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Trading Secrets

A Law Blog on Trade Secrets, Non-Competes, and Computer Fraud

California Attorney General Provides Some Guidance on Cybersecurity

Posted in Cybersecurity, Data Theft, Privacy

Cross-Posted from The Global Privacy Watch

With all the high-profile cybersecurity breaches that seem to be in the news lately, there is a plethora of “guidance” on cybersecurity. The Attorney General of California has decided to add to this library of guidance with her “Cybersecurity in the Golden State” offering. Cybersecurity is a pretty mature knowledge domain, so I am not quite sure why General Harris has determined that there needs to be additional guidance put in place. However, it is a good reminder of the things that regulators will look for when assessing whether or not “reasonable security” was implemented in the aftermath of a breach. And while there isn’t anything new in the guidance, what is informative is what is not there. Continue Reading

The Two Billion Dollar Zhu Zhu Pet, Sold for $5k: Puffing in Trade Secret Misappropriation Pleadings May be Perilous

Posted in Trade Secrets

Zealous advocacy, copious use of Latin, and literary devices advantageously applied to attack our adversaries’ arguments.  These are the cornerstones of American legal representation. 

These tools are part of the modus operandi of every lawyer.  This article may use dead language and assonance as running themes, but some lawyers take zealous advocacy ad infinitum.  Such attorneys are rarely even admonished by the courts, much less sanctioned.  That said, the Ninth Circuit has approved sanctions against an attorney for “misrepresentations” made in the complaint of a trade secret lawsuit.  

Wait a minute…the COMPLAINT?  The boiler-plate statement “upon information and belief” was somehow omitted?  After the trial is over and the bad, bad defendant is found to be not so bad after all, is not all that bluster and bravado in the complaint long forgotten? 

N.B.: Perhaps not. 

Although they are called “pleadings” for a reason, statements in the pleadings must be at least “grounded in fact” to pass muster as fact, even in the complaint.  Synonyms used to impress clients might better be left to other writing exercises, e.g., fantasy novels and fairy tales. 

In Heller v. Cepia LLC et al., 11-cv-1146 (N.D. Cal. 2011), Jason Heller claimed that Cepia, the makers of “Zhu Zhu Pets” robot toy hamsters, used the same features and accessories he had disclosed to toy manufacturers in his prototype designs.  Mr. Heller asserted, inter alia, that the manufacturers forwarded his trade secrets to Cepia, who then used his ideas in the Zhu Zhu Pets products.

In the 2011 complaint, Mr. Heller’s attorney alleged that visitor logs at one of the manufacturers “appeared to confirm” that Cepia had visited the manufacturer.  Mr. Heller then “confronted” the manufacturing company who “refused” to provide information about any relationship with Cepia.

Sort of benign, isn’t it?  Some visitor logs and a request for additional information that was denied.  Prima facie this does not seem to be sanctionable writing or behavior.  Yet the use of “appeared to confirm” and “confronted” are why Mr. Heller’s attorney was sanctioned.

This puffing seems rather tame in comparison to the damages Mr. Heller sought: over $2,000,000,000.  Yes, two Billion.  For a toy hamster?  Such a damage request, seemingly made rather boldly across several pages in the complaint, appears somewhat less bona fide than something couched with “appeared,” and, remarkably, did not even rate a de minimis or dicta mention by the court as raising any cause for concern.

No wonder there are so many cries for tort reform.

Fast forward to a year later, where, on joint stipulation, the complaint was dismissed against Cepia with prejudice.  Per se no sanctions, right? 


In part of the quid pro quo for the dismissal stipulation, Cepia received Mr. Heller’s acknowledgement that “he did not find any evidence that Cepia had any access to any of Mr. Heller’s hamster toy ideas or information” in the documents and evidence produced during discovery.

First mistake: saying, “There is nothing in any of the evidence showing the defendant was bad.”  Because then the complaint looks like, oh, a big lie.

Second mistake: letting a client say, “There is nothing in any of the evidence showing the defendant was bad.”  Because then it looks like the attorney fabricated the complaint ab initio.  And yes, now sanctions may be apropos, in this case to the tune of $5,000.00 from the Northern District of California.

Mr. Heller’s attorney appealed, arguing in his appellate brief that “in hindsight, my wording could have been better.”  Admitting the wording was misleading is likely a third mistake. 

Mr. Heller’s attorney then tried to save the day, ibid, by arguing that his letter to one of the manufacturers constituted a “method of confronting them on the issues.”

Fourth mistake: unless you are a Court of Appeals for the Federal Circuit judge, you are not allowed to construe the meaning of words de novo.

Confront means “to oppose or challenge (someone) especially in a direct and forceful way” or “to directly question the action or authority of (someone).” (Merriam-Webster).

The complaint implied that someone went to the defendant’s place of business and spoke to them face-to-face, or challenged them to prove they were innocent.  Nope.  His attorney dashed off a quick note saying, in essence, “Hey, thanks for the visitor logs, can you tell us a little more about your relationship with Cepia?”  The defendants did not answer.  There was no confrontation, and the visitor logs didn’t confirm any visits by Cepia.

In reality, the only mistake here was somebody thinking it was a good idea to make the defendants appear uncooperative and/or hiding the truth.  Had the complaint contained the facts, instead of something that sounded a little more ominous, the lawsuit would have still gone forward exactly as it did. 

Everything except for the sanctions.

For more on Heller v. Cepia, see the Law360 Article.

Recent Decision Affirms Significant Protections for Confidential Information in United Kingdom

Posted in International, Practice & Procedure, Trade Secrets

By Ming Henderson and Razia Begum

With the increasing number of disputes and client queries regarding confidential information in the United Kingdom, the recent case of Personnel Hygiene Services Ltd & ors v. Rentokil Initial UK Ltd , EWCA Civ 29, 29 January 2014, serves as a useful reminder of the extensive protection of confidential information. 

The Court of Appeal, considered whether the obligations under a confidentiality agreement continued to apply after the parties entered into a second agreement which contained no such express obligations. The court upheld the first instance decision, finding that the confidentiality obligations continued to apply to protect trade secrets ( in this case, information relating to customers and services).  Although not a landmark decision, this may seem a surprising judgment , particularly as the second agreement contained an entire agreement clause which would usually be interpreted as replacing all previous contractual arrangements. The court made no reference  to this point in the judgment. 

In this case, Rentokil (the party in receipt of the confidential information) appealed against an injunction preventing it from contacting customers of UK Hygiene (the provider of the confidential information). UK Hygiene had terminated the second agreement in order to deal directly with the ultimate customers. Following this, Rentokil directly approached UK Hygiene’s customers with a  goal to provide services to them using, in part, confidential information (about customers and services) under the first agreement.

