"Inventioneering" offers a warning and prescription for how US companies can compete against burgeoning IP powerhouses, especially those in China
In this IP Watchdog post, Ed Flinchem, Chief Data Scientist at TurboPatent, visualizes 40 years of patent trends.
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This New York Times article discusses how AI technology will not replace lawyers anytime soon, but instead, is pushing them up the intellectual ladder by automating tasks at the bottom.
Clients are willing to pay for a lawyer's experience and intellectual work, but not for any routine work. Task-based automation excels at replacing, or assisting with the routine work.
The patent process is often long and costly, full of repetitive, time-consuming, and tedious tasks. Errors occur far too frequently. These factors make it highly suitable to gain improved efficiency and quality through automation.
This case study provides a look at one area of the patent process, demonstrating how automation can offer dramatic improvements.
TurboPatent, experts in automating and streamlining the patent process, today unveiled SmartShell. The revolutionary new software uses predictive analytics, Natural Language Processing (NLP), and other automation and artificial intelligence technology to significantly simplify the process of responding to patent examiners.
TurboPatent examined Tech Center 2800 at three levels of detail: the overall statistics; a breakdown of the allowance rate by stage of prosecution; and finally, all the way down to the extremes of variation exhibited by individual examiners. The deepest investigation exposes a range of patterns unobserved in a focus on allowance rates alone. Relatively small changes in allowance rates, for example +/-15%, correlate to a 2x change in the effort and cost of an allowance.
“How can I measure the worth of my patent portfolios?” is a question asked frequently by VPs of intellectual property, patent managers, licensing professionals, and other people responsible for the maintenance and monetization of corporate patent portfolios in the ICT space. In other words, are there benchmarks against which quality and value may be determined in a patent portfolio?
In fact, there are such benchmarks, and they are based upon a very few facts that are well-known in the patent industry.
Quixey is a Silicon Valley-based technology company that provides users with easy access and engagement with the content and functionalities within apps. Recently, Quixey utilized the TurboPatent Machine to assess the quality of their patent portfolio. The TurboPatent Machine quickly delivered Quixey a TurboPatent Portfolio Report -- a deep and highly efficient analysis of Quixey’s published pending and granted patent assets.
Patenting can be a long and costly process, and the uncertainty of the outcome can greatly impede the patentee’s ability to budget and plan effectively. In an ideal world, a practitioner (and by extension, their client) would know which art unit will be assigned to examine a case prior to the completion of drafting. Knowing that the outcome of prosecution is more likely to be negative, costly, and/or narrow could make all the difference for applications likely to fall in a small set of truly challenging art units. With this knowledge, the client and practitioner may revise the application to better the odds of a more favorable outcome.
The good news is that machine learning and analytics have matured to the point where one can “look into the future” and distinguish outcomes for a patent application with various probabilities. Access to such information and predictions is now available, for a price. However, not all output is equal. Given the same raw data, technology applied to analyze the data can yield different results.