Semantic Network Data Preparation for Patent Analysis
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Solution Overview
Problem
Traditional methods for searching and analyzing large datasets, such as patent databases, lack precision and struggle to provide sophisticated information on the relative merit of patents due to the complexity and volume of data, leading to errors and inefficiencies in identifying valuable information.
Innovation Solution
A method and system for preparing data for analysis by utilizing identifier and associative attributes to form networks of data records, selecting relevant networks based on size, subject matter, and citation links, and visualizing the data to facilitate more accurate and comprehensive analysis.
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 quick, but the precision and accuracy of search results deteriorate due to the complexity and volume of data
Solution Approach 1:
The patent divides the patent record into multiple semantic networks based on different attributes (e.g., technical field, inventor, citation relationships). Each network is constructed by segmenting the data into meaningful groups connected by relationships, allowing precise retrieval within specific networks while maintaining overall search efficiency.
Solution Approach 2:
The patent introduces semantic networks as intermediary structures between the user query and the patent database. These networks act as mediators that organize and pre-process patent information, enabling more accurate search results without requiring users to manually analyze complex patent documents.
2Measurement precision
If detailed analysis of each patent specification is performed, then the accuracy of merit assessment improves, but the time required and complexity of the process increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing patent data to construct semantic networks before actual analysis is needed. Patent records are pre-organized into networks based on their attributes and relationships, so that when analysis is required, the data is already structured and ready for quick retrieval and assessment without time-consuming manual processing.
Solution Approach 2:
The patent creates a simplified representation (copy) of patent information organized into semantic networks. Instead of requiring direct analysis of the full patent specification, the system works with this structured copy that captures essential relationships and attributes, enabling rapid merit assessment while preserving the necessary information accuracy.
3Quantity of substance
If patent databases contain more records to reflect increased technological development, then the comprehensiveness of data improves, but the complexity of data processing and search increases
Solution Approach 1:
The patent segments the large patent database into multiple manageable semantic networks organized by different attributes and relationships. This segmentation allows the system to handle vast quantities of patent records by processing and retrieving information from specific networks rather than the entire database, reducing overall processing complexity while maintaining data comprehensiveness.
Data Source
AI summary
A method of preparing data for analysis, comprising the steps of receiving an initial data set including a plurality of records, each of the plurality of records including an identifier attribute and an associative attribute that identifies a further one or more records;receiving the further one or more records identified by the associative attribute in each of the plurality of records; andassociating the further one or more records with the initial data set to form a final data set.