Annotating Discursive Roles of Sentences in Patent Descriptions (2023)

Abstract: Patent descriptions are a crucial component of patent applications, as they are key to understanding the invention and play a significant role in securing patent grants. While discursive analyses have been undertaken for scientific articles, they have not been as thoroughly explored for patent descriptions, despite the increasing importance of Intellectual Property and the constant rise of the number of patent applications. In this study, we propose an annotation scheme containing 16 classes that allows categorizing each sentence in patent descriptions according to their discursive roles. We publish an experimental human-annotated corpus of 16 patent descriptions and analyze challenges that may be encountered in such work. This work can be base for an automated annotation and thus contribute to enriching linguistic resources in the patent domain.

## introduction patent applications represent a first step in obtaining exclusive rights over an invention. analyzing these documents enables inventors to understand technological trends, avoid potential litigation, and assess the competition. the patent description, a substantial part of the patent application, provides detailed information about the invention. although specific segments have to be present in order to have the application accepted, the information can be provided without any pre-imposed order. patent descriptions should be well organized in order to communicate the invention's technical details, advantages, and scope with more clarity. this, in turn, helps patent examiners to review applications more efficiently, reduces the likelihood of misinterpretation or ambiguity, and increases the chances of obtaining a patent grant with a well-defined scope of protection. the contributions of our work are as follows: ## related work the analysis of document structure allows for a deeper understanding of the author's thought process and facilitates the retrieval of specific information within the document. many previous studies have focused on the analysis of technical documents, particularly scientific papers. for example, @xcite created a multi-layered annotated corpus of 40 scientific papers in the domain of computer graphics, with each sentence annotated according to its rhetorical role. @xcite created a corpus by manually annotating 75 articles in the domain of intercellular cancer pathways. each article was divided into clauses which are classified into one of the following categories: goal, fact, result, hypothesis, method, problem, implication, none. this corpus was used to develop a discourse tagger for claim extraction and evidence fragment detection @xcite . patent applications are another type of technical document that has garnered researchers' interest. a patent application usually consists of various components, including a title, abstract, description, one or more claims, drawings, and classification information. current patent text analysis mainly focuses on claims or abstracts to improve claim readability @xcite , such as using them to build an engineering knowledge graph @xcite , or to aid in patent classification models @xcite . however, the less-structured and much longer patent descriptions, an essential part of understanding patents, receive little attention. to our knowledge, only @xcite analyzes the structure of patent descriptions, but they focus solely on four content types: field, problem, solution, and effect. nevertheless, patent descriptions contain much richer information, including technical term definitions in context, advantages of the invention, and drawing descriptions. exploring the structure of patent descriptions can be used to acquire patent writing skills -for humans and machines. writing a good patent description not only requires an understanding of legal knowledge, but also requires expertise in relevant technical fields. furthermore, mastering the structure of a patent description enables the extraction of reliable features, which may be useful for patent text modeling, specific to domains, assignees, and legal goals of the patent. the goal of this study is to apply the discourse structure analysis, a common practice in scientific papers, to the whole patent descriptions while considering their unique writing style. to achieve this, we design an annotation scheme to label each sentence in the description according to its discursive role. ## annotation scheme the patent description is typically divided into several sections. under the patent cooperation treaty (pct), the description contains mainly (wipo, 2022): title of invention, technical field, background art, summary of invention, brief description of drawings, and description of embodiments. the field section specifies the technical domain to which the invention belongs. the background section discusses the prior art related to the invention, identifies previously encountered problems, and explains how the proposed invention may offer solutions to one or more of these issues. the summary section highlights the key features and advantages of the invention. this is commonly followed by a section that provides a concise overview of the content present in each illustrative drawing, if they are included. lastly, the detailed descrip-tion section should encompass greater detail of the claimed invention by way of examples (embodiments), describing figures in detail and defining little known or specially formulated technical terms when necessary, to further clarify the structure and functioning of the invention. based on the above essential elements recognized in a patent description and with the assistance of a patent attorney, we initially designed a set of 12 labels corresponding to the bold elements above. key features and examples (embodiments) were combined, represented by the label embodiment, as the invention is usually described by introducing its features. following this, two annotators collaboratively annotated two patent descriptions and identified a need to distinguish between the advantage and problem labels, to clarify whether these pertain to the invention itself or to existing technologies. in addition, we added the label other for sentences that don't fall into any of the established 14 categories. this set of 15 labels was applied by two annotators on the first test dataset of 8 patent applications. we noticed that the section title label does not cover all kinds of titles within a patent description, since different applicants may introduce subsections with additional titles according to their writing styles. the annotators found it difficult to decide on the class of non-standard titles: are they section titles or part of the embodiment? to remedy this difficulty, a 16 th label section subtitle was added following the annotation of the first dataset. this new label also allows for an elementary encoding of the scope of the main sections, whenever they are indicated by section titles. it is this set of 16 labels that has reached consensus and is deemed operational and representative for annotating the discursive role of sentences within patent descriptions. ## corpus annotation 4.1 corpus preparation to build our annotation corpus, we use patent applications published by the european patent office (epo). these patent applications are classified using the cooperative patent classification (cpc) system, which comprises eight domains ( we extracted the description section from each patent application, each description is then segmented into sentences before being annotated. we use scispacy 1 combining with special rules to perform sentence splitting. for example, patent claims, which are usually extremely long sentences separated by semicolons, could be copied into description. in order to balance the size of each sentence, semicolons are also considered as ending punctuation. we chose to stay on the sentence level because delving into a finer-grained level would require not only knowledge in linguistics but also expertise in various technical domains. for instance, the following sentence, which we simply classify as being of type figure description, could be broken down into sub-sequences that detail the interaction between elements described in the figure: cpu 16 controls first conveyance section 21 to move conveyance pallet 40 to the loading position, and controls multi-joint robot 24 so that robot-side attachment section 27 grips pallet-side attachment section 42 below conveyance pallet 40 (refer to fig. 8 ). ## conclusion in this paper, we have proposed an annotation scheme adapted to the specific writing style of patent applications. as an exploratory work, we defined a set of 16 labels to categorize each sentence in a patent description according to their discursive roles. the initial results show that such work is feasible, since strong agreement is achieved for most categories. however, challenges remain. considering the aforementioned difficulties, we propose the following improvement to the future work: allow multi-labeling for ambiguous sentences or consider implementing a multi-layer annotation scheme. in the first level, include classes corresponding only to the five common sections of a patent description, followed by additional specific categories in subsequent layers as necessary. in conclusion, our annotation work is an ongoing process. we plan to expand our dataset once the relevant labels have been established, and to employ active learning methods to streamline the annotation process. we believe that a such linguistic resource in the patent domain could contribute to enhancing the accuracy of tasks such as patent classification, patent novelty detection, patent information retrieval, and, most central to qatent, computer-assisted patent drafting.