Now more than ever, technology is influencing the way we do business, communicate, move through and around the world, access and manage our homes, receive and administer healthcare, make purchases, secure information, and protect our most valuable property. Morrison Foerster keeps pace with this rapidly evolving landscape across our internationally recognized corporate, finance, intellectual property, and litigation practices. MoFo Tech brings together our insights relating to fields such as blockchain and distributed ledger, cloud and as-a-service technologies, driverless cars, drones, fintech, computer hardware and software, the Internet of Things, semiconductors, and telecommunications. The MoFo Tech blog offers real-time, in-depth analyses of the trends and complex issues shaping the global technology industry.<h2>ABOUT MORRISON FOERSTER</h2>Morrison Foerster is a leading global law firm that transforms complexity into advantage for its clients. Our clients include some of the largest financial institutions, banks, consulting and accounting firms, and Fortune 100, technology, and life sciences companies. Highlighting the firm’s commitment to client service, leadership in market-changing deals and impact litigation, and values-based culture, Morrison Foerster has been repeatedly recognized as one of the top 10 firms on The American Lawyer’s A-List. Year after year, the firm receives significant recognition from Chambers and The Legal 500 across their various guides, including Global, USA, Asia‑Pacific, Europe, UK, Latin America, and FinTech Legal. Our lawyers passionately care about delivering legal excellence while living our values. Morrison Foerster has a long-standing commitment to creating a culture that respects and celebrates differences, while providing an inclusive environment. The firm has achieved Mansfield Certification Plus since 2018 as a result of successfully reaching at least 30 percent women, communities of color, and LGBTQ+ lawyer representation in a notable number of current leadership roles and committees. The firm also has a long history of commitment to the community and society through providing pro bono legal services, including litigating for civil rights and civil liberties, improving public education and fostering the wellbeing of children, advocating for veterans, promoting international human rights, enforcing the right to asylum, and safeguarding the environment.

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Scott Llewellyn’s practice focuses on complex litigation in federal and state courts around the country, with an emphasis on patent and trade secret disputes. Over the past decade, he has worked on some of the most significant, high–profile IP cases involving technology for televisions, smartphones, and self–driving cars, relying on 40 years of experience with computer hardware and software. Scott also has significant experience with business divorce cases before courts and arbitrators, involving a mix of trade secret, patent, tort, and contract claims relating to technology development and implementation.Scott also has successfully tried general commercial arbitration and agency matters, briefed and argued various appeals in federal appellate court, and tried more than 20 cases for the Denver City Attorney’s office, most of them jury trials.Scott also has an active pro bono practice, having worked on a variety of criminal and immigration appeals, and having represented Colorado criminal defense and mental health organizations in high–profile suits against the state of Colorado for violations of the constitutional rights of defendants.At MoFo, Scott serves on the board of the Morrison & Foerster Foundation. In the broader legal community, he serves on the boards of the Colorado Lawyers Committee and Drop in the Bucket (a charity that builds wells and sanitation systems in sub–Saharan Africa).After law school, Scott clerked for the Honorable Clarence A. Brimmer, District of Wyoming, and the Honorable Diarmuid F. O’Scannlain, Ninth Circuit. He was also previously an adjunct professor at the Georgetown University Law Center.

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Scott Llewellyn’s practice involves complex litigation before state and federal courts around the country, with an emphasis on patent and trade secret disputes for high-tech companies, to which he brings more than 35 years of experience with computer hardware and software, in particular relating to the processing and transmission of audio, video, voice, and other types of data. Mr. Llewellyn has also tried commercial arbitration and agency matters; briefed and argued various appeals in federal appellate court; and tried more than 20 cases for the Denver city attorney’s office, most of them jury trials.

