Patent Document Summarization Using Conceptual Graphs

The rapid change of trading values from tangible assets to Intelectual Property has put both businesses and academia in a race to acquire and protect the rights to exploit such property. This is mainly accomplished in the form of patent issuing by the governments, being time consuming and complicated due to the vast amount of documents that need to be analyzed in order to assert the novelty or validity of a patent application. Patent information retrieval research is thus growing quickly to support document analysis across multiple domains and information systems. One of the big challenges in patent analysis is the identification of the elements of innovation (concepts, processes, materials) and the relations between them, in the patent text. This paper presents a method for extracting semantic information from patent claims by using semantic annotations on phrasal structures, abstracting domain ontology information and outputting ontology-friendly structures to achieve generalization. An extraction system built upon the method is briefly evaluated on a document sample from INPI, the Brazilian patent office, a challenging information source.

Lecture Notes in Computer Science, 2004

Natural Language Processing techniques for text-mining and information retrieval are finding application in the analysis of many kinds of documentation, from technical documentation to World Wide Web. Particularly, Functional Analysis techniques are based on the extraction of the interactions between the entities described in the document: these interactions are expressed as Subject-Action-Object (SAO) triples (obtainable using a suitable syntactic parser) which represent a concept in its most synthesizing form. In this work, the techniques developed for a functional analysis of patents and their implementation in the PAT-Analyzer tool are presented. The same technique has been properly tailored and applied to the analysis of software requirements documents. Current work in the direction of the development of a SAO-based Content Analysis of technical documentation is presented.

Patents are valuable source of knowledge and are extremely important for assisting engineers and decisions makers through the inventive process. This paper describes a new approach of automatic extraction of IDM (Inventive Design Method) related knowledge from patent documents. IDM derives from TRIZ, the theory of Inventive problem solving, which is largely based on patent's observation to theorize the act of inventing. Our method mainly consists in using natural language techniques (NLP) to match and extract knowledge relevant to IDM Ontology. The purpose of this paper is to investigate on the contribution of NLP techniques to effective knowledge extraction from patent documents. We propose in this paper to firstly report on progress made so far in data mining before describing our approach.

Abstract This paper presents the theoretical bases and quantitative results of an activity consisting in manually annotating part-whole and motion relations in patent documents. The aim of this activity was creating a gold standard for the evaluation of an automatic relation extraction tool developed by FBK-irst within the PA-TExpert project.

Information Processing & Management, 2017

Patent search is recall-driven, which goes hand in hand with at least a partial sacrifice of precision. As a consequence, patent analysts have to regularly view and examine a large amount of patents. This implies a very high workload. Interactive analysis aids that help to minimize this workload are thus of high demand. Still, these aids do not reduce the amount of the material to be examined, they only facilitate its examination. Its reduction can be achieved working with patent summaries instead of full patent documents. So far, high quality patent summaries are produced mainly manually and only a few research works address the problem of automatic patent summarization. Most often, these works either replicate the summarization metrics known from general discourse summarization or focus on the claims of a patent. However, it can be observed that neither of the strategies is adequate: general discourse state-of-the-art summarization techniques are of limited use due to the idiosyncrasies of the patent genre, and techniques that focus on claims only miss in their summaries important details provided in the other sections on the components of the invention introduced in the claims. We propose a patent summarization technique that takes the idiosyncrasies of the patent genre (such as the unbalanced distribution of the content across the different sections of a patent, excessive length of the sentences in the claims, abstract vocabuly, etc.) into account to obtain a comprehensive summary of the invention. In particular, we make use of lexical chains in the claims and in the description of the invention and of aligned claim-description segments at the subsentential level to assess the relevance of the individual fragments of the document for the summary. The most relevant fragments are selected and merged using full-fledged natural language generation techniques.

The availability of huge amount of data from a bewildering variety of sources leads to the well-identified nowadays paradox: an overload of information means no usable knowledge. Text Mining and Knowledge Management technologies are therefore assuming a key role for many organizations: in order to propose competitive products or services it is necessary to minimize the resources dedicated to the accomplishment of repetitive tasks and to focus on "creative" activities. Moreover, innovation is basically limited by psychological inertia on one hand, and by lacks of knowledge on the other. This paper presents a set of tools developed by the authors for increasing knowledge extraction from patents. A proposal for a Patent Mining suite is therefore discussed.

