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Identifying and Assessing the Patent Landscape for L4/L5 Autonomous Vehicle Technologies

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Identifying and Assessing the Patent Landscape for L4/L5 Autonomous Vehicle Technologies Identifying and Assessing the Patent Landscape for L4/L5 Autonomous Vehicle Technologies Nermin Sejdic Eldin Abaz Djavid Sehic Tech+IP Advisory, LLC Tech+IP Advisory, LLC Tech+IP Advisory, LLC Sarajevo, Bosnia and Herzegovina Sarajevo, Bosnia and Herzegovina Sarajevo, Bosnia and Herzegovina [email protected] [email protected] [email protected] Chuck Fish Ed Fish Tech+IP Advisory, LLC Tech+IP Advisory, LLC Furlong, PA, USA Washington, DC, USA [email protected] [email protected] Abstract— Patent landscape information is often used to accelerate on a global scale and has been fueled by literally chart technical developments in important technology domains billions in R&D from a wide variety of companies. And the and to assess relative strengths and weaknesses among prize is clear: the worldwide autonomous vehicle (“AV”) competitors developing products using such technologies. market is estimated to hit $96 billion by 2025 and grow to Autonomous driving is one such area. This paper describes a nearly $3.5 trillion by 2050. At the same time, analysts methodology for categorizing patents that map to industry- standard definitions of Level 4 and Level 5 autonomous vehicles estimate that up to 15% of all new vehicles sold in 2030 will and provides a “first cut” methodology for assessing relative be fully self-driving and by 2035 as many as 23 million AVs patent importance using a modified patent citation analysis that will be on US roadways [3]. eliminates potential self-citation bias and aggregates at the patent family and geographic levels to better map to Intellectual From a technology perspective, self-driving vehicles Property (“IP”) industry practices and reflect the global depend on a wide array of sensors, actuators, cameras, market. To test the methodology and simplify the exercise, our efficient processors, a host of AI and machine learning search was focused on the 59 manufacturers who had permits to frameworks, and many other technologies. For example, test autonomous vehicles on California, USA streets as of video cameras distinguish traffic lights, read road signs, track December 4, 2020 [1]. other vehicles, and look for people and objects nearby, and Keywords— Autonomous vehicle, intellectual property, patent lidar sensors bounce pulses of light off the vehicle’s landscape, patent analysis, autonomous driving ecosystem environment to measure separations, distinguish street edges, and distinguish highway markings [2]. I. INTRODUCTION A vehicle capable of detecting its environment and As the autonomous vehicle market has matured, efforts operating without a human behind the wheel needed for some have been undertaken to standardize terminology concerning aspect of its operation is commonly referred to as a “self- the capabilities that define the levels of autonomous driving driving vehicle” [2]. Articles about advances in autonomous functions. A widely adopted lexicon was developed by the driving appear in technological and business journals nearly Society of Automotive Engineers (SAE) and several other every day, and companies like Tesla have seen the value of groups, including the US Department of Transportation. their enterprises grow rapidly as developments occur and are Outlined in Fig. 1., the framework is a 1-5 system where reported. The pace of development in this field continues to Level 1 is a completely manual vehicle and Level 5 Fig. 1. Five Levels of Autonomous Driving [3] 1 Copyright (C) 2021 Tech+IP Advisory, LLC. All rights reserved. is a completely independent vehicle with no limitations on search to the 59 manufacturers who had permits to test where it can drive. In this framework, Level 4 vehicles are autonomous vehicles on California streets as of December 4, recognized to be self-driving, though they incorporate a 2020 [1]. cockpit such that a driver could still be in control. By contrast, Level 5 connotes “Full Automation,” that is the vehicle can While this paper provides what we believe to be useful fully operate without human intervention and the vehicle insights into how patents will become increasingly relevant performs all driving duties [4]. in the autonomous vehicle world, just as they did with 4G and 5G technologies in the communications world, we have made An outgrowth of the billions of dollars spent on the R&D, the effort to lay out our search and other patent landscaping innovations, and technologies that come together to create an methods so others can reproduce them and contribute to their autonomous driving experience is a vast amount of advancement. As further explained below, we have intellectual property (IP) that is protectable under the laws of endeavored to normalize the data in the currency of the patent nearly every country. While it is beyond the scope of this markets – so-called “patent families” -- and to present a paper to explain the types, purposes, and requirements of IP distinctly global view because the R&D, productization, use, protection, as such information is readily