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The U.S. Patent and Trademark Office (USPTO) has issued <a href="https://www.federalregister.gov/documents/2024/02/13/2024-02623/inventorship-guidance-for-ai-assisted-inventions" target="_blank">guidance</a> regarding patentability and inventorship for inventions made with the assistance of artificial intelligence (AI), clarifying the Office’s position that such inventions are patent-eligible so long as at least one human inventor made a significant contribution to conception of the invention.In addition to announcing its view that AI-assisted inventions may be patent-eligible, the USPTO provided several guiding principles that it believes should inform determinations as to whether and when a human should be considered an inventor when using AI assistance.  Finally, the USPTO provided two detailed examples to guide examiners, practitioners, and inventors in determining inventorship for AI-assisted inventions.BackgroundThe Federal Circuit’s 2022 decision in Thaler v. Vidal, 43 F.4th 1207 (Fed. Cir. 2022) established that <a href="https://www.mofo.com/resources/insights/220809-federal-circuit-agrees-with-the-uspto" target="_blank">only a human inventor—not an AI system—can be validly named as an inventor</a> on a U.S. patent application.  Thus, under Thaler, inventions that are invented entirely by AI systems are not eligible for U.S. patent protection.  However, the Thaler court explicitly acknowledged that its decision did not confront the more nuanced question of “whether inventions made with the assistance of AI are eligible for patent protection.”  Thaler v. Vidal, 43 F.4th at 1213.Following the Thaler decision, the Biden administration’s <a href="/topics/ai-executive-order-patent-issues" target="_self">AI Executive Order</a> directed the USPTO director to weigh in on the very issue flagged as unresolved by the Thaler court.  The AI Executive Order of Nov. 1, 2023, charged the USPTO director to “publish guidance to USPTO patent examiners and applicants addressing inventorship and the use of AI, including generative AI, in the inventive process.”  Executive Order 14110, Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, 88 Fed. Reg. 75191-75226 (Nov. 1, 2023).The USPTO has now issued that guidance (Inventorship Guidance for AI-Assisted Inventions, 89 Fed. Reg. 10043-10051 (Feb. 13, 2024)) and is seeking public comment on it for a 90-day period.USPTO Position: Significant Human Contribution to Conception Is RequiredThe standard endorsed by the USPTO in its guidance for AI-assisted inventions is the following:  “While AI systems . . . cannot be listed as inventors on patent applications or patents, the use of an AI system by a natural person(s) does not preclude a natural person(s) from qualifying as an inventor (or joint inventors) if the natural person(s) significantly contributed to the claimed invention.”  89 Fed. Reg. at 10046. Critically, significant contribution to an invention requires significant contribution to the conception of the invention.  Traditionally, conception is defined as the formation of an invention in the mind of an inventor.  Conception is distinct from reduction to practice, which involves the processes of actual construction, successful testing, validation, or demonstrating an invention’s workability.  While reduction to practice may occur simultaneously with conception in some situations, significant contribution to reduction to practice alone is not enough for a human contributor to be an inventor if the human contributor did not contribute in any significant manner to conception.Pannu FactorsTo determine whether a human inventor has made a significant contribution to the conception of an invention, the USPTO looks to the Federal Circuit’s Pannu factors.  (See Pannu v. Iolab Corp., 155 F.3d 1344, 1351 (Fed. Cir. 1998).)  While the Pannu factors were promulgated by the Federal Circuit with respect to determining inventorship among multiple human contributors, the USPTO guidance says that these factors should apply similarly in determining inventorship among cooperative human and AI contribution.The Pannu factors provide the following requirements of an inventor:  “. . . (1) contribute in some significant manner to the conception or reduction to practice of the invention; (2) make a contribution to the claimed invention that is not insignificant in quality, when that contribution is measured against the dimension of the full invention; and (3) do more than merely explain to the real inventors well‑known concepts and/or the current state of the art.”  Id. at 1351 (citing to Fina Oil Chem. Co. v. Ewen, 123 F.3d 1466, 1473, 43 USPQ2d 1935, 1941 (Fed. Cir. 1997); and Ethicon, Inc. v. United States Surgical Corp., 135 F.3d 1456, 1460, 45 USPQ2d 1545, 1548 (Fed. Cir. 1998)) (emphasis added).Principles for Applying Pannu FactorsIn addition to invocation of the Pannu factors, the USPTO provided several “principles” that it says should inform application of the Pannu factors in situations where AI is leveraged to assist in creation of an invention.  These principles articulated in the USPTO guidance include the following:<ol><li>A natural person’s use of an AI system in creating an AI-assisted invention does not negate the person’s contributions as an inventor.  The natural person can be listed as the inventor or joint inventor if the natural person contributes significantly to the AI-assisted invention.</li><li>Merely recognizing a problem or having a general goal or research plan to pursue does not rise to the level of conception.  A natural person who only presents a problem to an AI system may not be a proper inventor or joint inventor of an invention identified from the output of the AI system.  