Introduction

The acquisition of AI companies has become one of the most consequential deal categories in technology. Global investment in generative AI surged to $25.2 billion in 2023, and the global AI market is projected to reach $407 billion by 2027. With that growth has come a wave of acquisitions by major technology companies seeking to add AI capabilities to their platforms, and with those transactions has come a rapidly evolving diligence playbook.

This article is part of a twelve-week series on autonomous systems, physical AI, and patent strategy. In our prior articles, we established an expanded taxonomy of patentable inventions across the physical AI landscape, presented a comprehensive IP strategy playbook for autonomous systems companies, and examined the data on whether patent filings help physical AI companies raise capital. Here, we turn to the buyer’s side of the equation: what do acquirers now diligence in AI and autonomy deals?

In a 2021 article, we examined this trend through the lens of Apple’s AI acquisitions. From 2016 to 2020, Apple acquired more AI companies than any other buyer, including several with technology applicable to autonomous vehicles (AVs) and electric vehicles (EVs): Drive.ai, Emotient, Vilynx, Xnor.ai, Lighthouse AI, Spectral Edge, and Spektral. The data revealed a striking pattern. The three companies Apple acquired for over $100 million, including Drive.ai ($200M, 19 patent assets), Xnor.ai ($200M, 20 patent assets), and Emotient ($100M, 22 patent assets), each had approximately 20 U.S. patent assets and went from founding to acquisition in just four years. Companies with materially smaller portfolios commanded lower valuations and took longer to be acquired. Patent portfolios, in short, appeared correlated with both acquisition value and speed to exit.

That finding remains relevant today, but the buyer’s diligence playbook has expanded dramatically. AI acquisitions in the autonomy and physical AI space now demand investigation across six critical pillars that go well beyond counting patent assets.

1. Proprietary Data: The New Crown Jewel

The original article focused on patents as the key asset driving acquisition value. Today, proprietary data has emerged as an equally critical, and in many cases more important, diligence target. As the cost of programming AI-powered algorithms and large language models comes down, buyers are looking beyond pure engineering talent and seeking to confirm that the target controls unique and proprietary datasets. It’s no longer just about the algorithms and large language models; it’s about having access to proprietary data that no one else can get, and the ability to use that data in the AI system in the manner desired.

For AI companies, the real value often lies in intangible assets, including layered, multifunction algorithms, large language models, and exclusive access to proprietary datasets that together produce valuable answers and drive monetization. Buyers conducting diligence must now trace the “genealogy of the data, from its origin to the assignment to the buyer” and confirm that the seller is transferring contractual rights sufficient to use the data for the buyer’s intended purpose.

In practice, this means diligence teams should ask: What can each party do with the data? Are there confidentiality obligations? Use restrictions? Geographic storage location considerations? If insights from the data lead to innovations, who owns those? If the data leads to incorrect conclusions, who is on the hook? These questions are especially critical in the autonomous systems context, where data pipelines may involve automated labeling, active learning, data compression for high bandwidth sensor streams, privacy-preserving collection, and federated learning across distributed fleets.

2. Patent Assets: From Counting to Characterizing

Our 2021 analysis demonstrated a correlation between patent portfolios and acquisition outcomes. That finding holds, but the diligence inquiry has become more sophisticated. Buyers today are not simply counting patent assets. They are characterizing them.

Patent diligence should now assess claim scope, validity, enforceability, infringement risk, freedom to operate, third-party challenges, ownership, chain of title, inventor assignments, and maintenance fee status. For AI-specific patents, diligence teams should examine whether claimed inventions disclose sufficient technical detail rather than functioning as “black box” claims, including architecture, training methods, inputs, preprocessing, inference, runtime updates, and concrete technical improvements.

In the autonomous systems space, as established in the first article in this series, the patentable technology stack has expanded to cover perception and sensor fusion, AI and ML models and training methods, planning and decision-making systems, edge computing architectures, simulation and testing, safety and redundancy systems, human and machine interfaces, fleet and swarm management, and data pipeline innovations. Filing strategy should also be aligned with product development and funding milestones, and the portfolio should be organized by product line, technology area, and geography.

At the pre-exit stage, acquirers should confirm that patent assignments are recorded, inventor declarations are complete, maintenance fees are current, and ownership chains are clean. Patent ownership risk is acute in this space because development frequently involves cross-functional teams, university labs, government research institutions, open-source communities, specialized consultants, joint development partners, and pre-existing founder IP.