available, for these and patenting efforts are also global. purposes, we focus on patents: that type of IP where in exchange for disclosing the invention to the public and II. METHODS meeting legal requirements for issuance of a patent, the inventor (or her assignee) is entitled to preclude others from A. Patent Landscape Methodology making, using, and selling the patented invention for a period Our landscape was created through a series of iterative of 20 years. Again, there are a host of details and exceptions searches and analyses using the patent database and tools in the complex global patent system, but many companies provided by the Innography software platform owned, and around the world seek and obtain patent protection for their widely licensed to the industry, by CPA Global. As inventions due to the strategic value patents deliver to their background, patent documents comprise text in which, owners and other stakeholders. among other things, inventors are required to fully describe their invention. Using a common approach, patents have At the same time, many companies monitor and assess sections that include a title, an abstract, a “description of the issued and pending patents to gain competitive intelligence invention”, and claims (which delimit available legal and to track and assess strategic opportunities and risks in protection). We used all the above sections in our searches business functions such as product development, sales, with regards to several factors including a frequency of business development, and legal. Google has indexed and keyword iteration and keyword weighting. made searchable many US and other patents, and the patent offices of many jurisdictions have websites where patents and In addition, we performed searches of technical materials pending patent applications can be searched [5]. and market literature to identify and extract potentially relevant technological “keywords” and some relevant patents As patents have become very important in many business (which we use as “seeds” for searches). Patent classification and technology sectors, companies and others create codes (the schemes used by patent offices to classify patent “landscapes” of patents and patented technologies in a applications based on the subject matter and route them to manner that closely mirrors the way venture capitalists and patent examiners with experience in those specified fields – strategic / management consulting / research organizations think the Dewey Decimal System of patent offices) were also construct corporate and product landscapes and ecosystems. collected, analyzed, and further used to filter candidate In the patent world, this kind of landscape often focuses on a keywords to obtain relevant data. In this iterative process, the particular technology or set of technologies [6], [7]. goal was to search, analyze, and re-search in an attempt to weed out search terms and seeds that yield large numbers of In our experience, however, what is often missing in irrelevant patents (“false positives”) while not removing published patent reports is a bridge between the relevant patents (“false negatives”). market/strategic analyst landscapes and the technology/patent landscapes; in many cases a completely Tables I-III. lay out the iterative use of keywords, both different lexicon is used. Without this bridge, a big portion of sematic and classification, in our study. The asterisk (*) after data is missing from what should otherwise be a better and each word in the keyword query represents an operator that more useful analysis. captures different combinations of characters after the actual term, for example, self-driv* includes self-driving, self-drive, 2020 was not only a transformational year for AV etc. technology but also a major change for the landscape of AV Our search strategy was implemented as follows: patents. Thus, we decided to look at using the SAE Framework for Transportation as a starting point to 1. Keywords linked to the presence of autonomous Level 4 categorize patented inventions and the underlying and 5 technologies were searched in the abstract, title, technological innovations that make up the Autonomous and claim sections of the patent documents by keyword Vehicle landscape. We focused on Level 4 and Level 5 terms such as: autonomous vehicle, self-driving, because most of the attention, much of the R&D expense, driverless, fully automated vehicle, highly automated and, quite simply, the future, resides here. To simplify the vehicle, self-parking vehicle, etc. Additionally, process and for feasibility reasons, we initially limited our keywords from abstract, title, and claim need to be 2 Copyright (C) 2021 Tech+IP Advisory, LLC. All rights reserved. described further in detail with another set of keywords “non-autonom*” OR “non autonom*” OR to collect patents related to the specific subject matter “driver assist*” OR “partial automat*”) (Level 4 and 5 patents). Some of these keywords are NEAR/5 ("drive" OR "driving" OR =vehicle machine learning, deep learning prediction, object OR "vehicle" OR “automobil*” OR “bus” detection, trajectory plan, etc. OR “van” OR “truck”))) (@* inno_utility_patent) TABLE I. KEYWORD SEARCH 3.
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