Hunt for Buried Treasures: Extracting Unclaimed Embodiments from Patent Specifications (2023)

Abstract: Patent applicants write patent specificationsthat describe embodiments of inventions. Some embodiments are claimed for a patent,while others may be unclaimeddue to strategic considerations. Unclaimed embodiments may be extracted byapplicants later and claimed incontinuing applications togain advantages over competitors. Despite being essential for corporate intellectual property (IP) strategies,unclaimed embodiment extraction is conducted manually,and little research has been conducted on its automation. This paper presents a novel task ofunclaimed embodiment extraction (UEE)and a novel dataset for the task. Our experiments with Transformer-based modelsdemonstratedthat the task was challenging as it requiredconducting natural language inference onpatent specifications, which consisted oftechnical, long, syntactically and semanticallyinvolved sentences. We release the dataset and code to foster this new area of research.

## introduction patents provide inventors the right to exclude others from using their inventions in exchange for disclosing how to make and use inventions by writing patent specifications. patents have thus incentivized innovation and benefited industries. given the increasing number of patent applications even during the covid-19 pandemic (wipo, 2022b), it is important to streamline patent application processes with technologies. a patent specification describes an invention (figure 1 ) by specifying one or more ways of embodying the invention, so that people skilled in the art can make and use it. a patent specification also contains claims that specify which embodiment applicants want to patent by stating the technical features necessary for the embodiment. here, an invention refers to a mental construct inside the ✔ 1. a method, performed by at least one computer system, of retrieving documents in response to a search query that includes one or more phrases, the method comprising: ## experiments we conducted experiments of uee with baseline models based on transformer @xcite to see the difficulty of the task ( §4.1). we also conducted experiments of making the decisions (i) and (ii) in §2 as independent tasks for a better understanding of the task ( §4.2 and §4.3). these experimental results show the following: ## related work patent document processing nlp systems for real-world applications in, for instance, ecommerce @xcite , medical @xcite , and legal areas @xcite has gained attention, probably because it has become more feasible to serve practical needs thanks to the success of transformer-based models and pre-training methods @xcite . patent document processing has also been studied extensively @xcite @xcite . its most studied areas are machine translation @xcite @xcite and information retrieval @xcite @xcite . there have recently been studies that would directly facilitate patent applications. @xcite created a dataset for summarizing patents and proposed baselines. @xcite proposed a method for claim generation formulated as text summarization. aslanyan and wetherbee (2022) created a dataset for phrasal matching for better patent similarity measurement. @xcite proposed a method for predicting whether a given patent application would be approved. however, to the best of our knowledge, no study has addressed uee; we are the first to do that. natural language inference nli has showcased the comprehension ability of nlp systems @xcite @xcite and provided datasets for their training @xcite . introducing diverse nli tasks would then push the boundary of nlp. recent studies have introduced new nli tasks targeting real-world applications @xcite @xcite . our decision (ii) is a novel realworld nli for patents that poses a new challenge. ## conclusion we introduced uee, a novel nlp challenge, and created a corresponding dataset. our experiments showed that uee was challenging due to the difficulty of making the decision (ii). we hope that the research community will address this challenge by utilizing the uee dataset and code that we created and released.