However, a significant contribution could be shown by the way the person constructs the prompt in view of a specific problem to elicit a particular solution from the AI system.</li><li>Reducing an invention to practice alone is not a significant contribution that rises to the level of inventorship.  Therefore, a natural person who merely recognizes and appreciates the output of an AI system as an invention, particularly when the properties and utility of the output are apparent to those of ordinary skill, is not necessarily an inventor.  However, a person who takes the output of an AI system and makes a significant contribution to the output to create an invention may be a proper inventor.  Alternatively, in certain situations, a person who conducts a successful experiment using the AI system’s output could demonstrate that the person provided a significant contribution to the invention even if that person is unable to establish conception until the invention has been reduced to practice.</li><li>A natural person who develops an essential building block from which the claimed invention is derived may be considered to have provided a significant contribution to the conception of the claimed invention even though the person was not present for or a participant in each activity that led to the conception of the claimed invention.  In some situations, the natural person(s) who designs, builds, or trains an AI system in view of a specific problem to elicit a particular solution could be an inventor, where the designing, building, or training of the AI system is a significant contribution to the invention created with the AI system.</li><li>Maintaining “intellectual domination” over an AI system does not, on its own, make a person an inventor of any inventions created through the use of the AI system.  Therefore, a person simply owning or overseeing an AI system that is used in the creation of an invention, without providing a significant contribution to the conception of the invention, does not make that person an inventor.</li></ol>89 Fed. Reg. at 10048-10049 (emphasis added).Human Inventorship Is Determined Claim by ClaimConsistent with the principle that inventorship among multiple human inventors is determined on a claim-by-claim basis, the USPTO guidance sets forth that human inventorship for AI-assisted inventions is also determined on a claim-by-claim basis.The most interesting implication of this claim-by-claim principle with respect to AI-assisted inventions is that each claim must have a valid human inventor.  As set out in some detail in the examples provided by the USPTO with its guidance, the implications for dependent claims are somewhat interesting.  For example, a dependent claim that recites a minor contribution made entirely by AI may still be included in an application, so long as the underlying independent claim has a valid human inventor who made a significant contribution to the conception of the overall inventive concept.Other Guidance NotesOther points in the USPTO guidance that may be of interest to inventors and practitioners include the following:<ul><li>Design patents and plant patents – The USPTO’s guidance applies equally to design and plant patents as it does to utility patents.</li><li>Duty of disclosure – The duty of disclosure for information material to patentability, under which anyone involved in preparation or prosecution of a patent application must submit material information to the Office in an Information Disclosure Statement, applies to information that is material to determining inventorship.</li><li>Duty of reasonable inquiry – According to the USPTO’s guidance, practitioners may be required to inquire about whether and how AI is being used in the inventive process, in order to fulfill their duty of reasonable inquiry to obtain information that may be material to patentability (which may thus be required to be disclosed to the Office).</li><li>Priority claims – While the U.S. courts have foreclosed the possibility that a patent application can validly name an AI system as a sole inventor or joint inventor, this is not the case in all countries.  Thus, the USPTO guidance addresses the question of whether and how priority can be claimed in U.S. applications to foreign applications listing an AI inventor.  According to the guidance, a U.S. application cannot claim priority to a foreign application that names an AI system as a sole inventor, but a U.S. application may claim priority to a foreign application that names humans and AI systems as joint inventors.  The U.S. application must itself name only human inventors, and the claims of the U.S. application should be reviewed to ensure that the listed human inventor contributed in a significant manner to each of them.</li></ul>USPTO AI-Assisted Invention ExamplesIn addition to the guidance memorandum itself, the USPTO also provided two detailed examples, outlining how it believes its AI-assisted invention principles should be applied.  The first example explores situations in which a publicly available generative AI system is used to create a transaxle design for a remote-control car.  The second example explores situations in which a ready-to-use deep neural network (DNN) prediction model is used, sometimes in combination with bespoke generative AI models, in discovery and development of therapeutic compounds.A brief summary of the USPTO examples is provided below.Example 1 – Transaxle for a remote-control carIn this example, toy car engineers prompt a publicly available generative AI system to create a transaxle design:  “Create an original design for a transaxle for a model car, including a schematic and description of the transaxle.”