Robinson Cole LLP
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Daniel J. Lass concentrates on the preparation and prosecution of patent applications in electro-mechanical and mechanical-related areas and other disciplines. With a degree in mechanical engineering, Dan has extensive experience handling patent applications related to varied technologies, including power systems, lighting and controls, high and low voltage industrial and consumer devices, power tools, medical devices, and manufacturing systems, in both U.S. and worldwide jurisdictions.

Dan brings a wealth of experience to his job as a result of his three years of patent agent work prior to joining Robinson+Cole. Dan has extensive experience in drafting and prosecuting applications for medical devices, hydrocarbon production, and virtual reality, both domestically and internationally.

Dan also works with the Data Privacy + Cybersecurity and Artificial Intelligence Teams, advising clients on compliance with state and federal privacy laws.

He is registered to practice before the United States Patent and Trademark Office.

  • University of Maryland (Bachelors)
    • B.S., Mechanical Engineering
    • B.A., History
    • Pi Tau Sigma
    • Phi Alpha Theta
  • American University, Washington College of Law (Juris Doctor)
    • American University Law Review
    • Intellectual Property Brief
    • Student Bar Association

  • U.S. Patent and Trademark Office
  • District of Columbia

Publications


Data Privacy + Cybersecurity Insider teaser
April 30, 2026

Data Privacy + Cybersecurity Insider

Data Privacy + Cybersecurity Insider teaser
February 26, 2026

Data Privacy + Cybersecurity Insider

Data Privacy + Cybersecurity Insider teaser
February 19, 2026

Data Privacy + Cybersecurity Insider

Data Privacy + Cybersecurity Insider teaser
April 30, 2026

Data Privacy + Cybersecurity Insider

Data Privacy + Cybersecurity Insider teaser
February 26, 2026

Data Privacy + Cybersecurity Insider

Data Privacy + Cybersecurity Insider teaser
February 19, 2026

Data Privacy + Cybersecurity Insider

Data Privacy + Cybersecurity Insider teaser
February 12, 2026

Data Privacy + Cybersecurity Insider

Data Privacy + Cybersecurity Insider teaser
December 24, 2025

Data Privacy + Cybersecurity Insider

Data Privacy + Cybersecurity Insider teaser
May 16, 2025

Data Privacy + Cybersecurity Insider

Data Privacy + Cybersecurity Insider teaser
March 13, 2025

Data Privacy + Cybersecurity Insider

Data Privacy + Cybersecurity Insider teaser
February 13, 2025

Data Privacy + Cybersecurity Insider

Data Privacy + Cybersecurity Insider teaser
February 6, 2025

Data Privacy + Cybersecurity Insider



Data Privacy + Cybersecurity Insider teaser
February 12, 2026

Data Privacy + Cybersecurity Insider

Data Privacy + Cybersecurity Insider teaser
December 24, 2025

Data Privacy + Cybersecurity Insider

Data Privacy + Cybersecurity Insider teaser
May 16, 2025

Data Privacy + Cybersecurity Insider

Data Privacy + Cybersecurity Insider teaser
March 13, 2025

Data Privacy + Cybersecurity Insider

Data Privacy + Cybersecurity Insider teaser
February 13, 2025

Data Privacy + Cybersecurity Insider

Data Privacy + Cybersecurity Insider teaser
February 6, 2025

Data Privacy + Cybersecurity Insider


News


June 23, 2025

Capital Markets + Securities Group Represents Dr Ashleys Limited in Merger with Impact BioMedical Inc

Robinson + Cole’s Capital Market +Securities group was pleased to represent Dr Ashleys Limited, a global pharmaceutical product developer with operations in Hong Kong and Latvia, in the execution of a definitive merger agreement on June 21, 2025, with Impact BioMedical Inc., a Texas-based developer of biopharmaceutical technologies. The transaction was announced on June 23, 2025, and is expected to close later this year after satisfying applicable regulatory clearance, including SEC review, and customary closing conditions. The team advising the transaction is led by Group chair Mitchell Lampert and counsel Zhuoyao (Joy) Hui and included member Tiange (Tim) Chen, with cross-disciplinary support from Christine Bromberg, John Mutchler, Virginia McGarrity, Ze'-ev Eiger, Benjamin Daniels, Kathleen Porter, Daniel Lass, and IP Specialists Christina Engel and Noelle Melnik. Read the press release.

