PatentNext Summary: Generative Artificial Intelligence (GenAI) patent application filings continue to rise at the U.S. Patent and Trademark Office (USPTO), with a significant concentration in Tech Center 2100, which focuses on computer architecture and software, particularly AI and simulation technologies. GenAI inventions commonly face Section 103 (obviousness) and Section 101 (subject matter eligibility) rejections, with the latter being a frequent challenge for computer-related inventions. Despite this, Tech Center 2100 boasts a relatively high allowance rate of 79%, while other centers, like 2600 and 2400, achieve even higher rates of up to 97%. Avoiding Tech Center 3600, known for its stringent Section 101 rejections, remains a strategic consideration for inventors seeking patent protection. 

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For the years leading up to the end of 2024, the United States Patent and Trademark Office (USPTO) experienced a surge in filings for Generative AI (GenAI) inventions.

The chart below illustrates filings by Technology (“Tech”) Center over time, spanning from 2000 to 2024.

It is important to note that the downward slope on the right-hand side of the graph is due to the 18-month “Publication Delay,” during which information about newer patent application filings is not yet publicly available. See 37 CFR § 1.211.

This chart organizes patent application filings by Tech Center. As shown, the majority of AI-related patent applications are concentrated in Tech Center 2100 (represented by the dark blue color in the graph), which covers “Computer Architecture and Software” inventions. This is unsurprising, as Tech Center 2100 encompasses Art Unit 2120, which focuses on “AI & Simulation/Modeling.”

Tech Center 2100 received most of the AI-related patent application filings. For instance, in 2021, Tech Center 2100 accounted for 337 GenAI-related invention filings, while the remaining Tech Centers each handled five or fewer such filings.

The graph below highlights the rejection bases for GenAI-related patent applications. 

Graph | Rejection bases count relative to rejection year between 2002 and 2024

As expected, GenAI-related inventions most commonly faced Section 103 (obviousness) rejections. However, Section 103 rejections are generally the most common type of rejection across all categories.

Additionally, the graph shows that Section 101 (Subject Matter Eligibility) rejections were the second most common for GenAI inventions. Section 101 rejections are prevalent in computer-related inventions, a category that includes GenAI.

Despite these challenges, there is encouraging news for GenAI inventors. The two most common Tech Centers (2100 and 2600) boast high allowance rates. The chart below outlines patent application allowance rates by Tech Center. 

Bar Chart | Allowance rate by tech centers

As seen above, GenAI-related patent applications reviewed by examiners in Tech Center 2100 (dark blue bar) achieved a relatively high allowance rate of 79%. Other Tech Centers, such as 2400 and 2600, perform even better, with allowance rates of 90% and 97%, respectively.

In contrast, Tech Center 3600 (orange bar) handles a mix of business-related technologies, including “Transportation, Construction, Electronic Commerce, Agriculture, National Security, and License and Review.” Certain Art Units within Tech Center 3600, such as Art Unit 3620 (“Business Methods”), are notorious for issuing patent-eligibility rejections under 35 USC § 101. These rejections can be challenging to overcome, contributing to the lower allowance rate of 63% for this Tech Center.

As such, avoiding Tech Center 3600 remains a prudent strategy for patentees.

For further guidance on this approach, readers can explore PatentNext’s articles on best practices for patenting AI inventions. See How to Patent an Artificial Intelligence (AI) Invention: Guidance from the U.S. Patent Office (USPTO) and How to Patent Software Inventions: Show an “Improvement”.

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Please note that the charts and their related information in this article are provided courtesy of Juristat. The charts and information were obtained by searching for search terms (“generative artificial intelligence” OR “Large Language Model” OR “Transformer” OR “diffusion”) using the fields Title, Abstract, and Claims and the filters USPC 706 (“Data processing: artificial intelligence”) and CPC G06N (“Computing Arrangements Based On Specific Computational Models”) in the Juristat app.

Subscribe to get updates to this post or to receive future posts from PatentNext. Start a discussion or reach out to the author, Ryan Phelan, at rphelan@marshallip.com or 312-474-6607. Connect with or follow Ryan on LinkedIn.