<ul><li>Scenario 1 of Example 1:</li><ul><li>An independent claim recites the AI-generated transaxle design.</li><li>The USPTO guidance explains that listed human inventors are not proper because they did not make a significant contribution to the conception of the AI-generated invention.</li></ul><li>Scenario 2 of Example 1:</li><ul><li>A dependent claim, dependent from the independent claim reciting the AI-generated transaxle design, recites the use of steel as a material, which was selected by a human inventor.</li><li>The USPTO guidance explains that listed human inventors are not proper because selection of a common material is not a significant contribution to the overall claim scope.</li></ul><li>Scenario 3 of Example 1:</li><ul><li>Human engineers modify the AI-generated axle design, and an independent claim recites the modified axle design.</li><li>The USPTO guidance explains that a listed human inventor is proper because human engineers made significant contributions to conception by making significant alterations to the original AI-generated design.</li></ul><li>Scenario 4 of Example 1:</li><ul><li>A dependent claim, dependent from the independent claim reciting the modified, human-generated transaxle design, recites the use of aluminum as a material, which was conceived in part by the human engineer in reliance on an AI-generated suggestion.</li><li>The USPTO guidance explains that a listed human inventor is proper because human engineers made significant contributions to the conception of the underlying independent claim, which are not negated by the use of AI assistance in the conception of the dependent claim.</li></ul><li>Scenario 5 of Example 1:</li><ul><li>The creator and owner of the publicly available general-purpose generative AI system used by the remote-control car engineers does not otherwise participate in the inventive process, but is listed as an inventor on an independent claim.</li><li>The USPTO guidance explains that the AI engineer is not proper as an inventor in this case because creation of a general-purpose AI system (without designing, building, or training the AI system in view of a “specific problem” to elicit a “particular solution” in a manner that significantly contributes to the invention of the solution created with the AI system) does not alone constitute a significant contribution to solutions created using the AI system.</li></ul></ul>Example 2 – Developing a therapeutic compound for treating cancerIn this example, drug researchers use a ready-to-use DNN prediction model to select candidate compounds for cancer treatment.  The researchers provide drug-target pairs as input in Simplified Molecular Input Line Entry System (SMILES) string format, and the prediction model outputs candidate compounds with the highest predicted binding affinity.<ul><li>Scenario 1 of Example 2:</li><ul><li>Wet-lab researchers manually identify and synthesize structural modifications to the AI‑identified candidate compounds.</li><li>Independent claim 1 is directed to a method for identifying a compound comprising using the DNN, synthesizing an intermediate, and synthesizing a compound by introducing structural modifications.</li><ul><li>The USPTO guidance explains that the wet-lab researchers are proper inventors because of their significant contributions to the intermediate synthesis and creation of structural modifications, whereas the AI researcher who did imputation using the DNN is not proper as an inventor (as the use of the DNN was an insignificant contribution in quality when measured against the scope of the entire claim).</li></ul><li>Independent claim 2 recites the structurally modified compound itself.</li><ul><li>The USPTO guidance explains that, similar to claim 1, the wet-lab researchers are proper inventors, whereas the AI researcher who did imputation using the DNN is not proper as an inventor.</li></ul></ul><li>Scenario 2 of Example 2:</li><ul><li>AI researchers build, train, and fine-tune a new generative neural net system that accepts SMILES inputs and generates an optimized drug compound (not previously synthesized).  The AI researchers apply the generative AI system downstream from the DNN prediction model to generate structurally modified versions of the candidate compounds identified by the DNN.  Wet-lab researchers then synthesize the structurally modified compounds.  An independent claim is directed to one such structurally modified compound.</li><li>The USPTO guidance explains that both the wet-lab researchers who synthesized the compounds and the AI researchers who built, trained, and fine-tuned the generative AI model in view of a specific problem to elicit a particular solution are proper inventors.</li></ul></ul>While the examples outline certain situations in which human beings have not made any significant contribution to the conception of a claimed invention, and therefore cannot be listed as valid inventors, the examples seem overall (particularly with respect to the second example relating to AI-assisted drug discovery) to seek to provide pathways by which inventors, practitioners, and examiners can establish valid human inventorship.  The examples, however, still leave significant uncertainty as to what amount of human contribution is required to establish valid human inventorship.  Notably, Example 2 is silent on any scenarios in which structural modifications are not made to a candidate compound identified or generated by a ready-to-use AI system, which may signal a scenario in which proper human inventorship of a claim to such a compound could be challenged.

