The Story of AI in Patents
By World Intellectual Property Organization (WIPO)
Artificial intelligence (AI) is fast becoming a part of our everyday lives. Where a few decades ago, only humans could play chess or read handwriting, both of these tasks are now routinely performed by AI-equipped machines. Today, researchers are working on ever more ambitious applications of AI, which will revolutionize the ways in which we work, communicate, study and enjoy ourselves.
Yet concerns exist about the nature of AI and the challenges it may pose to humanity. Any policy response to these concerns requires a common factual basis for discussions among decision makers.
That is why the World Intellectual Property Organization chose AI as the first topic in its new WIPO Technology Trends research series.
The WIPO Technology Trends: Artificial Intelligence report draws on data in patent applications and combines it with analysis of scientific publications to create a technical framework for understanding AI innovation. The data-analysis is complemented by interviews with experts in the field on what the future may hold for AI.
History of AI
Artificial intelligence (AI) emerged in the 1950s, with the first mention of the term coming during the Dartmouth Summer Research Project on Artificial Intelligence in 1956.
Since that time innovators and researchers have published over 1.6 million AI-related scientific publications and filed patent applications for nearly 340,000 AI-related inventions.
But the history of AI hasn't always been smooth sailing. Periods of optimism, success and growth were followed by disappointment, contraction and regrouping; AI "summers" gave way to AI "winters" as the nascent discipline struggled to find its feet.
Recent rapid growth in computing power and communications technologies has enabled the compilation and sharing of large volumes of data, opening up many new areas for AI technological development.
Five of the original participants in the 1956 Dartmouth Summer Research Project on AI. From left to right: Trenchard More, John McCarthy, Marvin Minsky, Oliver Selfridge, Ray Solomonoff. (Photo: © Dartmouth College)
Five of the original participants in the 1956 Dartmouth Summer Research Project on AI. From left to right: Trenchard More, John McCarthy, Marvin Minsky, Oliver Selfridge, Ray Solomonoff. (Photo: © Dartmouth College)
Growth in AI patent applications plus
historical milestones
1956
First mention of
the term “AI”
1956-74
Golden years of
government funding
1974-80
Unrealistic expectations
coupled with limited
capacities lead to
the first AI "winter"
1980-87
Knowledge-based
expert systems
herald new
optimism and focus
1987-93
Sudden collapse of
specialized hardware
industry leads to the
second AI "winter"
1993-2011
AI starts to become data
driven, computers increase
in power, and both optimism
and successes return
2012-today
More data, increased
connectedness and
greater computer power
bring new breakthroughs
and an AI patent boom
Techniques
Machine learning is the dominant artificial intelligence (AI) technique. It is found in 40% of all AI-related patents studied and the technique grew at an average rate of 28% every year from 2013-2016.
Percentage of all AI-related patents that mention machine learning
Within machine learning, the specific techniques currently revolutionizing AI are deep learning and neural networks. Both of these techniques are instrumental in transforming automatic translation, for example.
Mentions of deep learning in patent filings grew annually at an average rate of 175% from 2013-16. Mentions of neural networks grew annually at an average rate of 46% over the same period.
Case study
Machine learning and perfumery
The ability to craft a fragrance is something that takes master perfumers years of experience to develop. A group of IBM researchers and skilled perfumers at Symrise, a global producer of flavors and fragrances, got together to explore how to use AI to do just that.
Mixing artistic and scientific thought into one big pot resulted in Philyra – an AI product composition system that can learn about formulas, raw materials, historical success data and industry trends.
Philyra uses new, advanced machine learning algorithms to sift through hundreds of thousands of formulas and thousands of raw materials, helping identify patterns and novel combinations. As Philyra explores the entire landscape of fragrance combinations, it can detect gaps in the global fragrance market for which entirely new fragrance formulas can be designed.
The first two fragrances to be produced using Philyra will be for Brazilian cosmetics company O Boticário and are scheduled for release in 2019.
Image: Senior Perfumer David Apel at work in the Symrise Creative Studio New York (Photo: Symrise)
Applications
Computer vision, which includes image recognition (critical for self-driving cars, for instance), is the most popular functional application of artificial intelligence (AI). It was mentioned in 49% of all AI-related patents and grew annually at an average rate of 24% over the period 2013-16.
Percentage of all AI-related patents that mention computer vision
The other two top areas in functional applications are natural language processing (14% of all AI-related patents) and speech processing (13%).
