Patents In The Field of Artificial Intelligence

500 days of Artificial Intelligence #4

I am challenging myself to write and think about the topic of artificial intelligence for the next 500 days with the #500daysofAI.

Summary — numbers from 2013–2016 (WIPO, 2019)

  • Patents in AI are growing rapidly quicker than any other category within technology
  • Machine learning is the dominant AI-technique. Deep learning and neural networks are the fastest growing techniques
  • The field with the highest amount of patents is telecommunications
  • The most frequent combination in filing is deep learning and computer vision
  • Companies are dominating AI patenting. The company with the most AI-related patents is IBM
  • China is growing rapidly in the amount of AI-related patents

World Intellectual Property Organization

WIPO is the global forum for intellectual property services, policy, information and cooperation. They are a self-funding agency of the United Nations, with 192 member states. It was established in 1967 so it has been around for some time. Their mission is to lead the development of a balanced and effective international intellectual property (IP) system that enables innovation and creativity for the benefit of all.

WIPO Technology Trends 2019: Artificial Intelligence

A recent report by the World Intellectual Property Organization (WIPO) attempts to shed some light of the developments of patents within AI since the 1950s. The report is said to be written with contributions from many of the big names in AI related to business (see for yourself).

  1. Continue promoting the free and open sharing of AI knowledge and resources
  2. Promote increased understanding of AI

What Does the Stats Say About AI Patents?

A core point here is this. There is a high growth in AI-related patents.

  • Filings of machine learning-related patent have grown annually on annual average by 28 percent, with 20,195 patent applications filed in 2016 (compared with 9,567 in 2013).
  • Deep learning and neural networks are the fastest growing techniques in terms of patent filings. Deep learning up 175% from 2013 to 2016 (2,399 patent filings). Neural networks grew 45% in the same period (to 6,506).
  • Among AI functional applications computer vision is most popular. Computer vision is mentioned in 49 percent of all AI-related patents (167,038 patent documents)
  • AI for robotics grew by 55%.

What fields are the patents within?

Twenty application fields were identified in the present analysis and at least one was mentioned in 62 percent of the total identified AI patent data.

What combinations are we seeing?

Nearly 70 percent of inventions related to AI mention an AI technique, application or field in combination with another. The most frequent combinations in patent filings are:

  • computer vision with transportation
  • telecommunication and security
  • ontology engineering with natural language processing
  • and machine learning with life and medical sciences

Companies dominate AI patenting

Companies from Japan, the United States and China dominate patenting activity.

Of the top 20 companies filing AI-related patents, 12 are based in Japan, three are from the U.S. and two are from China. Japanese consumer electronics companies are particularly heavily represented.

  • IBM 8,290
  • Microsoft 5,930
  • Toshiba 5,223
  • Samsung 5,102
  • NEC 4,406.

China is growing rapidly in AI patenting

The State Grid Corporation of China has leaped into the top 20, increasing its patent filings by an average of 70 percent annually from 2013 to 2016, particularly in the machine learning techniques of bio-inspired approaches, which draw from observations of nature, and support vector machines, a form of supervised learning.

So what about Europe?

Four European public research organizations feature in the top 500 list; the highest-placed European institution is the German Fraunhofer Institute, which is ranked 159th, while the French Alternative Energies and Atomic Energy Commission (CEA) is in 185th position.

AI Policy and Ethics at Student at University of Copenhagen MSc in Social Data Science. All views are my own.