GlobeNewswire
November 16, 2022

Robinson+Cole Continues Significant Strategic Expansion with New Office in Washington, DC

June 23, 2025

Capital Markets + Securities Group Represents Dr Ashleys Limited in Merger with Impact BioMedical Inc

Robinson + Cole’s Capital Market +Securities group was pleased to represent Dr Ashleys Limited, a global pharmaceutical product developer with operations in Hong Kong and Latvia, in the execution of a definitive merger agreement on June 21, 2025, with Impact BioMedical Inc., a Texas-based developer of biopharmaceutical technologies. The transaction was announced on June 23, 2025, and is expected to close later this year after satisfying applicable regulatory clearance, including SEC review, and customary closing conditions. The team advising the transaction is led by Group chair Mitchell Lampert and counsel Zhuoyao (Joy) Hui and included member Tiange (Tim) Chen, with cross-disciplinary support from Christine Bromberg, John Mutchler, Virginia McGarrity, Ze'-ev Eiger, Benjamin Daniels, Kathleen Porter, Daniel Lass, and IP Specialists Christina Engel and Noelle Melnik. Read the press release.

GlobeNewswire
November 16, 2022

Robinson+Cole Continues Significant Strategic Expansion with New Office in Washington, DC

Data Privacy + Cybersecurity Insider


EU AI Act Update: Omnibus Talks Stall, but Clock Is Still Ticking

Talks between European Union legislators broke down on Wednesday as they tried to agree on proposed amendments to the EU AI Act. At the center of the debate is the Digital Omnibus on AI, first introduced in November 2025, which would delay several key compliance deadlines under the Act. If approved, the Digital Omnibus would push back the compliance date for high-risk AI systems classified under Annex III from August 2, 2026, to December 2, 2027. For products already regulated under existing EU harmonization legislation and listed in Annex I, the deadline would shift to August 2, 2028. Not everyone is on board, though. Some within the EU oppose including certain products, like medical devices, in the Act’s scope under Annex I, arguing that those products are already sufficiently governed by sector-specific laws. In their view, layering the AI Act on top of existing regulation would create unnecessary and burdensome double regulation, and sector-specific frameworks are the better tool for overseeing these AI systems. With legislators unable to reach a consensus, they agreed to pause talks and will likely resume negotiations next month. Organizations should keep a close eye on these discussions, because the outcome will directly shape compliance timelines. That said, the current deadlines still stand. Unless a compromise is reached, the Act’s obligations for high-risk AI systems begin taking effect in August 2026. That means now is the time to start building out governance programs. Practical steps include documenting AI systems currently in use, mapping them against the Act’s risk classifications, and putting processes in place to meet the Act’s transparency requirements.

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Using Trademarks to Protect IP from AI

The fight between creators and big tech has mostly been focused on the alleged copyright infringement of using creative works in AI training data. However, trademark law might be the next battleground as creators look for additional ways to protect their work from AI-related misuse. Actor Matthew McConaughey recently received U.S. Registration No. 8,070,191 for his famous line, “Alright, Alright, Alright” delivered in the 1993 movie Dazed and Confused. The registration is for a sensory mark that protects the distinct intonation he made famous in the film.  Trademarks protect a brand’s identity. Unlike copyright infringement, trademark infringement generally turns on whether a mark is used in commerce in a way that is likely to cause consumer confusion. That means using a trademarked phrase as an AI input, or having it appear in an AI output, is not automatically trademark infringement. Even so, trademarks can be a useful tool for artists looking to protect their identity from deepfakes and other AI-generated content that improperly imitates them. In that sense, trademark law may offer creators another path to push back against AI developers or users who try to profit from an artist’s identity without permission.

