Dynamic Expertise Inference via Data Source Clustering
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Solution Overview
Problem
Existing methods for determining expertise in technical assistance are inefficient due to reliance on static résumés and profiles, leading to longer case resolution times as they fail to account for dynamic changes in an individual's skills and expertise.
Innovation Solution
A computing device scans various data sources associated with an individual, categorizes instances based on recognized terms, clusters positive contributions, ranks clusters by size and frequency, and infers expertise using machine learning and natural language processing to dynamically determine current and relevant expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If static résumés and profiles are used to determine expertise, then the system structure is simple and easy to maintain, but the case resolution time increases and expertise accuracy decreases
Solution Approach 1:
The patent transforms the static expertise determination system into a dynamic one by continuously scanning multiple data sources (résumés, GitHub profiles, LinkedIn profiles, blog posts, forum contributions) and updating expertise profiles in real-time. This allows the system to adapt to changing skills and expertise levels, reducing case resolution time while managing complexity through automated dynamic updates.
Solution Approach 2:
The system automatically scans and processes data from multiple sources without manual intervention. The expertise profiles are self-updated based on scanned data, eliminating the need for manual profile maintenance and reducing the burden on system operators while improving expertise accuracy.
2Measurement precision
If multiple data sources are scanned and processed dynamically, then expertise accuracy improves and case routing becomes more efficient, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the expertise determination process into distinct modules: scanning data from multiple sources, categorizing instances by recognized terms, clustering positive contributions, ranking clusters by size and frequency, and inferring expertise. This modular segmentation improves accuracy while managing complexity through organized, independent processing stages.
Solution Approach 2:
The system introduces an intermediary processing layer that scans and synthesizes data from multiple external sources (GitHub, LinkedIn, blogs, forums) before integrating it into the expertise determination system. This intermediary layer manages the complexity of multiple data sources by providing a unified processing interface.
3Adaptability or versatility
If traditional structured database filters are used for expertise identification, then the implementation is straightforward and fast, but the expertise identification fails to capture dynamic skill changes
Solution Approach 1:
The patent creates a universal expertise determination system that processes multiple types of data sources (structured résumés, semi-structured profiles, unstructured blog posts, forum contributions) through a single multi-functional processing framework. This universal approach captures dynamic skill changes across diverse sources while managing complexity through unified processing logic.
Solution Approach 2:
The system dynamically changes the parameters of expertise profiles by continuously scanning and updating skills, technologies, and expertise areas based on new data from multiple sources. This allows expertise profiles to adapt to skill evolution, project experiences, and emerging technologies, transforming static parameters into dynamic, evolving attributes.
Data Source
AI summary
In one embodiment, a computing device scans a plurality of available data sources associated with a profiled identity for an individual, and categorizes instances of the data sources according to recognized terms within the data sources. Once determining whether the profiled identity contributed positively to each categorized instance, categorized instances that have a positive contribution by the profiled identity may be clustered into clusters. The computing device may then rank the clusters based on size of the clusters and frequency of recognized terms within the clusters, and can then infer an expertise of the profiled identity based on one or more best-ranked clusters. The inferred expertise of the profiled identity may then be stored.