Georgia Supreme Court, Edible IP, LLC v. Google, LLC, Docket No. S21G0798

Internet Law

 

Internet Search Algorithms

 

Keyword Advertising Program

 

Advertisement

 

Trade Name

 

Trademark

 

Goodwill

 

Third Party’s Right to Use a Trade Name

Civil Theft of Personal Property

Conversion

 

Trademark Infringement

 

Failure to State a Claim

 

“Money Had and Received” ((Common Law) Unjust Enrichment))

 

Georgia Law

 

 

 

This case involves Google LLC’s application of internet search algorithms, which it uses to auction off search terms for profit to advertisers, and the interests of Edible IP, LLC, which seeks to exercise control over the profit generated from its trade name and associated goodwill. In 2018, Edible IP brought an action against Google arising from Google’s monetization of the name “Edible Arrangements” without permission in its keyword advertising program. Google moved to dismiss the complaint, or in the alternative, to compel arbitration. The trial court granted the motion, dismissing the complaint on several grounds, including that it failed to state a claim, and alternatively compelling the parties to arbitration. Edible IP appealed from that order, and the Court of Appeals affirmed the dismissal for failure to state a claim. See Edible IP, LLC v. Google, LLC, 358 Ga. App. 218 (854 SE2d 565) (2021). We granted certiorari to address whether the trial court properly granted Google’s motion to dismiss. For the reasons that follow, we conclude that Edible IP has not stated a cognizable claim for relief and therefore affirm.

 

The Court of Appeals summarized the relevant facts underlying this appeal as follows:

The complaint alleges that Edible IP owns the trademarks, trade names, and other intellectual property associated with Edible Arrangements, a business consisting of websites and “brick-and-mortar” franchises that sell, among other things, floral-shaped arrangements of fresh-cut fruit. To support these websites and franchises, Edible IP licenses the use of its intellectual property to various entities. Edible IP, however, maintains ownership of the intellectual property, which includes the trademark/trade name “Edible Arrangements,” as well as the goodwill generated by the brand.

 

Google operates an internet search engine that allows individuals to search for information by typing relevant words into a search bar. Using algorithms that analyze the search terms and requested information, Google returns “organic” results of the query on a results page. According to the complaint, Google monetizes its search engine by “selling . . . ‘keywords’” to advertisers that “trigger advertisements on the search results page when Google users search for the keyword term.”

 

Keyword advertising is purchased through an auction-like process, with prospective advertisers bidding on terms suggested by Google. The auction includes general terms like “shoes” and “mother’s day gift,” as well as trade names such as “Edible Arrangements.” Google has never contracted with Edible IP for the right to use the Edible Arrangements trade name, and Edible IP has not otherwise given Google permission to include its trade name in the keyword advertising program. Nevertheless, Google began auctioning the trade name to advertisers in approximately 2011. As described by the complaint, Google places advertisements purchased through the keyword program “in a more attractive location on the results page than its ‘organic’ results in an effort to drive consumer behavior and get those consumers to click on the ad rather than Google’s ‘organic’ results.”

 

Based on these and other allegations, Edible IP sued Google for theft of personal property, conversion, money had and received, and civil Racketeer Influenced and Corrupt Organizations (“RICO”) violations. Google moved to dismiss the complaint, arguing that any claims alleged by Edible IP needed to be arbitrated, that a forum selection clause deprived the trial court of personal jurisdiction over Google, and that the complaint failed to state a claim upon which relief could be granted.

 

Google also requested that the trial court compel the parties to arbitration. 

Edible IP has alleged four claims: (1) civil theft of personal property; (2) conversion; (3) money had and received; and (4) violations of Georgia’s RICO Act. We will address each of the claims in turn.

 

a.   Civil Theft of Personal Property

Georgia law authorizes the owner of property to bring a civil action to recover damages from any person who either (1) willfully damages the owner’s personal property or (2) commits a theft as defined in OCGA § 16-8-2. See OCGA § 51-10-6 (a).

 

In its complaint, Edible IP (…), alleging that “Google has committed theft by taking, in violation of OCGA § 16-8- 2” by “unlawfully taking and otherwise appropriating Edible IP’s property by selling that property without permission to others and keeping the proceeds for itself.” Edible IP identified the property as its “trade name ‘Edible Arrangements’ and the good will and reputation associated with that name.”

 

(…) We are, thus, first required to determine whether Edible IP’s trade name and associated goodwill are “property” within the meaning of the civil theft statute and, if so, the contours of the associated property rights and whether Edible IP has sufficiently alleged that Google has wrongfully “appropriated” this property.

 

(…) “Goodwill” has been defined as “essentially the positive reputation that a particular business enjoys. This ‘positive reputation’ manifests itself as an expectancy that a business has of continued patronage from its customer.” 38 Am. Jur. 2d Good Will § 1. See also Armstrong v. Atlantic Ice & Coal Corp., 141 Ga. 464, 466 (81 SE 212) (1914) (“Good will is the favor which the management of a business wins from the public, and the probability that old customers will continue their patronage and resort to the old place.” (citation omitted)); Goodwill, Black’s Law Dictionary (11th ed. 2019) (defining goodwill as “a business’s reputation, patronage, and other intangible assets that are considered when appraising the business”).

 

We have expressly recognized that goodwill is a type of intangible property interest. See Nat. Assn. for Advancement of Colored People v. Overstreet, 221 Ga. 16, 29 (4) (a) (142 SE2d 816) (1965) (“It is uniformly recognized that good will is a species of property and constitutes a valuable asset of the business of which it is a part.” (citation omitted)). See also Reis v. Ralls, 250 Ga. 721, 723 (1) (301 SE2d 40) (1983) (“It appears clear that in addition to a trademark, a trade name, along with the goodwill it represents, may be the subject of an Article 9 security interest.”).

 

Construing these factual allegations in favor of Edible IP for the purpose of analyzing Google’s motion to dismiss, as we must, Edible IP has sufficiently alleged a property interest in the trade name “Edible Arrangements” and its associated goodwill within the meaning of OCGA § 51-10-6.

 

(…) Under each of these statutes, it is clear that trade names are only protected from use by others to the extent that such use is deceptive or there is a likelihood of confusion by the public.

 

Here, Edible IP has not alleged that Google’s use of the “Edible Arrangements” trade name in its keyword advertising program causes any confusion, and in fact, has disclaimed in the complaint that it is “seeking any . . . relief for any consumer confusion.” Thus, we see no basis in Georgia statutory law for Edible IP’s claim that Google has appropriated the “Edible Arrangements” trade name simply by using it in Google’s algorithms and keyword advertising programs.

 

And although Edible IP disclaims asserting any federal trademark claim, a review of federal trademark law is instructive on the contours of a third party’s right to use a trade name. Like Georgia statutory law and the common law, federal trademark law offers only limited trademark and trade name protection. For example, the doctrine of fair use permits reference to a competitor’s trade name in an advertisement.

 

(…) We see no reason to extend civil theft in Georgia to encompass the mere use of a trade name, without implicating consumer confusion, when doing so would subvert Georgia trademark law, federal trademark law, and the common law of trademark infringement.

 

(…) In sum, given Edible IP’s express disavowal of the element of consumer confusion in the complaint, it cannot state a claim for civil theft arising from the use of its trade name and associated goodwill.

 

Accordingly, the trial court did not err in granting Google’s motion to dismiss Edible IP’s claim for civil theft.

 

b. Conversion

Although Georgia law may provide relief for the conversion of certain intangible property, we have never extended that tort to claims based on the mere use of a trademark or on trade name infringement, and we decline to do so now.

 

Again, the cases Edible IP relies upon are readily distinguishable. For example, in English & Sons, Inc. v. Straw Hat Restaurants, Inc., 176 FSupp.3d 904 (N.D. Cal. 2016), the court concluded that the plaintiffs had “converted” the defendant’s trademark and other intellectual property by wrongfully registering ownership of the trademark with theU.S. Patent and Trademark Office – not just by simple use. See id. at 923 (2).

