The Internet of Things and its legal dilemmas

Guest Post by: Giulio Coraggio/Partner at DLA Piper

The Industrial Internet of Things also known as Industry 4.0 has the highest potentials of growth within the IoT, but it hides relevant legal issues.

The figures of the Industrial Internet of Things

According to McKinsey, the IoT will have a total potential economic impact of $3.9 trillion to $11.1 trillion a year by 2025.

But within such figures, it is interesting to see that the highest expectations are around the usage of IoT technologies within factories, as showed in the chart on this side.

Such growth derives, among others, from the possibility to drive efficiency by means of the monitoring of industrial processes, of manufacturing machines and of delivery chains through sensors that can, among others,

  1. predict malfunctionings or failures and therefore avoid downtimes;
  2. identify any lack of service within the manufacturing process, enabling corrections to improve productivity and cut costs; and
  3. track the whole manufacturing process, including workers by means for instance of wearable technologies and geolocation systems, to avoid errors and the misusage of devices or machines and enable their exploitation in a more efficient manner.

The “hidden” legal issues of Industry 4.0 technologies

I had referred in the past to Big Data as the “money maker” of the Internet of Things. And this “formula” is even more valid when it comes to Industry 4.0 technologies.

Are “industrial” data personal data?

Data collected from factories can have a different nature. If such data can be associated, directly or indirectly, to an individual, this obviously triggers privacy issues. This is not a restricted scenario because the efficiencies of the IoT require also to track individuals.

The recent changes introduced by the Italian Jobs Act enable the usage of technologies allowing the monitoring of employees in order to either improve the productivity of a company or perform the working activity. European laws such as the upcoming EU General Data Protection Regulation (GDPR) would prevail over national laws. Therefore the new flexibility provided by the Jobs Act might not result to be 100% reliable and require for instance to run a data protection impact assessment under the terms of the GDPR.

Additionally, once personal data is collected, the goal is to use it with the lowest possible level of restrictions. Therefore the implementation of pseudonymization, segretation or encryption technologies is valuable to further exploit it.

How do you protect data and IIoT technologies?

A tricky issue is to identify the most appropriate right to be used in order to protect data generated from factories. Such data can be confidential information or trade secrets, but is there a copyright or at least a database sui generis right on it?

In a period of time when the protection of software through intellectual property rights such patents is not at its hype, it shall be assessed whether the usage of IoT technologies led to the creation of a protectable model of business.

Finally, when Industrial Internet of Things technologies are adapted to the manufacturing process of the different customers, an issue pertains to the potential design rights on these customizations, especially when there is a relevant contribution from the customer.

Who is the owner of the data?

There is no easy answer to this question. When it comes to personal data, individuals to whom it relates have privacy rights on such data which cannot be waived. Individuals can grant their consent to the usage of its data, but shall keep the control at any time on it, with the right of subsequently withdraw the consent previously granted.

But, as mentioned above, the same data might be confidential information, a trade secret or represent the intellectual property of companies that for instance created large databases containing such data.

Subject to privacy law restrictions, the economic exploitation of data can be contractually agreed. And this is particularly relevant also in the light of the new data portability right that is provided by the EU Privacy Regulation. The right of an individual to have his data ported to the next supplier might not prevent the previous supplier to agree with its customer, at the time of the initial contractualization, a restriction on the usage of the same data for business purposes, even when ported to another supplier. It will be interesting to see the position of data protection authorities on the matter, but this is a fascinating topic.

Is data kept secure?

Cyber risk is exponentially becoming a threat for any business. The EU Data Protection Regulation requires to implement “appropriate” security measures. But this is not just a question of bearing large IT investments since, as recently happened, very smart guys might find an access into a system and all of a sudden the most secure system might become insecure.

And this risk is one of the reasons why interoparability of Internet of Things technologies is having such a hard time. The system of a different supplier interconnected to your system might be the source of a cyber attack. But the Internet of Things requires an “orchestration” of different technologies from different suppliers and you cannot do IoT alone.

Security is a dynamic concept and requires the implementation of organisational and technical measures aimed at limiting the risk of access to information systems and enabling the immediate reaction to a cyber attack. The implementation of a privacy by design approach and the reliance on a cyber risk insurance policy can help, but the whole internal organisation of a company has to change.

What liability if things go wrong?

In case of interconnected technologies such as those of the Industry 4.0, when there is a malfunctioning it is difficult to determine the perimeter of the liability of each supplier. And the matter is even more complicated when it comes to artificial intelligence systems which rely on a massive amount of collected data so that it might be quite hard to determine the reason why a machine took a specific decision at a specific time.

