Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Patent Litigation Prediction: A Convolutional Tensor Factorization Approach
Authors: Qi Liu, Han Wu, Yuyang Ye, Hongke Zhao, Chuanren Liu, Dongfang Du
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on real-world data demonstrate the effectiveness of our framework. |
| Researcher Affiliation | Academia | Anhui Province Key Lab. of Big Data Analysis and Application, University of S&T of China Decision Sciences & MIS Department, Drexel University |
| Pseudocode | No | The paper describes its method using mathematical formulations and diagrams but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described. |
| Open Datasets | Yes | We provide empirical validation on a real-world dataset which contains two parts: patent lawsuit cases crawled from Patexia4 and patent documents collected from the USPTO5. 4https://www.patexia.com/ 5https://www.uspto.gov/ |
| Dataset Splits | No | The paper describes training and testing splits, such as 'randomly sample training set according to the ratio ranging from 20% to 80% and the rest serve as testing set,' but does not explicitly specify a distinct validation set or its split percentage. |
| Hardware Specification | Yes | All the experiments are run on a Tesla K20m GPU |
| Software Dependencies | No | The paper mentions using 'Tensorflow' but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The size of latent dimension of Ui, Vj, and Pk is set as 10... we set the number of slices in NCNN as 32... the number of words in each slice is set as 300. ...the best performing values of λU, λV , λP and λW are listed as 10 4, 10 3, 10 5 and 10 6 and the learning rate is set as 0.1. |