What Does a Patent Examiner Do?

Often times, the term “patent examiner” is used as an abbreviation to describe an individual who is responsible for examining a patent for validity, or to determine if a claim is valid. While a patent examiner is responsible for determining if a claim is valid, he or she is not responsible for deciding whether the invention is infringing another person’s patent. Instead, the decision to grant or deny a patent rests with the legal experts.

Qualifications

Applicants for the position of patent examiner must have a bachelor’s degree in science or engineering. Candidates are also expected to demonstrate mathematical and scientific training. Typically, the position is advertised in newspapers, magazines, scientific journals, and online.

The qualifications of a patent examiner vary by jurisdiction and position. Generally, the qualifications for the position include a degree in the sciences, legal training, and five years of professional experience.

The EPO requires that all officials be proficient in one or more of its official languages. For example, a candidate for a position in the chemical series must be able to read and understand the Japanese specification.

The European Patent Office (EPO) hires examiners from nationals of member states. The training program is paid and includes classroom learning, tutoring from senior patent examiners, and a probationary period.

Obfuscation of the invention should be penalized

Among the legions of patent applicants, a single obfuscated application is all too commonplace. The only way to combat the scourge is to up the ante by instituting a multi-faceted patent policy. The mainstay of such a policy is a thorough review of the application in question. In the process, a more robust review of the applicant’s portfolio will be sparked. Fortunately, the patent examiner is well versed in such matters and has a knack for sniffing out the unwary slacker. In fact, the obfuscated applicant’s heaviest offender may be booted from the ranks in less time than it took to write the letter. Nevertheless, the obfuscated applicant may be a more viable prospect in the long run. Its predecessors have a bad rep, but the new broom is an exception in the making.

Claims mapping in any format

Using artificial intelligence to map claims to products can make the patent dollar go much faster. There are several methods to accomplish this task. While some are more efficient than others, all can be used to get the job done.

A patent data management system or engineering data management system are useful tools to achieve this. Alternatively, there are also software tools designed to make the task easier.

A claim map is a structured database of claim limitations. These limitations define the specific steps to be performed or the physical materials to be used. It can be tricky to figure out what to include.

For example, a claim reciting the function of all processors could be over-broad. In contrast, a claim reciting the functions of each of the processors may be less obvious.

Searching for prior art

Whether you’re a scientist, inventor or patent examiner, you need to know how to search for prior art. It’s important for you to do this before filing a patent. It can help you understand the scope of your invention and it can also cover modifications.

If you’re an expert in a technical field, you may be able to do an effective prior art search on your own. However, you should also consult a patent attorney or IP professional for additional advice.

To search for prior art, you should start by developing a search strategy. Some common strategies include keyword searches, classification searches and citation searches. Using Boolean search strings can improve your results.

You can also use Google’s natural language keywords to perform a prior art search. This is a simpler search that can turn up millions of results.

AI could help find prior terms for the same structural and functional elements

Despite the hype surrounding AI, there are still many ways to perform the same task a human can. This is particularly true of analytics and data management, where a small set of specialists are responsible for gleaning valuable insights from an otherwise impersonal set of data. As such, they are in a good position to identify the most relevant data from the most irrelevant. The best part is that such a symbiotic relationship could be realized at scale, if a large enterprise would be willing to take the leap.

However, the challenge is to distill this voluminous trove of data into meaningful insights that can be applied in an organizational setting. This is where a nimble AI solution comes into play. As an example, one such exemplar is IBM’s Watson, a cloud-based AI solution able to handle the data influx of hundreds of thousands of customers and employees alike. Using Watson, companies are able to better respond to customer queries, analyze and improve customer service, and streamline processes such as inventory management.