How can liability clauses and service levels be arranged if the efficiency of the technology depends also on information coming from other systems which might not be 100% correct, might be corrupted or just victim of a cyber attach that is due to interconnected technologies? Service levels might not for instance be “static” as they might increase/decrease during the lifetime of a product due to the occurrence of external factors which are not the typical force majeure events.

Such variables make more difficult to build a defence in a potential dispute, especially in case of physical harm caused to individuals since at that stage product liability provisions preventing liability exemptions would apply.

This topic is really fascinating, what is your view on the matter? This is the time when companies shall address such such issues, also because of the upcoming tax savings that are going to be provided for Industry 4.0 technologies.

If you found this article interesting, please share it on your favourite social media!


Giulio Coraggio advises some of the major land-based and online gaming operators as well as game suppliers on their business in Italy and their expansion in other jurisdictions

Full Bio

Antidilution Explained: Full-Ratchet and Weighted-Average Provisions

There are two principal ways to formulate antidilution provisions, capitalizing the terms to make it clear we are talking about the ones which have substantive bite—the “Full Ratchet” and the “Weighted Average.” Full Ratchet provisions are the real killers, at least from the founder’s point of view. They provide that, if one share of stock is issued at a lower price, or one right to purchase stock is issued at a lower aggregate price (exercise price plus what is paid, if anything, for the right), then the conversion price of the existing preferred shares [1] is automatically decreased, that is, it “ratchets down,” to the lower price. [2] Depending on how many shares (or rights) are included in the subsequent issue, this can be strong medicine. A brief example will illustrate.

Assume Newco, Inc. has one million common shares and one million convertible preferred shares outstanding, the founder owns all the common, and the investors own all the preferred, convertible into common at $1 per share. Newco then issues 50,000 shares of common at $ .50 per share because it desperately needs $25,000 in cash. To make the example as severe as possible, let us say the investors control the board and they make the decision to price the new round of financing at $.50. Suddenly the preferreds’ conversion price is $.50, the founder goes from 50 percent of the equity to under 33.3 percent, and all the company has gained in the bargain is $25,000. Indeed, a Full Ratchet would drop the founder from 50 percent of the equity to 33.3 percent if the company issued only one share at $.50. This is a harsh result, indeed. When a really dilutive financing occurs, say shares have to be sold at MO per share, the founder drops essentially out of sight. The company takes in $5,000 and the founder goes down under 9 percent, never to recover because he does not have the cash to protect himself in subsequent rounds. In the jargon of venture capital, he has been “burned out” of the opportunity. There is no other provision so capable of changing the initial bargain between the parties with the dramatic effect of Full Ratchet dilution. When venture capitalists are referred to as “vulture capitalists,” it is likely the wounded founders are talking about dilutive financings and a Full Ratchet provision. [3]

The more moderate position on this issue has to do with Weighted Average antidilution provisions. There are various ways of expressing the formula but it comes down to the same central idea: The investors’ conversion price is reduced to a lower number but one which takes into account how many shares (or rights) are issued in the dilutive financing. If only a share or two is issued; then the conversion price does not move much; if many shares are issued—that is, there is in fact, real dilution—then the price moves accordingly.

The object is to diminish the old conversion price to a number between itself and the price per share in the dilutive financing, taking into account how many new shares are issued. Thus, the starting point is the total number of common shares outstanding prior to the dilutive financing. The procedure to achieve the objective is tomultiply the old conversion price per share by some fraction, less than one, to arrive at a new conversion price; the latter being smaller than the former, the investors will get more shares on conversion and dilute the common shareholders (the founder) accordingly. The fraction is actually a combination of two relationships used to “weight” the computation equitably. The first relationship is driven by the number of shares outstanding, the weighting factor, meaning that the calculation should take into account not only the drop in price but the number of shares involved—the significance of the dilution, in other words. [Call the number of shares outstanding before the transaction—A.]

The fraction, then, takes into account the drop in price and expresses that drop in terms that can be mathematically manipulated with the first number to get a combined, weighted result. The relationship is between the shares which would have been issued for the total consideration paid if the old (i.e., higher) conversion price had been used versus the shares actually issued (i.e., the shares issued at the new price.) [Call these two numbers—C and D.]

The combination of these two relationships—number of shares outstanding and the comparative effect of the step down in price (expressed in number of shares)—is a formula:

((A + C) ÷ (A + D)) x Old Conversion Price

If the shares which would have been issued at the old (i.e., higher) price is (as indicated) the number in the numerator, the fraction or percentage will be less than one. This fraction (say 1/2 or .50) is multiplied by the existing (or initial) conversion price to obtain a lower conversion price, which means in turn that more shares will be issued because the conversion price produces the correct number of shares by being divided into a fixed number, usually the liquidation preference of the preferred stock.

It is open for theorists to argue about the fairness of that result, but the above formula has the advantage of economy of expression. If one wants to use a Weighted-Average antidilution formula, the above is one commonly used (albeit expressed in different terms).

There, are, of course, different ways of expressing the formula. In the case of warrants and options, for example, the contract is often expressed in terms of a specific number of shares obtained at a fixed exercise price. Simply adjusting the exercise price may mean that a holder gets the same number of shares but pays a little (or a lot) less. In such an instance, the trick is to continue the exercise price as is but to adjust upwards the number of shares resulting from exercise, which can easily be done by reversing the formula—(A plus D) divided by (A plus C).

The calculations get more complex, as rounds of financing multiply. If the investors in round one (holding series A preferred) enjoy a conversion price of $1, and the price for the round two (series B) investors is $1.50, and the round three (series C) preferred is convertible at $4 and there then occurs a dilutive financing at $.50, all the conversion prices are affected, but it takes a computer to figure out who is entitled to what number of shares, particularly since investors in the various rounds will tend to overlap. (In this connection, one occasionally encounters a formula which keys off accumulated dilution. Thus, in the example cited, and depending on the amount raised in each instance, only the series C preferred holders would get an adjustment in their conversion price; the earlier investors would hold fast because the Weighted Average price of all subsequent rounds, taken together, is above their price.)

There are a number of other confusions which can easily creep into the drafting of the section. For example, can the conversion price go up? The answer is ordinarily no, at least by virtue of cheap stock antidilution. Does the exercise price which is ratcheted down always mean the original conversion price or the conversion price immediately preceding the dilutive event? The answer can vary but usually the latter is meant. If the conversion price goes down from $10 to, say, $5, a subsequent round at $7 doesn’t budge it again. An adjustment in the conversion price is usually pegged to the issuance of cheap stock or the right to buy cheap stock, say another convertible or an option. If the option lapses, is the adjustment reversed? Ordinarily, no. Other dilutive events are referenced in conventional financing documents, including extraordinary dividends. Absent care in drafting, a distribution of cash or property can ratchet the conversion price down to a negative number.

For more information on Venture Capital, please visit VC Experts

New Investor Technology to Provide Insights into Investing in Private Companies

Genesis by Lagniappe Labs
Platform Resulted from Collaboration by Investment Data and Software Development Industry Leaders

VC Experts and Switchback Partners, a software development team based in Austin, Texas that specializes in stock market trading and analytic platforms, join to reduce inefficiencies in the private market analysis and transaction space. The new entity, Lagniappe Labs, federates large volumes of private company information from many difficult to acquire sources. Their platform, “Genesis”, provides tools that allow users to build their own analysis on technicals around the specific private financing deal terms, Valuations and Multiples, Cost of Capital, and many other models to satisfy research due diligence practices. Genesis also allows users to build their own indexes and themes on private companies and then compare those against similar public companies.

Read the full Press Release

Adaptive Insights – Where’s the Valuation at Now?

Screenshot 2015-06-30 14.23.57

Amended & Restated Certificate of Incorporation 6/22/2015

Based on documents uncovered by VC Experts the cloud finance intelligence company is worth $453m. View the Amended and Restated Certificate of Incorporation filed June 22, 2015 and check out the funding facts below. Want more? You can view more private company valuations, deal terms, and price per share information with our Intelligence Database.
Adaptive Insights

Trends in Financial Technology Investing

There is no denying that the Financial Technology sector is on the rise. As the companies start maturing and certain outliers build credibility with big raises the investors will continue to flock. ACE Portal’s Co-Founder and CFO, Carl Torrillo, interviewed Ross Barrett, Co-Founder of VC Experts, on the trends in financial technology (fintech) investing, valuations, and average investment size. Take a moment and see what trends are shaping this industry.

Incase you missed our Fintech Overview Report feel free to take at look at it here.

View the FinTech Report

View the FinTech Report

Friday Feature Company: Beepi, Inc.

Early Stage Tear Down

View the Early Stage Deal Term Report

After posting our Early Stage Deal Term Report we decided to dive deeper into one of the companies that caught our attention. Beepi is the first and only 100% online peer-to-peer marketplace to buy or sell a car. According to their website they “…take up to 9% of all transactions. To put that number in perspective: dealers mark cars up to 54%. [They] are able to offer low prices because [they] connect buyers and sellers directly and don’t have overhead like salespeople or physical lots…”. Beepi has raised north of $57M based on regulatory filings and we valued them at $166 M post-money after their Series B round.  You can see the investment profile below.

beepi_logo_transp_2Investment Data

View the Entire Company Report for Beepi, Inc.