Opportunity Network System for Career Path Discovery
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Solution Overview
Problem
Conventional career development approaches rely heavily on personal and professional networks, limiting users to only visible opportunities, making it difficult to discover potential next positions and determine a good fit, leading to unseen opportunities being missed.
Innovation Solution
An opportunity network system that utilizes machine learning, statistical, and natural language processing techniques to analyze comprehensive databases of resumes and profiles, identifying potential next positions and generating position-detail profiles to determine a degree of match for users, enabling communication with relevant individuals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users rely on personal and professional networks for career development, then accessibility and ease of operation are improved, but the quantity and diversity of visible opportunities are limited
Solution Approach 1:
The patent introduces an intermediary system (opportunity network system with machine learning algorithms) that mediates between users and career opportunities. The system automatically analyzes resumes, profiles, and career data to identify potential opportunities beyond users' immediate networks, acting as a mediator that expands visibility without requiring users to manually search or rely solely on personal connections.
Solution Approach 2:
The patent transitions from a two-dimensional approach (direct personal networks) to a multi-dimensional approach by incorporating machine learning analysis, statistical modeling, and comprehensive data processing. This dimensional expansion allows the system to uncover opportunities across multiple layers of professional relationships and industries that would be invisible through conventional network-only approaches.
2Quantity of substance
If users expand their professional networks to see more opportunities, then the quantity of visible opportunities increases, but the complexity of the system and time required increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and storing comprehensive career data, resumes, and profiles in databases before users need opportunity information. The machine learning models are pre-trained on historical career data, enabling the system to quickly generate personalized opportunity recommendations without requiring complex real-time analysis during user interaction.
Solution Approach 2:
The system performs self-service by automatically analyzing user profiles, resumes, and career histories without requiring manual input from users. The machine learning algorithms autonomously identify patterns, predict suitable opportunities, and generate personalized recommendations, reducing the burden on users to manually navigate complex network structures.
3Loss of information
If users manually search for comprehensive information about positions, then information completeness is improved, but time consumption increases
Solution Approach 1:
The patent applies preliminary action by pre-extracting, normalizing, and storing comprehensive position information from numerous sources before users need it. The system maintains pre-processed databases containing detailed position profiles, skill requirements, and career progression data, enabling instant retrieval and presentation of complete information without requiring users to conduct manual research.
Solution Approach 2:
The patent replaces the mechanical manual search process with automated machine learning systems that process and analyze vast amounts of data rapidly. Instead of users manually searching through numerous websites and documents, the system uses AI algorithms to automatically gather, analyze, and synthesize comprehensive position information, dramatically reducing time consumption while maintaining information completeness.
Data Source
AI summary
The present disclosure provides a method for identifying and representing potential next positions based on current position of user of an opportunity network system, the method including: (a) collecting and pre-analysing a comprehensive database of resumes or profiles of users, (b) extracting normalized entity information from resumes or profiles about backgrounds of users, (c) normalizing of the entity information of the users using at least one of machine learning techniques or statistical techniques to obtain normalized entity information, (d) identifying a comprehensive set of possible subsequent positions for the user based on the current position, (e) generating a position-detail profile for one or more of possible subsequent positions based on the profiles of people who are currently in that position or who may have previously worked at the position, and (f) determining a degree of match between resume or profile information of the user and at least one the position-detail profile of the target position.