Big Tech companies have profited greatly from dominant market positions while riding largely for free over the top of fixed and mobile telecom networks and devices. The entire Information and Communications Technologies (ICT) ecosystem is enabled by a variety of interoperability technologies including 5G cellular, WiFi and HEVC/H.265 video compression that are openly available in published standards and embedded in components and end-products. How much, if anything, should beneficiaries pay for the capabilities upon which their standard-based implementations are built?
Thursday, 28 September 2023
Saturday, 23 September 2023
In May of 2023, the White House published a document titled, “United States Government National Standards Strategy for Critical and Emerging Technologies.” The Executive Summary states:
Strength in standards development has been instrumental to the United States’ global technological leadership. Standards development underpins economic prosperity across the country and fortifies U.S. leadership in the industries of the future at the same time. Bolstering U.S. engagement in standards for critical and emerging technology (CET) spaces will strengthen U.S. economic and national security. The U.S. Government has long engaged in these standards development processes through an approach built on transparency, private sector and public sector leadership, and stakeholder engagement—a process that reflects the United States’ commitment to free and fair market competition in which the best technologies come to market. Government support for scientific research and development (R&D), an open investment climate, and the rule of law have also been critical for U.S. standards leadership. America’s workers, economy, and society have benefited significantly as a result, as have those of like-minded nations alongside which the United States has collaborated to forge technological progress.
Today, however, the United States faces challenges to its longstanding standards leadership, and to the core principles of international standard-setting that, together with like-minded partners, we have upheld for decades. Strategic competitors are actively seeking to influence international standards development, particularly for CET, to advance their military-industrial policies and autocratic objectives, including blocking the free flow of information and slowing innovation in other countries, by tilting what should be a neutral playing field to their own advantage.
The United States must renew our commitment to the rules-based and private sector-led approach to standards development, and complement the innovative power of the private sector with strategic government and economic policies, public engagements, and investments in CET. By supporting our unrivaled innovation ecosystem and related international standards development as part of a modern industrial strategy, we can ensure that CET are developed and deployed in ways that benefit not only the United States but all who seek to promote and advance technological progress. Strengthening the U.S. approach to standards development will lead to standards that are technologically sound, earn people’s trust, reflect our values, and help U.S. industry compete on a level playing field.
This strategy outlines how the U.S. Government will strengthen U.S. leadership and competitiveness in international standards development, and ensure that the “rules of the road” for CET standards embrace transparency, openness, impartiality and consensus, effectiveness and relevance, coherence, and broad participation.
Professors Amanda Parsons and Salome Viljoen have published a draft on SSRN titled, “Valuing Social Data.” The draft is excellent, provides numerous insights and includes a very nice literature review.
Here is the abstract:
Social data production is a unique form of value creation
that characterizes informational capitalism. Social data production also
presents critical challenges for the various legal regimes that are
encountering it. This Article provides legal scholars and policymakers with the
tools to comprehend this new form of value creation through two descriptive
contributions. First, it presents a theoretical account of social data, a mode
of production which is cultivated and exploited for two distinct (albeit related)
forms of value: prediction value and exchange value. Second, it creates and
defends a taxonomy of three “scripts” that companies follow to build up and
leverage prediction value and describes the normative and legal ramifications
of these scripts.
The Article then applies these descriptive contributions to demonstrate how legal regimes are failing to effectively regulate social data value creation. Through the examples of tax law and data privacy law, it demonstrates these struggles in both legal regimes that have historically regulated value creation, like tax law, and legal regimes that have been newly tasked with regulating value creation by informational capitalism, like privacy and data protection law.
The Article argues that separately analyzing data’s prediction value and its exchange value may be helpful to understanding the challenges the law faces in governing social data production and the political economy surrounding such production. This improved understanding will equip legal scholars to better confront the harms of law’s failures in the face of informational capitalism, reduce legal arbitrage by powerful actors, and facilitate opportunities to maximize the beneficial potential of social data value.