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Of Counsel

Josh Crawford's practice focuses on domestic patent prosecution, international patent portfolio management, patent infringement and validity determinations, inter partes review proceedings, and IP lit

Joshua A. Crawford

Joshua Crawford

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Jon Bockman is the head of the firm’s Technology Patent Practice. In this capacity, he leads the firm’s prominent group of patent practitioners with degrees in electrical engineering, mechanical engineering, computer science, computer engineering, chemical engineering, biomedical engineering and physics. 

Jonathan Bockman

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The U.S. Patent and Trademark Office (USPTO) has issued guidance regarding patentability and inventorship for inventions made with the assistance of artificial intelligence (AI), clarifying the Office’s position that such inventions are patent-eligible so long as at least one human inventor made a significant contribution to conception of the invention.

USPTO Guidance: Inventions Made with AI Assistance Can Be Patent-Eligible

The evolution of artificial intelligence (AI) technologies has triggered a surge in the filings of patent applications, from machine learning models to applications of those models. See USPTO, Artificial Intelligence (AI) trends in U.S. patents (June 29, 2022) at 7. But because AI technologies rely on machine learning algorithms and computing power, AI inventions face challenges in light of the legal standards under Section 101 of the Patent Act.Section 101 sets forth eligibility requirements for patents, providing that they may be granted only for a “new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof.” 35 U.S.C. § 101. In Alice Corporation v. CLS Bank International, the U.S. Supreme Court set forth a two-part test to determine if a claimed invention is eligible for patent protection: (1) whether the claim of an invention is directed to a patent-ineligible concept, such as an abstract idea; and (2) if so, whether the claim recites an “inventive concept” sufficient to transform the abstract idea into a “patent-eligible application.” 573 U.S. 208, 217-18 (2014).In a recent case in the District of Delaware, the court found that the plaintiff’s patents claimed no more than an abstract idea “using known generic mathematical techniques.” See Recentive Analytics, Inc. v. Fox Corporation, No. 22-cv-1545-GBW, 2023 WL 6122495, *8 (D. Del. Sept. 19, 2023) (appeal pending). The two patents both involved machine learning techniques, directed to methods for (1) generating television network schedules, and (2) optimizing event schedules by considering a variety of parameters. Id. at *1–2.Regarding the first Alice prong, the court found that the patents recited collecting and analyzing information, a “familiar class of claims directed to a patent-ineligible concept.” Id. at *9. The court distinguished the patents from those in McRO, Inc. v. Bandai Namco Games America Inc. See 837 F.3d 1299 (Fed. Cir. 2016) (claims reciting software for animating lip synchronization and facial expressions of animated characters were not directed to an abstract idea). First, the television network maps produced by Recentive’s methods were “less tangible than the created animated characters” in McRO. Recentive at *8. Second, “changing a process where both humans and algorithms are trying to maximize TV ratings” was different from transforming “a traditionally subjective process performed by human artists into a mathematically automated process” in McRO. Id. Finally, the rules Recentive claimed were “conventional machine learning techniques,” unlike the “specific and unconventional” ones in McRO. Id.Regarding the second Alice prong, the court held that the patents did not involve an “inventive concept.” Id. at *12. Instead, “[t]he machine learning limitations [were] described only in broad functional terms and provide[d] little guidance on model parameters or training technique[.]” Id. For example, the patents vaguely disclosed “any suitable machine learning technique” and “using either a neural network or a support vector model and iteratively training it.” Id.Recentive sought to distinguish the claimed inventions from the way human brains might process the same information, emphasizing that “humans process data qualitatively, rather than quantitatively,” and that “[t]he number of possible solutions is far beyond what a human could process.” Id. The court rejected these arguments, reasoning that