Automation and Predictive Analytics in Patent Prosecution: USPTO Implications and Policy
Ga. St. U. L. Rev., Vol: 35, Issue: 4, Page: 1185
2019
- 375Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage375
- Downloads351
- Abstract Views24
Article Description
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 information in an artificial-intelligence-driven market. Based on the economics of information and institutional-design perspectives, the USPTO should develop a counteracting artificial-intelligence unit in response to artificial-intelligence proliferation.
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