The Art of Moneyball and Patent Prosecution
Using statistics to gather patent examiner intelligence is the process of identifying, gathering and using information about the characteristics of a particular examiner. By comparing the attributes of different examiners, it is possible to learn about the types of patents they are likely to issue. This can help patent owners, managers and lawyers to choose the best patent examiner for their inventions.
Our software enables you to play “Moneyball” with your patent applications. We will examine how this is done next.
Moneyball and Patent Prosecution
The concept of “Moneyball” with Patent Examiners refers to using data and analytics to make more informed decisions in patent prosecution. Just like how the book “Moneyball” applied data and analytics to baseball, the Moneyball approach to patent prosecution applies data and analytics to help companies make better decisions about how to prosecute their patents.
By using data and analytics, companies can gain a deeper understanding of Patent Examiner tendencies and decision-making processes, as well as trends in patent prosecution. This information can be used to inform the strategy for prosecuting a patent application, including the arguments and evidence to present in a response to a rejection.
For example, companies can use data on Patent Examiner decisions to determine the best office to file a patent application, predict the likelihood of a successful outcome, and determine the most effective arguments and evidence to present in a response to a rejection.
By leveraging data and analytics, companies can gain valuable insights into Patent Examiner tendencies and decision-making processes, helping them to prepare a more effective response and increase the chances of a favorable decision.
AI predicts examiner behavior based on history
Using a patent analytics platform helps you analyze examiner behavior, which can provide valuable insights for better prosecution outcomes. It can also be used to help identify cases that require extra attention or to spot actions against yellow examiners. Among other features, provides a distribution visualization, which lets you see the makeup of each type of USPTO examiner in any technology center. It even allows you to filter the dashboard so that you can find exactly what you are looking for.
The AI predicts the probability that an examiner will grant a patent, as well as the length of time it will take to obtain the patent. Aside from predicting the length of time it will take to obtain a patent, the AI value is also useful for informing drafting decisions before the application is submitted. The AI combines all pending cases in an examiner’s caseload. It does not penalize examiners for voluntary abandonments. You can anticipate examiner behavior and tailor your prosecution strategy to achieve the best possible outcome. The AI analysis is a great way to get ahead of the competition.
The AI takes into account the experience of each examiner. It also takes into account examiner behavior trends, such as the types of rejections and applications that examiners make. In fact, this is the most informative patent metric of all. You can gain insights into how examiners are behaving and how to make better decisions to improve the outcome of your patent application. The database provides comprehensive statistics about each USPTO examiner. The data is customizable, making it possible to use it in unique ways. You can access a number of other metrics, including the average pendency time for a patent. The tool also shows you the difficulty level of a patent in comparison to other patent examiners. You can also find out how many office actions were taken before a patent was granted. This allows you to set expectations with your clients before any obstacles occur.
Another useful feature is the Art Unit listing. The system lists all the examiners in each Art Unit. It also includes other statistics, such as representative allowance rates. For each individual Art Unit, you can view the number of office actions, and the overall makeup of the examiners. You can also generate reports for each examiner. In addition, you can see how the average office actions are distributed across different art units.
In addition to providing information about patent examiners, the patent analytics database also offers information about USPTO employees. You can learn about their tenure and experience at the USPTO. This information can help you decide how to manage your relationship with an examiner.
How you can use AI analysis of a particular Examiner
Leveraging prior history can help to predict how a Patent Examiner will decide on a response to a patent application. By analyzing the previous decisions made by a Patent Examiner, it is possible to understand their tendencies and the factors that influence their decisions.
For example, if a Patent Examiner has a history of rejecting similar patent applications for a particular reason, this information can be used to predict the likelihood of a similar rejection in the future. This can help inventors and patent lawyers to prepare a response that addresses the Patent Examiner’s concerns and increases the chances of a favorable decision.
Additionally, software tools can be used to analyze Patent Examiner history and performance, providing valuable insights into their tendencies and decision-making processes. This information can be used to inform the strategy for prosecuting a patent application, including the arguments and evidence to present in a response to a rejection.
In conclusion, leveraging prior history can be a valuable tool for predicting how a Patent Examiner will decide on a response to a patent application and improving the chances of a favorable decision. By using software to analyze Patent Examiner history, companies can gain valuable insights into their tendencies and decision-making processes, helping them to prepare a more effective response.
Moneyball and patent portfolio planning
The concept of “Moneyball” can also be applied to patent portfolio planning. By using data and analytics, companies can gain a deeper understanding of their patent portfolio, identify trends and opportunities, and make informed decisions about how to optimize their portfolio for their business goals.
For example, companies can use data on Patent Examiner decisions, patent ownership, and market trends to prioritize which patents to file, license, sell, or abandon. Additionally, companies can use data on the costs associated with maintaining a patent portfolio to make informed decisions about which patents to keep and which to divest.
By using data and analytics to inform their patent portfolio planning, companies can improve the efficiency and effectiveness of their patent portfolio management, reducing the risk of holding redundant or overlapping patents, and increasing the return on investment from their patent portfolio.
In conclusion, the Moneyball approach to patent portfolio planning involves using data and analytics to make informed decisions about how to optimize a patent portfolio for a company’s business goals. By leveraging data and analytics, companies can gain valuable insights into their patent portfolio, identify trends and opportunities, and make informed decisions to improve the efficiency and effectiveness of their patent portfolio management.