Using Big Data to identify real gem patents in a Patent Portfolio

Big Data analytics and Artificial Intelligence are transforming the patent industry. Efforts are being done to improve patent analysis by adopting Big Data and AI tools.

Big Data tools and technologies are being used in several ways in the patent world. Analyzing patents in aggregate, and comparing them via big data algorithms and analytics involves big data techniques of analyzing all available information.

Most companies have an IP management tracking system that keeps track of their own filings but highlight nothing about their portfolio strength relative to a competitor’s patents neither identify gaps in a buyer’s portfolio which can help them identify their patents which they might sell or out-license.

Big data analysis of patents in aggregate provides an additional layer of insight and guidance that cannot be achieved through patent-per-patent analysis.

While maintaining the vast patent portfolios, companies must make decisions about which patents to invest in and how to manage the overall portfolio. These decisions can be made easily with the help of Big data-derived insights for e.g., within a pool of patents, Big data analytics can be used to prioritize which patents to review in detail, this not only reduces the overall review efforts but also the time-frame.

Below are the few stages of a patent life-cycle where big data analysis may prove to be helpful in making decisions:

  1. Investments & Partnership in R&D –Big data analysis helps in informing and guiding about R&D investments. For potential R&D partnership opportunities, Big Data Analytics is very helpful in finding who is innovating in a technology.
  2. Decisions for Filling – In the world of intellectual property, the basic rule is to block an infringing competitor. Sometimes, patent filers specify which competitors and products each patent application hopes to block in the future. Hence, it is very useful to know each & every aspect of the competitor’s situation, future plans etc. while investing in filing a patent application.
  3. Prosecuting Attorney Selection – In order to make decisions like where to send patent applications based on their success rate, average latency and costs by art unit and other factors, various patent filers examine mix of the information like patent grant rates, latency and office action counts by art unit, examiner, patent filer and prosecuting attorney which can be easily comprehended with the help of big data.
  4. Handling of Office Actions – On the basis of similar prior situations, predictive analytics can show the likelihood of success for each type of office action response.
  5. Decisions for Buying Patents – When a company raises an offer to sell patents, Big data analytics is helpful in determining:
  • Any other stronger patents, in the relevant technology areas, than in a company’s current portfolio.
  • Any other new territory that a patent can cover.
  • Strongest patents in the selling portfolio which are to be considered in detail.
  1. Patents Licensing or Selling – On the basis of strength and applicability to other players patent filings, Big data is helpful in prioritizing patents and in finding out which competitors are patenting closest to the patents that are under consideration.
  2. Decisions for Renewal – By knowing the patent’s relative strength in a portfolio compared with the competitors, renewal decisions are made. To make these decisions, Big data analytics can help in making effective decisions in less time.
  3. Strategies for Litigation – Big data analytics tools help in understanding the data behind the litigation landscape and litigation history of the opposing counsel which help companies to select and budget counsel.

Hence, amalgamation of Big data analytics in the patent portfolio domain can boost up the targeted investments with an added layer of quality and value at each and every stage of patent portfolio.

Leave a Reply

Your email address will not be published. Required fields are marked *