Wednesday, 30 August 2023
The California Institute of Regenerative Medicine recently announced several grants to fund clinical stage research. Importantly, CIRM has many different requirements than those offered under the Bayh-Dole Act. The Press Release states:
South San Francisco, CA – The California Institute for Regenerative Medicine (CIRM), the world’s largest institution dedicated to regenerative medicine, today awarded $50.1 million to fund clinical-stage research projects aimed at advancing stem cell and gene therapy treatments for a variety conditions ranging from neurodegenerative diseases and blood cancers to HIV/AIDS.
The awards will support six projects in the Agency’s clinical program which provides funding for eligible stem cell and gene therapy-based projects through any stage of clinical trial activity.
The awards include: [Aspera Biomedicines, AcuraStem, Regenerative Path Technologies and the City of Hope].
Among the awards is a $12.4 million grant to support Regenerative Patch Technologies LLC in a Phase 2b clinical trial to evaluate the safety and efficacy of a retinal pigmented epithelial (RPE) implant. The implant will be evaluated in patients with geographic atrophy, a late-stage form of age-related macular degeneration (AMD), a common condition that can lead to vision loss in older adults.
The RPE is an important cell layer that supports the retina and plays a critical role in maintaining vision. In geographic atrophy, RPE cells break down over time, leading to impaired vision and a loss of independence.
The stem cell-based implant aims to promote the survival and function of the retina, protecting the eye from disease progression and potentially improving vision.
“This award supplies critical funding to support a Phase 2b clinical trial to achieve our goal of improving vision in patients with geographic atrophy”, said Jane Lebkowski, PhD, President of Regenerative Patch Technologies. “We want to thank CIRM for their support of this program.”
Geographic atrophy affects more than 8 million people worldwide and an estimated 1 million people in the United States. There are currently no approved therapies that are effective in improving vision in patients with geographic atrophy.
“CIRM is proud to continue to fund this groundbreaking stem cell therapy that has the potential to improve outcomes for the millions of people suffering from geographic atrophy,” said Maria T. Millan, M.D., President and CEO of CIRM. “This investment is follow-on funding to CIRM’s previous support to develop this therapy. It reflects our commitment to advancing cutting-edge science and underscores our dedication to addressing the unmet medical needs of those affected by degenerative diseases.”
This month’s clinical awards include two preclinical projects and four clinical-stage projects. That brings the number of CIRM-funded clinical trials to 95. For more information on CIRM’s clinical stage program, please visit our Funding Opportunities page.
The U.S. Copyright Office released today a notice of inquiry concerning AI and copyright. The Press Release states:
Today, the U.S. Copyright Office issued a notice of inquiry (NOI) in the Federal Register on copyright and artificial intelligence (AI). The Office is undertaking a study of the copyright law and policy issues raised by generative AI and is assessing whether legislative or regulatory steps are warranted. The Office will use the record it assembles to advise Congress; inform its regulatory work; and offer information and resources to the public, courts, and other government entities considering these issues.
The NOI seeks factual information and views on a number of copyright issues raised by recent advances in generative AI. These issues include the use of copyrighted works to train AI models, the appropriate levels of transparency and disclosure with respect to the use of copyrighted works, the legal status of AI-generated outputs, and the appropriate treatment of AI-generated outputs that mimic personal attributes of human artists.
The NOI is an integral next step for the Office’s AI initiative, which was launched in early 2023. So far this year, the Office has held four public listening sessions and two webinars. This NOI builds on the feedback and questions the Office has received so far and seeks public input from the broadest audience to date in the initiative.
“We launched this initiative at the beginning of the year to focus on the increasingly complex issues raised by generative AI. This NOI and the public comments we will receive represent a critical next step,” said Shira Perlmutter, Register of Copyrights and Director of the U.S. Copyright Office. “We look forward to continuing to examine these issues of vital importance to the evolution of technology and the future of human creativity.”
