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Solution Overview
Problem
The rapid growth of unique job titles and complex job descriptions has made it challenging to classify occupations effectively for digital processing, such as anti-money laundering regulations, leading to inefficiencies in computer analysis.
Innovation Solution
A system for dynamically classifying free-form occupational inputs into pre-defined categories using machine-learning algorithms, direct matching, synonym matching, and semantic matching, which streamlines computer processing and reduces software complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If free-form occupation inputs are used to accommodate diverse job titles, then adaptability is improved, but device complexity increases and processing efficiency decreases
Solution Approach 1:
The patent introduces an intermediary mapping system that translates free-form occupation inputs into standardized occupation categories. This intermediary layer (occupation mapping module) handles the complexity of diverse job titles internally while presenting a simplified standardized interface to downstream processing systems, thereby resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The system transforms occupation data from unstructured free-form text into structured standardized categories through parameter changes in data representation. By changing the parameter format from variable free-text to fixed standardized codes, the system maintains adaptability for diverse inputs while reducing complexity for processing.
2Adaptability or versatility
If free-form occupation inputs are used to capture diverse job titles, then adaptability is improved, but productivity decreases due to processing inefficiency
Solution Approach 1:
The system performs preliminary classification by mapping free-form occupation inputs to standardized categories before downstream processing. This preliminary action of standardization prepares the data in advance, enabling faster and more efficient processing in subsequent anti-money laundering analysis steps, thus resolving the productivity issue.
3Adaptability or versatility
If free-form occupation inputs are used to accommodate unique job titles, then adaptability is improved, but loss of information increases due to classification difficulty
Solution Approach 1:
The occupation mapping module serves as an intermediary that preserves information from free-form inputs by systematically mapping them to standardized categories rather than losing information in the translation process. This intermediary ensures that occupation information accuracy is maintained while still accommodating diverse job titles.
Data Source
AI summary
System and methods for classifying free-form occupation inputs into prescribed occupation inputs are provided. In one embodiment, a machine receives an occupation; generates a meaning vector; compares the meaning vector against meaning vectors associated with a prescribed set of occupations; determines meaning scores for the prescribed set of occupations based upon an affinity between vector and the vectors associated with the prescribed set of occupations; selects a subset of the prescribed set of occupations based upon the meaning scores for the prescribed set of occupations; presents a selectable list of the subset of prescribed occupations; receives an indication of a selection a particular prescribed occupation for the selectable list; and provides the particular prescribed occupation to a downstream-processing system that is configured to only handle occupations in prescribed set of occupations.