2013

This paper presents a machine learning approach to automatically extract concepts and the conceptual relations towards creation of Conceptual Graphs (CGs) from patent documents using shallow parser and NER. The main challenge in the creation of conceptual graphs from the natural language texts is the automatic identification of concepts and conceptual relations. The texts analyzed in this work are patent documents, focused mainly on the claim‟s section (Claim) of the documents. The task of automatically identifying the concept and conceptual relation becomes difficult due to the complexities in the writing style of these documents as they are technical as well as legal. The analysis we have done shows that the general in-depth parsers available in the open domain fail to parse the „claims section‟ sentences in patent documents. The failure of in-depth parsers led us, to develop a methodology to extract CGs using other resources. Thus in the present work we came up with a methodology...

Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR), 2014

Good readability of text is important to ensure efficiency in communication and eliminate risks of misunderstanding. Patent claims are an example of text whose readability is often poor. In this paper, we aim to improve claim readability by a clearer presentation of its content. Our approach consist in segmenting the original claim content at two levels. First, an entire claim is segmented to the components of preamble, transitional phrase and body, using a rule-based approach. Second, a conditional random field is trained to segment the components into clauses. An alternative approach would have been to modify the claim content which is, however, prone to also changing the meaning of this legal text. For both segmentation levels, we report results from statistical evaluation of segmentation performance. In addition, a qualitative error analysis was performed to understand the problems underlying the clause segmentation task. Our accuracy in detecting the beginning and end of preamble text is 1.00 and 0.97, respectively. For the transitional phase, these numbers are 0.94 and 1.00 and for the body text, 1.00 and 1.00. Our precision and recall in the clause segmentation are 0.77 and 0.76, respectively. The results give evidence for the feasibility of automated claim and clause segmentation, which may help not only inventors, researchers, and other laypeople to understand patents but also patent experts to avoid future legal cost due to litigations.

2009

Engineering and research teams often develop new products and technologies by referring to inventions described in patent databases. Efficient patent analysis builds R&D knowledge, reduces new product development time, increases market success, and reduces potential patent infringement. Thus, it is beneficial to automatically and systematically extract information from patent documents in order to improve knowledge sharing and collaboration among R&D team members. In this research, patents are summarized using a combined ontology based and TF-IDF concept clustering approach. The ontology captures the general knowledge and core meaning of patents in a given domain. Then, the proposed methodology extracts, clusters, and integrates the content of a patent to derive a summary and a cluster tree diagram of key terms. Patents from the International Patent Classification (IPC) codes B25C, B25D, B25F (categories for power hand tools) and B24B, C09G and H011 (categories for chemical mechanical polishing) are used as case studies to evaluate the compression ratio, retention ratio, and classification accuracy of the summarization results. The evaluation uses statistics to represent the summary generation and its compression ratio, the ontology based keyword extraction retention ratio, and the summary classification accuracy. The results show that the ontology based approach yields about the same compression ratio as previous non-ontology based research but yields on average an 11% improvement for the retention ratio and a 14% improvement for classification accuracy.

Proceedings of the …, 2009

With their abstract vocabulary and overly long sentences, patent claims, like several other genres of legal discourse, are notoriously difficult to read and comprehend. The enormous number of both native and non-native users reading patent claims on a daily basis raises the demand for means that make them easier and faster to understand. An obvious way to satisfy this demand is to paraphrase the original material, i.e., to rewrite it in a more appropriate style, or-even better-to summarize it in the language of preference of the reader such that the reader can rapidly grasp its essence. PATExpert is a patent processing service which incorporates, among other technologies, paraphrasing and multilingual summarization of patent claims. With the goal to offer the user the most suitable options and to evaluate alternative techniques that are based on different contextual and linguistic criteria, both paraphrasing and summarization implement "surface-oriented" strategies and "deep" strategies. The surface strategies make use of shallow linguistic criteria such as punctuation and syntactic and lexical markers. The deep strategies operate on deep-syntactic structures of the claims, using a full fledged text generator for synthesis of the paraphrase or summary, respectively.