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Extraterritorial Scope of the EU AI Act

Given its extraterritorial reach, companies outside Europe should start preparing for the EU AI Act now. In general, the Act will apply to companies that develop high-risk AI systems used in the EU and that provide outputs from those systems, even if the companies have no physical presence in Europe. Ahead of August 2026, companies, especially those who have no direct dealings within the EU, should begin auditing their systems and practices to determine whether they fall under the Act to avoid surprises of later enforcement actions. That starts with mapping how the organization uses AI to generate outputs and identifying where AI is being used by third-party vendors and partners. Once those AI use cases are documented, the next step is assessing whether any of documented cases could be considered high-risk, which may trigger compliance requirements under the Act. Companies using or deploying high-risk AI outside Europe should work with suppliers and customers to update contracts so they are notified when AI is used and can restrict AI systems and outputs from being shared in the EU, where appropriate. For companies that intend to do business in Europe, now is also the time to begin building a risk management program in preparation for the Act’s enforcement.

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Patentability Implications of the EU AI Act

Novelty is a core requirement for any invention to be patentable. Put simply, your invention generally cannot have been publicly disclosed before the patent application’s effective filing date. In the United States, 35 U.S.C. § 102 includes a one-year grace period for certain public disclosures made before you file—many other jurisdictions do not have this grace period. Europe, for example, generally applies an absolute novelty standard, where your invention can bar patentability if you publicly disclose first and file later. This is where the EU AI Act can create an unexpected patentability issue. The Act sets out a comprehensive framework for regulating AI and includes a mandatory registration requirement for AI systems considered “high-risk.” An AI system is considered high-risk when it relates to areas such as safety components, critical infrastructure, education, border control, and law enforcement. Before a high-risk AI system can be placed on the market, the provider must register the system with the EU Commission and submit information about the system in a searchable and publicly accessible EU database. If the information submitted includes enabling technical details, that registration can function as a public disclosure and can block patentability in absolute novelty jurisdictions, like Europe and China. Bottom line: if EU AI Act registration is on your roadmap, build IP planning into the timeline. Companies should consider preparing and filing patent applications at or before submitting information to the EU Commission, so their own disclosures do not become prior art against later-filed patent applications.

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Reverse Engineering in the Age of AI: Are Your Trade Secrets Still Safe?