Initial written comments are due by 11:59 p.m. eastern time on Wednesday, October 18, 2023. Reply comments are due by 11:59 p.m. eastern time on Wednesday, November 15, 2023. Instructions for submitting comments are available on the Office’s website. Commenters may choose which and how many questions to respond to in the NOI.
The NOI includes the following questions:
The Office has several general questions about generative AI in addition to the specific topics listed below. Commenters are encouraged to raise any positions or views that are not elicited by the more detailed questions further below.
1. As described above, generative AI systems have the ability to produce material that would be copyrightable if it were created by a human author. What are your views on the potential benefits and risks of this technology? How is the use of this technology currently affecting or likely to affect creators, copyright owners, technology developers, researchers, and the public?
2. Does the increasing use or distribution of AI-generated material raise any unique issues for your sector or industry as compared to other copyright stakeholders?
3. Please identify any papers or studies that you believe are relevant to this Notice. These may address, for example, the economic effects of generative AI on the creative industries or how different licensing regimes do or could operate to remunerate copyright owners and/or creators for the use of their works in training AI models. The Office requests that commenters provide a hyperlink to the identified papers.
4. Are there any statutory or regulatory approaches that have been adopted or are under consideration in other countries that relate to copyright and AI that should be considered or avoided in the United States? How important a factor is international consistency in this area across borders?
5. Is new legislation warranted to address copyright or related issues with generative AI? If so, what should it entail? Specific proposals and legislative text are not necessary, but the Office welcomes any proposals or text for review.
If your comment applies only to a specific subset of AI technologies, please make that clear.
6. What kinds of copyright-protected training materials are used to train AI models, and how are those materials collected and curated?
6.1. How or where do developers of AI models acquire the materials or datasets that their models are trained on? To what extent is training material first collected by third-party entities (such as academic researchers or private companies)?
6.2. To what extent are copyrighted works licensed from copyright owners for use as training materials? To your knowledge, what licensing models are currently being offered and used?
6.3. To what extent is non-copyrighted material (such as public domain works) used for AI training? Alternatively, to what extent is training material created or commissioned by developers of AI models?
6.4. Are some or all training materials retained by developers of AI models after training is complete, and for what purpose(s)? Please describe any relevant storage and retention practices.
7. To the extent that it informs your views, please briefly describe your personal knowledge of the process by which AI models are trained. The Office is particularly interested in:
7.1. How are training materials used and/or reproduced when training an AI model? Please include your understanding of the nature and duration of any reproduction of works that occur during the training process, as well as your views on the extent to which these activities implicate the exclusive rights of copyright owners.
7.2. How are inferences gained from the training process stored or represented within an AI model?
7.3. Is it possible for an AI model to “unlearn” inferences it gained from training on a particular piece of training material? If so, is it economically feasible? In addition to retraining a model, are there other ways to “unlearn” inferences from training?
7.4. Absent access to the underlying dataset, is it possible to identify whether an AI model was trained on a particular piece of training material?
8. Under what circumstances would the unauthorized use of copyrighted works to train AI models constitute fair use? Please discuss any case law you believe relevant to this question.
8.1. In light of the Supreme Court's recent decisions in Google v. Oracle America and Andy Warhol Foundation v. Goldsmith, how should the “purpose and character” of the use of copyrighted works to train an AI model be evaluated? What is the relevant use to be analyzed? Do different stages of training, such as pre-training and fine-tuning, raise different considerations under the first fair use factor?
8.2. How should the analysis apply to entities that collect and distribute copyrighted material for training but may not themselves engage in the training?
8.3. The use of copyrighted materials in a training dataset or to train generative AI models may be done for noncommercial or research purposes. How should the fair use analysis apply if AI models or datasets are later adapted for use of a commercial nature? Does it make a difference if funding for these noncommercial or research uses is provided by for-profit developers of AI systems?
8.4. What quantity of training materials do developers of generative AI models use for training? Does the volume of material used to train an AI model affect the fair use analysis? If so, how?