Key takeaway messages:

  • Extensive protection: Although we would always recommend inserting strong confidential information clauses that limit use after the term of the agreement or for other purposes, this decision provides comfort to those providing information to potential competitors that “confidential information” with a significant risk of misuse will be protected.
  • Prevention is better than cure: Though the point was not specifically raised in this case, companies should, if they are not already doing so, physically protect their confidential information (e.g. by installing passwords, limiting access by other means, etc.) and seek to retain control of it. This can be a far more effective and less costly approach in the long-term than litigating over confidential information in the hands of third parties. 

With international offices in London, Shanghai, Melbourne, and Sydney, Seyfarth Shaw’s trade secrets, computer fraud, and non-competes practice group provides national and international coverage for companies seeking to protect their information assets, including trade secrets and confidential information, and key business relationships.

Computer Fraud and Abuse Act Claims in the First Circuit – Will the Narrow Approach Prevail?

Posted in Computer Fraud, Computer Fraud and Abuse Act

The scope of the Computer Fraud and Abuse Act (“CFAA”), 18 U.S.C. § 1030, remains unsettled in the First Circuit after two decisions issued just weeks apart adopted differing approaches to the treatment of such claims.

The CFAA prohibits the intentional access of a computer without authorization or exceeding a party’s authorization to obtain information from a protected computer.  As we have previously reported here, courts are split on the interpretation of what constitutes unauthorized access or access that exceeds authorization.    In some jurisdictions, courts take a narrow view, limiting “unauthorized access” to true “hacking” cases, where a party improperly gains access to documents he or she is not authorized to obtain.  On the other hand, certain circuits have favored a broad interpretation that would prohibit a party from accessing documents to which he or she typically would have access, if such access were for an improper purpose (for example, an employee who misappropriates documents to which she legitimately had “technical access” for the benefit of a competitor).

At the beginning of December, Judge Denise Casper of the U.S. District Court for the District of Massachusetts granted in part a preliminary injunction in Enargy Power Co. Ltd. v. Wang, C.A. no. 13-cv-11348-DJC, based on an alleged violation of the CFAA.  The plaintiff had alleged that the defendant, a former employee, had instructed his assistant to encrypt certain files on the employer’s computer server, and directed her to transmit those files to him, all in violation of the CFAA.  Citing Advanced Micro Devices, Inc. v. Feldstein, et al., C.A. No. 13-40007, which we previously reported on here, Judge Casper noted that the defendant’s actions “employed an element of deception in that he acted without his employer’s consent or knowledge . . . and using his assistant as a conduit, who had every reason to trust that [the defendant] was acting within the scope of his authorization,” and accordingly those actions likely exceeded the scope of his authorization. 

Judge Casper’s reliance on the “means of a deception” language used in Feldstein suggests a trend in the First Circuit to limit the previously adopted broad interpretation to those cases where a defendant exceeds his authorization through some deceptive act with fraudulent intent.  It remains unclear whether a garden-variety misappropriation case, in which an employee obtains documents for the purpose of competing with his or her employer but without “hacking” into the employer’s computer servers, would be viewed as sufficiently “deceptive” so as to constitute a violation of the CFAA under the Feldstein and Enargy line of cases. 

Another case decided less than two weeks later took an even narrower view.  In Verdrager v. Mintz, Levin, Cohn, Ferris, Glovsky & Popeo, P.C., Judge Peter Lauriat of the Massachusetts Superior Court analyzed a law firm’s CFAA counterclaim against its former associate, who had alleged sex discrimination and retaliation.  The firm, Mintz Levin, alleged that the associate had conducted searches for documents related to her case on the firm’s document management system, and forwarded relevant documents to herself or her attorney.  Determining that the associate clearly had access to the documents she viewed and transmitted, Judge Lauriat found that “it was not the obtaining of the documents that creates the basis for [Mintz Levin’s] claims against [the associate], but for what use she sought to obtain them.”  Judge Lauriat held that the associate’s disloyalty could not form the basis of a CFAA violation, and that Mintz Levin’s failure to restrict the associate’s access to sensitive documents related to her case “further weaken[ed] [Mintz Levin’s] position” that the associate had violated the CFAA, and granted summary judgment in the associate’s favor.

In light of the unsettled landscape of CFAA actions in the First Circuit, employers must remain vigilant.  For example, where employers have reason to believe an employee may be using confidential documents for an improper purpose, immediate steps should be taken to restrict the employee’s ability to access such documents.  However, until Congress or the Supreme Court settles the matter for good, it remains unclear how courts in the First Circuit will treat CFAA claims against employees accused of misappropriation.

Trade Secrets: A New Framework

Posted in Cybersecurity, Trade Secrets

As a special feature of our blog –special guest postings by experts, clients, and other professionals –please enjoy this blog entry by Pamela Passman, President and CEO for the Center for Responsible Enterprise and Trade (CREATe.org)

-Robert Milligan, Editor of Trading Secrets

By Pamela Passman

Around the globe, dozens of countries are considering or enacting legal reforms to grapple with the growing misappropriation of trade secrets. As these changes lumber forward, it remains to be seen how new laws will be enforced, and whether legal remedies will offset the losses from theft.

In this uncertain landscape, companies must invest in practical, preventive measures to address the risk to their valuable intellectual assets, according to a new report, “Economic Impact of Trade Secret Theft: A framework for companies to safeguard trade secrets and mitigate potential threats.”

The report by the Center for Responsible Enterprise and Trade (CREATe.org) and PriceWaterhouseCoopers provides a fresh look at the problem of trade secret theft — including an estimate of the magnitude of the problem and analysis of the main types of perpetrators, their motivations and means. It also develops several scenarios suggesting how the effectiveness of regulation, the openness of the internet, and cyber threats could play out and impact the environment for the protection of trade secrets in the coming 10-15 years.

Practical measures in an uncertain world

For companies, this analysis provides a backdrop for addressing the immediate and pressing challenge: How to protect trade secrets in a rapidly changing and risky global marketplace?

The report offers companies an original framework for protecting valuable competitive information that has been developed through experience, investment and research.

A series of practical measures, the report argues, should be adopted throughout the company’s operations — and shared or required of contractors and business partners throughout the global supply chain, to the greatest extent practical.

The five-part framework — illustrated with the help of a fictional company, ABC Widget— starts with making an inventory of trade secrets.

ABC is billed as a large, global, publicly traded, U.S.-based alternative energy company with a widely dispersed global supply chain.

Get a handle on the goods

The inventory process starts with “a cross-functional team of senior executives, business unit leaders and corporate functional leaders” who are asked to make lists of key company information in five categories: product information, research and development, critical and unique business processes, sensitive business information, IT systems and information.

“Participants arrive at the working session with their lists, which they present, discuss, and compile into a master list that aligns with ABC’s views about what constitutes a trade secret. The meeting results in a categorized list of valuable trade secrets reflecting critical elements of ABC’s business model.”

With that, a team of security professionals moves into action:

“Using tools that search based on keywords and other identifiers, trade secrets from the master list are found on various servers, in files with non-relevant file names, and on shared-file sites created for reasons unrelated to the trade secret itself. The results for the location of each trade secret found are noted on the master list, to be incorporated later into the vulnerability assessment.”

The security team also works with the other business leaders to find trade secrets that are not digitized — things like hand written notes and prototypes — in an effort to make the inventory as comprehensive as possible.

Pick your poison

The second step is to assess the “threat actors” that present the greatest risk to the company’s assets, given its specific industry and areas of operation—and how company security systems measure up.

Various perpetrators — competing companies, transnational criminal organizations, “hacktivists,” nation-states and “malicious insiders” in the company have various means of stealing trade secrets and a variety of motivations, including pure profit, nationalistic advantage and political or social goals.

Companies involved in military technologies or dual-use technologies that have civilian and military applications, for instance, will need to factor in the threat from governments that have been known to steal information through cyber attacks or by dealing with “malicious insiders” who work for the company.

Where to put the money

From there, the report walks through level three of the framework — ranking trade secrets according to the impact that their theft would have on the business.

Step four is to assign a dollar cost to those hypothetical losses. This includes direct impact on performance, including lost sales revenue and market share. It also includes indirect losses, where there is damage to investor confidence, customer trust or other secondary impacts.

So, in the case of the fictional ABC alternative energy company, the report explores the indirect dollar impact from stolen source code:

ABC… investors may assert that the company lacks appropriate controls and protection processes to support sustainable growth, deciding to sell shares despite the absence of direct financial consequences of the theft. Also, if discussion of the theft trends on social media blogs or is covered by traditional media, it can influence long-term customers’ buying decisions. Similarly, the theft may erode the trust of the company’s key business partners.

After assigning costs to the damage, the company is in a position to make decisions and investments — step five — to invest its resources to mitigate the most significant potential threats to trade secrets.

This, of course, is the bottom line: Companies need to understand, assess and embrace their trade secrets, and develop security around them. In the global economy, this security is an investment, rather than a cost.


Pamela Passman is President and CEO of the Center for Responsible Enterprise and Trade, a non-profit organization working with companies to protect intellectual property and prevent corruption in global supply chains. Previously, Pamela was the Corporate VP and Deputy General Counsel, Global Corporate and Regulatory Affairs at Microsoft Corp and has practiced law with Covington & Burling (Washington, DC) and Nagashima & Ohno (Tokyo).

Upcoming Client Webinar: Employee Social Networking: Protecting Your Trade Secrets In Social Media

Posted in Social Media, Trade Secrets

On Thursday, April 24, 2014 at 12:00 p.m. Central, Seyfarth’s second installment of its 2014 Trade Secrets Webinar series will address the relationship between trade secrets and social media. 

The Seyfarth panel will specifically address the following topics:

  • Defining and understanding trade secrets in social media, including a deeper dive into how courts are interpreting ownership of and whether social media sites constitute property and preventing trade secret misappropriation or distribution through social media channels.
  • Discussing the National Labor Relations Board’s treatment of employer social media policies, whether it applies to you, and what steps should be taken to avoid potential penalties for violating NLRB policy.
  • Analyzing judicial treatment of ownership of social media handles, pages, and accounts and how developing internal company policy and/or contracts can obviate expensive litigation.
  • Discussion and analysis of recent employee privacy legislation, including discussion of the Trade Secrets Clarification Act, and how it may impact policies dictating mandatory turnover of social networking passwords and employee privacy concerns.




There is no cost to attend this program, however, registration is required. 

If you have any questions, please contact events@seyfarth.com.

*CLE: CLE Credit for this webinar has been awarded in the following states: CA, IL and NY. CLE Credit is pending for the following states: GA, NJ, TX and VA. Please note that in order to receive full credit for attending this webinar, the registrant must be present for the entire session.

Beware: Over-Inclusive Non-Compete Agreement May Be Unenforceable

Posted in Non-Compete Enforceability, Restrictive Covenants

An employment agreement non-competition provision stated that, for 18 months after termination, the employee shall not become employed by or act “directly or indirectly, as an advisor, consultant, or salesperson for, or become financially interested, directly or indirectly, [in an entity] engaged in the business of selling flavor materials.” Earlier this month, the North Carolina Court of Appeals held that the provision was impermissibly broad. Horner Int’l Co. v. McKoy, Case No. COA 13-964 (N.C. App., Mar. 4, 2014).

Summary of the case

McKoy, a plant manager in North Carolina, was a party to an employment agreement with Horner, a manufacturer of flavor materials for use in food and tobacco products. The agreement contained non-competition and trade secret confidentiality clauses. McKoy had been in the food processing and flavor industry for decades. He resigned after six years with Horner and went to work in New Jersey for a company that manufactured food and beverage flavoring items. Horner sued him and sought preliminary injunctions with respect to both clauses. Earlier this month, the trial court’s ruling — denying the motion for an injunction with respect to the non-compete but granting the injunction motion relating to the confidentiality provision — was affirmed on appeal.

The appellate court’s rulings

The appeals panel stated that it was guided by the familiar rules that employee covenants not to compete are disfavored but are enforceable if they are no broader than necessary to protect the employer’s reasonable business interests. The non-competition covenant here had no geographic limitations and was not restricted to performance of tasks similar to those McKoy performed for Horner. Further, the covenant purported to prohibit him from associating with any company selling flavoring materials even if that company’s products did not compete with Horner’s. Finally, because he was precluded from investing “directly or indirectly” in such a company, the appellate court concluded that the non-compete was intended to prevent him even from owning shares in a mutual fund that was a Horner stockholder. For all of these reasons, the court held that the covenant exceeded permissible boundaries.

The injunction relating to the confidentiality clause, however, was upheld. North Carolina law permits injunctions for actual or threatened misappropriation of trade secrets the employee knows and has the opportunity to use or disclose. McKoy had access to Horner’s trade secrets. By averring “with great detail and specificity the information Defendant has allegedly provided to his new employer,” Horner met the “sufficient particularity” pleading standard.