Artificial intelligence has dramatically broadened the capabilities of anyone looking to reverse-engineer public-facing products. What once took specialized skill, deep pockets, and many hours now requires little more than a curious mind and a powerful AI model. For companies built around valuable and confidential know-how, this shift has profound implications, especially for in-house counsel tasked with safeguarding trade secrets. How AI Is Changing Reverse Engineering Reverse engineering is the process of using publicly available information, like software code or a publicly available user interface, to discover nonpublic information about a product or process. Traditionally, this was a slow and expert-driven endeavor that required a significant amount of information. But with advances in AI—including code analysis tools, language models, and automated data scrapers, reverse engineering can be carried out with significantly less information and at a scale and speed previously unimaginable. Machine learning and predictive modeling allows AI to uncover hidden information and piece together proprietary logic from software outputs, reconstruct algorithms from behavioral patterns, and even deduce “secret sauce” ingredients that were once thought irretrievable. No company operating in the digital world, whether SaaS, traditional tech, or even non-tech with proprietary digital processes, is immune. Trade Secret Law: What Counts as “Improper Means”? In the US, trade secrets are protected under the Uniform Trade Secrets Act (UTSA) and the Defend Trade Secrets Act (DTSA). Both statutes define a trade secret as information that has economic value because it is not generally known or “readily ascertainable,” and reasonable efforts are used to keep it confidential. Crucially, these laws focus on misappropriation, wrongful acquisition or use, typically through “improper means.” However, both UTSA and DTSA have always made a major exception: information obtained by reverse engineering a publicly available product is not considered “improper means”—and therefore, is not misappropriation. Yet the AI era is disturbing what counts as “proper” and “improper.” Is deploying bots to scrape massive amounts of data “proper”? Is coaxing unexpected outputs from a generative AI model by way of “prompt injection” fair play, or does it cross the line into cyberattack territory? Recent legal cases signal that courts are struggling with these questions. Recent Cases: The Law Grapples with AI In a 2024 case, a company claimed competitors used “prompt injection” (manipulating generative AI with crafted inputs) to elicit sensitive outputs, allegedly extracting valuable trade secrets. Adding to the intrigue, the attackers used false credentials and impersonation—raising the specter of “improper means.” The Eleventh Circuit recently ruled that even when data is accessible to the public, how it’s accessed matters. Automated scraping of millions of insurance quotes, carried out with bots, was deemed “improper means,” casting doubt on companies relying purely on “technical public availability” as a shield. Underlying both decisions is a key trend: As AI makes it easier for outsiders to reconstruct proprietary information, courts are increasingly interrogating what makes a method of acquisition truly “improper.” Heightened Risk: When “Readily Ascertainable” Is Redefined A second risk looms: As AI tools become more adept at deducing secrets from public clues, courts may decide that information is, in fact, “readily ascertainable.” That could mean what was once securely covered by trade secret law might lose protected status, not because of a security lapse, but because technology makes it easier for competitors to deduce nonpublic confidential information from the outside. Protecting Trade Secrets in an AI-Powered World For in-house counsel and business leaders, the message is clear: the old playbook is no longer enough. Here are practical steps for safeguarding confidential information in the current environment: Reinforce Technical Barriers Implement rate limiting, CAPTCHA challenges, and advanced bot-detection tools, especially on SaaS platforms. Apply AI-powered monitoring for unusual patterns that might signal scraping or prompt injection attempts. Update Legal Protections Revise terms of service to expressly prohibit automated access, scraping, reverse engineering, and prompt injection. Make these clauses visible and actively enforce them. Incorporate explicit provisions regarding AI-specific attack vectors in your contracts and NDAs. Document all incidents and responses to show your “reasonable measures” in the event of future litigation. Revisit Traditional Best Practices Limit access to core confidential information for each trade secret to those on a genuine “need to know” basis, and monitor usage carefully. Maintain a robust trade secret management program, including labeling sensitive documents and regularly auditing access controls. Review employment and third-party agreements to confirm they’re up to date for the realities of AI-era risks. Stay Vigilant and Evolve. Conduct periodic reviews of your confidentiality policies. If your framework predates the AI surge of 2023, it’s time for an update. Educate internal teams on the new forms of reverse engineering and AI-enabled threats, including social engineering. Consider documenting how your company specifically addresses the risk of AI-assisted reverse engineering. Trade secret protection has always demanded vigilance, but the rise of AI has upped the stakes. The law continues to evolve as judges confront novel scenarios, but companies cannot afford to wait for clear answers. By implementing a multi-layered approach, combining legal vigilance, technical defenses, and up-to-date policies, businesses can better safeguard their intellectual assets and navigate the uncertain legal terrain of the AI age. Have your trade secret protocols kept pace with the march of AI? Now is the time to review, revise, and reinforce.

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Generative AI Training may not Qualify for the Fair Use Defense

Last week, the Copyright Office released the third and final part of its report exploring copyright-related issues posed by artificial intelligence (AI). Unlike the first two parts, the third was released as a “pre-publication” version. It was published less than a day after Dr. Carla Hayden, the Librarian of Congress, was fired by President Trump and a day before Shira Perlmutter, the Register of Copyrights, was fired by President Trump. Building off its earlier parts, the latest publication focuses on how copyright law and the fair use defense should be applied to companies that use copyrighted works to train AI models. The report concluded that companies presumptively infringe the copyright protections of others when they copy materials to use in training data. Additionally, the report concluded that the numerical parameters of the model can also be infringing when it can reproduce the copyrighted work as a memorized example. However, the fair use defense permits copying another’s work. Many uses of copyrighted works by an AI model are likely transformative. However, the Office concluded that commercializing copyrighted works in training data to compete with the original works is unlikely to fit the fair use exception. AI models rapidly create new works that imitate a creator’s style. The Office concluded that this market dilution weighs against the fair use argument for generative AI companies.  Recognizing that the copyrighted data is needed in the training data, the report concluded by exploring different licensing frameworks that companies can use to acquire the data. The report does not recommend that the government intervene to establish a licensing regime right away, but that the market should continue to develop. It is unclear if the Trump administration will rescind the report or issue a final report with changes. However, companies developing AI tools should likely consider the report regardless of the administration’s actions.