 

Other courts have likewise rejected attempts to expand the tort of conversion to encompass the type of intangible property traditionally protected within the scope of trademark law. See, e.g., Ortega v. Burgos, Case No. 12-CV-05421, 2014 U.S. Dist. LEXIS 70457, at *3 (II) (E.D.N.Y. May 22, 2014) (dismissing trademark conversion claim because “not only has this Court been unable to find any authority that recognizes trademark conversion, the leading treatise on trademark law states that, ‘every court to consider such a claim has rejected it’” (citation omitted)). See also 4 McCarthy, supra, § 25:9.50 (“Occasionally, a trademark owner will allege, either along with or instead of a traditional infringement claim, that its mark has been ‘converted’ by defendant. Every court to consider such a claim has rejected it. The author agrees that the tort of ‘conversion’ should not be stretched and deformed to substitute for the traditional law of trademark infringement.”).

 

Accordingly, we cannot say that the trial court erred in concluding that Edible IP had failed to state a claim for conversion.

 

c. Money Had and Received

In Count 3 of its complaint, Edible IP asserts a claim for money had and received, alleging that Google currently holds a sum of money belonging to Edible IP. The common law action for money had and received “is founded upon the equitable principle that no one ought to unjustly enrich himself at the expense of another.” Sentinel Offender Services, LLC v. Glover, 296 Ga. 315, 331 (4) (a) (766 SE2d 456) (2014) (citation omitted). This action “is maintainable in all cases where one has received money under such circumstances that in equity and good conscience he ought not to retain it.” Id. (citation and punctuation omitted).

 

Edible IP’s claim for money had and received again relies on its unavailing assertion that, rather than selling advertising space, Google is in essence selling Edible IP’s trade name and illegally profiting from it. And, despite Edible IP’s insistence that our courts have previously held that “the fact that money was received from a third person will not affect a defendant’s liability,” this holding does not change the futility of Edible IP’s claim for money had and received because Edible IP has no claim to the profits that Google has earned by selling advertising. Accordingly, for this reason and for the reasons discussed above in Divisions 1 (a) and (b), Edible IP cannot, as a matter of law, show that it is entitled to profits that Google earns through its keyword advertising program (even if the program uses the trade name “Edible Arrangements”) or that Google has been unjustly enriched at Edible IP’s expense, and this claim likewise fails.

 

 

Secondary sources: J. Thomas McCarthy, Conversion of a Trademark, 4 McCarthy on Trademarks and Unfair Competition, § 25:9.50 (5th ed. 2019) 

 

 

 

 

(Georgia Supreme Court, Feb. 15, 2022, Edible IP, LLC v. Google, LLC, Docket No. S21G0798)

Using Artificial Intelligence and Algorithms

 

Using Artificial Intelligence and Algorithms

 

By: Andrew Smith, Director, FTC Bureau of Consumer Protection

April 8, 2020

Republication

 

AI, Algorithms, Consumer, Privacy, Data, Bogus “Likes”, Chatbot, Facial Recognition, Voice-cloning Technologies, Title VII, Disparate Impact

 

How companies can manage the consumer protection risks of AI and algorithms.

 

 

 

BE TRANSPARENT.

 

Don’t deceive consumers about how you use automated tools. Oftentimes, AI operates in the background, somewhat removed from the consumer experience. But, when using AI tools to interact with customers (think chatbots), be careful not to mislead consumers about the nature of the interaction. The Ashley Madison complaint alleged that the adultery-oriented dating website deceived consumers by using fake “engager profiles” of attractive mates to induce potential customers to sign up for the dating service. And the Devumi complaint alleged that the company sold fake followers, phony subscribers, and bogus “likes” to companies and individuals that wanted to boost their social media presence. The upshot? If a company’s use of doppelgängers – whether a fake dating profile, phony follower, deepfakes, or an AI chatbot – misleads consumers, that company could face an FTC enforcement action.

 

Be transparent when collecting sensitive data. The bigger the data set, the better the algorithm, and the better the product for consumers, end of story…right? Not so fast. Be careful about how you get that data set. Secretly collecting audio or visual data – or any sensitive data – to feed an algorithm could also give rise to an FTC action. Just last year, the FTC alleged that Facebook misled consumers when it told them they could opt in to facial recognition – even though the setting was on by default. As the Facebook case shows, how you get the data may matter a great deal.

