The global patent data set is big by any measure, with millions of new patent documents and updates made public every week. For technology and intellectual property (IP) professionals, weeding through this data to determine new business opportunities and/or competitive risks is a crucial but daunting task.
Patent data is well suited for big data tools and techniques because of the volume, variety and velocity of changes. In fact, many of today’s patent analytics solutions are leading the way in the use of big data to drive deeper analysis.
Throughout the patent lifecycle – from the initial idea to prosecution, grant, maintenance and expiration – decisions are made about which patents to invest in and how to manage the overall portfolio. By providing additional context relative to other patent filers, big data-derived insights can be invaluable to guide important decisions around R&D investments, filing decisions, litigation strategies and competitive positioning.
New technologies have emerged that are transforming the way companies can analyze large data sets. It’s now possible to purchase access to millions of patents and billions of websites, but without the processing power and analytics capabilities to derive insight, this information is practically useless.
Cloud computing – access to unlimited, on-demand computing power – allows IP professionals to data mine in more than two billion web pages at a fraction of the cost of previous methods. Meanwhile, machine learning enables companies to analyze petabytes of data with unprecedented depth of understanding. Combined, these technologies enable predictive analytics, a science that gives organizations the ability to analyze data, find trends and discover anomalies that help shape future business strategies.
When a company reviews patents for renewal, identifying the revenues that each patent has generated against the cost of protection is a simple way to decide which ones to renew. Using predictive analytics, however, the IP team can identify both internal and external information to evaluate the patent’s strength and relevance. Internal information can be supplemented with external data on relevant markets, company market share and potential areas for future exploitation to deliver a richer and more nuanced overview of the patent’s potential that may outweigh the renewal cost.
Despite the obvious benefits of automation, some IP professionals still input information manually, making it vulnerable to human error. For example, in patent office documents over the past two decades, the company “International Business Machines Corporation” was spelled more than 1,000 different ways. Big data technologies such as rules engines with machine learning and textual analysis can fix these errors and provide much cleaner patent owner information.
Insights from Big Data Analytics technologies are likely to have a major impact on business planning and strategy, potentially providing opportunities for chief IP officers to take on more strategic roles within their organizations. Many of these tools exist today, with more advanced applications being developed on a daily basis. Companies that still manually search for relevant IP information are not only using their resources inefficiently, they’re missing the critical business insights that come from connecting disparate data sets.
Whether it’s the ability to process massive amounts of data or to analyze and evaluate it quickly, big data tools available today go wider and deeper than ever before, enabling IP professionals to make more informed decisions around their current and future IP portfolios. The real transformation will come over time – as more IP professionals embrace big data’s potential to drive critical decisions and outcomes in investment strategy, global revenue growth and product development.