Patent Novelty Assessment Accelerating Innovation and Patent Prosecution

• URL: http://arxiv.org/abs/2501.06956v1
• Date: Sun, 12 Jan 2025 22:25:46 GMT
• Title: Patent Novelty Assessment Accelerating Innovation and Patent Prosecution
• Authors: Kapil Kashyap, Sean Fargose, Gandhar Dhonde, Aditya Mishra,
• Abstract summary: 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.
• Score: 0.873811641236639
• License:
• Abstract: In the rapidly evolving landscape of technological innovation, safeguarding intellectual property rights through patents is crucial for fostering progress and stimulating research and development investments. This report introduces a ground-breaking Patent Novelty Assessment and Claim Generation System, meticulously crafted to dissect the inventive aspects of intellectual property and simplify access to extensive patent claim data. Addressing a crucial gap in academic institutions, our system provides college students and researchers with an intuitive platform to navigate and grasp the intricacies of patent claims, particularly tailored for the nuances of Chinese patents. Unlike conventional analysis systems, our initiative harnesses a proprietary Chinese API to ensure unparalleled precision and relevance. The primary challenge lies in the complexity of accessing and comprehending diverse patent claims, inhibiting effective innovation upon existing ideas. Our solution aims to overcome these barriers by offering a bespoke approach that seamlessly retrieves comprehensive claim information, finely tuned to the specifics of the Chinese patent landscape. By equipping users with efficient access to comprehensive patent claim information, our transformative platform seeks to ignite informed exploration and innovation in the ever-evolving domain of intellectual property. Its envisioned impact transcends individual colleges, nurturing an environment conducive to research and development while deepening the understanding of patented concepts within the academic community.

Related papers

• 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)
• Towards Automated Patent Workflows: AI-Orchestrated Multi-Agent Framework for Intellectual Property Management and Analysis [0.0]
  PatExpert is an autonomous multi-agent conversational framework designed to streamline and optimize patent-related tasks. The framework consists of a metaagent that coordinates task-specific expert agents for various patent-related tasks and a critique agent for error handling and feedback provision.
  arXiv  Detail & Related papers  (2024-09-21T13:44:34Z)
• PatentGPT: A Large Language Model for Patent Drafting Using Knowledge-based Fine-tuning Method [1.4496326701907591]
  Existing large language models (LLMs) often fall short in this IP creation domain due to their lack of specialized knowledge and context-awareness. We propose a groundbreaking framework for Knowledge Fine-Tuning (KFT) of LLMs, designed to endow AI with the ability to autonomously mine, understand, and apply domain-specific knowledge. Our model, PatentGPT, has demonstrated outstanding performance, scoring up to approximately 400% higher in patent related benchmark tests compared to state-of-the-art models.
  arXiv  Detail & Related papers  (2024-08-26T12:00:29Z)
• Structural Representation Learning and Disentanglement for Evidential Chinese Patent Approval Prediction [19.287231890434718]
  This paper presents the pioneering effort on this task using a retrieval-based classification approach. We propose a novel framework called DiSPat, which focuses on structural representation learning and disentanglement. Our framework surpasses state-of-the-art baselines on patent approval prediction, while also exhibiting enhanced evidentiality.
  arXiv  Detail & Related papers  (2024-08-23T05:44:16Z)
• Natural Language Processing in Patents: A Survey [0.0]
  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.
  arXiv  Detail & Related papers  (2024-03-06T23:17:16Z)
• 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)
• Machine Unlearning: A Survey [56.79152190680552]
  A special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning. This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality. No study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios. The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.
  arXiv  Detail & Related papers  (2023-06-06T10:18:36Z)
• 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)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims [59.64274607533249]
  AI developers need to make verifiable claims to which they can be held accountable. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
  arXiv  Detail & Related papers  (2020-04-15T17:15:35Z)

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.