 

If you make automated decisions based on information from a third-party vendor, you may be required to provide the consumer with an “adverse action” notice. Under the FCRA, a vendor that assembles consumer information to automate decision-making about eligibility for credit, employment, insurance, housing, or similar benefits and transactions, may be a “consumer reporting agency.” That triggers duties for you, as the user of that information. Specifically, you must provide consumers with certain notices under the FCRA. Say you purchase a report or score from a background check company that uses AI tools to generate a score predicting whether a consumer will be a good tenant. The AI model uses a broad range of inputs about consumers, including public record information, criminal records, credit history, and maybe even data about social media usage, shopping history, or publicly-available photos and videos. If you use the report or score as a basis to deny someone an apartment, or charge them higher rent, you must provide that consumer with an adverse action notice. The adverse action notice tells the consumer about their right to see the information reported about them and to correct inaccurate information.

 

EXPLAIN YOUR DECISION TO THE CONSUMER.

 

If you deny consumers something of value based on algorithmic decision-making, explain why. Some might say that it’s too difficult to explain the multitude of factors that might affect algorithmic decision-making. But, in the credit-granting world, companies are required to disclose to the consumer the principal reasons why they were denied credit, and it’s not good enough simply to say “your score was too low” or “you don’t meet our criteria.” You need to be specific (e.g., “you’ve been delinquent on your credit obligations” or “you have an insufficient number of credit references”). This means that you must know what data is used in your model and how that data is used to arrive at a decision. And you must be able to explain that to the consumer. If you are using AI to make decisions about consumers in any context, consider how you would explain your decision to your customer if asked.

 

If you use algorithms to assign risk scores to consumers, also disclose the key factors that affected the score, rank ordered for importance. Similar to other algorithmic decision-making, scores are based on myriad factors, some of which may be difficult to explain to consumers. For example, if a credit score is used to deny someone credit, or offer them less favorable terms, the law requires that consumers be given notice, a description of the score (its source, the range of scores under that credit model), and at least four key factors that adversely affected the credit score, listed in the order of their importance based on their effect on the credit score.

 

If you might change the terms of a deal based on automated tools, make sure to tell consumers. More than a decade ago, the FTC alleged that subprime credit marketer CompuCredit violated the FTC Act by deceptively failing to disclose that it used a behavioral scoring model to reduce consumers’ credit limits. For example, if cardholders used their credit cards for cash advances or to make payments at certain venues, such as bars, nightclubs, and massage parlors, they might have their credit limit reduced. The company never told consumers that these purchases could reduce their credit limit – neither at the time they signed up nor at the time they reduced the credit limit. That decade-old matter is just as important today. If you’re going to use an algorithm to change the terms of the deal, tell consumers.

 

ENSURE THAT YOUR DECISIONS ARE FAIR.

 

Don’t discriminate based on protected classes. Cavalier use of AI could result in discrimination against a protected class. A number of federal equal opportunity laws, such as ECOA and Title VII of the Civil Rights Act of 1964, may be relevant to such conduct. The FTC enforces ECOA, which prohibits credit discrimination on the basis of race, color, religion, national origin, sex, marital status, age, or because a person receives public assistance. If, for example, a company made credit decisions based on consumers’ Zip Codes, resulting in a “disparate impact” on particular ethnic groups, the FTC could challenge that practice under ECOA. You can save yourself a lot of problems by rigorously testing your algorithm, both before you use it and periodically afterwards, to make sure it doesn’t create a disparate impact on a protected class.

 

Give consumers access and an opportunity to correct information used to make decisions about them. The FCRA regulates data used to make decisions about consumers – such as whether they get a job, get credit, get insurance, or can rent an apartment. Under the FCRA, consumers are entitled to obtain the information on file about them and dispute that information if they believe it to be inaccurate. Moreover, adverse action notices are required to be given to consumers when that information is used to make a decision adverse to the consumer’s interests. That notice must include the source of the information that was used to make the decision and must notify consumers of their access and dispute rights. If you are using data obtained from others – or even obtained directly from the consumer – to make important decisions about the consumer, you should consider providing a copy of that information to the consumer and allowing the consumer to dispute the accuracy of that information.

