A novel patentability detection model based on Siamese network
Bibliographic Data
| ID | 21441503 |
|---|---|
| Authors | Ahmet Kayakökü (0000-0001-8946-1484, United States Patent and Trademark Office, corresponding author), Aslıhan Tüfekci (0000-0002-8669-276X, Gazi University) |
| Year | 2025 |
| Volume | 130 |
| Issue | 10 |
| Pages | 5401-5440 |
| Publication date | 2025-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Scientometrics (JOURNAL) |
| Journal identifiers | ISSN: 0138-9130 • E-ISSN: 1588-2861 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11192-025-05425-9 |
| OpenAlex | W4414374862 |
| Language | EN |
| Citations received | 1 |
| References cited | 64 |
Invention · Novelty · Patent law · Patentability · Similarity (geometry) · Timeline · Intellectual Property and Patents
A two-stage deep learning-based system for patent citation recommendation
Research on classification and similarity of patent citation based on deep learning
Patent document clustering with deep embeddings
A deep learning based method for extracting semantic information from patent documents
A doc2vec and local outlier factor approach to measuring the novelty of patents
Early detection of valuable patents using a deep learning model
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |