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IP Analysis & Open Innovation

Reshaping the automotive industry

2016

Table of Contents

Open innovation affirms importance of IP 3

IP’s role in defining the competitive landscape 5

Discover areas of competition and partnership 9

Ability to execute 11

The wider search for partners 13

Connected cars and smart cities 16

Effective IP research to generate optimum outcomes 17

New technologies give rise to different philosophies In industries where the stakes are high and innovation is rapidly accelerating, information from IP analysis becomes absolutely vital for determining the right areas in which to focus R&D efforts or mitigate risk. No more so, than in the automotive industry, where multiple technologies are combining in ways that will fundamentally reshape the relationship between humans and cars. In her keynote speech at the CES event in Las Vegas earlier this year, the CEO of General Motors, Mary Barra, predicted that the automotive industry will change more in the next five to ten years than it has done in the last fifty. 1 But while everyone in the automotive industry agrees that there will be major change, the exact nature of how the change will look is yet to be determined. On the one hand, you have companies such as GM, who state: “We believe the convergence of ridesharing and autonomous vehicles offers great opportunities to improve safety, reduce congestion and enhance transportation freedom for everyone, including the elderly and disabled.”2 It’s a view shared by Google, which adds: “Deaths from traffic accidents—over 1.2 million worldwide every year—could be reduced dramatically, especially since 94% of accidents in the U.S. involve human error.”3 Gil Pratt, CEO at Toyota, disagrees. He points out, “The vast majority of mainstream vehicles adopting autonomous driving features will be controlled by advanced driver assistance systems (ADAS) or ‘guardian angels’ that learn over time.” He refers to the Google concept as like being driven by artificially intelligent chauffeurs, commenting: “If you love to drive, the idea of a chauffeur is not fun.” 4

© 2016 All Rights Reserved 3

1 https://www.weforum.org/agenda/2016/01/the-next-revolution-in-the-car-industry/ 2 https://www.weforum.org/agenda/2016/01/the-next-revolution-in-the-car-industry/ 3 https://www.google.com/selfdrivingcar/ 4 http://www.computerworld.com/article/3079044/car-tech/ai-guardian-angel-vehicles-will-dominate-auto-industry-says-toyota-exec.html

Open innovation affirms importance of IP

Top Vehicle

Manufacturers

Toyota 10.4 million

Volkswagen 9.9 million

GM 9.6 million

Hyundai 8.0 million

Ford 6.0 million

World vehicle production, 2014,

OICA Survey

Using IP to speed up innovation and adoption of specific technologies The debate about where to draw the line on the level of automation needed in driving is a discussion that automotive R&D teams all around the world will be very familiar with. Part of the uncertainty arises because a full automation scenario alters the role of driving in our lives. It is not clear what consumers’ preferences will eventually be, and governments, for their part, are still deciding how to legislate. For R&D teams, it means that numerous possible scenarios must be factored in to product roadmap planning. It has created an environment where open innovation has been pursued by many in order to speed up the adoption of new technologies – Ford and Tesla, for example, have offered their patents for licensing in the hope of increasing the adoption of the electric vehicle (EV) and improving the supporting infrastructure. Tesla was almost acquired by Google in 2013 and some market commentators say that moves from Ford, Tesla and Toyota towards open innovation and speeding up the process of R&D is a response to new threats from Google - and also perhaps from Apple, who have the ability to completely disrupt the market and the business model.5 Using IP as the fly-on-the-wall With more open innovation and many potential alliances and licensing options, in combination with new technologies rapidly emerging, the automotive industry is just one area that shows it is becoming more important than ever to harvest the information contained in patents in order to determine the best competitive strategy and most efficient R&D direction.


5 http://www.ipwatchdog.com/2015/06/09/open-innovation-electric-vehicle-market/id=58374/

Open innovation affirms importance of IP

Definitions for emerging technologies It’s important to follow a search methodology in order to extract the best results from patent and IP information. Often, emerging technologies are only loosely defined and various manufacturers will refer to these technologies (or alternative competing technologies) with a variety of terms, some of which are only ever really used in patents. Finding the patent related terms This means it is important to consider the wider technology space as the first step, and then use that broad search to identify the key players and the technologies that they are focused on, before narrowing the search again to focus on the most direct competitors. This combination of inter-related broad and narrow searching will provide the best results for determining the overall competitive and R&D landscape. Example: ‘Autonomous driving’ In this example, we’ll drill down specifically on the ‘autonomous’ aspect of the competitive automotive landscape. The US Department of Transportation has issued its own definitions6 on this matter, which range from Level 1 to Level 4 (see right).


6 http://www.nhtsa.gov/About+NHTSA/Press+Releases/U.S.+Department+of+Transportation+Releases+Policy+on+Automated+Vehicle+Development

IP’s role in defining the competitive landscape

Levels of Automation

US Dept. of Transport

1 Function-specific Automation One or more specific control functions are automated, e.g. electronic stability control or pre-charged brakes.

Combined Function Automation Automation of at least two primary control functions designed to work in unison to relieve the driver of control of those functions. E.g. adaptive cruise control in combination with lane centering.

Limited Self Drive Automation Driver can cede full control of all safety-critical functions under certain traffic or environmental conditions

Full Self-Driving Automation Vehicle is designed to perform all safety-critical driving functions and monitor roadway conditions for an entire defined trip.

2

3

4

So let’s start with ‘autonomous vehicle,’ a term which appeared in 51% more patents in 2015 compared to 2014. The main companies filing using this term include Google, in first place, followed by Ford, Boeing, John Deere and BAE Systems.


7 https://www.washingtonpost.com/news/the-switch/wp/2015/06/22/google-didnt-lead-the-self-driving-vehicle-revolution-john-deere-did/


With this general term, the only automotive manufacturers to appear in the top six are Ford and GM. Other industry areas that are pursuing technologies in this market include aviation (Boeing and BAE Systems) and agricultural vehicles (John Deere). These are important from an adjacent applications point of view. One farmer that the Washington Post interviewed for a story covering Google’s self-driving car revolution explains: “We kind of laugh when we see news stories about self-driving cars, because we've had that for years." As the article explains, John Deere is the largest operator of autonomous vehicles, selling models equipped with auto-steering and self-guidance in more than 100 countries.7 While the broad term of ‘autonomous vehicle’ has enabled us to find organizations with a close interest in this general technological space, some of the large car manufacturers are missing. Clearly, they have defined the technology with different terminology. The information in patents already found will now help us to locate these alternative terms that other manufacturers might use to refer to the same or similar technology in their IP.

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2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Google

Ford

Boeing

John Deere

BAE Systems

General Motors

Patents using term ‘autonomous vehicle’

Google ramps up significantly in 2014, prompting response from Ford in 2015

Source: PatSnap Insights

Finding alternative terms As we can see, for example, from this circle chart, which displays segments of common keywords grouped into a 2-tier hierarchy, a frequent term used in conjunction with patents that use the phrase ‘autonomous vehicle’ is ‘autonomous driving.’ This can now be used to modify the focus of the search. Using this phrase, we find that GM, Ford, Volvo and Audi (Volkswagen) are all among the top six; plus Google and South Korean research institution, ETRI.



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2010 2011 2012 2013 2014 2015

General Motors

Google

Ford

Volvo

ERTI

Audi (VW)

Patents using term ‘autonomous driving’


Using keyword analysis to find technical terms A next step is to explore the top keywords that appear within these newly uncovered patents, through which it is possible to determine whether there are any other significant terms used in patents that relate to our subject:



Driver Assistance System Specifically here, ‘Driver Assistance System, 'or ‘Driving Assistance System’ and other variants, is another match for aspects of vehicle-related autonomous technology. In this space, we find Toyota at the forefront, with Nissan, Denso, Robert Bosch and Hyundai all in the mix. So, bearing in mind that Google and GM have expressed a different viewpoint of the future in terms of the approach to autonomous driving, we see differences in the exact technology focus in patenting activity as well – with car giants Toyota and Hyundai using different language in their patents. It does not mean that there is no overlap across these technology areas, or that the solution is necessarily completely different, but it does suggest that strengths and weaknesses could reside in different places. Exactly where these differences lie will require further analysis of IP information.


Compare IP Footprints With just the terms ‘autonomous vehicle,’ ‘autonomous driving’ and ‘driver assistance systems’ alone, we will not have captured every patent possible relating to this technology. Nevertheless for this illustration, we have quickly found the terms that uncover top market players and will allow us to compare now the technological approaches. We could use all the actors we have found in this space for our analysis, but let’s first of all pick out Google, GM, Ford, Toyota and John Deere.


Discover areas of competition and partnership

Here we can see that, from an IP perspective, each of organization has a different strength in different areas. And while Toyota is the leader in terms of total volume of patents, it’s evident that all the traditional automotive firms have their largest footprint in ‘conjoint control of vehicle sub-units,’ which is one of the areas that includes patents covering aspects such as automatic manoeuvring for parking and taking automatic action to avoid collision.8

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Overlaps and differences in expertise

8 http://www.cooperativepatentclassification.org/cpc/scheme/B/scheme-B60W.pdf

Google, on the other hand, has its largest footprint in ‘Systems for controlling or regulating non-electric variables,’ which deals with control of position,9 a key area also for John Deere. And Toyota leads in the ‘traffic control systems’ category, which has a distinct focus on the use of sensors or detectors to identify changing environmental factors that would impact driving behaviour.10 Of course, volume alone doesn’t necessarily mean that a specific organization leads in a specific sector. The company with the biggest advantage may be the one that has the most valuable patent in a specific category, or one that is the most cited by others. However, it does reveal which areas of technology are receiving most attention from a specific vendor, and can therefore be used as guidance for evaluating levels of expertise in certain technology areas. The road to collaboration The greatest advantage is to have widespread coverage of all areas. However, in an area such as the automotive industry – and especially given the range of technologies needed to make autonomous driving work – gaining knowledge in all necessary fields may not be possible or feasible. For this reason, open collaboration in automotive makes sense and a chart, such as the one above, reveals where partners with certain expertise can be found. For example, in the category G05D, namely controlling position of vehicles, an enterprise may not wish to work with Google or a direct competitor, but we can see that John Deere is an established player that has an R&D function with strong expertise here.


Discover areas of competition and partnership

9 http://www.cooperativepatentclassification.org/cpc/scheme/G/scheme-G05D.pdf 10 http://www.cooperativepatentclassification.org/cpc/scheme/G/scheme-G08G.pdf


Ability to execute

Here, we can see that Google, as a new entrant, is filing quickly and augmenting its portfolio at a higher growth rate than others. Meanwhile, General Motors and Toyota are leading in terms of diversification, as measured by the number of ‘complex’ patents, measured here by those that achieve multiple technology classifications. On the other hand, Ford scores highly on specialization, meaning that it has a high density of patents in specific areas and it leads in internationalization. Finally, John Deere leads in science-driven R&D and joint R&D.

Using indicators to empirically compare strengths

While volume of patents can be used as a proxy for expertise, factors such as most valuable patent or most citations can reveal the best partners for specific technology categories. The ability to execute certain R&D initiatives based on a range of metrics is an effective way to benchmark potential partners in a market that is moving increasingly towards an open collaboration environment.

Some of the ways in which this could be determined is to use indicators such as patent growth rate or a quality improvement metric – for instance, are a vendor’s patents being cited more? Other measurable factors could be evaluated, such as level of internationalization, diversification, range of specialisations, level of science-driven research, amount of joint R&D (as measured by co-patenting or joint creation of patents), or a score of market drive, namely how quickly can an organization turn leading edge technology into IP.

By combining such metrics together, it is possible to see a comparative chart showing the strengths and weaknesses of different companies in terms of IP strategy:

General Motors

Toyota

Google

John Deere

Ford

Source: PatSnap Insights, Radar Map

Assess whether potential partners have the right abilities

This distribution in different areas of strategy shows that there are good strategic, as well as technological reasons, for manufacturers to find complementary collaborative IP fits for technology areas. For instance, collaboration between Google and Ford would make sense from an IP strategy point of view, as Ford is strong in many areas where Google is weaker and vice versa. Indeed, prior to the Las Vegas CES event, a joint venture between the two was planned, according to a widely cited news report from Yahoo Autos.11

Although there was no announcement at CES, shortly after, The Wall Street Journal reaffirmed that a partnership was still at least under discussion. A look at the IP landscape, (below) however, shows that any agreement would certainly involve a lot of negotiation. Both Ford and Google have some of the greatest overlap of ‘autonomous driving’ related patents in the same technology category, one that is heavily populated by keywords relating to steering and navigation. This area has already been characterised by litigation between others.

It suggests that the stakes are high in this space and therefore joint venture deals are likely to be tough to negotiate, so finding the right partner and the right balance in the partnership would be crucial.


Ability to execute

Source: Autonomous Driving related terms, PatSnap Insights

11 https://www.yahoo.com/news/google-pairs-with-ford-to-1326344237400118.html

Google and Ford have most overlap in this area of the market, which overall has seen most litigation

The drive for differentiation with complementary technologies

Once the market has been evaluated from a competitive point of view, especially with regards to a range specific technology areas, the search can begin for complementary technologies, or partners with strengths in other fields, which now involves broadening the search to complete the competitive picture.

For example, it is widely understood that Apple, renowned for its secrecy, is working on a car. It is believed that Project Titan, as it is known, was given the green light by Tim Cook as far back as 2014.12

While there is no official confirmation of its existence, it is a logical conclusion. Google is not only Apple’s top rival, but the footprints of their patent portfolios are extremely similar, and furthermore both have the biggest footprint in the category ‘electric digital data processing,’ which appears as a key technology in patents relating to both ‘autonomous vehicles’ and ‘autonomous driving.’ Similarly, ‘pictorial communication,’ comes up as a key technology for ‘autonomous vehicles,’ ‘autonomous driving’ and searches related to driving assistance systems.


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12 https://www.yahoo.com/news/google-pairs-with-ford-to-1326344237400118.html

Rivals Apple and Google aim for similar patent portfolios Volume of patents by technology

The wider search for partners

Reading between the lines – clues in patents While Apple has never confirmed the existence of the car itself, their patents centre on aspects of the in-car experience, in line with its HomeKit solutions. This reflects the ‘connected car’ approach – leading a number of market observers to conclude that any car proposed by Apple will be self-driving.13 There are some further indications in Apple patents regarding this issue. For instance, in one abstract, Apple explains: “When individual persons or vehicles move through a transportation network, they are likely to be both actively and passively creating information that reflects their location and current behaviour. In this patent, we propose a system that makes complete use of this information. First, through a broad web of sensors, our system collects and stores the full range of information generated by travellers. Next, through the use of previously-stored data and active computational analysis, our system deduces the identity of individual travellers. Finally, using advanced data-mining technology, our system selects useful information and transmits it back to the individual, as well as to third-party users; in short, it forms the backbone for a variety of useful location-related end-user applications.” In the description, Apple goes on to say that the method provides “a very accurate real-time estimate of the vehicle's location.”14 Certainly not conclusive, of course, but it does show that a high level of expertise has built up within Apple that could be applied to the autonomous car solution.



13 See The Guardian report, https://www.theguardian.com/technology/2015/aug/14/apple-self-driving-car-project-titan-sooner-than-expected

14 http://share.analytics.patsnap.com/view/5C93858FE8663FBA71B1EDF12144DD5FCF1AB903D6908348CEDE8432095A1F35

Many clues about eventual strategy could be

held in specific patents



15 https://www.washingtonpost.com/news/innovations/wp/2016/04/28/why-tesla-is-the-spark-that-apple-needs/ 16 http://www.trustedreviews.com/news/apple-car-news-rumours-driverless-price-release-date-electric

Deepening or widening expertise? We also know that Apple has been poaching experts from electric car manufacturer, Tesla. And meetings took place between Tesla CEO Elon Musk and Apple’s acquisitions chief. With Apple’s IP footprint similar to Google’s and Tesla’s proficiencies in automotive combined with energy storage, an article in The Washington Post went as far as to say that it could be a marriage made in heaven.15 However, Tesla is not the only car manufacturer to have been involved in discussions with Apple. In a news article from Reuters, it was noted that several senior Apple employees had made visits to BMW facilities. The report goes on to say: “The dialogue ended without conclusion, because Apple appears to want to explore developing a passenger car on its own.”16

Tile chart of top 10 technologies within total portfolio, colour-grouped by tech

area (based on patent IPC)

Tesla

Toyota

Ford

Apple

Examining technology coverage Looking at the top 10 technologies in each company’s portfolio, Tesla’s areas of focus are a far closer match to Apple’s. Would this make it a good fit to deepen expertise?