PatentNext Takeaway: When deciding whether to patent AI-based inventions or maintain them as trade secrets, key considerations include the extent of public disclosure and the detectability of the AI model. Deploying an AI model in consumer-facing devices or making its output public often supports patenting to secure exclusivity. On the other hand, low detectability and sensitive training data, such as personal or medical information, may favor trade secret protection. Balancing these factors alongside the need for sufficient disclosure to meet patent requirements allows businesses to safeguard their innovations while mitigating risks.

Continue Reading AI-based Inventions: Patenting vs. Trade Secret Considerations

This article is co-authored by Phelan Simpkins, counsel for State Farm who oversees emerging technology licenses, among other key areas for the company. Phelan is speaking in his individual capacity. The views expressed herein do not necessarily reflect the view and position of State Farm.

PatentNext Takeaway: This post discusses the issues of divided infringement in U.S. patent law, specifically focusing on software-based patent claims. Divided infringement occurs when multiple parties collectively perform all the steps of a patent claim, but no single party carries out every element. This is particularly problematic for software patents, where actions can be split between different entities. This post reviews key case law, such as Akamai Techs. v. Limelight Networks and Travel Sentry v. Tropp, which illustrate scenarios where divided infringement disputes arise. The post emphasizes that patent drafters should focus on drafting claims that assign all essential steps or components of a system to a single entity to avoid costly litigation. This strategy helps ensure enforceability and minimizes the complexities of proving infringement when multiple actors are involved. This post concludes with best practices for drafting patent claims, encouraging practitioners to structure claims in ways that simplify enforcement and reduce reliance on third-party actions.

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Continue Reading Drafting Software-based Patent Claims to Avoid Costly Divided Infringement Issues

PatentNext Takeaway: This post highlights the FDA’s increasing regulatory efforts for artificial intelligence (AI) and machine learning (ML)-enabled medical devices (MLMDs), with a focus on managing device AI/ML updates through Predetermined Change Control Plans (PCCPs). The FDA emphasizes five guiding principles for PCCPs to ensure safety, risk management, and transparency for MLMDs throughout their lifecycle. The post also notes a significant rise in AI-based medical device FDA submissions and related patent filings, particularly since 2016, indicating growing interest in this technology.

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Introduction

The Food and Drug Administration (FDA) continues to engage in regulatory efforts for Artificial Intelligence (AI) and machine learning (ML) based software devices, which the FDA refers to as artificial intelligence/machine learning-enabled medical devices (MLMD). This article serves as an update to those efforts, as previously discussed on PatentNext. See PatentNext: The Intersection of Artificial Intelligence (AI), Life Sciences, Healthcare, and Intellectual Property (IP). 

AI-based Medical Device FDA Filings

Innovators of AI-based medical device inventions should familiarize themselves with the regulatory landscape, including the FDA’s position on Artificial Intelligence (AI) and machine learning (ML) based software devices, including MLMDs. 

The FDA has identified AI/ML as an important technology that it will monitor and regulate, stating that “AI/ML technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day,” and that “[t]he FDA may also review and clear modifications to medical devices, including software as a medical device, depending on the significance or risk posed to patients of that modification.” Artificial Intelligence and Machine Learning in Software as a Medical Device” (FDA.gov).

The FDA maintains a list of AI/ML-enabled Medical devices submitted to the FDA. The list includes devices submitted and authorized via 510(k) clearance, granted De Novo request, or an approved Premarket Approval Application (PMA). 

The chart below summarizes the number of AI/ML submissions made to the FDA through the years. 

Chart 1: Number of AI/ML Final Decisions by the FDA Per Year

Bar Chart | Comparing number of AI/ML final decisions by the FDA per year between 1995 and 2024.

As shown in the above Chart 1, AI/ML decisions started increasing exponentially around the 2016 timeframe. This correlates with AI-medical device patent filings, as shown below in this article.

As shown in the below Chart 2, by far, the Radiology FDA panel received the most submissions (723 submissions) compared to any other group. The Cardiovascular FDA panel received the second most submissions (98 submissions). The remaining panels each received 35 or fewer submissions. 

Chart 2: AI/ML Submissions by FDA Panel Type

Bar Chart | Comparing AI/ML Submissions by FDA Panel Types to number submitted.

The FDA’s Predetermined Change Control Plans (PCCPs) for updating AI/ML-enabled medical devices 

Even after being initially approved by the FDA, an AI/ML-enabled medical device may be subject to additional approval when its AI model is updated. This is because an update to a medical device’s underlying AI model may cause the AI model, and thus the AI/ML-enabled medical device, to operate differently from when it was originally approved by the FDA.

