Automated Single-Label Patent Classification using Ensemble Classifiers

• URL: http://arxiv.org/abs/2203.03552v1
• Date: Thu, 3 Mar 2022 08:47:15 GMT
• Title: Automated Single-Label Patent Classification using Ensemble Classifiers
• Authors: Eleni Kamateri, Vasileios Stamatis, Konstantinos Diamantaras, Michail Salampasis
• Abstract summary: We present an innovative method of ensemble classifiers trained with different parts of the patent document. To the best of our knowledge, this is the first time that an ensemble method was proposed for the patent classification problem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many thousands of patent applications arrive at patent offices around the world every day. One important subtask when a patent application is submitted is to assign one or more classification codes from the complex and hierarchical patent classification schemes that will enable routing of the patent application to a patent examiner who is knowledgeable about the specific technical field. This task is typically undertaken by patent professionals, however due to the large number of applications and the potential complexity of an invention, they are usually overwhelmed. Therefore, there is a need for this code assignment manual task to be supported or even fully automated by classification systems that will classify patent applications, hopefully with an accuracy close to patent professionals. Like in many other text analysis problems, in the last years, this intellectually demanding task has been studied using word embeddings and deep learning techniques. In this paper we shortly review these research efforts and experiment with similar deep learning techniques using different feature representations on automatic patent classification in the level of sub-classes. On top of that, we present an innovative method of ensemble classifiers trained with different parts of the patent document. To the best of our knowledge, this is the first time that an ensemble method was proposed for the patent classification problem. Our first results are quite promising showing that an ensemble architecture of classifiers significantly outperforms current state-of-the-art techniques using the same classifiers as standalone solutions.

Related papers

• LC-Protonets: Multi-label Few-shot learning for world music audio tagging [65.72891334156706]
  We introduce Label-Combination Prototypical Networks (LC-Protonets) to address the problem of multi-label few-shot classification. LC-Protonets generate one prototype per label combination, derived from the power set of labels present in the limited training items. Our method is applied to automatic audio tagging across diverse music datasets, covering various cultures and including both modern and traditional music.
  arXiv  Detail & Related papers  (2024-09-17T15:13:07Z)
• A Comprehensive Survey on AI-based Methods for Patents [14.090575139188422]
  AI-based tools present opportunities to streamline and enhance important tasks in the patent cycle. This interdisciplinary survey aims to serve as a resource for researchers and practitioners working at the intersection of AI and patent analysis.
  arXiv  Detail & Related papers  (2024-04-02T20:44:06Z)
• PaECTER: Patent-level Representation Learning using Citation-informed Transformers [0.16785092703248325]
  PaECTER is a publicly available, open-source document-level encoder specific for patents. We fine-tune BERT for Patents with examiner-added citation information to generate numerical representations for patent documents. PaECTER performs better in similarity tasks than current state-of-the-art models used in the patent domain.
  arXiv  Detail & Related papers  (2024-02-29T18:09:03Z)
• 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)
• Multi-Label Knowledge Distillation [86.03990467785312]
  We propose a novel multi-label knowledge distillation method. On one hand, it exploits the informative semantic knowledge from the logits by dividing the multi-label learning problem into a set of binary classification problems. On the other hand, it enhances the distinctiveness of the learned feature representations by leveraging the structural information of label-wise embeddings.
  arXiv  Detail & Related papers  (2023-08-12T03:19:08Z)
• Adaptive Taxonomy Learning and Historical Patterns Modelling for Patent Classification [26.85734804493925]
  We propose an integrated framework that comprehensively considers the information on patents for patent classification. We first present an IPC codes correlations learning module to derive their semantic representations. Finally, we combine the contextual information of patent texts that contains the semantics of IPC codes, and assignees' sequential preferences to make predictions.
  arXiv  Detail & Related papers  (2023-08-10T07:02:24Z)
• Multi label classification of Artificial Intelligence related patents using Modified D2SBERT and Sentence Attention mechanism [0.0]
  We present a method for classifying artificial intelligence-related patents published by the USPTO using natural language processing technique and deep learning methodology. Our experiment result is highest performance compared to other deep learning methods.
  arXiv  Detail & Related papers  (2023-03-03T12:27:24Z)
• Open-Set Automatic Target Recognition [52.27048031302509]
  Automatic Target Recognition (ATR) is a category of computer vision algorithms which attempts to recognize targets on data obtained from different sensors. Existing ATR algorithms are developed for traditional closed-set methods where training and testing have the same class distribution. We propose an Open-set Automatic Target Recognition framework where we enable open-set recognition capability for ATR algorithms.
  arXiv  Detail & Related papers  (2022-11-10T21:28:24Z)
• Class-Specific Semantic Reconstruction for Open Set Recognition [101.24781422480406]
  Open set recognition enables deep neural networks (DNNs) to identify samples of unknown classes. We propose a novel method, called Class-Specific Semantic Reconstruction (CSSR), that integrates the power of auto-encoder (AE) and prototype learning. Results of experiments conducted on multiple datasets show that the proposed method achieves outstanding performance in both close and open set recognition.
  arXiv  Detail & Related papers  (2022-07-05T16:25:34Z)
• 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)
• Multi-Agent Reinforcement Learning with Temporal Logic Specifications [65.79056365594654]
  We study the problem of learning to satisfy temporal logic specifications with a group of agents in an unknown environment. We develop the first multi-agent reinforcement learning technique for temporal logic specifications. We provide correctness and convergence guarantees for our main algorithm.
  arXiv  Detail & Related papers  (2021-02-01T01:13:03Z)

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.