The US Patent and Trademark Office (USPTO) recently issued a report on the potential impact of artificial intelligence (AI) on the world in general and on the patent system specifically.
The USPTO even used AI to compile its report. It used a machine learning algorithm to determine the volume, nature, and evolution of AI and its component technologies as represented in US patents from 1976 to 2018.
Let’s start with a few facts about AI and patents cited in the Report:
The top US AI patent owners are, not surprisingly, the major US technology companies. IBM is number one, with almost 47,000 patents awarded from 1976 to 2018. Number two is Microsoft, with 22,000. Other top-ten AI patent owners include Google, Intel, AT&T, Oracle, Amazon, and Apple.
As the Report notes, the U.S. National Institute of Standards and Technology (NIST) defines AI technologies and systems to comprise software and/or hardware that can learn to solve complex problems, make predictions or undertake tasks that require human-like sensing (such as vision, speech, and touch), perception, cognition, planning, learning, communication, or physical action.
For patent applications, the USPTO defines AI as including one of eight component technologies:
Knowledge processing involves representing and deriving facts about the world. Knowledge processing is an essential technique for enabling autonomous robots to do the right thing to the right object in the right way. Using knowledge processing, the robots can achieve more flexible and general behavior and better performance.
The USPTO gives the example of US Patent No. 7,685,082, issued to Intuit, which “describes an algorithm that uses a pre-defined “knowledge base” to automatically detect accounting errors. One application is real-time error detection for online income tax preparation.”
The use of AI technology for speech recognition includes applications like Apple’s Siri (US Patent No. 10,043,516) and Amazon’s Alexa.
AI requires a lot of computing power. The AI hardware category includes computers and components designed to meet the special requirements of AI.
For example, IBM’s Watson supercomputer, which defeated two human champions on Jeopardy, is covered by a number of patents for both its hardware and software.
Evolutionary computation mimics natural selection in the world of biology. An AI-based process can test a large number of different models or hypotheses quickly and then determine which one performs best. That knowledge can be “fed back” into the system, allowing it to “mutate” and become even better at doing a task. U.S. Patent No. 7,657,494, issued to the oil and gas company Chevron USA Inc., describes “an evolutionary approach to predicting available petroleum reserves. The invention’s computerized method evaluates a large number of competing models and selects the model with the highest performance by using a genetically inspired algorithm that “mutates” through different options.”
Just like some AI applications recognize spoken words, natural language processing can understand written language.
U.S. Patent No. 8,930,178, issued to the Cincinnati Children’s Hospital Medical Center, which uses text to build an ontology by simulating various human memory approaches. The resulting ontology can be used to increase the efficiency of various healthcare administrative tasks such as assigning billing codes to clinical records.
And just as AI can recognize and interpret spoken and written words, it can also identify and react in response to images.
For example, AI can be used to detect abnormalities in medical scans. U.S. Patent No. 10,055,843, issued to the Mayo Foundation for Medical Education and Research and to Arizona State University, automates the detection of abnormalities in images taken during colonoscopies. AI in self-driving vehicles can identify and respond to road hazards, and AI face-recognition technology can be used to identify suspects in security camera images.
AI can be used detect problems via visual, sound, or other conditions. For example, U.S. Patent No. 10,031,490, issued to Fisher-Rosemount Systems Inc., may help to reduce costly workflow analyses when abnormal conditions occur in processing plants.
Clearly, there can be overlaps in all of these areas. For example, U.S. Patent No. 7,392,230, titled “Physical neural network liquid state machine utilizing nanotechnology,” is classified by our methodology as both machine learning and AI hardware component technologies.
The above Figure illustrates the long-term trends from 1976 through 2018 in the volume of public AI patent applications and their share among all public patent applications. Because of changes made by the American Inventors Protection Act (AIPA) at the end of 1999 and its implementation period (the gray area in the Figure), the trends are most informative after 2002.
Over that 16-year period, annual AI patent applications increased by more than 100%, rising from 30,000 to more than 60,000. Although all patent applications at the USPTO increased during that time, the share of AI applications, which adjusts for this overall trend, also shows notable growth—from 9% in 2002 to nearly 16% by 2018.
The Figure below shows the number of public AI patent applications in each component technology from 1990 to 2018.
The largest are planning/control and knowledge processing. These two components include inventions that control systems, develop plans, and process information. They are the most general AI component technologies and patents in other component technologies such as machine learning often include an element of planning/control or knowledge processing.
Since 2012, patent applications in machine learning and computer vision show pronounced increases. Both of these AI technologies were central to the 2012 success of AlexNet, which was part of the 2010 ImageNet Large Scale Visual Recognition Challenge. AlexNet was a watershed achievement that changed the technological trajectories for image recognition and machine learning, particularly for deep learning.
Notably, patent applications in AI hardware have increased along with those in computer vision. The close association of applications in these two component technologies probably reflects the interplay between advances in image recognition and the need for computational power and performance.
Specialized hardware includes accelerators for computer processors and specialized memory. Other applications of AI, such as autonomous vehicles, also involve specialized hardware.
Whether AI turns out to be as revolutionary as electricity or the semiconductor depends, in part, on the ability of innovators and firms to successfully incorporate AI inventions into existing and new products, processes, and services. The consensus so far has been that AI has the potential to fundamentally change how people perceive the world around them and live their daily lives.