Non-compete covenants must be limited in scope not only with respect to time and geography, but also concerning the activities which are prohibited. Horner teaches that an employer’s use of virtually limitless phrases such as “directly or indirectly” and “financially interested” can be risky. Also, purporting to extend the covenant to services beyond those actually performed for the employer, and locales where the employee did not work, may doom the enforceability of the non-compete. Violation of a confidentiality clause may be enjoined, however, if the employee’s access to the employer’s trade secrets is demonstrated, they are described in sufficient detail, and the likelihood the employee may exploit or divulge the confidential information is shown.

Global Business 101: Hire Your Competitor as a “Consultant”

Posted in Espionage, Trade Secrets

Why spend millions of dollars employing a bunch of bright, talented employees to develop your business when you can just hire a worker from your rival to steal all their research?  As on every test you took in school, isn’t getting the right answer more important than figuring out how to solve the problem?

Competition for business is fierce.  Small price differences or lower development costs can win your company any number of contracts.  How can one effectively compete in today’s marketplace?

Some companies, in a word, cheat.

Korea-based KCC Silicones hired chemist Michael Agodoa of Michigan-based rival Wacker Chemical Corp. as a consultant in 2010.  Rather than taking years to determine the proper formulas and millions in experimentation costs on plastics used in extrusion, silicone mold making materials, and elastomers, KCC employed Mr. Agodoa to steal over 100 of Wacker’s trade secrets.

Isn’t this what business people call a “win-win” situation?   Mr. Agodoa probably made enough extra cash to have a nice vacation or two.  KCC didn’t have to waste any time testing and certainly received a large “ROI” (return on investment).  Business, after all, is business.

Wacker Chemical spent two decades developing and refining their manufacturing methods.  Mr. Agodoa’s defense was that he shared Wacker’s knowledge and experience “in the spirit of scientific cooperation” over a period of two years.  In other words, KCC bought eighteen years’ worth of intelligence for the price of one consultant.

Facing up to forty-six months behind bars, Mr. Agodoa plea-bargained his way to a two-year federal prison sentence and a $7,500.00 fine.  Wacker Chemical’s losses were estimated at more than $15 million.

Trade secrets are valuable weapons in today’s global marketplace.  Trade secrets are often very costly to develop, and may provide the competitive edge for your company.  Although Mr. Agodoa only faced federal trade secret charges, the addition of even harsher criminal penalties under the Economic Espionage Act will hopefully make employees think twice about taking that moonlighting “consulting” job.

Federal prosecution for trade secret theft is a “closing the door after the horse has left the barn” approach.  Yes, it is nice to know that your trade secrets are protectable and additional statutes are increasing that protection.  As employers, however, it may be prudent to make your employees aware of the potential penalties before they decide to sell your proprietary information.  The Office of the National Counterintelligence Executive is a good place to look for materials that may be useful in your workplace.

As with students of every age, the temptation is to take the path of least resistance.  Just as with students, the reward is potentially great; your company could win that lucrative contract or catch up to the competition in a short amount of time.  Just find a way to “borrow” your competitor’s know-how.

With trade secret theft, and the Economic Espionage Act, the penalty is not a few days of vacation from school and a meeting with some toothless academic honor board.

You, like Mr. Agodoa, get to head straight to a federal penitentiary.

For more details on U.S. v. Agodoa, 13-cr-20525, (E. D. Mich. 2014), see the Rubber News article and the Bloomberg News article.

Big Data and IP Business Strategy

Posted in Computer Fraud, Cybersecurity, Data Theft, Privacy, Social Media

As a special feature of our blog –special guest postings by experts, clients, and other professionals –please enjoy this blog entry about the big data and IP business strategy by technology lawyer and IP strategist Joren De Wachter. Joren serves as a Co Chair with me on the ITechLaw Intellectual Property Law Committee and has an excellent blog of his own on current technology issues. Enjoy Joren’s article and for more on Big Data, please see our webinar on the Big Data Revolution.

-Robert Milligan, Editor of Trading Secrets

By Joren De Wachter

Big Data is an important technological change happening around us.

How should businesses react? What is the right business strategy? And, as part of such business strategy, what is the right Intellectual Property Strategy?

It can be the difference between success and failure.

1. What is Big Data?

“Big Data” is the revolution happening around us in the creation, collection, communication and use of digital data.

In the last couple of years, humanity’s capacity to create data, to communicate and to process them, has increased manifold.

According to IBM, we produced 2.5 Exabyte (that’s 2,500,000,000,000,000,000 or 2.5×1018 bytes) of data every day in 2012.

But the total amount of data, already beyond easy intuitive grasp, is not the key characteristic of Big Data. Its key characteristic is the continued exponential growth of those data.

While 90% of all data in existence today was created in the last two years (which means that, in less than 18 months, we create more data than has been created since the beginning of humanity, roughly 150,000 years ago), the most important point is this: those 90% created in the last two years, that what we consider to be enormous amounts of data today, will be dwarfed into complete insignificance in a couple of years’ time.

Humans are not very good at really understanding exponential mathematics, or at grasping its impact. This blogpost will give an insight into how technology businesses and their investors should plan and prepare for the Big Data Tsunami that is heading their way. And not just today, but for the foreseeable future. For there is no indication that this doubling of computational power (Moore’s law), the doubling of storage capacity or the doubling of communication capability – each occurring in 18 months or less – is about to slow down in the next couple of years.

This presents every business with a serious challenge: how will the emergence of Big Data affect the way the business uses its intangible assets? As we know, most assets these days are intangibles, also known as Intellectual Capital or Intellectual Property.

Arguably, Intellectual Capital is at the heart of any innovative business. Using it better will be the recipe for future business success. And failing to use it better will be a recipe for failure.

2. Framing the discussion around Big Data and Intellectual Capital Strategy.

a) What do we mean by “data”, and what is the difference with “information” and “intelligence”?

Data is a very wide concept. Everything created digitally is covered. From every document on your desktop to any picture posted by any user of social media. But it’s much more than that. It also means that, e.g. all the 150 million sensors of the Large Hadron Collider in Geneva, delivering data 40 million times per second, are included in the concept of data. If all of those data would be recorded, they would exceed 500 Exabyte per day – 200 times more than the world creation of data per day according to IBM as referred to above. However, those data are not actually produced, recorded or processed – before that happens, massive filtering takes place. In reality, the LHC produces a mere 25 Petabyte per year of data (a Petabyte is 1×1015, so 1/1000th of an Exabyte).

The implication is that there are enormous potential amounts of data that will be created and processed, once our computing and communication capability allow for it.

But “data” means more than that. It also includes everything created by any kind of sensor, but also by any camera, the input of any user, any person operating a computing device (PC, mobile, tablet, etc). Any project, any plan, any invention, any communication is also included.

And all of those data increase exponentially, roughly every year or so.

What is the relation between “data” and “information”? In essence, they are the same. Every bit of data has information.  The nature of that information and its potential value are determined by analyzing it. This is where we start talking about the meaning of data, and the intelligence that can be extracted from them.

However, while many definitions and approaches are possible in respect of how information becomes useful, and about the value of analysis and intelligence, a simple observation will be sufficient for the purpose of this blogpost.

That observation is that any subset of data, such as useful data, or intelligent data, or structured data, will grow in a similar, exponential fashion.

The implication of this observation is that it is not only “data” that grows exponentially, but also, by necessary implication, “knowledge” or “useful data”. So, we need to assess Intellectual Property strategies in a world where the amount of knowledge grows exponentially.

b) The importance of algorithms

The analysis of data, or indeed pretty much any meaningful way of using data, is done through using algorithms.

An algorithm describes a process for calculation, processing or reasoning, and allows to extract meaning and understanding from data. This, in turn, increases the value of the data – algorithms make the data speak to us.

This is where data turn into intelligence; it is through applying algorithms that data start to make sense, and can give us additional information.

One of the great potentials of Big Data is the capability to recombine data from different sources, and compare and analyze them. This allows finding new correlations – something that will help us understand how society works, and how one phenomenon works on another.

For example, analysis of the raw data of drug prescription in the National Health Service in England and Wales, allows to find correlation with certain hospital visits for conditions that are indirectly caused by certain drugs, which have not been noticed by the clinical trials (or which the drug companies have kept hidden from publishing).

The potential value of Big Data, and the use thereof, is enormous. McKinsey, the consultancy, estimates that Open Data would add between $3tn and $5tn to the world economy – that’s an economy with a size somewhere between Germany and Japan.

And algorithms are the key to unlocking this value of Big Data; hence they will be key in any IP Strategy.

c) What is Intellectual Property Strategy?

Intellectual Property Strategy means that business understands what their Intellectual Capital consists of, and uses it in the most optimal way to support the business strategy. It looks at a lot more than just patents or copyrights, the technical aspects of IP rights, but considers the whole range of Intellectual Capital.

Key aspects of IP Strategy consist of recognizing and understanding Intellectual Capital, assessing how they are used to support the business model and keeping the right balance between an open approach and using protection techniques (such as patents or copyright), by taking into consideration the impact of Open Innovation and Open Source.

In essence, it is the tool to bring innovation to market, and to scale innovation to new markets.

3. Impact of Big Data on IP Strategy.

This blogpost will look at how Big Data will impact IP Strategy from five angles. These five are 1) patent strategy 2) ownership of data 3) copyright 4) secrecy/know-how and 5) IP value and strategy of algorithms.
They are all essential parts of an IP Strategy.

a) Patent strategy.

Patent strategies can be quite different from one industry to another.

However, there are some common elements to consider, and they can be summed up in two observations. The observations are that both obtaining and using patents will become much harder. As a consequence, the business value of patents is likely to drop significantly.

Obtaining patents will become much harder

The observation is straightforward, but very important: if the amount of available information doubles every 18 months, the amount of prior art also doubles every 18 months.big data and patents linear prior art.

Patents are exclusive rights, granted on novel and non-obvious technical inventions. The granting of patents is based on the assumption that the patent offices will know existing technology at the time of the patent application, and refuse the application if the technology described is not “novel”.

However, if the amount of existing information grows exponentially, this means that in principle, the rejection rate of patents must also grow exponentially, to the point where it will reach 100%.

The reason is simple: the number of patents does not double very fast – in the last 50 years, it has doubled only twice in the US. The exponential growth of prior art (remember – this is a phenomenon humans intuitively struggle to understand) means that the amount of information that would disallow the granting of a patent – and that is any patent – also grows exponentially.

So, unless the granting of patents also grows exponentially, the area of technology that is patentable will shrink accordingly. Since patents are granted by human operators (patent examiners), and the number of patent examiners cannot grow exponentially (within a couple of years, most of the workforce would have to consist of patent examiners, which would be absurd), the number of patents will fall behind. On an exponential basis.

More importantly, if the patent offices would do their job properly, and only grant patents on technology that is actually new, the rejection rate would soar, and would reach very high levels (up to 100%), within a relative short time span.

This means that the risk of having a patent rejected two or three years through the application process, will rise significantly.

However, this phenomenon is not very visible yet. One of the key reasons why the impact of the Big Data explosion of accessible information is not very visible at the moment in the way patents are being granted, is because the patent offices don’t actually look at prior art in a way that takes into account the exponential growth of non-patented technology information.

Most patent examination processes review existing patent databases, and will establish novelty against existing patents rather than the actual state of technology. This made sense in a world where the speed of information creation was not an issue, or ran generally parallel to the rate of technology patenting. However, as non-patented technology (and, more particularly, information thereon) doubles every 18 months, the relevance of patent databases to establish whether something is new, takes a significant nosedive.

It is not clear to me whether patent offices realize the exponentially growing insignificance of their traditional data-approach. Once they do, though, they only have two options. The first is to reject most, if not all, patent applications. The second option is to ignore reality, and grant patents on non-novel inventions. However, this will (and arguably, already does) create huge problems in enforcing or using patents, as explained further below.

Either way, from a purely theoretical level, a novelty-based patent system is unsustainable in an environment of exponentially growing prior art or publicly available information.

From an IP Strategy point of view, this means that businesses will have to become much more selective and knowledgeable in their decision process on what to patent, and how to patent it.

This will affect both the scope of patents (which, in order to remain effective, is likely to become much more narrow), and the rate of success/failure of patent applications, both of which will have a significant impact on the return on investment in patent exclusive rights being sought and used by a business and its investors.

Use of patents

A similar problem affects the potential use of patents as part of an IP Strategy. There are a number of ways in which patents can be used, but the core function of a patent is to act as an exclusive right – a monopoly – on the production or distribution of a product or service.

This means that “using” a patent effectively means suing a competitor to have them blocked access to market, or charge them a license for allowing them to sell.

However, depending on the specifics of the legal system involved, when a patent holder wishes to enforce a patent, the defendant often can invoke that the patent should not have been granted, because there was prior art at the time the patent was granted.

And, while patent offices do not seem to have a clear incentive to take into account actual reality, including the exponentially available information created by Big Data, when reviewing the application, the situation is very different for a defendant in a patent lawsuit.

They will have every incentive to establish that the patent should never have been granted, because there was pre-existing prior art, and the information in the patent was not new at the time of application.

And one important consequence of Big Data will be that the information available to defendants in this respect, will also grow exponentially.

This means that, again, from a theoretical level, the probability of being able to defend against a patent claim on the basis of prior art, will grow significantly. Because of the lag of time between patent applications and their use in court (it takes several years for an application to be granted, and it may take more time before a court decides on it), the effect of the recent explosion of information as a result of Big Data is not very visible in the patent courts yet. But this is a ticking time-bomb, and, if and to the extent procedural rules do not interfere with the possibility of invoking prior art to invalidate a patent, there is a high likelihood we will see the rates of invalidation in courts increase steeply.

From an IP Strategy point of view, this means that an offensive IP Strategy, consisting of suing competitors or others based on your patent portfolio, becomes more risky. While the costs will continue to rise, the potential of a negative outcome will also increase significantly.

There is a second important issue around use of patents that needs to be addressed here as well. It relates to the algorithmic aspect of patents.

A patent is, of itself, an algorithm. It describes the process of a technical invention – how it works (at least, that’s what a patent is theoretically supposed to be doing).

It is therefore quite possible that a lot of algorithms around analysis of Big Data will become patented themselves. It  could be argued that this will act as a counterweight against the declining value and potential of patents described above. However, I do not  believe that the effect will be anything more than marginal.

The reasons for my opinion are the three challenges affecting the potential value of a patent on algorithms analyzing Big Data.

The first is that many of these algorithms are, in fact, not technical inventions. They are theoretical structures or methods, and could therefore easily fall into the area of non-patentable matter.

The second is that algorithmic patents are particularly vulnerable to the ability by others to “innovate” around them. It is quite unlikely that a data analysis algorithm would be unique, or even necessary from a technical point of view. Most data analysis algorithms are a particular way of doing similar things, such as search, clever search, and pattern recognition. There is, in actual fact, a commoditization process going on in respect of search and analytical algorithms.

As a general rule, in order to become patentable, such algorithms must be quite specific and focused. The broader they are described, the higher the likelihood of rejection because of the existence of prior art. However, this reduces their impact from the perspective of using them to block others access to market. A slightly different algorithm, yielding sufficiently similar analytical intelligence, but outside the scope of the first patent, will often (in my experience almost always) be available. This is due to the generic nature of the different aspects of most data analytical algorithms – it’s basically always a combination of checking, calculating, filtering and compressing information (sometimes with visualization or tagging and creation of metadata added); but the potential ways in which these can be combined quickly becomes unlimited.

In practice, it means that a patent around data analysis can almost always be circumvented with relative ease.

But the third challenge is the most important one.

Patents are “frozen” algorithms. The elements of the algorithm described in a patent are fixed. In order to have a new version of the algorithm also protected, the patent will either have to be written very vague (which seriously increases the risk of rejection or invalidity) or will have to be followed up by a new patent, every time the algorithm is adapted.

And the key observation around Big Data algorithms is that, in order to have continued business value, they must be adapted continuously. This is because the data, their volume, sources and behavior, change continuously.
Compare it to the core search algorithms of Google. These algorithms are continuously modified and updated. Indeed, in order to stay relevant, Google must continuously change its search algorithms – if they didn’t do so, they would drop behind the competition very quickly, and become irrelevant in a very short time.

The consequence is that, even if a business manages to successfully patent Big Data analytical algorithms, and avoids the pitfalls described above, such patent will lose its value very quickly. The reason is simple: the actual algorithms used in the product or service will quickly evolve away from the ones described in the patent. Again, the only potential answer to this is writing very broad, vague claims – an approach that does not work very well at all.

In other words: the technology development cycles of algorithms applied to Big Data analytics and intelligence are much too short to be accommodated by patents as they exist today.

Therefore, the use of patents will decline significantly; their value for business needs to be continuously re-assessed to address this observation.

From an overall IP Strategy point of view, this means that businesses will have to become much more selective in applying for and using patents. Conversely, investors will have to re-assess their view on the value that patents add to a business.

b) Ownership of data

Data ownership is an interesting, and developing area of law. In most countries, it is theoretically possible to “own” data under the law. The legal principle applied will differ, but is typically based on some kind of protection of the effort to create or gather the data, and will allow to block or charge for access or use.

However, there are a number of challenges related to ownership of data.

These challenges are based on the fact that Big Data is typically described by three characteristics: Volume, Velocity and Variety.

Volume stands for the ever growing number of data, as explained above. Velocity stands for the speed required to gain access to and use data, and Variety stands for the fact that data sources and formats multiply and change constantly.

From an ownership perspective, these charactistics lead us to two ways in which the traditional concept of data ownership is challenged by Big Data.

The first is the simple observation that data are a non-rivalrous commodity. That means that one person’s use of data does not necessarily prohibit or reduce the value of use of those data by another person, or by another 10,000 persons.

From a technical perspective, re-use of data is the most common, and obvious, way of approaching data.

But the challenge is neither technical nor legal; it is based on business models and interests.

And those business models and interests point us to two very relevant facts: a) most data are generated by someone else, and b) the value of data increases by their use, not the restriction on their use.

The first fact is obvious, but its relevance is underestimated. Most of the business value in Big Data lies in combining data from different sources. Moreover, the actual source of data is often unknown, or derives from different levels of communication. Data from customers will be combined with data from suppliers. Data from government agencies will be combined with data from machines. Internal data need to be compared with external data. Etc. Etc.

Therefore, there is a clear push towards opening up and combining data flows – this is the most efficient and best way to create business value.

And while it is true that from a legal and risk management perspective, many people will indicate the risks related to opening up data flows (and those risks are real), it is my perspective that those risks, and the costs related to tackling them, will drown in the flood of business value creation generated by opening up and combining data flows.

Add in the observation that many governments are currently considering how much of their data will become open. The likely trend is for much, much more public data to be made available either for free, or for a nominal access fee. This, in turn, will increase the potential of re-usability and recombination of these data, pushing in turn businesses to open up, at least partly, their own data flows.

And this leads to the second fact. The value of data is in its flow, not its sources.

Big Data can be compared to new river systems springing up everywhere. And the value of a river is in having access to the flow, not control over the sources. Of course, the sources have some relevance, and control over specific forms or aspects of data can be valuable for certain applications.

But the general rule is, or will be, that gaining and providing access to data will be much more valuable than preventing access to data.

As a result, the question of “ownership” of data is probably not the right question to ask. It does not matter so much who “owns” the data, but who can use them, and for what purpose.

And, as the number of sources and the amount of data grows, it is the potential of recombining those aspects, that will lead to exponential growth in how we use and approach data.

The river analogy comes in handy again: as the number of sources and data grows, the number of river systems also grows – and they will be virtually adjacent to each other. If you can’t use one, you jump to another one; the variety on offer will make control or ownership in practice virtually impossible to operate.

