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13 results for “USPTO”
USPTO_PIP_Dataset
<p><strong>USPTO-PIP Dataset:</strong> For the perspective classification task, we used the dataset presented by Wei et al. [32] based on patents collected from the USPTO. In</p><p>this dataset, meta information including image perspectives has been automatically extracted from captions. We processed the data to extract the most common (more than 1000 samples) perspective labels (e.g., left view, perspective) and identified a class taxonomy (Table 1, right) covering 2, 4, and 7 classes. We</p><p>utilize this information to compile the USPTO-PIP dataset for patent image perspective (PIP) classification from images</p><p><strong>Paper title:</strong> Classification of Visualization Types and Perspectives in Patents</p><p><strong>Paper link: </strong><a href="https://link.springer.com/chapter/10.1007/978-3-031-43849-3_16 ">https://link.springer.com/chapter/10.1007/978-3-031-43849-3_16 </a></p><p><strong>Git repository link:</strong> <a href="https://github.com/TIBHannover/PatentImageClassification">https://github.com/TIBHannover/PatentImageClassification</a> </p><p> </p><p><strong>Cite as:</strong></p><p>Ghauri, J.A., Müller-Budack, E., Ewerth, R. (2023). Classification of Visualization Types and Perspectives in Patents. In: Alonso, O., Cousijn, H., Silvello, G., Marrero, M., Teixeira Lopes, C., Marchesin, S. (eds) Linking Theory and Practice of Digital Libraries. TPDL 2023. Lecture Notes in Computer Science, vol 14241. Springer, Cham. https://doi.org/10.1007/978-3-031-43849-3_16</p><p> </p><p>OR</p><p> </p><p>@InProceedings{10.1007/978-3-031-43849-3_16,</p><p>author="Ghauri, Junaid Ahmed</p><p>and M{\"u}ller-Budack, Eric</p><p>and Ewerth, Ralph",</p><p>editor="Alonso, Omar</p><p>and Cousijn, Helena</p><p>and Silvello, Gianmaria</p><p>and Marrero, M{\'o}nica</p><p>and Teixeira Lopes, Carla</p><p>and Marchesin, Stefano",</p><p>title="Classification of Visualization Types and Perspectives in Patents",</p><p>booktitle="Linking Theory and Practice of Digital Libraries",</p><p>year="2023",</p><p>publisher="Springer Nature Switzerland",</p><p>address="Cham",</p><p>pages="182--191",</p><p>abstract="Due to the swift growth of patent applications each year, information and multimedia retrieval approaches that facilitate patent exploration and retrieval are of utmost importance. Different types of visualizations (e.g., graphs, technical drawings) and perspectives (e.g., side view, perspective) are used to visualize details of innovations in patents. The classification of these images enables a more efficient search in digital libraries and allows for further analysis. So far, datasets for image type classification miss some important visualization types for patents. Furthermore, related work does not make use of recent deep learning approaches including transformers. In this paper, we adopt state-of-the-art deep learning methods for the classification of visualization types and perspectives in patent images. We extend the CLEF-IP dataset for image type classification in patents to ten classes and provide manual ground truth annotations. In addition, we derive a set of hierarchical classes from a dataset that provides weakly-labeled data for image perspectives. Experimental results have demonstrated the feasibility of the proposed approaches. Source code, models, and datasets are publicly available (https://github.com/TIBHannover/PatentImageClassification).",</p><p>isbn="978-3-031-43849-3"</p><p>}</p><p> </p>
USPTO patent data: 250k random sample and NPEs' patents
<p>This package includes:</p> <ol> <li>Two Stata .dta files consisting of information on patents assigned by the United States Patent and Trademark Office between 1976 and 2014: a random sample of 250 000 US patents, and data on patent owned by Intellectual Ventures, RPX, and several other companies. The variables for example include: grant date, application date, forward and backward citations, renewals, claims and others.</li> <li>Source codes and methods used in generating and analyzing the two data files.</li> </ol> <p>A bachelor thesis with further information will be linked here.</p>
USPTO-LLM: A Large Language Model-Assisted Information-enriched Chemical Reaction Dataset
<p>USPTO-LLM is an <strong>information-enriched chemical reaction dataset</strong> that provides more side information (reaction conditions and reaction steps division) for developing new reaction prediction and retrosynthesis methods and inspires new problems, such as reaction condition prediction. It comprises over <strong>247K chemical reactions</strong> extracted from the patent documents of USPTO (United States Patent and Trademark Office), encompassing abundant information on reaction conditions. </p> <p>We employ large language models to expedite the data collection procedures automatically with a reliable quality control process. The extracted chemical reactions are organized as <strong>heterogeneous directed graphs</strong>, allowing us to formulate a series of prediction tasks, such as reaction prediction, retrosynthesis, and reaction condition prediction, in a unified graph-filling framework.</p>
Link Compustat – USPTO Patent Assignment Dataset
<p>This page provides the data resulting from linking assignees and assignors in the USPTO Patent Assignment Dataset to Compustat gvkeys. We work with a version of the USPTO PAD that was gracefully shared with us by Stuart Graham. Such version precedes by one year the first release available at the USPTO website (https://www.uspto.gov/ip-policy/economic-research/research-datasets/patent-assignment-dataset). The version that we use covers 5,534,135 transactions recorded at the USPTO between January 1970 and January 2013 (inclusive). While the first transaction date is January 1970, the number of transactions recorded in the initial years is negligible. Data coverage seems sufficient for the years 1981-2012.</p> <p> </p> <p>If you use the code or data, please cite the following two papers:</p> <p> </p> <p>Arque-Castells, P., and Spulber, D. (2022). Measuring the Private and Social Returns to R&D: Unintended Spillovers versus Technology Markets. Journal of Political Economy. <a href="https://doi.org/10.1086/719908">https://doi.org/10.1086/719908</a></p> <p> </p> <p>Arqué Castells, Pere and Spulber, Daniel F., Firm Matching in the Market for Technology: Business Stealing and Business Creation (September 17, 2021). Northwestern Law & Econ Research Paper No. 18-14, Available at SSRN: https://ssrn.com/abstract=3041558 or <a href="http://dx.doi.org/10.2139/ssrn.3041558">http://dx.doi.org/10.2139/ssrn.3041558</a></p> <p> </p>
