{"url":"/task/patent-classification","name":"Patent classification","slug":"patent-classification","description_markdown":"Patent reviewers usually are responsible to classify the patent applications, i.e., they assign the design codes. This is time-consuming due to the numerous classification codes. For instance, the U.S. design patent system has 33 classes which are further divided into subclasses. Given that design patents include both titles and visual content, the goal of the experiment is patent classification by integrating titles, captions, and images. Although a single design patent can be associated with multiple design codes, we focus on the primary classification—solving the task as a multi-class classification problem.","categories":[{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":36,"papers_with_code":9,"benchmarks":0,"benchmark_tables_in_archive":0,"benchmark_tables_shown":0,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":2,"subtasks":0,"parent_tasks":1},"benchmarks":[],"datasets":[{"url":"/dataset/dapfam","name":"DAPFAM","full_name":"A Domain‑Aware Patent Retrieval Dataset Aggregated at the Family Level","num_papers_in_archive":1},{"url":"/dataset/impact-patent","name":"IMPACT Patent","full_name":"A Large-scale Integrated Multimodal Patent Analysis and Creation Dataset for Design Patents","num_papers_in_archive":1}],"subtasks":[],"parent_tasks":[{"url":"/task/multi-class-classification","name":"Multi-class Classification"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":9,"of":9,"tagged_in_all":36,"items":[{"url":"/paper/hybrid-model-for-patent-classification-using","title":"PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT","date":"2021-03-22","arxiv_id":"2103.11933","repositories_listed":2,"syntology":null},{"url":"/paper/impact-a-large-scale-integrated-multimodal","title":"IMPACT: A Large-scale Integrated Multimodal Patent Analysis and Creation Dataset for Design Patents","date":"2024-12-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-taxonomy-learning-and-historical","title":"Adaptive Taxonomy Learning and Historical Patterns Modelling for Patent Classification","date":"2023-08-10","arxiv_id":"2308.05385","repositories_listed":1,"syntology":null},{"url":"/paper/event-based-dynamic-graph-representation","title":"Event-based Dynamic Graph Representation Learning for Patent Application Trend Prediction","date":"2023-08-04","arxiv_id":"2308.09780","repositories_listed":1,"syntology":null},{"url":"/paper/a-novel-patent-similarity-measurement","title":"A Novel Patent Similarity Measurement Methodology: Semantic Distance and Technological Distance","date":"2023-03-23","arxiv_id":"2303.16767","repositories_listed":1,"syntology":null},{"url":"/paper/technological-taxonomies-for-hypernym-and","title":"Technological taxonomies for hypernym and hyponym retrieval in patent texts","date":"2022-11-14","arxiv_id":"2212.06039","repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-on-sentence-embedding-models","title":"A Survey on Sentence Embedding Models Performance for Patent Analysis","date":"2022-04-28","arxiv_id":"2206.02690","repositories_listed":1,"syntology":null},{"url":"/paper/clusterdatasplit-exploring-challenging","title":"ClusterDataSplit: Exploring Challenging Clustering-Based Data Splits for Model Performance Evaluation","date":"2020-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/190602124","title":"PatentBERT: Patent Classification with Fine-Tuning a pre-trained BERT Model","date":"2019-05-14","arxiv_id":"1906.02124","repositories_listed":1,"syntology":null}],"syntology_records":0,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}