Research on key generic technology prediction based on graph neural networks under the perspective of patent citation - An example from the field of genetic engineering

In this research, we adopted graph neural network models for key generic prediction based on cited patent data. Through the construction of the patent citation network and the design of a key generic evaluation system, 20879 relevant patents and 51,610 irrelevant patents were screened out. Further, we utilized the LDA topic model to interpret technical topics at a finer granularity. Finally, to test the effectiveness of this method, we took the field of genetic engineering as an example for key generic technology prediction, with an accuracy rate of 95%., These datasets were obtained by the Incopat patent database for cited patents (2013-2022) in the field of genetic engineering.
Details for the datasets are provided in the README file.
This directory contains the selection of the patent datasets.
1) Table of key generic indicators for nodes (partial 1).csv
This file consists of 10 indicators of patents: technical coverage, patent families, patent family citation, patent cooperation, enterprise-enterprise cooperation, industry-university-research cooperation, claims, citation frequency, layout countries,and layout countries.
2) Table of key generic indicators for nodes (partial 2).csv
This file consists of 10 indicators of patents: technical convergence, cited countries, inventors, citations,homologous countries/areas, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, and PageRank.
3) patent.content
The content file contains descriptions of the patents in the following format:<ID_number> <t..., , This README file was generated on 2023-11-25 by Mingli Ding.

## GENERAL INFORMATION

1. Title of Dataset: Cited patents in the field of gene engineering

2. Author Information
Investigators Contact Information
Name: Mingli Ding; Wangke Yu; Ran Li; Wenyu Ma; Lu Tan; Yuying Wang
Institution: Jingdezhen Ceramic University
Address: Jingdezhen, Jiangxi, China
Email:

3. Date of data collection:2013-2022

## DATA & FILE OVERVIEW

1. File List:

A) Table of key generic indicators for nodes (partial 1).csv

B) Table of key generic indicators for nodes (partial 2).csv

C) patent.content

D) patent.cites

E) Graph neural network modeling highest accuracy for different dimensions.csv

F) Prediction effects of key generic technologies.csv

### DATA-SPECIFIC INFORMATION FOR: Table of key generic indicators for nodes (partial 1).csv

1. Number of variables: 10

2. Number of cases/rows: 72489

3. Variable List:

* technical coverage: number of national economic class...