There is an unlimited number of insights to uncover in a global patent database. This type of collection is so vast that, until quite recently, it was impossible to uncover all of the relevant information held within the world’s patents. All of the benefits of a comprehensive prior art search, at any point in the innovation lifecycle, were limited by the constraints of human labor. Only with the rise of artificial intelligence and natural language processing have the hidden insights previously locked in patent databases come to light.
Now, AI-backed patent search software allows us to answer questions we didn’t know we had about the landscapes our technologies exist within.
Uncovering Licensing Opportunities
An IP.com client within the oil and gas industry was exploring the patent landscape surrounding one of its underwater oil detection technologies. This system would show the client where to drill to access underwater fuel deposits. The client completed a search within InnovationQ Plus® that included the phrase “detecting subsurface objects.” The search results, displayed in both a keyword list and semantic map, included a number of expected words, including “drilling,” “fracking,” and “seismic.”
What the client didn’t expect to see in their global patent database search results were terms related to the healthcare industry. The results contained numerous references to ultrasound technology, which detects anomalies in the human body such as swelling and fetuses. In hindsight, this similarity seems obvious. However, we don’t think of a swollen organ or pregnancy as a “subsurface object,” so we weren’t able to ask the kinds of questions that would have led to this answer on our own.
This hidden insight allowed our client to identify and pursue licensing and partnership opportunities within the healthcare industry.
Understanding the Technology Landscape
An electric bike company used InnovationQ Plus to see how its industry was changing over time and identify areas for future growth. When the organization glanced through suggested filters and reviewed the search results as a semantic map, it realized that many relevant technologies were not patented by bike manufacturers. Rather, the entities holding patents semantically similar to this electric bike company’s concepts for the future were car manufacturers.
The company then realized that, as the electric bike industry advanced, the technology landscape included the sophisticated innovations used in electric cars. This hidden insight allowed the company to see which companies are home to relevant inventions, beyond its traditional competitors.