How R&D leaders can leverage AI tools to drive competitive advantage
In this article we’ll discuss how R&D leaders leverage AI tools in patent landscape analysis to drive competitive advantage. We’ll cover three AI tools: patent and literature discovery, focus and data extraction.
The number of patents grows every year. In 2018, there were 3.3 million patent applications, representing a 5.2% increase from the previous year. That’s equivalent to around 9,000 patents a day, meaning there are tens of millions of published patents and patent application references available to review. Moreso, it’s generally assumed that 80% of published science and technology information contained in patents is not published anywhere else. This makes patent databases a gigantic treasury mine of knowledge.
That’s where patent landscape analysis comes into play, enabling R&D leaders to find “white spots” and gain competitive advantage in the market. Patent landscape analysis or “patent mapping” is a comprehensive study of a particular field of technology, enabling large businesses, universities, start-ups and research organizations to understand trends and explore rewarding business product development opportunities.
Advancements in artificial intelligence (AI) are presenting opportunities for R&D leaders to drive competitive advantage for their patent landscape analysis.
Three AI tools to drive competitive advantage
Over the past few years, AI has become a lot easier and more accurate for processing scientific documents. Three of the most relevant areas for R&D leaders and patent landscape analyses are:
Discovering patents and papers
Narrowing down huge lists of documents to a succinct reading list
Extracting key data from texts, tables and charts
Discovering literature and patents
Intellectual Property departments are often running patentability searches (also known as prior art search or novelty search) on submitted inventions from R&D departments. Using AI technologies, researchers can find similar patents based on a provided description of the company’s patent idea. The engine reads your provided texts and identifies key concepts based on similarity — saving researchers time, as they will quickly discover whether an idea already exists. The new-found time enables R&D leaders to focus on driving competitive advantage and finding a niche for development.
Narrowing down your reading list
After finding documents, researchers can apply AI to narrow down the list, based on relevant and irrelevant concepts. Going manually through big sets of papers and patents is time-consuming, but the engine understands the context of the article and rates its relevance. More so, it identifies the similarity between each document and the description provided by the researcher. As a result, researchers save up to 80% of their time.
Extracting key data
Many patent analysts, R&D managers, researchers and data scientists go through tens of patents daily, manually analyzing and drawing out the most important information from text, tables and graphs. The process lends itself to automation, as it’s time-consuming and error-prone for humans. AI-enhanced tools find key data points from patents and automatically add the information to a prepared excel sheet. This helps avoiding human errors and most importantly saves time.
Key takeaways
The number of patents grows at an unprecedented rate, and it’s time-consuming and difficult to analyze them. AI technology enables R&D leaders and their teams to spend more time analyzing the actual data in patents — as the engine does a lot of the manual work. This includes discovering relevant patents, narrowing down long lists of documents based on specific criteria and lastly extracting all key data points. This way, researchers save time during the patent landscape analysis and drive competitive advantage in their market.
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