it is irrelevant whether a human brain would process the information the same way as AI. Rather, “[t]he relevant question is whether the machine learning processes are mathematical algorithms.” Id. The court found that no amount of data or computing power made a meaningful difference: “humans can engage in the mathematical techniques to perform machine learning (albeit slowly).” Id.Claims directed to novel methods for improving prior art neural networks, however, can still be eligible. The court noted that a “Method for Training a Neural Network for Facial Detection” identified in the USPTO’s guidance (M.P.E.P. § 2106.04(a)(1)) described such methods as “an expanding training set using mathematical transformations and the minimization of false positives using a distinctive training method.” Recentive, 2023 WL 6122495 at *6. Recentive’s claims were not analogous. Instead, they merely “relate[d] to the application of machine learning techniques to a manual process.” Id.With Recentive in mind, patentees should be prepared to articulate what makes their AI claims patentable, for example, by focusing on specific and unconventional machine learning techniques claimed in the patents.

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Innovative Foundry Technologies v. TSMC et al. (District of Delaware; International Trade Commission)

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Bel Power v. Monolithic Power Systems (Western District of Texas)

Waymo v. Uber (Northern District of California)

CSPC Dophen v. Hu (Eastern District of California)

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Lily Li is a partner in the firm’s Litigation Group. She has represented companies in high-stakes patent litigation, trade secret matters, competitor cases, non-practicing entity assertions, and commercial disputes.

Lily Li

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Associate

Steven McKeever is an associate in Morrison Foerster’s Palo Alto office who specializes in litigating emerging, high-tech issues. 

Steven M. McKeever

Steven McKeever

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AI Trends For 2024 - The “Abstract Ideas” Behind Artificial Intelligence Inventions

AI Trends For 2022 - Export Control/CFIUS

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The evolution of artificial intelligence (AI) technologies has triggered a surge in the filings of patent applications, from machine learning models to applications of those models. 

AI Trends For 2024 - The “Abstract Ideas” Behind Artificial Intelligence Inventions

In 2023, generative AI took center stage—the year was filled with rapid advancements in the capabilities of large language models (LLMs). Heading into 2024, businesses continue to see great potential for generative AI and are eager to integrate the technology into internal workflows and customer-facing applications.Developers are engaging in a variety of techniques to improve the performance of LLMs for domain-specific tasks. At times, these efforts have been loosely referred to as “fine tuning.” True “fine tuning” involves the further training of an LLM with new data (i.e., the “re-weighting” of the LLM’s parameters). However, “fine tuning” is at times overused to describe techniques that do not involve updating an LLM’s weights.Fine tuning can be time- and resource-intensive. As a result, developers often prefer to tailor LLMs for domain-specific tasks using prompt engineering. As the name suggests, prompt engineering consists of crafting high-quality inputs.Retrieval-augmented generation (RAG) is a prompt engineering technique that consists of an intermediary step between a user’s submission of a prompt and the LLM’s generation of output. In this step, an LLM-based application retrieves additional information of greater relevance and higher quality that was not part of its training, such as:<ul><li dir="ltr">recent data from search engine results (e.g., ChatGPT’s “Browse with Bing” functionality) and</li><li>proprietary datasets (e.g., a custom GPT that retrieves from an enterprise’s knowledge base).</li></ul>The user’s original prompt is then augmented with the retrieved information and provided to the LLM. As a result, a RAG-enabled application may generate more relevant outputs, and potentially fewer hallucinations, without any retraining.We anticipate that the adoption of RAG will continue to grow in 2024. When assessing the suitability of a RAG-enabled application for a given use case, businesses should consider the sources from which the application retrieves data.

