On the Patent Claim Eligibility Prediction Using Text Mining Techniques

With the widespread of computer software in recent decades, software patent has become controversial for the patent system. Of the many patentability requirements, patentable subject matter serves as a gatekeeping function to prevent a patent from preempting future innovation. Software patents may easily fall into the gray area of abstract ideas, whose allowance may hinder future innovation. However, without a clear definition of abstract ideas, determining the patent claim subject matter eligibility is a challenging task for examiners and applicants. In this research, in order to solve the software patent eligibility issues, we propose an effective model to determine patent claim eligibility by text-mining and machine learning techniques. Drawing upon USPTO issued guidelines, we identify 66 patent cases to design domain knowledge features, including abstractness features and distinguishable word features, as well as other textual features, to develop the claim eligibility prediction model. The experiment results show our proposed model reaches the accuracy of more than 80%, and domain knowledge features play a crucial role in our prediction model

Patent Eligibility: Exploring the Intersection Between Patent Law and Biomedical Data

The world was fundamentally changed by the rampant spread of COVID-19 in 2020. This is not the first and will not be the last time the world is faced with a pandemic. Thus, it is essential to  take the necessary steps now to be prepared in the future. This Note will address how patent law can protect inventions incorporating the biomedical data to prevent future pandemics. The Note compares U.S. and European Patent Regimes to determine which system is better at protecting biomedical data. Lastly, this Note proposes changes to the U.S. Patent Regime to help increase its compatibility with biomedical data

Big data and data repurposing – using existing data to answer new questions in vascular dementia research

Introduction: Traditional approaches to clinical research have, as yet, failed to provide effective treatments for vascular dementia (VaD). Novel approaches to collation and synthesis of data may allow for time and cost efficient hypothesis generating and testing. These approaches may have particular utility in helping us understand and treat a complex condition such as VaD. Methods: We present an overview of new uses for existing data to progress VaD research. The overview is the result of consultation with various stakeholders, focused literature review and learning from the group’s experience of successful approaches to data repurposing. In particular, we benefitted from the expert discussion and input of delegates at the 9th International Congress on Vascular Dementia (Ljubljana, 16-18th October 2015). Results: We agreed on key areas that could be of relevance to VaD research: systematic review of existing studies; individual patient level analyses of existing trials and cohorts and linking electronic health record data to other datasets. We illustrated each theme with a case-study of an existing project that has utilised this approach. Conclusions: There are many opportunities for the VaD research community to make better use of existing data. The volume of potentially available data is increasing and the opportunities for using these resources to progress the VaD research agenda are exciting. Of course, these approaches come with inherent limitations and biases, as bigger datasets are not necessarily better datasets and maintaining rigour and critical analysis will be key to optimising data use

A study of patent thickets

Report analysing whether entry of UK enterprises into patenting in a technology area is affected by patent thickets in the technology area

Generating Rembrandt: Artificial Intelligence, Copyright, and Accountability in the 3A Era--The Human-like Authors are Already Here- A New Model

Artificial intelligence (AI) systems are creative, unpredictable, independent, autonomous, rational, evolving, capable of data collection, communicative, efficient, accurate, and have free choice among alternatives. Similar to humans, AI systems can autonomously create and generate creative works. The use of AI systems in the production of works, either for personal or manufacturing purposes, has become common in the 3A era of automated, autonomous, and advanced technology. Despite this progress, there is a deep and common concern in modern society that AI technology will become uncontrollable. There is therefore a call for social and legal tools for controlling AI systems’ functions and outcomes. This Article addresses the questions of the copyrightability of artworks generated by AI systems: ownership and accountability. The Article debates who should enjoy the benefits of copyright protection and who should be responsible for the infringement of rights and damages caused by AI systems that independently produce creative works. Subsequently, this Article presents the AI Multi- Player paradigm, arguing against the imposition of these rights and responsibilities on the AI systems themselves or on the different stakeholders, mainly the programmers who develop such systems. Most importantly, this Article proposes the adoption of a new model of accountability for works generated by AI systems: the AI Work Made for Hire (WMFH) model, which views the AI system as a creative employee or independent contractor of the user. Under this proposed model, ownership, control, and responsibility would be imposed on the humans or legal entities that use AI systems and enjoy its benefits. This model accurately reflects the human-like features of AI systems; it is justified by the theories behind copyright protection; and it serves as a practical solution to assuage the fears behind AI systems. In addition, this model unveils the powers behind the operation of AI systems; hence, it efficiently imposes accountability on clearly identifiable persons or legal entities. Since AI systems are copyrightable algorithms, this Article reflects on the accountability for AI systems in other legal regimes, such as tort or criminal law and in various industries using these systems

AI & IP Innovation & Creativity in an Age of Accelerated Change

From a glimmer in the eye of a Victorian woman ahead of her time, AI has become a cornerstone of innovation that “will be the defining technology of our time.” Around 2016, the convergence of computing power, funding, data, and open-source platforms tipped us into an AI-driven 4IR. AI can make a difference in accelerating disruptive innovation by bringing a data-driven approach to invention and creation. To do so, the law must embrace change and innovation as an imperative in a journey towards an ever-shifting horizon. In the creative arts, the work for hire doctrine provides a pragmatic legal vehicle for interests to vest and negotiated by the commercial interests best placed to encourage investment in both the technology and its downstream uses. Like human-generated work, AI-generated work is an amalgam of mimicry mined from our own learning and experience. The training data it draws upon, both for expressive and non-expressive sues, are merely grist for AI’s mill. Consequently, fair use must be liberally applied to prevent holdup by copyright owners and stifle transformative uses enabled by AI. AI can also be used to decipher complex copyright infringement cases such as those involving musical compositions. In the technological arts, the controversy will revolve around who owns innovative breakthroughs primarily or totally attributed to AI. How should these breakthroughs affect the regard for the notion of PHOSITA? How does AI change the equation when it comes to infringement? And how can AI help save the patent system from obsolescence? In these, AI both enables and challenges how we reward individuals whose ingenuity, industry, and determination overcame the frailty of the human condition to offer us inventions that make our lives more efficient and pleasurable. It will take a clear-eyed view to ensure that copyright and patent laws do not impede the very progress they were designed to promote

Predicting Policyholder Behavior and Benefit Utilization: An Analysis on Long-Term Care Insurance

In order to better serve their customers, a project to create a methodology for identifying variables that could indicate future long-term care insurance usage was commissioned by Ability Resources, Inc. As a basis for constructing a predictive model, tools such as SAS and Excel were implemented. A k-means clustering algorithm in SAS was utilized to group policyholders with similar characteristics, and a performance evaluation was executed in Excel. Together, these processes created a tool that determined the impact each characteristic had on policyholder benefit utilization. The validity of the process was assessed by applying it to supplemental data generated by the team. After several trials, the Variable Identification Procedure proved accurate