While computer vision, natural language processing and speech processing are the three most important functional applications in terms of the total number of patent filings, others such as robotics and control methods are emerging and growing fast.
Case study
Using speech processing to turn radio discussions into policy data
In Uganda, where most of the population lives in rural areas, radio is a vibrant platform for public discussions, information sharing and news. Talk shows and phone-ins are popular ways for people to voice their needs, concerns and opinions.
In this pilot project, UN Global Pulse and the Stellenbosch University in South Africa built speech recognition technology that uses machine learning to convert public discussions in radio broadcasts into text that can be read in several of the languages spoken in Uganda, including Luganda, Acholi, Lugbara and Rutooro.
There is a wealth of data that can be extracted from public radio conversations and these data can be mined to support sustainable development and humanitarian efforts. Insights about the spread of infectious diseases, or the way people move during a disaster, or how they perceive healthcare campaigns or access to jobs and education, can be derived from radio talk.
In order to protect the right to privacy, the project employs specific tools such as data anonymization, restricting access to the data during project implementation and destroying the data once the project is concluded.
Findings from this pilot project continue to be analyzed to understand how the data gathered can be applied to advance the SDGs.
Image: @UN Global Pulse 2019; Case: Using Machine Learning to Analyze Radio Content in Uganda
Fields
The top fields in which artificial intelligence (AI) technologies are employed are:
- telecommunications: computer networks/internet, radio and television broadcasting, telephony, videoconferencing, and VoIP
- transportation: aerospace/avionics, autonomous vehicles, driver/vehicle recognition, transportation and traffic engineering
- life and medical sciences: bioinformatics, biological engineering, biomechanics, drug discovery, genetics/genomics, medical imaging, neuroscience/neurorobotics, medical informatics, nutrition/food science, physiological parameter monitoring, public health
Percentage of all AI-related patents filed in telecoms, transportation or life and medical sciences
Looking at the ten-year period from 2006-16, growth in transportation technologies stands out. Representing just 20% of applications in 2006, by 2016 transportation accounted for one-third of applications (with more than 8,700 filings).
Telecommunications has remained at around 24% during this ten-year period, but the proportion of filings mentioning business, document management and publishing or life and medical sciences has decreased.
AI application
fields
Top three application fields
in which AI-related patents
are filed
#1
Telecommunications
51,273 applications (15%)
#2
Transport
50,861 applications (15%)
#3
Life and medical sciences
40,758 applications (12%)
Case study
Saving lives with AI telecoms technology
Sudden unexplained death in epilepsy (SUDEP) claims a life every seven to nine minutes. The Empatica Embrace is the first smart watch that uses AI to detect potentially life-threatening convulsive seizures.
The watch continuously runs a seizure-detection algorithm, built using machine learning. The AI algorithm within is a support vector machine. This form of supervised learning is trained by collecting lots of data from wearables. An expert neurologist is then asked to provide a medically accurate label for each time chunk of the data.
The labels and data are used to train the support vector machine, enabling it to learn how to map data sensed from the wearer’s wrist to labels likely to be given to that data by an expert human. The resulting trained support vector machine is programmed into every watch, where it runs continuously, looking for events that might be a dangerous seizure.
When it detects such an event, it communicates with another piece of software (perhaps on a paired smartphone) that issues alerts and makes calls and text messages. In addition, the software logs the data and event timing, so it can be reviewed later by a medical professional.
The device was cleared by the FDA (U.S. Food and Drug Administration) in January 2018, and has already been credited with helping save lives.
Image: Empatica
Leaders
Companies represent 26 out of the top 30 artificial intelligence (AI) patent applicants. Most of these are multinational firms active in consumer electronics, telecommunications or software.
Number of companies that feature in the top 30 AI patent applicants
Acquisitions are relatively common, with 7 out of the top 20 companies having acquired AI firms. Among them, Alphabet has acquired the largest number (18) of AI companies.
Most AI-related patent filings are made at the patent offices in the United States of America (152,981 filings) and China (137,010). Both countries combine a high number of innovations in AI and potential as a market for AI-related inventions. Filings under WIPO's Patent Cooperation Treaty (PCT System) represent 20% (67,662) of the total number of AI-related filings.
