Membership Analyzing Method for Dynamic Organizational Structure
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for displaying hierarchical relationships within groups, such as business or family organizations, often rely on outdated organizational charts, making it difficult to obtain accurate and up-to-date membership information.
Innovation Solution
A membership analyzing method that classifies members based on predetermined keywords and uses a classification model to determine hierarchical relationships, generating a tree structure graph that displays member information, including job positions and personal details, to accurately represent the organizational structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If organizational charts are displayed on webpages for viewing membership information, then members can access hierarchical relationships, but the information becomes outdated and inaccurate over time
Solution Approach 1:
The system automatically crawls data from multiple sources including company websites, news media, and social platforms to self-update organizational structure information without manual intervention. The classification model automatically processes collected data to identify and categorize members, eliminating the need for manual chart updates while maintaining information accuracy.
Solution Approach 2:
The system performs preliminary data collection and classification by crawling and storing raw membership data from multiple sources before actual analysis is needed. This preliminary action ensures that when analysis is required, the data is already prepared and categorized, reducing the time needed to generate accurate organizational structures.
2Reliability
If manual updates of organizational charts are performed, then information can be kept current, but significant time and effort are required
Solution Approach 1:
The patent replaces manual mechanical updating processes with an automated computer-based system. The system uses web crawling technology to automatically collect data and employs machine learning classification models to process and categorize membership information, substituting human manual work with automated computational processes that maintain higher reliability and efficiency.
Solution Approach 2:
The system introduces an intermediary classification model that acts as a mediator between raw data collection and final organizational structure presentation. This intermediary automatically processes and validates data from multiple sources, ensuring reliability while eliminating the need for manual verification and updating processes.
3Measurement precision
If multiple data sources are crawled and classified using predetermined keywords, then accurate membership information can be obtained, but the system complexity increases
Solution Approach 1:
The system segments the complex classification task into distinct components: data crawling from multiple sources, keyword-based preliminary classification, and machine learning model-based refined classification. This segmentation allows each component to be optimized independently, maintaining high classification accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The classification model serves multiple functions: it classifies members based on keywords, identifies hierarchical relationships, validates data accuracy, and adapts to different data sources. This multi-functionality reduces the need for separate systems for each task, thereby managing complexity while maintaining comprehensive classification accuracy.
Data Source
AI summary
Disclosed is a membership analyzing method, including: obtaining data information of all members of a target group; classifying members with same first type information into a data set based on predetermined keywords; obtaining the type of first type information and data sets on condition that all members have been successfully classified into a corresponding data set, otherwise, if there is any remaining member failed to be classified into any data set, inputting data information of the remaining member into a predetermined classification model, to obtain the type of first type information and a data set of each remaining member; determining a tree structure of data sets based on the type of the first type information, and producing a graph for the tree structure; setting a same identification for the nodes of the same data set, and displaying second type information of each member in a display area.