Detectability is another critical factor in valuing AI patents. Features visible in product operation, user interfaces, or product documentation are easier to detect and enforce; features retained solely in cloud infrastructure may be better maintained as trade secrets.

3. Technical Authenticity: Guarding Against “Fake AI”

A risk that did not feature prominently in our 2021 analysis is the problem of “fake AI.” Technical diligence should test whether the AI solution is authentic, robust, scalable, and aligned with business objectives. Without thorough technical diligence, investors face exposure to misrepresented solutions lacking genuine capabilities, potentially leading to substantial financial losses.

The rise of “AI Washing” is the intentional overstating of a product or service’s AI capabilities to make it appear more innovative than it actually is. This practice has drawn significant enforcement attention. In March 2024, the SEC brought its first-ever settled charges against two investment advisers, Delphia and Global Predictions, for misrepresenting their use of AI to attract investors. Both companies claimed to use AI technologies they did not actually possess. Delphia paid a civil penalty of $225,000, and Global Predictions paid $175,000.

For buyers in AI and autonomy deals, the lesson is clear: diligence must go beyond reviewing marketing materials and pitch decks. Technical diligence should independently evaluate algorithms and models, actual AI functionality and deployment, data privacy and security measures, and compliance with relevant regulations. Buyers should also ensure that public statements about AI capabilities have been reviewed by legal counsel and are accurate.

4. Regulatory Exposure: A Rapidly Expanding Landscape

In 2021, the regulatory landscape for AI was sparse. It is no longer. According to the Stanford University AI Index Report, the number of AI-related regulations in the U.S. rose from just one in 2016 to 25 in 2023, growing by 56.3% in 2023 alone. The count of U.S. regulatory bodies crafting AI regulations climbed to 21 in 2023, up from 17 in 2022, with newcomers including the U.S. Department of Transportation, the U.S. Department of Energy, and OSHA.

On the international front, the EU AI Act has established the world’s first comprehensive AI regulatory framework, classifying AI systems into four risk levels (unacceptable, high, limited, and minimal), each with corresponding compliance obligations. At the same time, antitrust officials at the DOJ, FTC, European Commission, and the UK’s Competition and Markets Authority have pledged to remain vigilant regarding AI’s risks to competition.

For AI acquisition diligence, this means buyers need to understand where the target operates and where users can access the product in order to determine the breadth and scope of applicable laws. Key regulatory areas include data privacy (GDPR, CCPA, HIPAA), algorithmic bias, ethical AI standards, antitrust scrutiny, and industry-specific regulations. AI acquisitions are facing special scrutiny from antitrust and competition regulators, who are concerned with not only monopolistic practices and anti-competitive behavior but also the effect of AI on jobs.

Purchase agreements should reflect this regulatory complexity with tailored representations and warranties covering ownership, noninfringement, data privacy compliance, cybersecurity integrity and maintenance, and disclosure of known risks or limitations. Buyers may further seek indemnities, holdbacks, escrow arrangements, and increasingly, third-party insurance to manage post-closing regulatory risk.

5. Cybersecurity: A Growing Attack Surface

As autonomous systems become more connected, cybersecurity diligence has become a standalone pillar of AI acquisition analysis. According to a 2023 report by Upstream, automotive cyberattacks spiked by 380% in just one year, from 2021 to 2022, affecting telematics, infotainment systems, electronic control units, and remote keyless entry systems. As the number of computing devices, sensors, and communication interfaces increases, so does the attack surface available to malicious actors.

AI-specific cybersecurity risks extend beyond conventional threats. Adversarial data, meaning subtle, almost unnoticeable changes to training data, can undermine how an AI algorithm works and cause unexpected outcomes, destroying the positive impact of AI. It is critical that companies ensure the security of their training data and their software. Bad data, adversarial inputs, compromised sensors, or weak connected infrastructure can affect not only privacy and IP, but also safety and real-world system behavior.

Buyers should assess security by design, secure development and networking practices, product testing, vulnerability monitoring, incident response programs, and supplier security controls. Importantly, cybersecurity is not the same as compliance: while compliance with data privacy regulations is necessary, it may not always be sufficient to protect data. Companies should implement security measures beyond minimum regulatory requirements, including eliminating vulnerabilities at the design stage and continuously monitoring for emerging threats.

6. Rights to Use Training Data: The New IP Frontier

Perhaps the most significant shift since our 2021 article is the emergence of training data rights as a freestanding diligence category. Many AI models have been trained on third-party data without appropriate authorization or consent from copyright owners, resulting in potential downstream liability to end users of such models.

Buyers should examine whether the target’s training data was lawfully collected and whether the target has contractual rights sufficient for the buyer’s intended use. Diligence should review existing agreements to determine whether customer or third-party data may be used for training vendor systems or improving the vendor’s own products. Copyrights associated with training materials should be confirmed as properly licensed or excluded.

The regulatory trajectory further underscores this risk. Proposed legislation like California’s AB2013 would require developers of AI systems to publish documentation of the datasets used to train those systems. As transparency requirements expand, gaps in training data provenance that might have gone unnoticed in an earlier era could become material liabilities.

Conclusion: A New Diligence Framework for AI and Autonomy Deals

When we published our original article in 2021, patent assets were the primary lens through which buyers evaluated AI acquisition targets. The data from Apple’s acquisitions, as discussed in our 2021 article, illustrated the value of a deliberate patent strategy: companies with approximately 20 patent assets commanded valuations exceeding $100 million and exited within four years.

That finding endures, but it is no longer sufficient. As AI companies move from research projects to production systems that operate in the physical world, the risk profile for AI-centric business models is so different from acquisitions of other technology businesses that a new approach is required. Buyers today must conduct a retargeted due diligence investigation that spans proprietary data, patent characterization, technical authenticity, regulatory mapping, cybersecurity assessment, and training data rights.

For AI companies developing AV/EV related technology, or any physical AI system, a strong patent portfolio remains a powerful signal of innovation and proprietary technology that can multiply valuation. But the companies best positioned for acquisition will be those that can demonstrate not only patent strength, but also clean data provenance, genuine technical capabilities, regulatory readiness, robust cybersecurity, and defensible rights to the data that trains their systems. In our next article in this series, we ask where the physical AI patent wars may begin, as autonomy expands from vehicles into drones, robotics, defense systems, industrial automation, and AI-enabled hardware.

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DISCLAIMER: The information provided herein may not be applicable in all situations and should not be acted upon without specific legal advice based on particular situations.

September 2026

For autonomous systems and physical AI companies, patent cadence can be part of the fundraising story when it maps to defensible shipped technology. This article examines the latest data on patent-funding correlation and translates it from the AV sector to the broader physical AI market.

Introduction

Physical AI is attracting capital at a breakneck pace. In 2025, robotics and physical AI startups raised a record $27.6 billion across 1,009 deals, more than double the $13.7 billion across 851 deals in 2024.[1] In the first quarter of 2026 alone, physical AI startups raised $16.3 billion across 492 deals, while autonomous machines delivered a historic $29 billion across 118 transactions.[2] These figures reflect a sector-wide conviction that AI systems operating in the physical world, including robots, drones, autonomous vehicles, surgical platforms, defense systems, and agricultural machines, represent the next major commercial frontier.

With this surge comes an increasingly important question for founders, investors, and acquirers: does a company’s patent portfolio correlate with its ability to raise capital, command higher valuations, and achieve faster exits? In a 2021 article, we examined this question through the lens of Apple’s acquisitions of AI and autonomous vehicle (AV) companies and found a striking pattern.[3] The current article revisits and broadens that analysis, drawing on new data to assess whether patent filings can help physical AI companies raise capital across the full spectrum of autonomous systems.

This article is the third in a twelve-week series on autonomous systems, physical AI, and patent strategy. In our first article, we established an expanded taxonomy of patentable inventions across the physical AI landscape.[4] In our second article, we presented a comprehensive IP strategy playbook for autonomous systems companies, including guidance on aligning patent filings with product and funding milestones.[5] Here, we turn to the empirical question: what does the data tell us about the relationship between patent activity and funding outcomes?

1. The Original AV Finding: Patents and Exit Correlation

Our 2021 article, “How Are Electric/Autonomous Vehicle Patent Filings Correlated With Funding Events?” examined Apple’s AI acquisition activity from 2016 to 2020. During that period, Apple acquired more AI companies than any other buyer, including several with technology applicable to autonomous vehicles and electric vehicles. The data revealed a notable correlation between patent portfolio size, acquisition value, and speed to exit.

