Spot Innovation Trends, Opportunities and Risks by Digging into Patent Data
Loyal readers (and anyone caught in an elevator with me) already know my passion for Patent Analysis. Inventors, entrepreneurs and other innovators should pay close attention as the process is low risk and the results are always educational.
As a quick recap, Patent Analysis takes statistical advantage of the roughly 5 million patent applications and granted patents being published around the world annually . For a quick refresher on the mechanics of Patent Analytics, see these posts: Data Mining: 4 Ways to Use Patent Analysis for Any Size Business and Data Mining: Take a 3-D Tour of Patent Analysis.
Since the purpose of each one of these patent applications and granted patents is to lock others out of a future market, each one will provide dozens of valuable clues surrounding innovations. With large numbers, they make a rich data set to sift through and interpret.
Traditionally, the typical users of Patent Analysis are very large companies already familiar with the method and having the right tools and knowledge to leverage the results for maximum benefit.
But smaller companies and inventors should look more closely at Patent Analytics as this research is fast, relatively low cost, uses real data, minimizes risk and helps direct money and other innovation resources into the most appropriate direction. Accordingly, companies can find maximum benefit from this method when they identify areas where they need to innovate today, not tomorrow, or when they chose to redirect their R&D efforts.
Digging a little deeper into innovation territory
Patent publications can reveal unexpected innovation information. Here are some quick examples.
1. Global perspective. When your competitors are filing IP for their innovative ideas, in which country is the first filing? What is their advantage in using this methodology, assuming that it’s part of their strategy? Do other trends come into focus when you take an international view? Uh-oh, not looking beyond the U.S? This would be a mistake since U.S. patent applications only represent 25 percent of the world’s total.  Identifying your competitor’s methodology early also allows you to refine your competitive intelligence program to spot their innovations even earlier. Improving your early warning radar helps ensure your innovation teams’ efforts are directed for maximum benefit.
2. Expert identification. People change jobs but they don’t leave their knowledge behind. If you need a subject matter expert to improve your current innovation or to come up with new ideas for your next innovation, look at the inventors behind the IP being filed. It’s common to see people working in the same area but at different companies. Heads up to companies that like to poach employees!
3. Tip offs from the big dogs. I don’t claim that patents are a perfect predictor of future markets. They do, however, provide some insight into the types of opportunities others see in a given space. Accordingly, if you see companies known for their early due diligence filing large numbers of patent applications in a particular area, they likely feel that there’s appreciable opportunity for their innovation(s) to fill that future market opportunity rather than a casual poker bet. On the other hand, they may have already beaten you to the punch. Be mindful of each company’s strategy as some companies are known to file patent applications on every idea they have to support their patent cloud.
4. Licensing and financial information. Companies looking to start a new R&D project, scouting for innovative technologies, or an investor looking for the next great opportunity need to know the overall trends of patent filings in a specific area. The tool that shows activity can be compared to the familiar S curve shown here :
In case you’ve never seen this curve before, it shows how technologies are slowly accepted in their early years (on the left side of the graph) but then are purchased at increasingly faster rates (in the middle of graph) which then slow down again as they near market saturation (on the right of the graph).
Businesses often use the S-shaped market adoption curve to help them assess when a potentially innovative technology is starting to be adopted or is nearing maturity. So, the more activity, the more likely people are seeing a technology space as innovative and more of an opportunity. Conversely, a decaying amount of activity may signal that future market opportunities are slipping or that there’s fewer innovation opportunities remaining in that area. If so, it may be time to redirect your innovative activities and investments elsewhere.
5. What innovations are your competitors up to these days? Just like the S-curve description for a particular technology, looking at this information constrained to one or more of your competitors can provide an indication of where they’re spending their money today and where they wish to be tomorrow. Accordingly, their innovation path may be somewhat predictable while you monitor for unanticipated deviations in their innovation program. Ultimately, you will have more time to react if you can spot these variations early and then choose whether to beat them to the punch with the same type of innovation, alter your innovation process to provide a different solution, or simply choose to not alter your current R&D process.
Values of Patent Analytics for Innovators
- Helps identify new competitors, technology maturity, risks and opportunities.
- Brings together the relationship between technology, IP and markets.
- Ensures that our view of the future is verifiable with data.
- Brings lots of real data points into focus quickly and at a reasonable cost since it eliminates or minimizes the tedious process of reading lots of patents or patent applications.
- Helps identify trends quickly.
- Can be used on many business segments.
- Should not be used to the exclusion of other data, but can round out information from other sources.
Would this type of information affect your company’s strategy? Are there other areas of Patent Analytics you’d like to see discussed in a future column? Drop me a note in the comments below.
 Alanf777 (Wikimedia)