Autonomous driving has had a meteoric rise, becoming one of the primary uses of AI aside from its other computational and language processing functions. Alongside other patented analytical systems including hardware such as exterior cameras and motion sensors, AI enables autonomously driven vehicles to navigate a wide variety of terrain.
While AI has had a significant role in advancing the technology, the patent history of self-driving cars extends back to the 1970s. The University of Tsubaka created the first self-driving car in 1977, and it was able to achieve speeds of up to 20 miles an hour using analog cameras alone. Following that, the first patent for a system enabling a car to guide itself was filed in 1986 by Ernst Dickmanns in Germany, a professor at Bundeswehr University Munich and a former postdoctoral scholar of control engineering at Princeton University. In patent no. DE3619824A1, Ernst described a system “image sensor which is attached to the vehicle and whose image data are processed by an information-processing device”, a system which he tested by incorporating them into two mercedes 500 SEL’s that ended up hitting 130 km/h on Autoroute 1 of France.
With the early advent of this technology that manifested itself in non-patent and patent literature, its rise was aided by effective protection and use of prior art. Upon such systems, current AI-powered systems were created to further interpret and use the complex data provided by the monitoring system described in the patents for the technology’s earliest iterations. It is precisely this data that companies such as IBM seek to use even more effectively going forward.
The connectivity that is required to enable AI-powered self-driving systems can be further harnessed to provide users of self-driving cars (via rideshare or vehicle ownership) more control over their experience and further added benefits. IBM’s recent patents detail the use of “available resources”, described as non-critical resources and data generated by the systems used to power autonomous vehicles, to enhance rider experiences. For example, the use of connectivity required to power software and hardware in the car can be harnessed to provide users internet and database connection that can be used for anything from working to gaming or streaming entertainment. The extent of use of such resources would be determined by excess capacity in the car systems, to prevent overusage that could infringe on the required capacity for proper operation of the car’s self-driving systems.
Such patents enable companies endeavoring to popularize autonomous vehicles to provide additional value to consumers that are often skeptical of these systems. Additionally, due to the capital intensive nature of developing such systems in the first place, using all available data and capacity enables companies to maximize the profit from their systems. To further maximize this profit and efficiently utilize capital, the IP strategy to protect such systems must also be considered. While algorithms themselves cannot be patented, the relevant hardware and steps of software algorithms can be patented to maximize the market power of industry leaders in the AI and automotive sectors. Additionally, such patents also provide a quality standard for future development, which is important to ensure that future technology developments meet at least the same standards as current technology. For efficient IP management, AI-powered IP portfolio management software such as patlytics.ai enable companies to manage IP portfolios in a capital efficient manner while comparing them to the most updated patent databases. This ensures not only cost-efficient IP management, but continuously improved IP portfolios due to the ability to compare them with USPTO and WIPO databases. With ease of infringement and invalidity detection, companies can focus their attention and capital on developing the future of automotive technology.