8.5. Under the fourth factor of the fair use analysis, how should the effect on the potential market for or value of a copyrighted work used to train an AI model be measured? Should the inquiry be whether the outputs of the AI system incorporating the model compete with a particular copyrighted work, the body of works of the same author, or the market for that general class of works?
9. Should copyright owners have to affirmatively consent (opt in) to the use of their works for training materials, or should they be provided with the means to object (opt out)?
9.1. Should consent of the copyright owner be required for all uses of copyrighted works to train AI models or only commercial uses?
9.2. If an “opt out” approach were adopted, how would that process work for a copyright owner who objected to the use of their works for training? Are there technical tools that might facilitate this process, such as a technical flag or metadata indicating that an automated service should not collect and store a work for AI training uses?
9.3. What legal, technical, or practical obstacles are there to establishing or using such a process? Given the volume of works used in training, is it feasible to get consent in advance from copyright owners?
9.4. If an objection is not honored, what remedies should be available? Are existing remedies for infringement appropriate or should there be a separate cause of action?
9.5. In cases where the human creator does not own the copyright—for example, because they have assigned it or because the work was made for hire—should they have a right to object to an AI model being trained on their work? If so, how would such a system work?
10. If copyright owners' consent is required to train generative AI models, how can or should licenses be obtained?
10.1. Is direct voluntary licensing feasible in some or all creative sectors?
10.2. Is a voluntary collective licensing scheme a feasible or desirable approach? Are there existing collective management organizations that are well-suited to provide those licenses, and are there legal or other impediments that would prevent those organizations from performing this role? Should Congress consider statutory or other changes, such as an antitrust exception, to facilitate negotiation of collective licenses?
10.3. Should Congress consider establishing a compulsory licensing regime? If so, what should such a regime look like? What activities should the license cover, what works would be subject to the license, and would copyright owners have the ability to opt out? How should royalty rates and terms be set, allocated, reported and distributed?
10.4. Is an extended collective licensing scheme a feasible or desirable approach?
10.5. Should licensing regimes vary based on the type of work at issue?
11. What legal, technical or practical issues might there be with respect to obtaining appropriate licenses for training? Who, if anyone, should be responsible for securing them (for example when the curator of a training dataset, the developer who trains an AI model, and the company employing that model in an AI system are different entities and may have different commercial or noncommercial roles)?
12. Is it possible or feasible to identify the degree to which a particular work contributes to a particular output from a generative AI system? Please explain.
13. What would be the economic impacts of a licensing requirement on the development and adoption of generative AI systems?
14. Please describe any other factors you believe are relevant with respect to potential copyright liability for training AI models.
Transparency & Recordkeeping
15. In order to allow copyright owners to determine whether their works have been used, should developers of AI models be required to collect, retain, and disclose records regarding the materials used to train their models? Should creators of training datasets have a similar obligation?
15.1. What level of specificity should be required?
15.2. To whom should disclosures be made?
15.3. What obligations, if any, should be placed on developers of AI systems that incorporate models from third parties?
15.4. What would be the cost or other impact of such a recordkeeping system for developers of AI models or systems, creators, consumers, or other relevant parties?
16. What obligations, if any, should there be to notify copyright owners that their works have been used to train an AI model?
17. Outside of copyright law, are there existing U.S. laws that could require developers of AI models or systems to retain or disclose records about the materials they used for training?
Generative AI Outputs
If your comment applies only to a particular subset of generative AI technologies, please make that clear.
18. Under copyright law, are there circumstances when a human using a generative AI system should be considered the “author” of material produced by the system? If so, what factors are relevant to that determination? For example, is selecting what material an AI model is trained on and/or providing an iterative series of text commands or prompts sufficient to claim authorship of the resulting output?
19. Are any revisions to the Copyright Act necessary to clarify the human authorship requirement or to provide additional standards to determine when content including AI-generated material is subject to copyright protection?
20. Is legal protection for AI-generated material desirable as a policy matter? Is legal protection for AI-generated material necessary to encourage development of generative AI technologies and systems? Does existing copyright protection for computer code that operates a generative AI system provide sufficient incentives?