Electricity, including distributing power and circuitry

Measuring , testing and calculation

Transportation and vehicles

Mechanical engineering, machines, gearing


17 http://www.zdnet.com/article/hyundai-to-develop-fully-autonomous-cars-by-2030/ 18 http://www.geektime.com/2016/03/28/following-cisco-samsung-aims-to-turn-tiny-daegu-into-south-koreas-first-major-smart-city/ 19 http://www.labcities.com/embedded-sensors-big-data-analytics-make-cities-run/

Connected cars and smart cities

Call for collaboration Apple’s ‘secret’ approach to innovation is certainly at odds with Tesla and others in the automotive industry. The consensus is that the industry will have to be reliant on open innovation in order to bring together multiple tech areas. This, even more so, when considering that the concept of autonomous connected cars must be developed in line with principles shaping the wider development of smart cities, as well as governmental and legislative bodies. In terms of ‘connected cars,’ it is Samsung who have patented heavily in this area. It is able to work with authorities and the national telecoms incumbent SKT to provide an infrastructure for connected devices, including vehicles. And it’s here where we find Hyundai patenting heavily, as its R&D team focuses on vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) and vehicle-to-everything (V2X) communications.17 As an article about a project in South Korea’s manufacturing city of Daegu explains, “The new communication infrastructure will incorporate cloud computing, and allow for heavier loads of data, smart energy systems for public utilities, and accommodation for electric cars and autonomous vehicles. Samsung is also making its IP available to local technology companies to experiment with.”18

Daegu is not the only urban area that has been pursuing these new technologies. Songdo, near Seoul, has been touted as the world’s first smart city to be built from scratch. As Labcities explains: “The smart city project in Songdo stipulates that all city traffic will be measured and controlled through RFID tags, which will be installed on the cars. These RFID tags will send the location of the cars to a traffic monitoring unit.”19 Meanwhile, in order to achieve its ultimate goals, Samsung lists a range of major partner companies, including FCA Fiat Chrysler Automobiles, Willis Towers Watson, AT&T, SEAT, Cisco and Tantalum Corporation. The question is, whether the same open, collaborative approach to new innovation could fit with Apple’s penchant for secrecy. Will Apple have to change? Would a foray into the automotive sector alter Apple’s approach to innovation?

Artist impression of Daegu start-up centre

Summary of IP-led approach to competitive analysis From Google’s fleet of autonomous cars in sun-kissed California, through to Ford’s autonomous snow driving in Michigan, all manufacturers are on the road to autonomous cars. Even Toyota, which is reluctant to replace the thrill of a manual driving experience with an artificial substitution, has signalled its intent to embrace autonomous driving. CEO, Mr. Toyoda, explains in a recent interview: “I have hopes that what we are studying now could be used beyond the automotive business.”20 However, navigating R&D in an environment where the stakes are high, development expensive, and collaboration in specific technology areas vital, IP analysis is critical to securing a successful outcome. Being able to identify the areas where it makes most sense to collaborate, adopt an open innovation approach, license technologies (or alternatively remain closed) is the foundation of competitive success. Competitive analysis which follows the structure of combining broader technology searches with narrow technology searches on the most sensitive or business critical aspects of an organization’s roadmap will uncover adjacent technology opportunities, reveal IP strategies and find patents or technology areas that are either beneficial to your R&D direction, or those that could pose the greatest risk. This has never been more important than when considering the impact of open innovation approaches. • Broad search the terms relating to the technology, such as ‘autonomous driving,’ and evaluate the technology trend, as well as determine early starters versus those playing catch-up • Use the broad search results to find more specific terms within those patents, or references to alternative technologies that highlight different competitors’ approaches to their product roadmap • Create a technology matrix, detailing the technologies where competitors are strongest or weakest. Include other technology areas, such as ‘connected car,’ and locate overlapping IP areas as well as those that are specific to one technology area or another. This helps identify gaps in competitor portfolios, or discover potential partners to fill a gap in ones own portfolio, or reveal licensing opportunities. • Review the IP strategy strengths and weaknesses, to determine whether a potential partner’s approach is complementary to your goals • Define all the technology areas that need to be open and collaborative, versus closed and guarded; and developed internally, or sourced elsewhere


Effective IP research to generate optimum outcomes

20 http://www.wsj.com/articles/behind-toyotas-late-shift-into-self-driving-cars-1452649436

About PatSnap PatSnap is the leading IP Analytic and Management platform that empowers even non IP-proficient users to understand this competitive landscape. Through our simple yet powerful interface, IP is now accessible to everyone allowing you to compete and innovate on a global scale. Since it was founded in 2007, PatSnap has served thousands of customers in more than 30 countries worldwide including IBM, National Institutes of Health, United States Department of Defence, MIT and Singapore’s Agency for Science, Technology and Research. Find out more: www.patsnap.com

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http://www.patsnap.com/