The FDA acknowledges that traditional medical device regulation wasn’t designed to address technology that can update an already approved device, e.g., “The FDA’s traditional paradigm of medical device regulation was not designed for adaptive artificial intelligence and machine learning technologies. Many changes to artificial intelligence and machine learning-driven devices may need a premarket review.” “Artificial Intelligence and Machine Learning in Software as a Medical Device” (FDA.gov).

Further, the FDA acknowledges that changes to an AI model deployed on an approved AI medical device can be significant, e.g., “certain changes to [machine learning-enabled medical devices] MLMDs, such as changes to a model or algorithm, may be substantive or significant. For this reason, they can require regulatory oversight, such as additional premarket review. Such regulatory expectations may not always coincide with the rapid pace of MLMD development.” Predetermined Change Control Plans for Machine Learning-Enabled Medical Devices: Guiding Principles (FDA.gov).

In order to address this issue, the FDA has released a set of five (5) “guiding principles” to manage and monitor MLMDs, having deployed AI models for performance and re-training risks. Id. The FDA looks to these five guiding principles to develop or review Predetermined Change Control Plans (PCCPs). 

The 5 Guiding Principles for developing a PCCP are summarized below.

1. Focused and Bounded: A PCCP should describe specific changes that a manufacturer intends to implement. Such changes should be limited to modifications within the intended use or intended purpose of the original MLMD.

2. Risk-based: A PCCP should be driven by a risk-based approach throughout the MLMD’s product lifecycle to ensure that individual and cumulative changes remain appropriate over time for the MLMD and its use environment.

3.  Evidence-Based: A PCCP should involve collection/monitoring evidence to ensure the ongoing safety and effectiveness of the MLMD, demonstrate that the benefits outweigh the associated risks, and establish that the risks are adequately managed and controlled.

4. Transparent: A PCCP should provide transparency to users and/or stakeholders of the MLMD by, e.g., characterization of data used in the development and modifications of a model deployed on the MLMD, comprehensive testing for planned changes, characterization of the MLMD before and after implementation of changes, and monitoring, detection, and response to deviations in MLMD performance.

5. Total Product Lifecycle (TPLC) Perspective: A PCCP should consider the lifecycle of the product by employing risk management practices during the lifecycle of the MLMD and by using and supporting existing regulatory, quality, and risk management measures throughout the TPLC to ensure device safety by monitoring, reporting and responding to safety concerns.

Currently, the 5 Guiding Principles do not replace existing FDA regulatory procedures or constitute approval of any updated AI/ML-enabled Medical device. Instead, the 5 Guiding Principles are to “facilitate and foster ongoing engagement and collaboration among stakeholders on the PCCP concept for MLMD” and to “lay a foundation for PCCPs and encourage international harmonization.” Id.

AI-based Medical Device Patent Filings

The number of AI-based Medical Device Patent filings follows a similar trend compared to the number of AI/ML submissions made to the FDA, as shown above in Chart 1.

For example, the below chart shows AI and Life Science patent filings by Technology (“Tech”) Center over time from 2000 to 2024, where a spike exists in filing activity post-2016 (note that the right-most side of the graph slopes downward because of the 18-month “Publication Delay,” during which information for newer patent application filings, is not yet publicly available. See 37 CFR § 1.211).

Graph | Filings by tech centers from 2000 to 2024 compared to publications delay.

The above chart is provided courtesy of Juristat. The chart and information were obtained by searching for “software AND medical AND device AND (‘artificial intelligence’ OR ‘machine learning’)” and using the fields Title, Abstract, Description, and Claims in the Juristat app.

After the spike in 2016, the above chart shows continued activity up to the present day. The above chart organizes patent application filings by USPTO Tech Centers. As shown, most AI-based medical device patent filings fall within Tech Center 2600 (“Communications”), having received, for example, about 2500 filings in the year 2022. This tech center includes art units that focus on computer graphic processing (art unit 2615) and image analysis (art unit 2660), which are important to radiotherapy and radiology inventions. These technical areas can relate to radiology, which uses imaging technology, and thus correlates to Chart 2 above regarding FDA filings in the radiology field. 

Given the increased interest in AI and medical device technology and the increase in FDA submissions, as discussed above, we can expect further filings in this space in the coming years.

