Proposal Support System Using Co-occurrence Network Analysis
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Solution Overview
Problem
Existing proposal support systems struggle to efficiently generate new proposal ideas that combine sales activity information with external information across different fields, leading to difficulties in finding relevant ideas and a lack of efficiency in the proposal generation process.
Innovation Solution
A proposal support system that utilizes a co-occurrence network to acquire and analyze specific words from multiple fields, extracting company names and associating them with trending words and focus words, thereby suggesting new proposal ideas that enhance the value of sales proposals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual search of all information is performed to generate proposal ideas, then comprehensive idea coverage is achieved, but time consumption increases significantly
Solution Approach 1:
The patent replaces manual information search and analysis (mechanical human effort) with an automated computer-based system that performs co-occurrence network analysis, text mining, and proposal idea generation. The system automatically extracts relationships between words, identifies trending topics, and generates proposal ideas without requiring manual review of all information sources.
Solution Approach 2:
The system enables self-service by automatically generating proposal ideas through automated information processing. The computer system independently performs data collection, analysis, and idea generation without continuous human intervention, allowing users to obtain proposal suggestions on demand without manual research efforts.
2Adaptability or versatility
If cross-domain information integration is implemented to enhance proposal value, then proposal quality improves, but system complexity increases
Solution Approach 1:
The patent implements a universal information processing system that handles multiple data sources, fields, and analysis methods through a single integrated platform. The system can process various types of information (text data, co-occurrence networks, company information) and apply different analysis techniques (text mining, network analysis, association rule mining) uniformly, enabling cross-domain integration without requiring separate systems for each function.
Solution Approach 2:
The system uses co-occurrence networks as an intermediary structure to integrate information across different domains. By representing relationships between words from different fields in a unified network format, the system can identify connections between disparate domains and generate cross-domain proposal ideas without directly managing the complexity of multiple information sources.
3Adaptability or versatility
If organizational thinking is expanded to multiple fields for social issue resolution, then solution scope increases, but idea generation difficulty increases
Solution Approach 1:
The patent replaces the mechanical process of organizational thinking and cross-field analysis with automated computational methods. The system performs co-occurrence network analysis, text mining, and association rule mining to identify relationships across multiple fields, eliminating the need for manual cross-domain analysis while expanding solution scope to include social issues and global challenges.
Solution Approach 2:
The system adds a new dimension to idea generation by incorporating co-occurrence network analysis and cross-field information integration. By representing information in a multi-dimensional space (words, fields, relationships, companies), the system can identify patterns and connections that are not apparent in traditional single-field analysis, thereby expanding solution scope while managing complexity through structured data representation.
Data Source
AI summary
A system acquires a specific first word from a network in which each first word of a first word group in a first sentence group including a first field name in a source out of a first source related to a first field and a second source related to a second field different from the first field is a node, and a relationship between two first words is a link connecting the nodes, extracts company names in the second field related to the specific first word, from a second sentence group including a second field name and the specific first word from the source, associates the specific first word, a specific second word, and the company names in the second field, based on information regarding the specific second word that co-occurs with the specific first word in a second word group in the second sentence group, and outputs a result.