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Most scientific publishers require authors to submit their manuscripts with a text reporting their parallel activities which might be considered as Conflicts-of-Interest (COI), including patent-related ones. The patenting activities that are disclosed by authors or institutions as COI in articles within Conflicts-of-Interest Statements (COIS) are generally analyzed in the literature either within small datasets or using non-systematic methodologies. This study proposes methods for assessing COIS presence and their main features across biomedical topics and journals, particularly with respect to the patent filing details that may be disclosed herein. These methods have been established and tested in the freely available PubMed database by searching the literature indexed herein during the period 2011–2022. However, when comparing the results of such searches within PubMed and a selection of journals’ websites for a specific topic (such as COVID-19), COIS appear unevenly available and searchable in PubMed owing to the varying practices that each journal implements. Thus, COIS appear a possibly underestimated source of patent information but the search and analysis of COI disclosures in biomedical literature requires well-designed and controlled strategies for identifying the relevant evidences for a given scope, such as prior art analysis and legal or strategic evaluation of patenting activities.

The advent of construction robotics (CR) has been rendered technically and economically viable by the proliferation of microprocessors and a decline in costs. A captivating epoch in CR has emerged, and it defies conventional interpretation. We ask how to extract the frontier research topics from patents, which countries are driving innovation in CR, and what policies are contributing to the current situation. This paper uses patent analysis and text mining to study 1831 C R-related patents and finds four clusters from the Derwent Innovations (DI) database. The patent remaining life impact (PRLI) index is calculated. The results show an increase in the number of CR-related patents annually. China leads in the number of patent publications, while the US and other patent organizations have played critical roles in the field. An improved technology evolutionary path tracing visualization identifies several hotspots. Additionally, the policy strategy matrix (PSM) analyzes the industrial stimulation policies of the two countries with the most considerable proportion, providing an explanation for predicting future technology research and development (R&D) and the direction of optimization.

University patent information systems can help evaluate university performance, create patenting policies, and meet researcher information demands. Since information system development is time-consuming and expensive, it is important to ensure the need for the system and include users in the process. This study aims to assess the need for Patent Information System of Iranian Medical Universities (PISIMU). In this cross-sectional descriptive study, a need assessment was conducted among the potential users of PISIMU. A researcher-made questionnaire was used to collect the required data. Descriptive and analytical statistics and the SPSS software were used for data analysis. The findings showed that both Iran's Ministry of Health and Medical Education and Iranian medical universities continuously collect information to assess university and faculty performance. The respondents thought that PISIMU was necessary for managing and providing patent information (60.4 %), evaluating university/faculty performance (84.6 %), increasing industry collaboration and investment (90.1 %), and motivating inventors to innovate (80.8 %). It is necessary to consider the users' needs and specified objectives in developing PISIMU.

We present a novel systematic approach called three-window analytics for identifying technological trends for chitin, the second-most abundant polymer found in nature, and its derivative, chitosan. For this analytics, we used patent documents filed from 2006 to 2020 under the Patent Cooperation Treaty (PCT) containing chitin and chitosan in the title, abstract, and claims to build code-keyword matrices. These matrices merged into a matrix containing relevant patent data across three-time windows called the three-window matrix. Two types of three-window matrices were constructed for the International Patent Classification (IPC) and Cooperation Patent Classification (CPC) systems. A combination of two ternary forms was developed to systematically categorize seven technological trends: active, inactive, emerging, declining, emerging to active, declining to active, and volatile. We focused on energy applications for both classification systems to identify emerging applications. In the main finding, chitin-based and chitosan-based technologies exhibit an emerging trend in manufacturing non-active parts and electrodes for batteries under the patent classification codes, H01M 2 and H01M 4, for both systems. These findings suggest emerging innovative activities incorporating chitin and chitosan into electrochemical battery technology to enhance battery performance and efficiency for storing electrical energy from renewable energy sources. This is further supported by recent Scopus-indexed academic literature published with similar keywords found in previously granted patents. The insights from the three-window analytics highlight how chitin and chitosan are ideal sustainable materials for environmentally friendly energy storage systems contributing to the green energy transition toward net zero.

Contemporary patent analytics employs a holistic approach whose purpose is to extract insights from aggregate analysis of patent landscapes. For the fruits of this endeavour to be accessible to executives, public servants and other individuals who value their time, the insights must be graphical in nature. In this paper, time evolved ranked Pareto distribution of patent family counts per assignee are analyzed by power law analysis. A graphical representation is presented which provides instantaneous insights into comparative scale and consolidation of technology landscapes. A number of specific data-analytical issues have been investigated and their impact on the validity of the results have been bounded and best-known methods proposed.

Welcome to the latest quarterly Literature Listing intended as a current awareness service for readers indicating newly published books, journal, and conference articles on IP management; Information Retrieval Techniques; Patent Landscapes; Education & Certification; and Legal & Intellectual Property Office Matters. The current Literature Listing was compiled end-November 2023. Key resources include Scopus, Digital Commons, publishers' RSS feeds, and serendipity! This article gives a selection of interesting references to whet your appetite - the full list of references can be found in the companion datafile.

Tumor-infiltrating lymphocytes (TILs) is a type of adoptive cell therapy that addresses the challenges associated with cancer treatment. In this approach, T cells from a patient's tumor are harvested and stimulated to activate and expand them. Once a significant number of these cells have been generated, and reintroduced into the patient's body, where they can effectively recognize and eliminate cancer cells. However, conducting research in this field whit various challenges, including limited access to necessary technological devices for TILs isolation and expansion. To address this issue, a search was conducted to identify technological devices that could be used in adoptive cell therapy, using patent exploration strategies. The search began by identifying the best-known patent databases platforms and selecting four to explore. Over 300,000 documents were initially found, but applying various filters to reduce the number of relevant documents to approximately less than 500. The information obtained was then cross-referenced and analyzed to identify the top some technologies with the highest potential for use in the isolation and expansion of TILs. Finally, the interfaces of the search platforms were compared to identify their differences.

Recent advances in Deep Learning based prior art search has enabled the development of easy-to-use prior art search engines that accept natural language search queries and provide improved search performance. However, unlike conventional keyword-based techniques where the results are readily interpreted by the presence of queried keywords, Deep Learning based techniques act like a black box. As a result, it is difficult for users to articulate their information in order to obtain optimal results. In this paper, we share insights on query well-formedness from extensive experimentation with PQAI,¹ an open source Deep Learning based prior art search engine. We study the effects of various query parameters such as grammar, specificity, and verbosity on the search results and show that ill-formed queries containing grammatical errors, non-essential content, and broad terminology adversely affect the relevance of search results. We also develop a number of Machine Learning models, viz. Grammatical Error Detection Model (GEDM), Query Specificity Model (QSM), and Query Verbosity Model (QVM), to identify and mitigate commonly encountered issues with ill-formed queries. The data, survey forms, and code relating to this work will be released to the community². Towards future breakthroughs, critical areas of query understanding in prior art search for advancing research are given in the end.