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primary topic: Neural Patent Classification beyond Title and Abstract: Leveraging Patent Text and Metadata

Neural Patent Classification beyond Title and Abstract: Leveraging Patent Text and Metadata
type: Article
abstract: Intellectual property violations involve substantial litigation and license costs, because of which patent search is of utmost importance. Over the years, patent corpora have amassed millions of patents, making manual searches impractical. Patent classification techniques help domain experts to search and analyze patents. On submission to an examination office, a patent application is assigned with labels from pre-defined patent taxonomies, e.g., Cooperative Patent Classification (CPC) and International Patent Classification (IPC). CPC/IPC classification helps to route patent applications to the correct department and assists in performing prior art searches. In addition to CPC/IPC classification, we address the classification task associated with the Patent Landscape Study (PLS), a process that allows organizations to search patents, categorize them into custom labels, and analyze them to derive crucial insights. This thesis significantly contributes to the improvement of patent classification systems by addressing the key challenges described below. Most of the existing CPC/IPC classification datasets provide only limited texts of the included patents and are, therefore, insufficient for our experiments. In response to this issue, we release a CPC classification dataset that includes the full texts of patents. Further, the unavailability of open-source datasets is a major bottleneck for the automation of PLS. To address this challenge, we curate, enrich, and release three open-source datasets from two diverse domains. Despite CPC/IPC classification being a hierarchical multi-label classification task, most prior neural models have not considered the hierarchical taxonomy when designing model architectures and have often predicted labels only for a single level. We make a major contribution with our memory-efficient model architecture, which shares a single transformerbased language model across multiple classification heads, one for each label in the taxonomy, and…
date: 2024
author list: authors
type: EPrint
type: DoctoralThesisEPrint
is part of: repository
has document: Neural Patent Classification beyond Title and Abstract: Leveraging Patent Text and Metadata (Other)
has document: Neural Patent Classification beyond Title and Abstract: Leveraging Patent Text and Metadata (PDF)
subject: 004 Informatik
subject: 020 Bibliotheks- und Informationswissenschaft
subject: 600 Technik, Medizin, angewandte Wissenschaften

Neural Patent Classification beyond Title and Abstract: Leveraging Patent Text and Metadata (PDF)
type: Document
has file: thesis_Subhash_Chandra_Pujari_2024-07-30.pdf
has part: thesis_Subhash_Chandra_Pujari_2024-07-30.pdf

Subhash Chandra Pujari
type: Person
given name: Subhash Chandra
family name: Pujari

Neural Patent Classification beyond Title and Abstract: Leveraging Patent Text and Metadata (Other)
type: Document
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Neural Patent Classification beyond Title and Abstract: Leveraging Patent Text and Metadata (Other)
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Neural Patent Classification beyond Title and Abstract: Leveraging Patent Text and Metadata (Other)
type: Document
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Neural Patent Classification beyond Title and Abstract: Leveraging Patent Text and Metadata (Other)
type: Document
is version of: Neural Patent Classification beyond Title and Abstract: Leveraging Patent Text and Metadata (PDF)
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Neural Patent Classification beyond Title and Abstract: Leveraging Patent Text and Metadata (Other)
type: Document
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HTML Summary of #35223 Neural Patent Classification beyond Title and Abstract: Leveraging Patent Text and Metadata
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primary topic: Neural Patent Classification beyond Title and Abstract: Leveraging Patent Text and Metadata

004 Informatik
type: Concept
preferred label: 004 Data processing Computer science @en

020 Bibliotheks- und Informationswissenschaft
type: Concept
preferred label: 020 Library and information sciences @en

600 Technik, Medizin, angewandte Wissenschaften
type: Concept
preferred label: 600 Technology (Applied sciences) @en