Natural Language Processing in Patents: A Survey

• URL: http://arxiv.org/abs/2403.04105v2
• Date: Mon, 12 Aug 2024 18:30:06 GMT
• Title: Natural Language Processing in Patents: A Survey
• Authors: Lekang Jiang, Stephan Goetz,
• Abstract summary: Patents, encapsulating crucial technical and legal information, present a rich domain for natural language processing (NLP) applications. As NLP technologies evolve, large language models (LLMs) have demonstrated outstanding capabilities in general text processing and generation tasks. This paper aims to equip NLP researchers with the essential knowledge to navigate this complex domain efficiently.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Patents, encapsulating crucial technical and legal information, present a rich domain for natural language processing (NLP) applications. As NLP technologies evolve, large language models (LLMs) have demonstrated outstanding capabilities in general text processing and generation tasks. However, the application of LLMs in the patent domain remains under-explored and under-developed due to the complexity of patent processing. Understanding the unique characteristics of patent documents and related research in the patent domain becomes essential for researchers to apply these tools effectively. Therefore, this paper aims to equip NLP researchers with the essential knowledge to navigate this complex domain efficiently. We introduce the relevant fundamental aspects of patents to provide solid background information, particularly for readers unfamiliar with the patent system. In addition, we systematically break down the structural and linguistic characteristics unique to patents and map out how NLP can be leveraged for patent analysis and generation. Moreover, we demonstrate the spectrum of text-based patent-related tasks, including nine patent analysis and four patent generation tasks.

Related papers

• Patent Novelty Assessment Accelerating Innovation and Patent Prosecution [0.873811641236639]
  This report introduces a ground-breaking Patent Novelty Assessment and Claim Generation System. Our system provides college students and researchers with an intuitive platform to navigate and grasp the intricacies of patent claims. Unlike conventional analysis systems, our initiative harnesses a proprietary Chinese API to ensure unparalleled precision and relevance.
  arXiv  Detail & Related papers  (2025-01-12T22:25:46Z)
• EvoPat: A Multi-LLM-based Patents Summarization and Analysis Agent [0.0]
  EvoPat is a multi-LLM-based patent agent designed to assist users in analyzing patents through Retrieval-Augmented Generation (RAG) and advanced search strategies. We demonstrate that EvoPat outperforms GPT-4 in tasks such as patent summarization, comparative analysis, and technical evaluation.
  arXiv  Detail & Related papers  (2024-12-24T02:21:09Z)
• PatentEdits: Framing Patent Novelty as Textual Entailment [62.8514393375952]
  We introduce the PatentEdits dataset, which contains 105K examples of successful revisions. We design algorithms to label edits sentence by sentence, then establish how well these edits can be predicted with large language models. We demonstrate that evaluating textual entailment between cited references and draft sentences is especially effective in predicting which inventive claims remained unchanged or are novel in relation to prior art.
  arXiv  Detail & Related papers  (2024-11-20T17:23:40Z)
• Pap2Pat: Towards Automated Paper-to-Patent Drafting using Chunk-based Outline-guided Generation [13.242188189150987]
  We present PAP2PAT, a new challenging benchmark of 1.8k patent-paper pairs with document outlines. Our experiments with current open-weight LLMs and outline-guided generation show that they can effectively use information from the paper but struggle with repetitions, likely due to the inherent repetitiveness of patent language.
  arXiv  Detail & Related papers  (2024-10-09T15:52:48Z)
• A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models [71.25225058845324]
  Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation. Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge. RA-LLMs have emerged to harness external and authoritative knowledge bases, rather than relying on the model's internal knowledge.
  arXiv  Detail & Related papers  (2024-05-10T02:48:45Z)
• Unveiling Black-boxes: Explainable Deep Learning Models for Patent Classification [48.5140223214582]
  State-of-the-art methods for multi-label patent classification rely on deep opaque neural networks (DNNs) We propose a novel deep explainable patent classification framework by introducing layer-wise relevance propagation (LRP) Considering the relevance score, we then generate explanations by visualizing relevant words for the predicted patent class.
  arXiv  Detail & Related papers  (2023-10-31T14:11:37Z)
• PaperCard for Reporting Machine Assistance in Academic Writing [48.33722012818687]
  ChatGPT, a question-answering system released by OpenAI in November 2022, has demonstrated a range of capabilities that could be utilised in producing academic papers. This raises critical questions surrounding the concept of authorship in academia. We propose a framework we name "PaperCard", a documentation for human authors to transparently declare the use of AI in their writing process.
  arXiv  Detail & Related papers  (2023-10-07T14:28:04Z)
• Source Attribution for Large Language Model-Generated Data [57.85840382230037]
  It is imperative to be able to perform source attribution by identifying the data provider who contributed to the generation of a synthetic text. We show that this problem can be tackled by watermarking. We propose a source attribution framework that satisfies these key properties due to our algorithmic designs.
  arXiv  Detail & Related papers  (2023-10-01T12:02:57Z)
• The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications [8.110699646062384]
  We introduce the Harvard USPTO Patent dataset (HUPD) With more than 4.5 million patent documents, HUPD is two to three times larger than comparable corpora. By providing each application's metadata along with all of its text fields, the dataset enables researchers to perform new sets of NLP tasks.
  arXiv  Detail & Related papers  (2022-07-08T17:57:15Z)
• Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
  Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome. Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations. We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
  arXiv  Detail & Related papers  (2021-06-14T20:48:48Z)
• Summarization, Simplification, and Generation: The Case of Patents [0.0]
  This survey aims at a) describing patents' characteristics and the questions they raise to the current NLP systems, b) critically presenting previous work and its evolution, and c) drawing attention to directions of research in which further work is needed. To the best of our knowledge, this is the first survey of generative approaches in the patent domain.
  arXiv  Detail & Related papers  (2021-04-30T09:28:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.