The conclusion on ownership is again best illustrated by our river analogy: we should not focus on who owns the land that is alongside the river; we should focus on being able to use the flow, and extract value from that.
From a practical perspective, it means that “ownership” of data should be looked at from a different angle: businesses should not focus on acquiring ownership of data, but on expanding different ways of using data, regardless of their source.

c) Copyright

Copyright is a remarkably inept system for the Information Society. Its nominal goal is to reward authors and other creators. In real life, it mainly benefits content distributors.

Originally, copyright was typically granted for the expression of creative activity: writing a book or a blog, creating or playing music, making a film, etc.

However, copyright also applies to software code, based on the observation that code is like language, and therefore subject to copyright. As such, copyright covers the code, but not the software functionality expressed through the code.

But does copyright apply to Big Data? And if so, does it have business value?

Data is information. Copyright does not apply to the semantic content or meaning of text written by human authors. In other words, it is not the message that is covered, but the way the message is formulated. If only one formulation is possible, then there is no copyright protection, because there is no creative choice possible. That is, very abridged, what copyright theory states.

Logically, this means that most data will fall outside of copyright. Any data generated by machines or sensors will not be covered by copyright. Any statistical or mathematical data is, as such, not covered by copyright.

That means that a very large subset of Big Data will not be covered by copyright. This legal observation will not stop many businesses from claiming copyright. Claiming copyright is easy: there is no registration system, and there is no sanction attached to wrongfully claiming copyright or claiming copyright on something that cannot be covered by copyright (e.g. machine-generated data).

Another large subset of Big Data is, in theory, covered by copyright, but in practice, the copyright approach does not work. This subset relates to all user generated data. Any picture, video or other creation posted by any social media user online is, in theory, covered by copyright. But that copyright is never actually used.

Users will not be allowed to claim copyright protection against the social media platform (the terms of use will always include extremely liberal licenses, allowing the social media platform to do pretty much what they want with the content).

More importantly, the value of all that user generated content lies in using it in ways that copyright is structurally unable of handling. User generated content, in order to have value, must be freely available to copy and paste, tag, adapt, create derivatives of, and, fundamentally, share without limitation. It is the opposite of what copyright tries to achieve (a system of limited and controlled distribution and copying).

Again, the analysis points to the inappropriate nature of our Intellectual Property system.

Most business value in using Big Data will be in open breach of copyright, typically by ignoring it or, at best, pay some lip service to it (as e.g. Facebook or other large social media do), or will be dealing with data that are not under copyright, but have not necessarily been recognized yet as such by the court system.

As a result, the copyright aspect of any IP strategy in Big Data will first and foremost have to make the analysis of a) whether copyright applies, and b) whether it adds any business value.

Since the applicability of copyright on machine or user generated content is partly in legal limbo, an appropriate solution for some businesses may be to use the creative commons approach. It helps to ensure that data are shared and re-used, hence increasing their value, and allows, from a practical perspective, to ignore the question whether or not copyright applies. If it applies, the creative commons license solves the problem. If it does not apply, and the data can be freely used, the end result will be, from a business perspective, similar.

A final point on database rights, a subset of copyright for a specific purpose, developed in the European Union.

While database rights may look as a system specifically designed for Big Data, reality shows otherwise.

Database rights don’t protect the actual data, they protect the way in which data are organized or represented.

In a typical Big Data situation, they would apply to the structured result of an algorithmic analysis of a dataset. Or they could apply to a relational database model, the way in which an application will sort data that is delivered to it, before specific functionality is applied to it.

While these have potentially quite a lot of value, the concept of protecting them through a copyright-related system suffers from the same weaknesses of copyright itself.

The value of such databases directly derives from a) access to the underlying data  and b) the algorithmic process of selecting and manipulating the data, both of which are not covered by database rights.  The end result of the exercise, as a snapshot, is covered by database rights. But the logic of importing, selecting and other functions on them, are not.

In other words, it’s another Intellectual Property Right that does not focus on the business value of Big Data – which is why nobody really talks about it.

d) Secrecy/know-how

Secrecy and know-how protection can be a very valuable asset of businesses. The most classic example is of course the secret formula of Coca-Cola. It’s not actually protected by a formal Intellectual Property Right (anyone is free to copy e.g. cooking recipes), but it has significant business value, and it is protected by other legal instruments. Typically, contract law, with confidentiality agreements, will play a big role in protecting business secrets and know-how, and most legal systems allow businesses to bring legal claims against competitors, business partners or employees who disclose or use secret information in unauthorized ways.

By and large, this approach is used by many businesses. Often, the strategy around protecting Intellectual Capital will consist of understanding what the business secrets are, and building appropriate procedures of protection or disclosure.

Yet, a key consideration for this part of any IP and business strategy is the word “secret”.

Secrecy has a major downside: it means you can’t talk about, use or disclose whatever is secret in a way that allows others to find out about it. The challenge here is that a lot of the value of Big Data depends, as we have seen repeatedly, on the ability to have access, and preferably open or free access, to as much data as possible.

This means that there is a natural market-driven pressure to businesses, in a Big Data environment, to prefer the use (and therefore an open approach) to data, rather than to limit or restrict use. While it is true that access to certain data can be very valuable, this approach is typically based on the assumption that one knows the data available, and understands at least the most important value considerations in respect of these data.

This is where Big Data presents an important shift: not only does it become much harder to know who owns or generates which data, or what is in those data, it also becomes much riskier not to grant relatively free access to data. This is because there is a lot of relevant, but not necessarily obviously visible, value in the data. A lot of value in Big Data comes from recombining data from different sources, or approaching data in a different way (e. g. compressing data in a visual or topographic way in order to discover new patterns).

As a result, businesses that open up their data are more likely to retrieve value from those data, and those that do, will retrieve more value from the data that is most open and accessible.

These developments will change habits within businesses, who will be pushed by market forces and the need to be more efficient, to open up more and more data sets and data sources. Inevitably, this will clash with strategies to keep information secret.

While it is, in theory, uncertain which way this conflict will play out, we need to be reminded again of the exponential growth of Big Data. The logical consequence of this exponential growth is that the pressure to open up is likely to be much stronger, and yield more direct benefit, than the longer term strategy to keep things secret because one day that may yield an additional benefit.

In other words, it will become much harder for businesses to keep things secret, and there will be growing pressure to open up data streams.

From an IP Strategy point of view, this means that understanding and selecting those intangible assets that have more value as a secret than as an open, accessible intangible asset will become more difficult, but, arguably, also more important. On the other hand, businesses that reject the knee-jerk reaction to keep as much as possible hidden or secret, may find that they evolve faster and generate more new business opportunities. It is not a coincidence that Open Innovation has become such a tremendous success. Big Data is likely to reinforce that evolution.

e) Intellectual Property and value of algorithms.