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Justin Haan is a partner in the firm’s Technology Transactions Group in the San Francisco office, advising leading technology industry pioneers to help them navigate their most critical IP and technology needs.

Justin H. Haan

Justin Haan is a partner in the firm’s Technology Transactions Group and advises clients on a range of issues in the technology and media industries. He also drafts technology license agreements on behalf of licensors and licensees involving a wide range of technologies, including virtual reality devices, cloud platforms, Internet of Things devices, unmanned aerial vehicles, and artificial intelligence applications. Mr. Haan received his J.D., with a certificate in law and technology, from the University of California, Berkeley.

Justin Haan

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In 2023, generative AI took center stage—the year was filled with rapid advancements in the capabilities of large language models (LLMs). Heading into 2024, businesses continue to see great potential for generative AI and are eager to integrate the technology into internal workflows and customer-facing applications.

AI Trends For 2024 - Retrieval-Augmented Generation (RAG) to Generate Richer Outputs

Generative artificial intelligence (“GenAI”) challenges EU copyright law, from the AI training to the protectability and liability risks of its output. The EU’s forthcoming AI Act takes first steps towards AI-specific regulation with its provider obligations on training data transparency and copyright policy.Text and data mining (TDM) exception for AI training: GenAI applications have been trained with millions of pieces of copyrighted content scraped from the internet without explicit consent from the rightsholders. In the EU, reproductions made in the course of such training are permitted by the TDM exception of the 2019 EU Copyright Directive, unless rightsholders express machine-readable opt-outs. Not yet harmonized standards for such technical opt-outs and the issue that AI models currently cannot retroactively “forget” individual training content are practical challenges for which crucial developments should be monitored in 2024.Training data transparency: The EU AI Act will include a training data transparency obligation for providers of general-purpose AI (GPAI) models. The transparency obligation will no longer be limited to copyrighted training data, as initially proposed by the EU Parliament. It now requires providing general information on the datasets and databases used with further guidance to be provided by the future EU AI Office.AI output to be assessed under general copyright law: The EU AI Act will require GPAI model providers to put in place a policy to respect existing EU copyright law, which applies to the entire AI cycle from the input level to the output level. Based on the existing copyright framework, this implies that copyrighted works that have been used for training on the input level must not be recognizable on the output level, unless copyright exemptions apply, or consent of the copyright owner has been given. The recognizable use on the output level is not covered by the TDM exception. Whether there can be copyright protection of the actual output will not be regulated by the AI Act; it will be left to general copyright principles.In order to act in a safe legal copyright environment, all interest groups should seek to develop joint solutions. Stay tuned!Susan Bischoff contributed to authoring this blog post.

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Christiane Stuetzle is a partner in Morrison Foerster's German office in Berlin and Co-Chair of the firm's Global Film & Entertainment Practice.

Christiane Stuetzle

Christiane Stuetzle is a partner of Morrison Foerster (International) LLP, based in the Berlin office. She is the Co-Chair of the Global Film & Entertainment Practice of Morrison & Foerster. She is a Certified Specialist for Copyright and Media Law & Arbitrator on the International Arbitration Panel of the IFTA (Independent Film & Television Alliance).

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Japan: Privacy Updates on Handling Personal Information in the “Digital Market”

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Generative artificial intelligence (“GenAI”) challenges EU copyright law, from the AI training to the protectability and liability risks of its output. The EU’s forthcoming AI Act takes first steps towards AI-specific regulation with its provider obligations on training data transparency and copyright policy.