Top 500 AI patent
applicants
Companies make up
333 of the top 500 AI
patent applicants
Universities and public
research organizations
make up 167 of the top 500
109 of these 333 companies
are US-based
110 of these 167 universities and
public research organizations
(PROs) are based in China
The leading company
is IBM
The leading university/public
research organization is
the Chinese Academy
of Sciences (CAS)
Case study
Smarter farming using a Microsoft AI system
Microsoft’s FarmBeats project combines low-cost soil sensors, aerial imaging, and vision and machine learning algorithms to complement a farmer's intuition, helping increase productivity and reduce costs.
A farmer places a small number of data-transmitting sensors in the ground and then attaches a smart phone to either a drone or a helium balloon to create an aerial map of their farm.
The data generated is a game changer. A farmer can decrease water use for irrigation by about 30% and use 44% less lime to control soil acidity. Information on soil temperature and moisture levels can help farmer better time seed planting, resulting in a more productive harvest. The aerial map can also be used to help predict flood patterns.
While most farm data systems require expensive transmitters and established infrastructure to transmit data, FarmBeats relies on a workaround that bypasses the need for internet or even mains power: it uses TV white space and is powered by solar energy. White spaces are unused TV broadcast spectrum – the “snow” you’ll sometimes see while flipping through channels. These gaps in spectrum are plentiful in the remote areas where most farms are located, so data can be sent over them the same way that data gets transmitted via broadband.
The FarmBeats team believes that the AI-enabled technology will give farmers around the world the tools they need to significantly increase global food production in a context of limited arable land and water.
Image: Getty Images/baranozdemir; Case: Microsoft - FarmBeats, AI and the IoT for Agriculture
The impact
Leaps in computational power are extending the AI revolution to beyond big multinationals and impacting businesses and academic organizations the world over. Ultimately, almost every activity and sector will benefit from the use of AI.
The impact of AI can already be seen in applications that people use every day, in transport, health, finance, law and other areas. As with every new technology, AI offers advantages to early adopters. However, it also poses many challenges.
We interviewed AI experts from around the world to complement our patent data research and provide a human dimension to the factual side of the analysis. The issues touched upon include:
Ownership and rights
- What ownership and regulatory models should apply to data, essential to the development of AI?
- Is a creation or invention generated by AI eligible for intellectual property protection and, if so, who owns those rights?
"Science contributes to the development of AI technology, but the role played by the private sector increases over time as it obtains IP rights over inventions."
Data privacy and ethics
- Free access to data can provide great, personalized experiences, but how open is too open?
- How can we ensure that citizens retain control over their personal information?
"Never before has our species been equipped to monitor and sift through human behaviors, physiology and biology on such a grand scale."
Security
- What is the best way to protect critical interconnected systems such as intelligent transportation?
- How can an increasing volume of data be kept safe?
"AI is also a bunch of numbers that are undecipherable, multiplied together in ways inexplicable to humans. If someone hacked in and changed a thousand numbers, how would people know?"
Superintelligence
- What happens if intelligent machines exceed the capabilities of the human brain?
- Is a move from narrow AI (using AI for individual tasks) to superintelligence desirable?
"Machine superintelligence would be a watershed moment: it would be the most important invention ever."
Employment
- Which jobs will AI change and how?
- How can intelligent machines fit into the world of work?
"AI will help machines perform dangerous tasks once done by humans. [...] It will also augment current jobs so that humans can be more accurate and efficient, and keep us safer."
The Technology Trends: Artificial Intelligence report contains more detail on everything presented in this feature piece.
"AI is the new electricity. I can hardly imagine an industry which is not going to be transformed by AI."
Andrew NG, CEO, Landing AI and deeplearning.ai
Credits
This feature was produced on the basis of the report "WIPO Technology Trends: Artificial Intelligence". Credits to the report team can be found within the report itself.
- Feature concept and content: Steven Kelly and Maria De Icaza (WIPO)
- Feature video and data visualizations: Edwin Hassink (WIPO)
Opening/closing video credits: Connected earth: riccardokolp / Vetta / Getty Images; Digital earth: mrcmrc / Getty Images; VR girl: SolStock / Getty Images; Agriculture drone: motionxcom / Getty Images; Fragrance: sergeysaraev / Getty Images; Smart watch: Kustvideo / Getty Images; Vector wave: KinoMasterskaya/Getty Images; Light green dotted backgrounds to infographics inspired by: Daria Dombrovskaya / iStock / Getty Images
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