20.1. If you believe protection is desirable, should it be a form of copyright or a separate sui generis right? If the latter, in what respects should protection for AI-generated material differ from copyright?
21. Does the Copyright Clause in the U.S. Constitution permit copyright protection for AI-generated material? Would such protection “promote the progress of science and useful arts”? If so, how?
22. Can AI-generated outputs implicate the exclusive rights of preexisting copyrighted works, such as the right of reproduction or the derivative work right? If so, in what circumstances?
23. Is the substantial similarity test adequate to address claims of infringement based on outputs from a generative AI system, or is some other standard appropriate or necessary?
24. How can copyright owners prove the element of copying (such as by demonstrating access to a copyrighted work) if the developer of the AI model does not maintain or make available records of what training material it used? Are existing civil discovery rules sufficient to address this situation?
25. If AI-generated material is found to infringe a copyrighted work, who should be directly or secondarily liable—the developer of a generative AI model, the developer of the system incorporating that model, end users of the system, or other parties?
25.1. Do “open-source” AI models raise unique considerations with respect to infringement based on their outputs?
26. If a generative AI system is trained on copyrighted works containing copyright management information, how does 17 U.S.C. 1202(b) apply to the treatment of that information in outputs of the system?
27. Please describe any other issues that you believe policymakers should consider with respect to potential copyright liability based on AI-generated output.
Labeling or Identification
28. Should the law require AI-generated material to be labeled or otherwise publicly identified as being generated by AI? If so, in what context should the requirement apply and how should it work?
28.1. Who should be responsible for identifying a work as AI-generated?
28.2. Are there technical or practical barriers to labeling or identification requirements?
28.3. If a notification or labeling requirement is adopted, what should be the consequences of the failure to label a particular work or the removal of a label?
29. What tools exist or are in development to identify AI-generated material, including by standard-setting bodies? How accurate are these tools? What are their limitations?
Additional Questions About Issues Related to Copyright
30. What legal rights, if any, currently apply to AI-generated material that features the name or likeness, including vocal likeness, of a particular person?
31. Should Congress establish a new federal right, similar to state law rights of publicity, that would apply to AI-generated material? If so, should it preempt state laws or set a ceiling or floor for state law protections? What should be the contours of such a right?
32. Are there or should there be protections against an AI system generating outputs that imitate the artistic style of a human creator (such as an AI system producing visual works “in the style of” a specific artist)? Who should be eligible for such protection? What form should it take?
33. With respect to sound recordings, how does section 114(b) of the Copyright Act relate to state law, such as state right of publicity laws? Does this issue require legislative attention in the context of generative AI?
34. Please identify any issues not mentioned above that the Copyright Office should consider in conducting this study.
It will be very interesting to see the responses. I wonder if AI was used to help generate the questions. I am sure someone will submit AI generated responses to the questions. I do wonder about moral rights [fn. 38 in the document].
Tuesday, 15 August 2023
President Biden takes a bow with the release of the Fact Sheet: “One Year after the CHIPS and Science Act, Biden-Harris Administration Marks Historic Progress in Bringing Semiconductor Supply Chains Home, Supporting Innovation, and Protecting National Security.” The Fact Sheet states:
One year ago, President Biden signed into law the CHIPS and
Science Act (CHIPS), which makes a nearly $53 billion investment in U.S.
semiconductor manufacturing, research and development, and workforce. The law
also creates a 25 percent tax credit for capital investments in semiconductor
manufacturing, and is helping to keep America at the forefront of innovation
and technological development. Semiconductors were invented in the United
States, but today we produce only about 10 percent of global supply—and none of
the most advanced chips. Similarly, investments in research and development
have fallen to less than 1 percent of GDP from 2 percent in the mid-1960s at
the peak of the space race. The CHIPS and Science Act aims to change this
by driving American competitiveness, making American supply chains more
resilient, and supporting our national security and access to key technologies.