On December 13, 2023, the IRS revealed a revamped leadership structure, introducing Chief Information Officer and Chief Operating Officer positions to leverage technology more effectively. The move aligns with IRS Commissioner Danny Werfel’s vision of employing AI and advanced analytics to streamline tax processes, facilitate communication, and optimize audits for both the IRS and taxpayers. This technological shift supports the IRS’s initiative to use AI in audit selection, addressing a decline in audits for high-income individuals, a strategy disclosed in a September 8, 2023 statement. The focus is on enhancing proficiency in detecting tax evasion, recognizing compliance threats, and refining case selection tools.The IRS has stated that audit rates for those earning less than $400,000 annually will remain unaffected. Additionally, AI safeguards will be implemented for Earned Income Tax Credit claims, aiming to prevent exploitation by unscrupulous tax preparers. The IRS’s plan involves expanding high-income and high-wealth compliance efforts, employing AI in initiatives like the High Wealth, High Balance Due Taxpayer Field Initiative. Specifically, the Large Partnership Compliance program, utilizing AI and machine learning, will scrutinize 75 of the largest U.S. partnerships, each possessing over $10 billion in assets, addressing historically limited examination coverage of such entities.

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Anthony J. Carbone is a partner in our New York office and co-chair of the Tax practice.

Anthony J. Carbone

Anthony J. Carbone is a partner in our New York office and co-chair of the Tax practice. He also serves as co-chair of the Federal Tax practice. His practice focuses on tax planning strategies and the tax consequences of a wide range of business, investment and capital markets transactions in the U.S. and internationally.

Anthony Carbone

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Kevin is a tax associate in the Boston office of Morrison Foerster.
Kevin advises clients on U.S. federal income tax considerations related to domestic and cross-border transactions that include mergers and acquisitions, debt and equity offerings, reorganizations, real estate investment trusts, capital markets transactions, structured products, and renewable energy.

Kevin Brown

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On December 13, 2023, the IRS revealed a revamped leadership structure, introducing Chief Information Officer and Chief Operating Officer positions to leverage technology more effectively. 

AI Trends For 2024 - IRS Adopts AI-Powered Audit Selection Tools, Hires Technology-Focused Leadership

Responsible AI isn’t just for BigTech. This coming year we can expect institutional, venture, and strategic investors to take an interest in whether and how well their funds and portfolio companies are identifying and managing the legal and ethical risks associated with their use of AI and, in particular, Generative AI. Similarly, the boards of companies will take an increased interest in these issues. As a result, these investors and board members will be hungry for tools that they can use to help them evaluate and then drive improvement in how their companies are addressing these concerns.AI raises many legal and ethical concerns, including accuracy, reliability, safety, privacy, security, fairness and bias, and sustainability. Responsible AI programs assess and mitigate these risks through solid governance practices and operational implementation.This past year, a number of AI industry standards launched that set forth the principal tenets of Responsible AI programs. We can expect in 2024 that implementation of these tenets will proliferate across sectors in both the public and private sectors.

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Stephanie assists companies with technology and life sciences transactions, representing both private and public companies, from emerging growth through the Fortune 50. In particular, Stephanie focuses on helping companies that leverage data through technology in structuring their business and commercial transactions.

Stephanie Sharron

Stephanie Sharron is partner in the Palo Alto office and a member of the firm’s Technology Transactions Group. Clients turn to Ms. Sharron for advice on how to leverage data through technology and the related issues that apply to their business models in light of the rapid changes in business, technology and law.

Stephanie Lynn Sharron

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AI Trends For 2022 - AI Industry Poised for Successful 2022

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Responsible AI isn’t just for BigTech. This coming year we can expect institutional, venture, and strategic investors to take an interest in whether and how well their funds and portfolio companies are identifying and managing the legal and ethical risks associated with their use of AI and, in particular, Generative AI. 

AI Trends For 2024 - Responsible AI isn't just for Big Tech