 

ENSURE THAT YOUR DATA AND MODELS ARE ROBUST AND EMPIRICALLY SOUND.

 

If you provide data about consumers to others to make decisions about consumer access to credit, employment, insurance, housing, government benefits, check-cashing or similar transactions, you may be a consumer reporting agency that must comply with the FCRA, including ensuring that the data is accurate and up to date. You may be thinking: We do AI, not consumer reports, so the FCRA doesn’t apply to us. Well, think again. If you compile and sell consumer information that is used or expected to be used for credit, employment, insurance, housing, or other similar decisions about consumers’ eligibility for certain benefits and transactions, you may indeed be subject to the FCRA. What does that mean? Among other things, you have an obligation to implement reasonable procedures to ensure maximum possible accuracy of consumer reports and provide consumers with access to their own information, along with the ability to correct any errors. RealPage, Inc., a company that deployed software tools to match housing applicants to criminal records in real time or near real time, learned this the hard way. The company ended up paying a $3 million penalty for violating the FCRA by failing to take reasonable steps to ensure the accuracy of the information they provided to landlords and property managers.

 

If you provide data about your customers to others for use in automated decision-making, you may have obligations to ensure that the data is accurate, even if you are not a consumer reporting agency. Companies that provide data about their customers to consumer reporting agencies are referred to as “furnishers” under the FCRA. They may not furnish data that they have reasonable cause to believe may not be accurate. In addition, they must have in place written policies and procedures to ensure that the data they furnish is accurate and has integrity. Furnishers also must investigate disputes from consumers, as well as disputes received from the consumer reporting agency. These requirements are important to ensure that the information used in AI models is as accurate and up to date as it can possibly be. And, the FTC has brought actions, and obtained big fines, against companies that furnished information to consumer reporting agencies but that failed to maintain the required written policies and procedures to ensure that the information that they report is accurate.

 

Make sure that your AI models are validated and revalidated to ensure that they work as intended, and do not illegally discriminate. Again, more lessons from the world of consumer lending, where credit-grantors have been using data and algorithms for decades to automate the credit underwriting process. The lending laws encourage the use of AI tools that are “empirically derived, demonstrably and statistically sound.” This means, among other things, that they are based on data derived from an empirical comparison of sample groups, or the population of creditworthy and noncreditworthy applicants who applied for credit within a reasonable preceding period of time; that they are developed and validated using accepted statistical principles and methodology; and that they are periodically revalidated by the use of appropriate statistical principles and methodology, and adjusted as necessary to maintain predictive ability.

 

HOLD YOURSELF ACCOUNTABLE FOR COMPLIANCE, ETHICS, FAIRNESS, AND NONDISCRIMINATION.

 

Ask questions before you use the algorithm. Going back to the 2016 Big Data report, the Commission warned companies that big data analytics could result in bias or other harm to consumers. To avoid that outcome, any operator of an algorithm should ask four key questions:

• How representative is your data set?
• Does your data model account for biases?
• How accurate are your predictions based on big data?
• Does your reliance on big data raise ethical or fairness concerns?

 

Protect your algorithm from unauthorized use. If you’re in the business of developing AI to sell to other businesses, think about how these tools could be abused and whether access controls and other technologies can prevent the abuse. For instance, just last month, the FTC hosted a workshop on voice-cloning technologies. Thanks to machine learning, these technologies enable companies to use a five-second clip of a person’s actual voice to generate a realistic audio of the voice saying anything. This technology promises to help people who have lost the ability to speak, among other things, but could be easily abused if it falls into the hands of people engaged in imposter schemes. One company that is introducing this cloning technology is vetting users and running the technology on its own servers so that it can stop any abuse that it learns about.

 

Consider your accountability mechanism. Consider how you hold yourself accountable, and whether it would make sense to use independent standards or independent expertise to step back and take stock of your AI. For example, going back to the algorithm that ended up discriminating against black patients, well-intentioned employees were trying to use the algorithm to target medical interventions to the sickest patients. Outside, objective observers who independently tested the algorithm were the ones who discovered the problem. Such outside tools and services are increasingly available as AI is used more frequently, and companies may want to consider using them.