Examining Examiners: The Top and Bottom 10 of TC 2800

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.

Read the complete analysis on IP Watchdog. 

Measuring the Worth of Patent Portfolios

“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 Utilizes the TurboPatent Machine to Assess Patent Portfolio Quality

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.

Framing Art Unit Predictions and Allowance Rates

Framing Art Unit Predictions and Allowance Rates

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.

Download a table with the allowance rate of each art unit, sorted by unit number