In the one year since CHIPS was signed into law, companies have announced over $166 billion in manufacturing in semiconductors and electronics, and at least 50 community colleges in 19 states have announced new or expanded programming to help American workers access good-paying jobs in the semiconductor industry. In total, since the beginning of the Biden-Harris Administration, companies have announced over $231 billion in commitments in semiconductor and electronics investments in the United States. This week alone, the Department of Commerce announced the first round of grants under CHIPS to support the development of open and interoperable wireless networks, and the National Science Foundation and Departments of Energy, Commerce, and Defense announced progress toward establishing the National Semiconductor Technology Center, which will help advance America’s leadership in semiconductor research and development.
One Year of Progress on Semiconductor Manufacturing and Innovation
Over the past year, agencies across the federal government have been developing and executing on programs established under CHIPS to encourage domestic semiconductor manufacturing, invest in research and development, and support supply chain resilience and workforce development. Key milestones in the Administration’s implementation of CHIPS include:
Supporting U.S. Semiconductor Manufacturing
- The Department of Commerce launched the first funding opportunity for the $39 billion in semiconductor manufacturing incentives provided in the Act just six months after CHIPS was passed. This funding opportunity covers funding for projects to construct, expand, or modernize facilities producing semiconductors and for projects that are making large investments in facilities to produce semiconductor materials and manufacturing equipment. As the Department assesses applications, economic and national security considerations will be key factors and the program will, among other objectives, aim to provide a supply of secure, national-security relevant semiconductors.
- Already, the Department of Commerce has received more than 460 statements of interest from companies for projects across 42 states interested in receiving CHIPS funding to invest across the semiconductor value chain from manufacturing to supply chains to commercial R&D.
- The Department of Commerce has also stood up CHIPS for America, a team of more than 140 people working to support implementation of all aspects of the CHIPS incentives program.
- The Department of the Treasury released a proposed rule in
March 2023 to provide guidance on the Advanced Manufacturing Investment
Credit, a 25% investment tax credit for companies engaged in semiconductor
manufacturing and producing semiconductor manufacturing equipment. The
Department of the Treasury also released a proposed rule in
June 2023 to allow companies to receive the full amount of the Advanced
Manufacturing Investment Credit as a direct payment from the Internal
Protecting National Security and Working with Allies and Partners
The Department of Commerce issued a proposed rule in March 2023 to implement the national security guardrails laid out in CHIPS. These guardrails are intended to prevent technology and innovation funded by the program from being misused by foreign countries of concern. The Department of the Treasury’s proposed rule in March 2023 implemented parallel guardrails for the Advanced Manufacturing Investment Credit.
- The Department of State announced in March 2023 its plans for implementing the International Technology Security and Innovation Fund to support semiconductor supply chain security and diversification, as well as adoption of trustworthy and secure telecommunications networks. The State Department has already announced partnerships with Costa Rica, Panama, and the OECD to explore opportunities to collaborate on the global semiconductor supply chain.
- The Department of Defense and Department of Commerce signed an agreement to expand their collaboration to make sure that CHIPS investments will position the United States to manufacture semiconductors essential to national security and defense programs.
- As it implements CHIPS, the Department of Commerce
has been in close touch with a number of partners and allies including the
Republic of Korea, Japan, the United Kingdom, India, and the European
Union. The United States is engaging with partners and allies to
coordinate government incentive programs, build resilient cross-border
semiconductor supply chains, promote knowledge exchange and collaboration
in developing next-generation technologies, and implement safeguards to
protect national security.
Creating Jobs and Workforce Pipelines for American Workers
- The White House announced an initial set of five Workforce Hubs to create pipelines for Americans to access good-paying jobs in the semiconductor industry and other industries seeing an increase in investments driven by President Biden’s Investing in America agenda – including CHIPS, the Inflation Reduction Act, and the Bipartisan Infrastructure Law. The White House also announced a national Workforce Sprint focused on creating pipelines into advanced manufacturing jobs, including in the semiconductor industry.
