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Littérature scientifique sur le sujet « Patentability prediction »

Auteur : Grafiati

Publié le 2 juin 2025

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Patentability prediction"

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Mr., Mohd Abdul Muneim Farzaan. "Revolutionizing Patent Processes: The Rise of AI-Assisted Patent Prosecution." Annual International Journal on Analysis of Contemporary Legal Affairs (AIJACLA) 5 (May 26, 2025): 220–29. https://doi.org/10.5281/zenodo.15521506.

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<em>The rapid advancement of Artificial intelligence is transforming innovation across industries, including intellectual property (IP). AI generated inventions has become increasingly prevalent worldwide and are reshaping its traditional processes of patent Drafting, examination and enforcement. AI tools predict patentability and automate patent searches with high efficiency. These advancements enable patent professionals enhance their decision making. An AI system generates novel ideas and inventions for intellectual property rights and for patent purposes and it also helps in learning machi

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Wang, Mei-Hsin, and Hui-Chung Che. "Intellectual capital forecasting for invention patent through machine learning model." Journal of Intellectual Capital 25, no. 7 (2024): 129–50. http://dx.doi.org/10.1108/jic-12-2023-0286.

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PurposeThis research explores support vector machine (SVM) with Gaussian radial basis function kernel (RBF) as the model and Analysis of Variance (ANOVA) for forecasting the invalidation re-examination decisions of China invention patents, it is beneficial to support patent monetization for corporate intellectual capital.Design/methodology/approachThere were 8,666 China invention patents with their existing invalidation re-examination decisions during 2000∼2021 chosen to conduct classification model training and prediction for the accuracy of invalidation re-examination decisions through SVM w

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Stefan, Sven Marcel, Katja Stefan, and Vigneshwaran Namasivayam. "Computer-aided pattern scoring (C@PS): a novel cheminformatic workflow to predict ligands with rare modes-of-action." Journal of Cheminformatics 16, no. 1 (2024). http://dx.doi.org/10.1186/s13321-024-00901-5.

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AbstractThe identification, establishment, and exploration of potential pharmacological drug targets are major steps of the drug development pipeline. Target validation requires diverse chemical tools that come with a spectrum of functionality, e.g., inhibitors, activators, and other modulators. Particularly tools with rare modes-of-action allow for a proper kinetic and functional characterization of the targets-of-interest (e.g., channels, enzymes, receptors, or transporters). Despite, functional innovation is a prime criterion for patentability and commercial exploitation, which may lead to

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Saladich Nebot, Javier. "Patents and Diagnostic Methods in the U.S.: The Subject Matter Eligibility Trap." Journal of Commercial Biotechnology 25, no. 1 (2019). http://dx.doi.org/10.5912/jcb876.

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Diagnostic methods have been gaining medical recognition and social importance as innovations that can be useful to provide individuals with a diagnosis, prognosis or prediction with regard to a condition that they currently have or that they are in risk of developing. Despite the great amount of resources deployed to produce these health technologies and their potential benefits for healthcare systems and patients or prospective patients alike, their exclusive protection in the United States has faced resistance from patent examiners and courts on the basis that diagnostics constitute a dubio

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Stefan, Sven Marcel, Katja Stefan, and Vigneshwaran Namasivayam. "Computer-aided Pattern Scoring (C@PS): A Novel Cheminformatic Workflow to Predict Ligands with Rare Modes-of-action." August 30, 2024. https://doi.org/10.5281/zenodo.13606805.

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<strong>Abstract: </strong>The identification, establishment, and exploration of potential pharmacological drug targets are major steps of the drug development pipeline. Target validation requires diverse chemical tools that come with a spectrum of functionality, <em>e.g.</em>, inhibitors, activators, and other modulators. Particularly tools with rare modes-of-action allow for a proper kinetic and functional characterization of the targets-of-interest (<em>e.g.</em>, channels, enzymes, receptors, or transporters). Despite, functional innovation is a prime criterion for patentability and commer

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Kaul, Nishit, Sameer Kaul, Bharti Bhat, et al. "SMART-Flow: Enhancing Fluid Dynamics Simulations with Machine Learning for Scalable Model Reduction and Accurate Prediction." Recent Patents on Engineering 19 (April 22, 2025). https://doi.org/10.2174/0118722121371511250322051853.

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Aims: To improve the efficiency of fluid dynamics simulations by applying model reduction and prediction techniques through machine learning, ultimately reducing computational costs while maintaining near-perfect accuracy. Background: Fluid dynamics simulations are critical in various engineering and physical science applications. However, their computational complexity often leads to significant time and resource consumption. To address this issue, integrating computational physics with machine learning- based methods offers promising opportunities for performance enhancements. Objective: To

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Bond, Eric W., and Zachary R. Luther. "International Patent Families: When to Apply." Review of International Economics, January 13, 2025. https://doi.org/10.1111/roie.12787.

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ABSTRACTWe model the decision of an innovating firm on where and when to make cross‐border patent applications given the rules of the Paris Convention and Patent Cooperation Treaty (PCT). We show that an innovating firm will have an incentive to apply at the beginning (end) of the Paris Convention application window when returns are front‐loaded (back‐loaded). The preliminary review provided by a PCT application expedites the review process in subsequent national applications, leading to the selection of firms with more productive innovations into the PCT route for cross‐border applications. W

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Chapitres de livres sur le sujet "Patentability prediction"

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Miyaoka, Kei, and Takako Akakura. "Construction of a Prediction Model for Pharmaceutical Patentability Using Nonlinear SVM." In Human Interface and the Management of Information. Information in Intelligent Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22649-7_20.

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