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Artists Protest AI Copyright Proposal in the U.K.

British Prime Minister Keir Starmer wants to turn the U.K. into an artificial intelligence (AI) superpower to help grow the British economy by using policies that he describes as “pro-innovation.” One of these policies proposed relaxing copyright protections. Under the proposal, initially unveiled in December 2024, AI companies could freely use copyrighted material to train their models unless the owner of the copyrighted material opted out. Although some Parliament members called the proposal an effective compromise between copyright holders and AI companies, over a thousand musicians released a “silent album” to protest the proposed changes to U.K. copyright laws. The album, currently streaming on Spotify, includes 12 tracks of only ambient sound. According to the musicians, the silent tracks illustrate empty recording studios and represent the impact they “expect the government’s proposals would have on musicians’ livelihoods.” To further convey their unhappiness with the proposed changes, the title of these twelve songs, when combined, reads, “The British government must not legalize music theft to benefit AI companies.”  High-profile artists like Elton John, Paul McCartney, Dua Lipa, and Ed Sheeran have also signed a letter urging the British government to avoid implementing these proposed changes. According to the artists, implementing the new rule would effectively give artists’ rights away to big tech companies.  The British government launched a consultation that sought comments on the potential changes to the copyright laws. The U.K. Intellectual Property Office received over 13,000 responses before the consultation closed at the end of February 2025, which the government will now review as it seeks to implement a final policy.

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Thomson Reuters Wins Copyright Case Against Former AI Competitor

Thomson Reuters scored a major victory in one of the first cases dealing with the legality of using copyrighted data to train artificial intelligence (AI) models. In 2020, Thomson Reuters sued the now-defunct AI start-up Ross Intelligence for alleged improper use of Thomson Reuters materials, including case headnotes in its Westlaw search engine, to train its new AI model. A key issue before the court was whether Ross Intelligence’s usage of headnotes constituted fair use, which permits a person to use portions of another’s work in limited circumstances without infringing on their copyright. Courts use four factors to determine whether a defendant can successfully use the fair use defense: (1) the purpose and character of the use; (2) the nature of the copyrighted work; (3) how much of the work was copied and was that a substantial part of the entire work; and (4) whether the defendant’s use of the work affected its value. In this case, federal judge Stephanos Bibas determined that each side had two factors in their favor. But the fourth factor, which supported Thomson Reuters, weighed most heavily in his finding that the fair use defense was inapplicable because Ross Intelligence sought to develop a competitive product. Lawsuits against other companies, like OpenAI and Microsoft, are currently pending in courts throughout the country, and decisions in those cases may involve similar questions about the fair use defense. However, Judge Bibas noted that Ross Intelligence’s AI model was not generative and that his decision was based only on Ross’s non-generative AI model. The distinction between the training data and resulting outputs from generative and non-generative AI will likely be key to deciding future cases.

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With Enough Human Contribution, AI-Generated Outputs May Be Copyright Protectable

After several months of delays, the U.S. Copyright Office has published part two of its three-part report on the copyright issues raised by artificial intelligence (AI). This part, entitled “Copyrightability,” focuses on whether AI-generated content is eligible for copyright protection in the U.S. An output generated with the assistance of AI is eligible for copyright protection if there is sufficient human contribution. The report notes that copyright law does not need to be updated to support this conclusion. The Supreme Court has explained that individuals can receive copyright protection when they translate an idea into a fixed and tangible medium. When an AI model supplies all creative effort, no human can be considered an author, thus no copyrightable work. However, when an AI model assists a human’s creative expression, the human is considered an author. The Copyright Office analogizes this to the principle of joint authorship because a work is copyright-eligible even if a single person is not responsible for creating the entire work. The contribution level is determined by what a person provides to the AI model. The Copyright Office reasoned that inputting a prompt by itself is not a sufficient contribution to be considered an author. The report analogizes this to a person hiring an artist, where the person may have a general artistic vision, but the artist produces the creative work. Additionally, because AI models generally operate as a black box, a user is cannot exert the necessary level of control to be considered an author.  However, when a user inputs a prompt in combination with their original work, the resulting AI-generated output is copyrightable for the material that is perceivable from their expression. The author’s own work helps provide the AI model with a starting point and limits the range of outputs. Finally, AI-generated content can be copyrightable when arranged or modified with human creativity. For example, while an AI-generated image is not copyrightable, a compilation of the images and a human-authored story can be protected by copyright. The Copyright Office is currently working on the third part of its report, which should be published later this year and will focus on the implications of using protected works to train AI models.

