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Automatically Extracting Meaning From Legal Texts: Opportunities And Challenges, Kevin D. Ashley
Automatically Extracting Meaning From Legal Texts: Opportunities And Challenges, Kevin D. Ashley
Georgia State University Law Review
This paper surveys three basic legal-text analytic techniques—ML, network diagrams, and question answering (QA)—and illustrates how some currently available commercial applications employ or combine them. It then examines how well the text analytic techniques can answer legal questions given some inherent limitations in the technology. In more detail, ML refers to computer programs that use statistical means to induce or learn models from data with which they can classify a document or predict an outcome for a new case. Predictive coding techniques employed in e-discovery have already introduced ML from text into law firms. Network diagrams graph the relations between …
Legal Intelligence Through Artificial Intelligence Requires Emotional Intelligence: A New Competency Model For The 21st Century Legal Professional, Alyson Carrel
Georgia State University Law Review
The nature of legal services is drastically changing given the rise in the use of artificial intelligence and machine learning. Legal education and training models are beginning to recognize the need to incorporate skill building in data and technology platforms, but they have lost sight of a core competency for lawyers: problem-solving and decision-making skills to counsel clients on how best to meet their desired goals and needs. In 2014, Amani Smathers introduced the legal field to the concept of the T-shaped lawyer. The T-shaped lawyer stems from the concept of T-shaped professionals who have a depth of knowledge in …
Automation & Predictive Analytics In Patent Prosecution: Uspto Implication & Policy, Tabrez Y. Ebrahim
Automation & Predictive Analytics In Patent Prosecution: Uspto Implication & Policy, Tabrez Y. Ebrahim
Georgia State University Law Review
Artificial-intelligence technological advancements bring automation and predictive analytics into patent prosecution. The information asymmetry between inventors and patent examiners is expanded by artificial intelligence, which transforms the inventor– examiner interaction to machine–human interactions. In response to automated patent drafting, automated office-action responses, “cloems” (computer-generated word permutations) for defensive patenting, and machine-learning guidance (based on constantly updated patent-prosecution big data), the United States Patent and Trademark Office (USPTO) should reevaluate patent-examination policy from economic, fairness, time, and transparency perspectives. By conceptualizing the inventor–examiner relationship as a “patenting market,” economic principles suggest stronger efficiencies if both inventors and the USPTO have better …