PIUG: Patent Information Users Group, Inc.: A History of The International Society for Patent Information Professionals

Efforts to view and analyze patents began soon after the first patents were filed in the novel system founded in the U.S. Constitution. In the succeeding 200 plus years, classification and indexing tools have evolved from paper to digital, with searching demanding ever-higher skills. Answering the need of patent researchers and analysts for advocacy, scholarship, and professional education, leading searchers founded the Patent Information Users Group, Inc., now the pre-eminent professional organization for patent searchers in the United States. It offers formal coursework for prospective patent searchers, colloquia, and conferences where novice searchers can master their craft. Searchers, who often work in isolation, benefit from the support network and collegiality of PIUG. Patent searching is both challenging and rewarding. It is vital for individuals seeking to secure rights to intellectual property and contributes to research in many fields: history, economics, finance, management, sociology, law, medicine, and government policy. It is a career path for academic and special librarians with knowledge of the sciences behind the inventions and is a core skill for those preparing for careers in the sciences and technology fields. Skills and applications for patent knowledge receive little treatment in college curriculum, leaving it to the individual to discover the range of tools, strategies, and practical uses of patents. This article describes the developments in patent searching technology and the work of PIUG’s founders and members that led to its creation, growth, and successes in professional education, advocacy, and outreach. Keywords: PIUG, patent searchers, professional education, librarian

Development of an information retrieval tool for biomedical patents

Supplementary material associated with this article can be found, in the online version, at doi: 10.1016/j.cmpb.2018.03.012 .Background and objective. The volume of biomedical literature has been increasing in the last years. Patent documents have also followed this trend, being important sources of biomedical knowledge, technical details and curated data, which are put together along the granting process. The field of Biomedical text mining (BioTM) has been creating solutions for the problems posed by the unstructured nature of natural language, which makes the search of information a challenging task. Several BioTM techniques can be applied to patents. From those, Information Retrieval (IR) includes processes where relevant data are obtained from collections of documents. In this work, the main goal was to build a patent pipeline addressing IR tasks over patent repositories to make these documents amenable to BioTM tasks. Methods. The pipeline was developed within @Note2, an open-source computational framework for BioTM, adding a number of modules to the core libraries, including patent metadata and full text retrieval, PDF to text conversion and optical character recognition. Also, user interfaces were developed for the main operations materialized in a new @Note2 plug-in. Results. The integration of these tools in @Note2 opens opportunities to run BioTM tools over patent texts, including tasks from Information Extraction, such as Named Entity Recognition or Relation Extraction. We demonstrated the pipelines main functions with a case study, using an available benchmark dataset from BioCreative challenges. Also, we show the use of the plug-in with a user query related to the production of vanillin. Conclusions. This work makes available all the relevant content from patents to the scientific community, decreasing drastically the time required for this task, and provides graphical interfaces to ease the use of these tools.This work is co-funded by the Programa Operacional Re- gional do Norte, under the “Portugal2020”, through the Euro- pean Regional Development Fund ( ERDF ), within project SISBI- Ref a NORTE-01-0247-FEDER-003381 . This study was also supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01- 0145-FEDER-00 6 684) and BioTecNorte operation (NORTE-01-0145- FEDER-0 0 0 0 04) funded by European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte.info:eu-repo/semantics/publishedVersio

Automating the search for a patent's prior art with a full text similarity search

More than ever, technical inventions are the symbol of our society's advance. Patents guarantee their creators protection against infringement. For an invention being patentable, its novelty and inventiveness have to be assessed. Therefore, a search for published work that describes similar inventions to a given patent application needs to be performed. Currently, this so-called search for prior art is executed with semi-automatically composed keyword queries, which is not only time consuming, but also prone to errors. In particular, errors may systematically arise by the fact that different keywords for the same technical concepts may exist across disciplines. In this paper, a novel approach is proposed, where the full text of a given patent application is compared to existing patents using machine learning and natural language processing techniques to automatically detect inventions that are similar to the one described in the submitted document. Various state-of-the-art approaches for feature extraction and document comparison are evaluated. In addition to that, the quality of the current search process is assessed based on ratings of a domain expert. The evaluation results show that our automated approach, besides accelerating the search process, also improves the search results for prior art with respect to their quality

Information attributes

In this chapter, we focus on the concept of information attributes. Information attributes are the properties of information and information objects that can be used to describe and differentiate information. Being able to differentiate information objects means we can select the most appropriate objects for our tasks and we can design information systems to organise and store information in useful ways. We start by providing a discussion on why being able to describe properties of information objects is important, and then we provide a description of essential concepts in this area to provide a theoretical background to information attributes. Following this, we highlight a selection of key information attributes to give a flavour of the kind of studies that have been conducted in this area before concluding with current trends in this research area

AI-assisted patent prior art searching - feasibility study

This study seeks to understand the feasibility, technical complexities and effectiveness of using artificial intelligence (AI) solutions to improve operational processes of registering IP rights. The Intellectual Property Office commissioned Cardiff University to undertake this research. The research was funded through the BEIS Regulators’ Pioneer Fund (RPF). The RPF fund was set up to help address barriers to innovation in the UK economy

Artificial intelligence for patent prior art searching

This study explored how artificial intelligence (AI) could assist patent examiners as part of the prior art search process. The proof-of-concept allowed experimentation with different AI techniques to suggest search terms, retrieve most relevant documents, rank them and visualise their content. The study suggested that AI is less effective in formulating search queries but can reduce the time and cost of the process of sifting through a large number of patents. The study highlighted the importance of the humanin-the-loop approach and the need for better tools for human-centred decision and performance support in prior art searching

AI-assisted patent prior art searching - feasibility study

This study seeks to understand the feasibility, technical complexities and effectiveness of using artificial intelligence (AI) solutions to improve operational processes of registering IP rights. The Intellectual Property Office commissioned Cardiff University to undertake this research. The research was funded through the BEIS Regulators’ Pioneer Fund (RPF). The RPF fund was set up to help address barriers to innovation in the UK economy