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RealPage Antitrust Consent Decree Proposed

In August 2024, the Department of Justice (DOJ) and eight states filed a civil antitrust lawsuit against RealPage Inc., alleging that its software was used to unlawfully decrease competition among landlords and maximize profits. Last week, the DOJ, now joined by ten states, filed an amended complaint alleging that landlords Greystar Real Estate Partners LLC, Blackstone’s LivCor LLC, Camden Property Trust, Cushman & Wakefield Inc., Pinnacle Property Management Services LLC, Willow Bridge Property Company LLC, and Cortland Management participated in the price-fixing scheme. These companies operate over 1.3 million residential units across 43 states and the District of Columbia. According to the amended complaint, these landlords shared sensitive information through RealPage’s pricing algorithm to decrease competition and increase corporate profits. Jennifer Bowcock, RealPage’s Senior Vice President of Communications, rebutted the allegations, arguing that issues with housing affordability stem from the limited supply of residential units and that the government should “stop scapegoating RealPage – and now [its] customers – for the housing affordability problems.” The DOJ also announced a proposed consent decree with Cortland Management, where the claims against Cortland would be resolved in exchange for agreeing to cooperate with the DOJ’s ongoing investigation against the remaining defendants. Under the terms of the proposed agreement, Cortland would be barred from using a competitor’s sensitive data to train a pricing model, pricing units with the assistance of an algorithm without court supervision, and soliciting or disclosing sensitive information with other companies to set rental prices. A spokesman for Cortland indicated that it is pleased with the outcome and is looking forward to “improv[ing the] resident experience” in 2025. Under the Tunney Act, P.L. 93-528, the proposed consent decree will be published in the Federal Register for a 60-day comment period, after which the court can enter final judgment. The case is United States v. RealPage Inc., dkt. no, 1:24-cv-00710 (LCB) (M.D.N.C. filed Aug. 23, 2024).

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New Jersey AG Says Anti-Discrimination Law Covers Algorithmic Discrimination

Last week, New Jersey Attorney General Matthew Platkin announced new guidance that the New Jersey Law Against Discrimination (LAD) applies to algorithmic discrimination, i.e., when automated systems treat people differently or negatively based on protected characteristics. This can happen with algorithms trained on biased data or with systems designed with biases in mind. LAD prohibits discrimination based on a protected characteristic like race, religion, national origin, sex, pregnancy, and gender identity, among other things. According to the guidance, employers, housing providers, and places of public accommodation who make discriminatory decisions using automated decision-making tools, like artificial intelligence (AI), would violate LAD. LAD is not an intent-based statute. Therefore, a party can violate LAD even if it uses an automated decision-maker with no intent to discriminate or uses a discriminatory algorithm developed by a third party. The guidance does not create any new rights or obligations. However, in noting that the law covers automated decision-making, the guidance encourages companies to carefully design, test, and evaluate any AI system they seek to employ to help avoid producing discriminatory impacts.