- At least 50 community colleges have already announced new or expanded semiconductor workforce programs. In July, the White House launched its first Workforce Hub in Columbus, Ohio, where Columbus State Community College announced a new partnership with Intel which will create a new semiconductor technician credentialing course, available this fall.
- The National Science Foundation is investing in the American semiconductor workforce through new initiatives focused on the manufacturing workforce, supporting researchers, and curriculum development. This includes partnerships with major semiconductor and technology companies.
- According to Handshake, student applications to
full-time jobs posted by semiconductor companies were up 79% in
2022-2023, compared to just 19% for other industries.
Investing in Innovation
- The Department of Commerce is partnering with the Department of Defense, the Department of Energy, and the National Science Foundation to establish the National Semiconductor Technology Center (NSTC), a critical part of the CHIPS research and development program that will support U.S. leadership in semiconductor innovation, cut down on the time and cost of commercializing new technologies, and develop the semiconductor workforce. The Department of Commerce has also outlined its strategy for the NSTC with respect to extending U.S. leadership in semiconductor innovation, reducing time to commercialization, and building a strong microelectronics workforce.
- The Department of Commerce is also continuing to work on other parts of its $11 billion R&D funding including the metrology program, the National Advanced Packaging Manufacturing Program, and up to three new Manufacturing USA Institutes.
- The Department of Defense released a Request
for Solutions for its Microelectronics Commons R&D program in
December 2022. This program will support hardware prototyping, the
transition of new technologies from lab-to-fab, and workforce training.
Source selection is currently underway.
Supporting Regional Economic Development and Innovation
- The Department of Commerce released a funding opportunity in May 2023 for Phase 1 of the $500 million Tech Hubs Program. This is an economic development program to develop centers of innovation across the country through support of regional manufacturing, commercialization, and deployment of key technologies.
- The Department of Commerce released a funding opportunity in June 2023 for Phase 1 of the $200 million Recompete Pilot Program, an initiative to support economic opportunity and create good jobs in persistently distressed communities.
- The National Science Foundation established a
new Directorate for Technology, Innovation, and Partnerships. This
Directorate has already launched the NSF Regional
Innovation Engines program, which is helping to support
innovation in geographies that have not received the full benefits of technology
advancement in past decades. In May 2023, NSF
announced 44 NSF Engines Development Awards spanning 46 U.S. states and
territories, each funded at up to $1 million over two years to plan
for a future NSF Engine. In August 2023, NSF
announced 16 finalists for the inaugural set of NSF Engines awards,
which are anticipated by the end of the year and will provide each NSF
Engine with up to $160 million over up to 10 years.
Support Wireless Innovation and Security
- This week, the Department of Commerce announced its first round of grants from its $1.5 billion Public Wireless Supply Chain Innovation Fund to support the development of open and interoperable wireless networks.
The U.S. Congressional Research Service has published a Report with a nice overview of the public performance right in sound recordings, including history as well as licensing rates and revenue trends. Importantly, the Report reviews two pieces of related proposed legislation. The Report, in part, states:
Two pieces of legislation introduced in the 118th Congress focus on public performance rights for sound recordings transmitted by broadcast radio. The first, a nonbinding resolution known as Supporting the Local Radio Freedom Act (LRFA, H.Con.Res. 13 and S.Con.Res. 5), would effectively declare support for maintaining the status quo. LRFA would resolve that Congress should not impose any new performance royalty (or other fee, tax, or charge) for the public performance of sound recordings by a local radio station via over-the-air broadcast or on any business for such public performance of sound recordings via an over-the-air broadcast. The second, the American Music Fairness Act (AMFA, H.R. 791 and S. 253), would expand the public performance right for sound recordings to include any audio transmission, including broadcast radio transmissions. AMFA would subject performances by radio stations to the statutory license applicable to noninteractive digital services and place caps on royalties for broadcast stations with annual revenue under $1.5 million in the preceding year (unless owned by an entity with annual revenue over $10 million).
