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.

This is inspired by the film 500 Days of Summer where the main character tries to figure out where a love affair went sour, and in doing so, rediscovers his true passions in life.

  • 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

So I started on the first day with a few definitions of artificial intelligence (AI). On my second day I went big (perhaps too big) discussing sustainable cities and AI. The third day I took a step back to briefly look at issues arising within funding of AI ethics. Thankfully I have friends on Facebook interested in the field of AI, and as such a picture caught my attention.

I am not familiar with Iplytics, and do not know the validity of this chart, however it sparked an interested in exploring the topic of patents within AI, so that is what I will do now. So where do we start? Let us start with WIPO!

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.

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).

Do not be surprised if there is a lot of repeating material in this post, I’ve tried to pull out some information and make it easier for you to access.

The stated intent of the report is to:

“to shift debate away from speculative interpretation and toward evidence-based projections, thereby informing global policymaking on the future of AI, its governance and the IP framework that supports it.”

In the first couple of pages it proposes three actions.

  1. To build more public-private partnerships
  2. Continue promoting the free and open sharing of AI knowledge and resources
  3. Promote increased understanding of AI

It then talks of building an ecosystem around AI to make it sustainable, however in this case it seems to be financial sustainability that is the focus unrelated to a wider understanding of the term.

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

The growth rates observed in the identified AI-related patent data are noticeably higher than the average annual growth rate for patents across all areas of technology, which was 10 percent between 2013 and 2016.

Got to love stats, right? Quantitative methods galore. If so further to this there are some interesting stats in the report worth taking notice of:

  • Machine learning is the dominant AI technique disclosed in patents and is included in more than one-third of all identified inventions (134,777 patent documents).
  • 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%.

Most reading this may already be familiar with what machine learning is, however it may be useful with a short explanation in passing.

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Comment: As a massive disclaimer as I mention machine learning — that is what I have been learning about so far within this field, programming in R (a programming language). As such I have only started my journey exploring what the field of AI holds. However at day four of writing about AI this may hopefully be forgiven by you the reader.

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.

These include, in order of magnitude:
1. telecommunications (mentioned in 15 percent)
2. transportation (15 percent)
3. life and medical sciences (12 percent)
4.personal devices
5. computing and human–computer interaction (HCI) (11 percent)

Other sectors featuring in the results include banking; entertainment; security; industry and manufacturing; agriculture; and networks (including social networks, smart cities and the Internet of things).

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:

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

These combinations suggest areas to watch for rapid developments in AI in the near future.

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

Companies represent 26 out of the top 30 AI patent applicants, while only four are universities or public research organisations.

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.

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.

Chinese universities are dominating academic contribution making up 17 of the top 20 academic players in AI patenting as well as 10 of the top 20 in AI-related scientific publications.

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.

I hope this was helpful! As always, if you have any thoughts about topics within the field of AI you want me to explore give me a comment.

Of course if you like the article also give it 50 claps. Clap clap clap.

Hope you have a wonderful day.

Reference:
WIPO (2019). WIPO Technology Trends
2019: Artificial Intelligence. Geneva: World Intellectual Property
Organization.

AI Policy and Ethics at www.nora.ai. Student at University of Copenhagen MSc in Social Data Science. All views are my own. twitter.com/AlexMoltzau