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Privacy Tip #427 – Ahead of the TikTok Ban, Users are Turning to Another Chinese App with Similar Privacy Concerns – What you Should Know

TikTok users are seeking alternate platforms to share and view content as the U.S. is set to ban the popular social media app on January 19, 2025. Instead of turning to U.S.-based companies like Facebook or Instagram, users are flocking to another Chinese app called Xiaohongshu, also known as RedNote. The app, which previously had little presence in the U.S. market, shot up to the most downloaded app in Apple’s app store this week. RedNote shares similarities to Yelp, where users share recommendations, but it also allows users to post short clips, similar to the soon-to-be-banned TikTok. While some of these TikTok users choose to switch to RedNote because of the similar short-form video format, other users appear to be purposefully choosing another Chinese-owned app as a form of protest. Either way, ordinary American and Chinese citizens can easily interact in new ways on the internet through RedNote. However, RedNote includes many of the same privacy and national security issues that the U.S. government raised concerning TikTok. Although many users ordinarily ignore privacy policies, RedNote’s privacy policy is written in Mandarin, making it even more difficult (and in some cases impossible) for users to understand. A translation of the privacy policy indicates that RedNote collects sensitive data like a user’s IP address and browsing habits. As a Chinese-based app, RedNote is also similarly subject to the Chinese data laws that led U.S. lawmakers to ban TikTok. The TikTok ban could eventually be extended to include RedNote and other Chinese (and other foreign country) apps national security and privacy concerns exist. With other short-form video services (e.g., Instagram Reels and YouTube Shorts) provided by U.S. companies, users do not need to expose their personal data to Chinese-based companies. Additionally, using RedNote to circumvent the TikTok ban could be problematic, particularly for government workers with security clearances. RedNote is not worth these risks, and Americans should avoid downloading it.

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Conclusion of Copyright Office’s Report on Artificial Intelligence Delayed Until 2025

This week, Director Shira Perlmutter indicated that the publication of part two of the U.S. Copyright Office’s three-part report on copyright issues raised by artificial intelligence (AI) would be further delayed. In her letter to the ranking members of the Senate Subcommittee on Intellectual Property and the House Subcommittee on Courts, Intellectual Property, and the Internet, Director Perlmutter indicated that although substantial progress had been made, the Office will not publish part two by the end of 2024 and now expects publication to occur in early 2025. Part two of the report will describe the copyrightability of generative AI outputs and will build on part one of the report on digital replicas. Following the publication of part two, Director Perlmutter indicated that the third and final part would be published in the first quarter of 2025. Part three will relate to “analyzing the legal issues related to the ingestion of copyrighted works to train AI models, including licensing considerations and the allocation of potential liability.”

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Predicting Date of Death with Artificial Intelligence

Launched in July 2024, Death Clock is an application that uses artificial intelligence (AI) to predict when its users will die. Death Clock trained its AI model using over 1,200 life expectancy studies. It then uses the answers from a questionnaire about the user’s physical health, like diet and exercise, to calculate each user’s date of death. While users of the free version will only receive this date, users of the paid version will receive lifestyle recommendations to help them live longer. Although the AI model includes a large amount of data, the data collected from individual users is currently limited. The current questionnaire is brief and does not delve extensively into family history or lifestyle habits. Including this additional data is likely necessary to receive more accurate results from the model. Improving the model’s accuracy is key for the economic calculations of different organizations, like the government and insurance companies. For example, people can better determine if they have saved enough for retirement. However, the increase in data collection comes at a risk — namely, user privacy and discrimination. Collecting more data for analysis and inclusion in the model exposes the data to a greater likelihood of being leaked if proper security and storage procedures are not followed. Additionally, implicit biases in the model may produce harmful outcomes (e.g., higher insurance premiums) for certain consumer groups. Therefore, it is crucial that models are developed with a diverse group of stakeholders and are used in a fair, unbiased, and privacy-conscious way.

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USPTO Employee AI Usage

Last year, as reported in a memo recently obtained by WIRED, the United States Patent and Trademark Office (USPTO) issued internal guidance that its examiners and other employees cannot use generative artificial intelligence (AI) for any purpose. The memo, authored by USPTO chief information officer Jamie Holcombe, expressed security and bias concerns associated with generative AI. However, the guidance does not fully limit an examiner’s ability to use AI. The USPTO press secretary clarified to WIRED that employees can still use approved AI programs. For example, USPTO examiners have access to the AI-based “Similarity Search” in the Patents End-to-End (PE2E) search tool.  The Similarity Search feature in PE2E allows examiners to input selected portions of a patent specification and receive related prior art that can be used to form rejections. Examiners must indicate in the application file that Similarity Search was used to put the public on notice.

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