USPTO Dataset for: Fast Chemical Reaction Condition Suggestion via Rule-Based Classification and Similarity Search
<p>USPTO database that is analyzed with Rxn-INSIGHT (<a href="https://github.com/mrodobbe/Rxn-INSIGHT">https://github.com/mrodobbe/Rxn-INSIGHT</a>).</p><p>This gzip file contains a very large Pandas DataFrame that can be loaded via pd.read_parquet('uspto_rxn_insight.gzip'). Because of the large size of the data, PyArrow version 13.0 must be used. </p><p>To use parquet in Pandas, install PyArrow and fastparquet using pip:</p><p>pip install pyarrow==13.0<br>pip install fastparquet</p>
[Data augmentation in a TTL] - Fictive dataset (27.5M) with up to 5k reactions per template // (13'953 template extracted from USPTO-FULL IBM version)
<p>Full generated fictive dataset, containing 27.5M reactions with up to 5000 reactions per radius 1 reaction template (13'953 reaction templates from USPTO-full, IBM version).</p> <p>Title of the manuscript:</p> <p>"Data augmentation in a Triple Transformer Loop retrosynthesis model"</p> <p>Abstract: </p> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div dir="auto"> <div>Reactions in the US Patent Office (USPTO) are biased towards a few over-represented reaction types, which potentially limits its usefulness for computer-assisted synthesis planning (CASP). To obtain an equilibrated dataset, we applied retrosynthesis templates to USPTO molecules as products (P) to generate starting materials (SM). We then used transformer T2 from our recently reported triple transformer loop (TTL) retrosynthesis model to predict reagents (R) for the SM®P reaction. Finally, we validated the prediction by requesting a high confidence prediction (>95%) for the prediction of P from SM+R by TTL transformer T3. We generated up to 5,000 reactions per template, resulting in 27.5 million validated fictive reactions covering the chemical space of the original UPSTO dataset. To exemplify the use of this dataset, we show that a single-step retrosynthesis transformer model trained with a template equilibrated subset of 1,097,374 fictive reactions outperforms the corresponding model trained on USPTO reactions only.</div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div></div>
PaRoutes 2.0 and USPTO-based models
<p>This contains</p> <ul> <li>PaRoutes 2.0 reference routes and stocks. </li> <li>USPTO-based models derived from pipelines in rxnutils and aizynthtrain</li> </ul>
Patent citation data for USPTO utility patents granted between 1976-2015 and for patents belonging to 30 technology domains
<p>These two data file contains information on patent citations for USPTO utility patents granted between 1976 and 2015 and for patents that have been classified in 30 specific technology domains.</p> <p>The file 'CITATION_INFO_no_neg_citlag.csv' is generated combining raw data <a href="https://www.patentsview.org/download/">freely dowloadable from patentsview.org</a> from which citations where the filing year of the citing patent is younger than the filing year of the cited one have been removed.</p> <p>The file 'CITATIONS_DOMAINS.csv' is a sample of the previous file that only includes citations made by patents belonging to one of 30 domains defined in the paper '<a href="https://www.sciencedirect.com/science/article/pii/S0040162520309264#ecom0001">Estimating technology performance improvement rates by mining patent data</a>' by Giorgio Triulzi, Jeff Alstott and Chris Magee.</p> <p>These two files complement another dataset <a href="http://dx.doi.org/10.17632/f4fj887y67.1">published on Mendeley Data</a>. The two datasets can be used, together with the code <a href="https://github.com/GiorgioTriulzi/TechnologyPerformanceImprovementEstimates">published on GitHub</a>, to replicate the main results from the paper.</p>
CPC Patent Classification: USPTO-70k-enriched
<p>The patent classification task falls under the category of hierarchical multi-label classification. A patent document contains `title`, `abstract`, `claims` and `description` as four textual fields. Because of the large text, most of the previous work focused on title, abstract and claims as patent fields. In the paper, we make use description as a more elaborate patent field. For evaluation, we create a new dataset (USPTO-70k-enriched) from the previously releasd USPTO-70k dataset which contains title and abstract as patent fields.</p> <p>Now, the dataset is enriched with four additional text columns, claims, brief-summary, fig-desc, detail-desc, where the later three columns are the subfield of description. Both the datasets are created from the bulk-data-dump provided by United States Patent and Trademark Office (USPTO) released under CC-BY-4.0.</p> <p>We also release the dataset under the same license, CC-BY-4.0.</p>
Fictive reactions validated by TTL with USPTO as source of molecules - equilibrated subset (1M)
Open the record for dataset details and reuse information.
USPTO dataset used by enviFormer
<p>This contains the exact version of the USPTO dataset used in the paper "Predictive Modeling of Biodegradation Pathways Using Transformer Architectures" by Brydon <em>et al.</em></p> <p>Also included is the pretrained enviFormer model that was created by pretraining on the USPTO dataset.</p>
PatentChem - USPTO granted chemistry patents 2001-2023
<p>USPTO granted patents 2001-2023 with CDX/MOL files present</p>
USPTO-50k
<p>USPTO-50K dataset</p>
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