Artificial intelligence (AI) is radically transforming all business sectors, bringing innovative solutions and opening new horizons -- and the IP field is no exception to this trend, as evidenced by our analysis of AI-related patent filings. Our research reveals that the number of patents filed since 2013 is increasing exponentially, with the patent data showing that AI is being deployed through various applications in the IP field.
Earlier this year, the Questel IP Consulting team undertook a patent data analysis of AI innovation in the IP sector for our Industry Outlook Research 'Beyond the Hype: How Technology is Transforming IP'. Here we summarize some of the key findings from that analysis and what the race to patent AI in the IP field means for IP professionals.
On the Rise: Patent Families for AI in IP
Our patent data analysis reveals a compound annual growth rate (CAGR) for AI-based innovations for IP of 30% between 2013 and 2023:
This boom in filing activity is the result of a multitude of players from different backgrounds seeking to patent AI technologies:
The AI Technologies Covered by Our Patent Data Analysis
As part of our research, Questel's IP Consulting team took a closer look at nine promising technologies:
1. Interactions with patent and trademark offices (PTOs):
Generative AI applications that assist in patent and trademark prosecution, e.g., submitting documents and responding to office actions.
2. Landscape, patentability, clearance, and invalidity search:
AI tools designed to automate patent, trademark, and design searches, including for patent data analysis.
3. Report drafting:
AI-driven solutions that compile and draft comprehensive reports, such as patent landscape analysis, to provide insights into the state of the art in a particular technology domain.
4. Patent drafting:
Generative AI systems that assist in drafting patent applications, including claims, detailed descriptions, and embodiments of the invention.
5. Identifying licensing opportunities:
AI technologies that analyze patent portfolios and market data to identify potential licensing opportunities.
6. Patent database improvement and categorization:
AI algorithms that enhance current patent search capabilities, allowing for more efficient identification and categorization of patents.
Interactive AI-powered chatbots that provide guidance and answers to common questions in the patent and trademark fields.
8. Image search (patent, trademarks & designs):
AI-enhanced image search technologies tailored for identifying and analyzing visual elements in trademarks, patents, and designs.
9. Patent translation:
AI systems specialized in translating patent documents across multiple languages, maintaining the precise and technical nature of the language used in patents.
AI and Maturity: The Hype Cycle
The Gartner Hype Cycle provides a helpful way to visualize the process a new technology goes through as it enters the mainstream. It enables businesses to understand where a new technology is in its lifecycle and to make informed decisions about when or if to invest in it.
Stage 1 'Technology Trigger' -- A new technology sparks interest and initial media coverage.
Stage 2: The Peak of Inflated Expectations -- As stories about the potential of the technology spread, excitement builds.
Stage 3: The Trough of Disillusionment -- When the initial promises aren't met immediately, enthusiasm wanes and the technology faces criticism.
Stage 4: The Slope of Enlightenment -- As the technology matures and practical applications are developed, interest starts to climb again.
The Race to Patent AI: Will Emerging Technology Replace IP Expertise?
Over the last few years, the IP sector has begun exploring AI's potential to provide new tools, features, and functionalities across many different fields. As shown by the patent data and patenting dynamics in this field, research into new features and improvement of existing ones remains incredibly dynamic.
Now the technology has been unleashed, this enthusiasm will not stop; however, we expect greater investigation, knowledge, and application to build a general understanding that AI-enabled technologies for IP are progressive rather than "magical" tools. In other words, we expect AI to improve and accelerate human work but not supplant human expertise. However, while it is likely that AI will not replace IP practitioners, those who do not use AI will most likely be replaced by those who do.