AI Patent Trends In The Automobile Sector

January 3, 2023

The last decade has seen a significantly upward patent filing trend in AI in automobiles. With AI permeating every industry, its advent in the automobile sector comes as no surprise. Prominent automakers are already utilizing the prowess of AI in manufacturing, transportation, as well as service. Both the subsets of AI, i.e., Machine Learning (ML) and Deep Learning (DL) have made a significant impact when it comes to patent filing. As every car manufacturer clamors towards AI to ensure their cars are future-ready, the sector is only going to witness more technological innovations.

AI Use Cases in Automotives

AI finds application in the entire value chain of the automotive segment. Below we explore its prospects in each:

Manufacturing
The manufacturing circuit consists of four stages namely, Design, Supply Chain, Production, and Post Production. This is the segment that AI has touched most deeply due to its wide applicability here. AI robots are already being used by companies like Hyundai and OTTO Motors to work alongside humans on the assembly line. Hyundai has successfully tested robotic exoskeletons or wearable robots that reduce human fatigue by lowering the impact on the lower body. This may result in increased productivity. Similarly, OTTO launched an intelligent material transport vehicle for their plant, based on AI. This self-driven vehicle can autonomously navigate through the manufacturing unit.
AI also finds use in the automotive supply chain. With complex supply chains spread across countries and continents, ensuring smooth operation is vital to productivity. Implementing AI in the supply chain helps regulate logistics, tracking, and management easily.
Transportation
Autonomously driven passenger vehicles are the ultimate goal for AI applications in this segment. Tesla currently leads the pack with its level 2 autonomous cars with its ‘Autopilot Driver Assistance System’ in the Model S car. Besides, the above there are several other use cases in transportation. AI-powered software for driver risk assessment, cloud computing, and driver monitoring too are being developed to offer more safety in cars and improve drive quality. The driver monitoring systems, for example, will be able to communicate and create maps, raise an alarm if the driver is falling asleep, detect tiredness, forecast weather, and offer other useful information that will assist the driver.
Services
The post-purchase services can also benefit immensely from the use of AI. The current AI software can already predict things like engine & battery performance to pre-warn the driver. This kind of predictive maintenance utilizes historical data like manuals, IOT data, service orders, etc., to make an assessment independently. In-car assistance too is becoming more mainstream as AI becomes accessible and cost-friendly.
AI is also being used in automotive insurance to digitalize and expedite the insurance process. Allowing customers to click and upload damaged vehicle photos using AI apps, allows makes the process smoother and quicker than traditional insurance.

Machine Learning Trends & Patents

Revenue generation from available resources is among the prime objectives of a company, and patent monetization plays a pivotal role in achieving this. It can open up avenues for an income source through royalties or a one-time sale. There are different types of patent monetization available to a company. Firms specializing in patent monetization can offer strategic guidance to companies to take the approach best suited for their business model. Machine Learning (ML) utilizes data and algorithms to learn and improve without being programmed to do so. This type of software uses historical data to make predictive outcomes. Of the 47000 ML patents filed in 2019, 3000 were related to the automobile industry. This was a three-fold increase compared to the previous year. The maximum number of patents were filed by China, followed by the European Union and the USA.

Deep Learning Trends & Patents

Deep learning is based on layers of neural networks that are designed to simulate the behavior of the human brain. It is capable of learning from unlabeled or unstructured data. DL too saw a sharp rise- almost 300%- in a patent filing in the year 2019 compared to 2018. 16,000 Deep Learning–based patent applications were filed in 2019, out of which 1800 were related to the automobile sector. Here too, China leads the way, with the US following in second place.

Conclusion

The use of AI in the automotive sector is nascent and the door is wide open for new tech to further revolutionize the developments. Industrial robots are likely to boost production in assembly lines while global sales of autonomous vehicles are predicted to rise from 33 million in 2019 to 80 million by 2032. With contributions from both automobile giants and start-ups and AI investment on the rise, the sector is likely to see new benchmarks in the immediate future.

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