A point that has been touched upon repeatedly is the value of algorithms in a world of Big Data.

Algorithms are the essential tools enabling businesses to make sense of, and create value out of, Big Data.

Yet algorithms are not, as such, protectable under formal Intellectual Property Rights.

Is this a problem for an IP Strategy? Not necessarily.

After all, an IP Strategy is not just about protecting or restricting access to Intellectual Capital, it is also about positive use of that Intellectual Capital to serve the strategic and operational needs of the business concerned.

The financial services industry has used complex algorithms for many years, particularly in the mathematical structures known as “quants” – the formulae used to operate in and track the highly complex mathematical environments of derivatives, online trading or future markets (not to mention the toxic products that are one of the causes of the crash of 2007-2008).

Yet, many of these systems do not enjoy any formal Intellectual Property Protection. No patents are used, no copyright applies. Secrecy does apply, of course, but the market data themselves are very open and visible; indeed, most of the algorithms depend on liquidity, if not of financial assets, then certainly of financial data.
The pattern is similar for algorithms around Big Data. While some secrecy can be tremendously important for specific parts of algorithmic use of Big Data, the liquidity and open nature of the data themselves will often be at least as, if not more, important.

To that needs to be added the need of continuous change and adaptation of such algorithms – in order to have business value, algorithms need to be “alive”. And in order to be alive, they need to be fed those huge amounts of data for which they have been created.

The analogy with bio- or ecosystems is not a coincidence.  Just like biosystems thrive on resources that are freely available as a result of ecological circumstances (the energy of the sun, the oxygen in the air, etc), Big Data ecosystems are emerging and evolving, based on free(ish) availability of data and data streams.

As a result, an IP Strategy towards algorithms will have to take into account their almost biological-like behavior. Clever strategies will therefore allow for processes of evolution and selection to occur – and it is likely that those processes that allow free access to data will outperform, through the force of evolutionary pressure, those that do not allow such free access.

It will be therefore key for any IP Strategy to look at the core algorithms that are at the heart of any business dealing with or affected by Big Data. That will be almost everybody, by the way. And such an IP Strategy will have to consider the benefits to be gained from an open approach, to the risks suffered from closing down access to that new lifeblood of the Big Data Information Age: the flow of data itself.

4. Conclusion

A recurring theme throughout this article has been that the traditional view on data and their use is being challenged.

That traditional view is based on making data and information artificially scarce, and trying to charge for it. Intellectual Property Rights are the most obvious ways of making non-rivalrous commodities such as ideas, technology and data artificially scarce.

Yet, as an inescapable consequence of the exponential growth of Big Data, that approach is now at risk of causing more damage to businesses, rather than providing benefits.

Big Data is like a river system. The value of Big Data is not in its many sources, but in gaining access to the flow, and using it for the strategic purposes of your business.

A traditional IP Strategy, focusing on ownership, is in our analogy akin to focusing on claiming land a couple of miles from the river. It is looking in the wrong direction, and misses most of the value of Big Data. While some ownership of a bit of river banks (the algorithms) may have value, our Big Data River is more complex than a simple estuary – it is like the Delta of the Nile – overflowing regularly, where riverbanks and plots of land all of a sudden disappear or get flooded. And a new Delta comes into existence every 18 months.

Therefore, as a conclusion, IP Strategies around Big Data should focus on the instruments to access and use the flow of data, rather than using outdated models of artificial scarcity that will be overtaken by the exponential growth of Big Data.


Courts Disagree on Meaning of “Interruption of Service” When Determining Loss Under the Computer Fraud And Abuse Act

Posted in Computer Fraud and Abuse Act

District courts are divided as to whether there is a private right of action under the Computer Fraud and Abuse Act (CFAA) for persons whose computer service is not interrupted but who nevertheless incur costs (a) responding to a CFAA offense, (b) conducting a damage assessment, or (c) restoring computerized data or programs as they were prior to the offense. A Georgia U.S. district court judge recently sided with those jurists who hold that a service interruption is not required. Southern Parts & Eng’r’g Co. v. Air Compressor Services, LLC, Case No. 1:13-CV2231-TWT (N.D. Ga., Feb. 19, 2014).

Case summary

Two employees of Southern, a manufacturer of air compressors, resigned and created a competitor corporation. Allegedly, both before and after their resignation, the two employees accessed Southern’s computerized confidential information, but the employees did not cause an interruption in the company’s computer service. Southern sued the employees in a Georgia federal court for violating the CFAA. The employees moved to dismiss on the ground that Southern had not sustained a compensable loss because no “interruption of service” had occurred. Acknowledging a split of authority, the Georgia judge ruled that a service interruption is not required, and so the motion to dismiss was denied.

Statutory interpretation

A jurisdictional requirement under the CFAA is a “loss” of at least $5,000 caused by a violation of the Act. The CFAA defines a “loss” as “any reasonable cost to any victim, including the cost of responding to an offense, conducting a damage assessment, and restoring the data, program, system, or information to its condition prior to the offense, and any revenue lost, cost incurred, or other consequential damages incurred because of interruption of service.” 18 U.S.C. § 1030(e)(11). Courts are divided as to whether the phrase “incurred because of interruption of service” (a) modifies “any reasonable cost to any victim,” or (b) applies only to “any revenue lost, cost incurred, or other consequential damages.”

Different interpretations

Some judges have concluded that the CFAA provides for recovery of expenses resulting from “responding to an offense, conducting a damage assessment, and restoring the data, program, system, or information to its condition prior to the offense,” regardless of whether there was an “interruption of service.” The other view is that an “interruption of service” is a condition precedent to any recovery.

The judge in the Southern Parts case adopted the former interpretation — “interruption of service” is not a prerequisite — as have judges in the Middle and Southern Districts of Florida, the Middle District of Louisiana, and the Southern District of Texas, and one judge in the Eastern District of Michigan. By contrast, judges in the Northern District of Florida, the Northern District of Illinois, the District of Maryland, and the Southern District of New York, and a different judge in the Eastern District of Michigan, disagree. (Those are not the only courts to have ruled on the issue.) Clearly, reasonable minds can differ!


The victim of a CFAA violation must demonstrate that it has incurred $5,000 in expense in a single year. In the absence of an “interruption of service,” there may be an opportunity for forum shopping. The victim might consider whether personal jurisdiction and venue would be proper in a court that allows CFAA suits to proceed even though no interruption occurred. If the victim selects such a forum, the alleged wrongdoer might consider the possibility of seeking to transfer the litigation either to a district that declines to adjudicate CFAA lawsuits when there has been no “interruption of service” or, at least, to a district that has not yet weighed in on the issue.