Our main conclusions:
- Artificial intelligence (AI) patents in traffic and transportation engineering have increased in recent years and have been accelerating since 2014.
- More than 5,000 new patent families were published in 2021, compared to around 2,000 in 2016.
- Interestingly, while one would expect transportation and traffic engineering companies (e.g. Siemens Mobility) to feature in the top 20 assignee list, the list is mostly made up of automotive companies and technological. It will be interesting to see how these companies exercise their patent rights in the years to come, with the potential for extensive licensing of their patented technology and collaboration with infrastructure manufacturers.
Artificial intelligence (AI) and machine learning have been gaining media attention lately. Various commentators have raised questions such as if it is a fad, if it can be trusted, and if it will eventually take over.
For us, as patent attorneys, we like to look at what patent data can tell us. This area in particular is interesting because innovation in traffic and transport engineering appears to be essential for the successful deployment of autonomous vehicles and managing the ever-increasing number of vehicles on the roads. This can provide insight into the motivation of automotive companies that innovate in this area. However, the data does not tell us why infrastructure manufacturers are not using AI to solve existing problems.
Below, we dig deeper into the patent data.
Patent Application Examples
The data used to compile the graphs in this article includes a wide range of innovations. An example patent application from the dataset relates to “a method for inferring the state of a traffic light when the traffic light is blocked by another object, for example a truck.” Another patent application example from the dataset is for “a drone configured to monitor parking space occupancy.”
Indeed, based on our experiences of assisting clients in identifying their intellectual property, many patents relate to inventions that inventors believe (wrongly!) qualify as criteria for granting patent protection.
Figure 1 below shows the trend of patent data in the field of artificial intelligence and traffic and transport engineering from 2004 to 2021. As shown in fig. 1, there was a significant increase in patent applications starting around 2012. This trend is consistent with other charts we have produced for other application areas where AI innovation is involved.
Trends in basic AI innovations are more exponential. Here, the trends, especially family and scholarship data, are increasing but are more linear. As we will see in more detail below, a number of the top patent applicants are well-established automotive OEMs. These companies can innovate taking into account automotive regulations, and therefore their filings may be more focused on specific areas, and therefore the number of filings has not accelerated as quickly as basic innovations in AI. Additionally, many key players are innovating in electric/hybrid vehicles – their IP focus (and budget) may be aimed at protecting this technology.
(Fig.1) Global patent filings on AI traffic and transport from 2004 to 2021
Patent applications, families and granted patent numbers are included in Figure 1, for the respective year of publication. Figure 1 is not cumulative.
Most patent offices consider AI innovations applied to traffic and transport engineering to be patentable because they have clear links to the real world through sensor data or control of physical equipment, for example traffic lights. This probably encouraged candidates to submit applications in this sector.
Subsidy data largely keeps pace with family data. This demonstrates that patent offices consider AI innovations in this application area to be patentable. It’s a great anecdote for any misconceptions about whether or not it’s possible to protect AI innovations, because at their core, they’re just algorithms. Here it is clear that innovations in AI are tuned, at least in this area of application.
It is positive to see that the data on grants also shows that patent examiners are following the increase in the number of filings. Therefore, it may still be possible to expedite prosecutions through the usual channels, for example PACE in Europe.
Given the increase in the number of filings, if you are breaking new ground in this area (and others relating to the application of AI to autonomous vehicles, for example), we recommend that you file your application as soon as possible in order to improve your chances of obtaining broad patent protection. The “pool” of prior art is increasing, which has the effect of forcing applicants to narrow down their patent claims, sometimes considerably.
In addition, the effects of the COVID-19 pandemic are not yet clear from patent data, due to the 18-month lag between filing and publication of patent applications. We will have to wait until next year and beyond to provide a clear assessment of the impact of the pandemic on filing numbers.
Distribution of jurisdictions
As part of the patent process, applicants are faced with a decision as to where to file. Patents are territorial rights, that is, a patent granted in the United States and applicable only in the United States. To protect the same invention in China, a granted Chinese patent would be required. The number of countries is proportional to the costs. Therefore, there is a balance to be struck, and it is a decision that any claimant must make. In fig. 2 below, we take a look at where applicants choose to file.
Data for 2019 includes the number of applications published in a country. The year 2019 was used to take account of accounting delays due to applicants using the PCT procedure.
(Fig.2) Global AI Traffic and Transport Patent Filings by Jurisdiction
The United States has had the highest number of patent publications for many years, although publications in China have seen a large proportional increase from 2016 to 2019. It will be interesting to see if this trend continues and if China will overtake the United States in the future. Publications at the European Patent Office (EPO) have been fairly constant between 2017 and 2019, ranking fifth in terms of number of publications.
Top 20 candidates for 2021
Figure 3 below shows the top 20 assignees for calendar year 2021.
(Fig.3) Top 20 AI Traffic and Transport Patent Assignees for 2021
To enter the top 20 of global applicants, an applicant must file at least 40 patent families in a calendar year. Due to the growing number each year, applicants filing in 2022 are likely to need more.
Many of the leadership positions in this field are held by well-known OEMs, such as Toyota, Honda and Nissan. This demonstrates that companies generally considered car manufacturers are themselves at the forefront of innovation in the fields of traffic and transport engineering, filing more patent applications than companies dedicated to this sector such as SiemensMobility.
Technology-focused companies such as Apollo Intelligent Driving, Baidu and Stradvision have high grant-to-family ratios in this space, which would indicate that patent offices view their applications favorably. These companies may also strategically file higher quality patent applications, preferring to obtain protection for the essential aspects of their innovations that they can license in the future.
Examples of patented technologies in this field include communication between vehicles, computer vision and image recognition, and intelligent management of traffic control systems such as traffic light networks.