Network Analysis Algorithm for Patent Data Clustering and Ranking
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Solution Overview
Problem
Traditional methods for searching and analyzing large patent databases lack precision, struggling to identify the relative merit or worth of patents due to the complexity and volume of data, leading to inefficient and error-prone results.
Innovation Solution
A method and system for analyzing and visualizing data that identifies clusters and determines the relative ranking of data records within a network by determining link values, shortest pathways, and collapsing pathways based on predetermined algorithms, allowing for the assignment of rankings and similarity indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional keyword search methods are used in patent databases, then the search process is simple and fast, but the precision and reliability of results deteriorate due to large data volumes and complex attributes
Solution Approach 1:
The patent replaces traditional mechanical keyword-matching search methods with network analysis algorithms that calculate link values, shortest pathways, and cluster relationships between patent records. This substitution enables the system to process large volumes of patent data while providing precise identification of relevant patents through automated network-based metrics rather than simple text matching.
2Measurement precision
If professional assistance and detailed study of each patent specification are required to judge relative merit, then measurement precision improves, but loss of time and ease of operation deteriorate
Solution Approach 1:
The patent implements self-service evaluation by automatically calculating network metrics (link values, pathway counts, cluster assignments) for each patent record without requiring manual expert review. The system autonomously processes and ranks patent data based on predetermined algorithms, eliminating the need for time-consuming manual analysis while maintaining consistent evaluation criteria across all records.
Solution Approach 2:
The patent transforms the evaluation process by changing from qualitative expert assessment to quantitative network-based parameters. By calculating objective metrics such as link values, shortest pathway frequencies, and cluster memberships, the system converts subjective patent merit judgments into measurable numerical parameters that can be automatically processed and compared.
3Ease of operation
If traditional search methods are used, then ease of operation is maintained, but reliability and measurement precision of results worsen due to inability to handle complex data structures
Solution Approach 1:
The patent introduces network analysis algorithms as an intermediary layer between the user's simple search query and the complex patent database. This intermediary automatically performs sophisticated calculations including link value determination, shortest pathway analysis, and cluster identification, shielding the user from complexity while ensuring reliable processing of intricate patent relationships.
Data Source
AI summary
A method for identifying clusters within a network including a plurality of nodes and links, comprising the steps of:determining a link value for each node in the network;determining a local maxima within the network by locating node values where the sum of link values are higher than the sum of link values for all adjacent nodes;determining a list of the shortest pathways between a local maximum and all other nodes in the network;collapsing the pathways in accordance with a predetermined algorithm; andassociating all nodes that remain connected to each local maximum along the pathways.