Structural Representation Learning and Disentanglement for Evidential Chinese Patent Approval Prediction

• URL: http://arxiv.org/abs/2408.12852v1
• Date: Fri, 23 Aug 2024 05:44:16 GMT
• Title: Structural Representation Learning and Disentanglement for Evidential Chinese Patent Approval Prediction
• Authors: Jinzhi Shan, Qi Zhang, Chongyang Shi, Mengting Gui, Shoujin Wang, Usman Naseem,
• Abstract summary: 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.
• Score: 19.287231890434718
• License:
• Abstract: Automatic Chinese patent approval prediction is an emerging and valuable task in patent analysis. However, it involves a rigorous and transparent decision-making process that includes patent comparison and examination to assess its innovation and correctness. This resultant necessity of decision evidentiality, coupled with intricate patent comprehension presents significant challenges and obstacles for the patent analysis community. Consequently, few existing studies are addressing this task. 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 to predict the approval of Chinese patents and offer decision-making evidence. DiSPat comprises three main components: base reference retrieval to retrieve the Top-k most similar patents as a reference base; structural patent representation to exploit the inherent claim hierarchy in patents for learning a structural patent representation; disentangled representation learning to learn disentangled patent representations that enable the establishment of an evidential decision-making process. To ensure a thorough evaluation, we have meticulously constructed three datasets of Chinese patents. Extensive experiments on these datasets unequivocally demonstrate our DiSPat surpasses state-of-the-art baselines on patent approval prediction, while also exhibiting enhanced evidentiality.

Related papers

• 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)
• ClaimBrush: A Novel Framework for Automated Patent Claim Refinement Based on Large Language Models [3.3427063846107825]
  ClaimBrush is a novel framework for automated patent claim refinement that includes a dataset and a rewriting model. We constructed a dataset for training and evaluating patent claim rewriting models by collecting a large number of actual patent claim rewriting cases. Our proposed rewriting model outperformed baselines and zero-shot learning in state-of-the-art large language models.
  arXiv  Detail & Related papers  (2024-10-08T00:20:54Z)
• 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)
• Rethinking State Disentanglement in Causal Reinforcement Learning [78.12976579620165]
  Causality provides rigorous theoretical support for ensuring that the underlying states can be uniquely recovered through identifiability. We revisit this research line and find that incorporating RL-specific context can reduce unnecessary assumptions in previous identifiability analyses for latent states. We propose a novel approach for general partially observable Markov Decision Processes (POMDPs) by replacing the complicated structural constraints in previous methods with two simple constraints for transition and reward preservation.
  arXiv  Detail & Related papers  (2024-08-24T06:49:13Z)
• Networks of Networks: Complexity Class Principles Applied to Compound AI Systems Design [63.24275274981911]
  Compound AI Systems consisting of many language model inference calls are increasingly employed. In this work, we construct systems, which we call Networks of Networks (NoNs) organized around the distinction between generating a proposed answer and verifying its correctness. We introduce a verifier-based judge NoN with K generators, an instantiation of "best-of-K" or "judge-based" compound AI systems.
  arXiv  Detail & Related papers  (2024-07-23T20:40:37Z)
• 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)
• Robust Saliency-Aware Distillation for Few-shot Fine-grained Visual Recognition [57.08108545219043]
  Recognizing novel sub-categories with scarce samples is an essential and challenging research topic in computer vision. Existing literature addresses this challenge by employing local-based representation approaches. This article proposes a novel model, Robust Saliency-aware Distillation (RSaD), for few-shot fine-grained visual recognition.
  arXiv  Detail & Related papers  (2023-05-12T00:13:17Z)
• 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)
• A Survey on Sentence Embedding Models Performance for Patent Analysis [0.0]
  We propose a standard library and dataset for assessing the accuracy of embeddings models based on PatentSBERTa approach. Results show PatentSBERTa, Bert-for-patents, and TF-IDF Weighted Word Embeddings have the best accuracy for computing sentence embeddings at the subclass level.
  arXiv  Detail & Related papers  (2022-04-28T12:04:42Z)
• 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)

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.