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Subscribe to get updates to this post or to receive future posts from PatentNext. Start a discussion or reach out to the author, Ryan Phelan, at rphelan@marshallip.com (Tel: 312-474-6607). Connect with or follow Ryan on LinkedIn.

PatentNext Takeaway:  WIPO published a Patent Landscape Report on GenAI.  The Patent Landscape Report discusses trends in GenAI, including trends in: GenAI scientific publications, GenAI patents, GenAI models, types of data used in GenAI, and GenAI application areas.

Continue Reading WIPO Issues a Patent Landscape Report on Generative AI

PatentNext Takeaway: The USPTO announced its 2024 Guidance Update on Patent Subject Matter Eligibility, particularly focusing on Artificial Intelligence (AI). Effective July 17, 2024, this guidance aims to address examination procedures for U.S. patent applications under 35 U.S.C. § 101, following President Biden’s executive order on the safe development and use of AI. The 2024 AI Guidance is designed to help USPTO personnel apply existing subject matter eligibility rules to AI-related inventions during patent examination, appeal, and post-grant proceedings. It includes case examples from the Federal Circuit, which, although not AI-specific, are relevant for understanding software-related arts. Additionally, it introduces hypothetical examples (new example claims 47-49) illustrating how AI-related patent claims will be analyzed for eligibility. These examples suggest that examiners will scrutinize AI-related claims more rigorously, emphasizing the need for detailed descriptions of how AI features improve technology or technical fields and/or provide a specific medical treatment. While the guidance does not constitute new law, it replaces previous guidance and is expected to be integrated into the MPEP eventually.

Continue Reading The USPTO Issues Guidance on Patenting Artificial Intelligence (AI)-related Inventions per 35 U.S.C. § 101 (Subject Matter Eligibility)

PatentNext Takeaway: Can text generated by artificial intelligence (AI) (e.g., an “AI-generated text”) constitute “prior art” pursuant to U.S. patent law? The answer to that question will impact whether AI-generated text can be used to preclude human inventions from issuing as patents in the United States. Third-party entities currently publish AI-generated text for the express purpose of preventing patent inventions from issuing. But this seems to run afoul of U.S. law requiring human “conception,” not to mention the U.S. Constitution, which seeks “[t]o promote the Progress of Science and useful Arts ….” Accordingly, it is possible that Congress or the courts will look not only the to related statutory text, but also to existing court decisions to preclude AI-generated text from constituting “prior art.”

Continue Reading Can Artificial Intelligence (AI) Generate Prior Art (e.g., a “Printed Publication”) pursuant to U.S. Patent Law?

I am excited to announce the publication of the Intellectual Property Owner (IPO)’s paper on Patent Marking regarding Software Medical Devices. 

The paper provides an in-depth analysis of patent marking laws as they apply to software and medical devices. It covers multiple jurisdictions, including the United States, the United Kingdom, France, and Germany. The paper addresses various types of medical devices and software platforms, such as external, implantable, cloud-based devices, and third-party devices. 

Continue Reading Patent Marking And Software Medical Devices (IPO Paper Announcement)

I am excited to announce the publication of the Intellectual Property Owner (IPO)’s Artificial Intelligence (AI) Patenting Handbook (the “AI Patenting Handbook”). This is a second, updated version of the AI patenting handbook. 

Continue Reading Artificial Intelligence (AI) Patenting Handbook: Version 2.0

PatentNext Takeaway:  The U.S. Patent and Trademark Office (USPTO) recently issued examination guidance regarding patentability for artificial intelligence (AI)-assisted inventions. The guidance states that AI-assisted inventions are not “category unpatentable.” Instead, when a natural person provides a “significant contribution” to an invention, such an invention can be patentable even if an AI system contributed to the invention. While the guidance does not constitute law, it is grounded in law, i.e., the Federal Circuit’s so-called Pannu factors, which serve as a test for ensuring that a natural person contributed, at least in part, to the conception of the invention as required in the Federal Circuit’s Thaler decision on AI inventorship. The guidance also provides several useful guidelines and examples to help patent practitioners determine what constitutes a “significant contribution” for purposes of establishing natural person inventorship and, thus, patentability for AI-assisted inventions.  

Continue Reading The U.S. Patent Office provides Inventorship Guidance for AI-Assisted Inventions