Tuesday, 8 August 2023
Among numerous legal, economic and commercial concerns about the European Commission’s proposed legislation for Standard Essential Patent (SEP) licensing, its plans for aggregate rate setting and mandatory Fair, Reasonable and Non-Discriminatory (FRAND) rate determinations in various technology standards raises all kinds of issues and alarms.
Following publication of the proposed legislation and impact assessment on April 27, the Commission has been seeking online feedback submissions by August 10, 2023.
In a previous posting here and in my initial feedback submission the Commission 14th June, I have argued against the Commission’s apparent intention to abandon the established approach of using comparable licensing agreements directly as benchmarks in FRAND rate determinations, and instead apportion rates among SEP owners based on their respective shares of total SEPs using the top-down approach. For example, I was critical about use of patent counting methods. My new feedback submission to the Commission focuses on aggregate royalty rate setting.
Any aggregate royalty rates set must be precisely defined, derived and applied. Aggregate rate setting for standards, as proposed by the Commission, will enable proposed rates to be depicted and manipulated in ways which are anticompetitive, unfair and will under-value patented standard-essential technologies. According to the proposed legislation, “‘aggregate royalty’ means the maximum amount of royalty for all patents essential to a standard.” The Commission also indicates “uncertainty about the SEP royalty burden” and that “Stakeholders consider that the FRAND licensing concept could benefit greatly from some clarification, notably with regard to the determination of an aggregate royalty burden.”
Aggregate royalty rates proposed to or set by the EUIPO could be in quantification of the total payment burden to be paid or of the rate to be used in determining individual FRAND royalty rates with the top-down approach. The latter should be a higher figure than the former to allow for SEPs that remain unlicensed and for which there is no payment.
Either of these aggregate royalty rate percentages might be derived somehow from among various different formulations of aggregate rates reported. However, these reported rates vary enormously, for example, global rates from more than 35% to less than 5% of a smartphone’s selling price. The maximum aggregate rate burden implementers will have to pay and the correct Aggregate Royalty Rate for Apportionment (ARRFA) in a top-down approach FRAND determination will fall well within those two extremes.
An alternative approach in aggregate rate setting is to estimate value in standards with use of techniques including hedonic pricing or conjoint consumer preference analysis, and then apportion value somehow between SEP licensors and implementers.
If aggregate rates are to be set at all—as they are for patent pools in their rate cards, but in the opinion of many is unnecessary and dysfunctional in bilateral licensing—such rates must be derived in the applicable context. Collective action—such as in patent pools—where some major licensors are typically also major licensees will tend to set rates that are lower than would be agreed bilaterally. Another crucial difference is that patent pool aggregate rates are the rates licensees actually pay.
In FRAND determinations for bilateral licensing there is always a shortfall between the ARRFA and what is actually paid because the SEPs in any given standard are never fully licensed. The aggregate rates from which bilateral licensing rates are derived are never fully paid due to notional royalty allocations to patents that remain unlicensed. Any aggregate royalty setting must recognize this difference if such rates are to be used to determine FRAND rates using the top-down approach.
To mitigate shortcomings in rate setting, some guiding principles must be established on what the “SEP royalty burden” and ARRFA should include and exclude, as well as how and by whom such rates should be derived and applied. The interests of both SEP owners and implementers must be safeguarded while reflecting industry realities with the many factors that shape varied financial and other terms in established licenses. Application of economic theory must have full and proper regard for what royalty figures reported in the industry represent and how licensing actually gets done.
My full submission to the Commission can also be downloaded from WiseHarbor.
 Feedback on draft EU legislation by Keith Mallinson, WiseHarbor; June 14, 2023
 Article 2 (10).
 Proposed regulation (page 8) and Impact Assessment (2.3.2)
 “A SEP holder or an implementer may request the competence centre for a non-binding expert opinion on a global aggregate royalty.” Article 18
 Various court decisions including Unwired Planet v. Huawei and InterDigital v. Lenovo have avoided or explicitly rejected aggregate rate setting, while others including Optis v Apple, also in the UK, have also primarily used comparable licensing benchmarks in their FRAND determinations.