The three companies Apple acquired for over $100 million each (Drive.ai ($200 million, 19 patent assets), Xnor.ai ($200 million, 20 patent assets), and Emotient ($100 million, 22 patent assets)) had approximately 20 U.S. patent assets and went from founding to acquisition in approximately four years.[6] Companies with materially smaller portfolios commanded lower valuations and took longer to be acquired. The table below summarizes the key data points:

Company	Acquisition Amount	Patent Assets	Years to Exit	Primary Technology	Relevance to AV/EV
Drive.ai	$200M	19	4	Autonomous driving	Direct
Xnor.ai	$200M	20	4	Edge AI inference	Perception/compute
Emotient	$100M	22	4	Facial recognition AI	HMI/sensing
Vilynx	$50M	5	9	Video analysis AI	Scene understanding
Spektral	$30M	2	3	Real-time image processing	Perception
Lighthouse AI	$17M	8	5	3D sensing AI	Depth perception
Spectral Edge	$8.1M	4	8	Image fusion	Sensor fusion

As illustrated above, the companies with approximately 20 patent assets consistently commanded valuations exceeding $100 million and reached acquisition within four years. Patent portfolios appeared correlated with both acquisition value and speed to exit. Notably, the analysis by IFI CLAIMS Patent Services, which tracked patent classification codes for 43 acquired EV technology startups, confirmed that patent activity provides valuable insight into R&D activity for investors, observing that “often the true value of a company lies with its intellectual properties.”[7]

2. Updated Evidence: Patent Portfolios and Startup Valuations

Since the 2021 article, multiple independent studies have confirmed and expanded the finding that patent portfolios correlate with startup valuations and funding outcomes. The evidence is no longer limited to a single acquirer’s deal history; it now spans thousands of startups across sectors and stages.

PitchBook’s 2023 research found that startups holding patents had a median pre-money valuation approximately 30% higher than non-patent startups.[8] A 2024 analysis of the Tech Coast Angels portfolio found that startups with 30 or more patents had over an 80% chance of achieving an exit, with exit valuation multiples averaging 38.7x compared to 1.4x for startups with no patents.[9] In 2025, two major studies provided additional confirmation: startups with patent protection commanded a 93% higher valuation at the angel stage and 51% higher at late stage, while being 6.4 times more likely to secure venture capital funding.[10] A separate 2025 report confirmed that venture-backed startups with patents were valued nearly twice as much as those without and were 47% more likely to raise VC funding.[11]

To be sure, these studies reflect correlation, not established causation. Companies that file patents may also tend to have stronger technical teams, more capital, or more mature products, and these factors may independently contribute to higher valuations. The data nonetheless suggest that patent portfolios are, at minimum, a meaningful input in the investor’s assessment of a company’s innovation profile and defensibility.

The following table summarizes the key findings:

Metric	Patent Startups	Non-Patent Startups
Median Pre-Money Valuation Premium	~30% higher (PitchBook, 2023)	Baseline
Exit Valuation Multiple	38.7x (30+ patents)	1.4x (no patents)
Angel-Stage Valuation Premium	93% higher	Baseline
Late-Stage Valuation Premium	51% higher	Baseline
Likelihood of Securing VC Funding	6.4x more likely	Baseline
VC-Backed Valuation Premium	~2x (nearly double)	Baseline
Likelihood of Raising VC	47% more likely	Baseline
Exit Achievement Rate (30+ patents)	>80%	Significantly lower

Taken together, these data points suggest that patent portfolios serve as both enforceable legal rights and quantifiable signals of innovation, defensibility, and commercial potential that investors and acquirers weigh when assessing valuations.

3. The Physical AI Funding Surge

The capital flowing into physical AI has reached unprecedented levels, making the patent-funding correlation increasingly consequential. In 2025, AI startups secured $131.5 billion in venture funding, representing approximately one-third of global VC investments. Global venture investors put $425 billion into more than 24,000 private companies in 2025, with AI companies capturing approximately $211 billion, according to Crunchbase.[12] Within this broader AI wave, the physical AI subsectors have attracted especially concentrated investment:

• Robotics and Physical AI: $27.6 billion across 1,009 deals in 2025 (PitchBook), more than double the $13.7 billion in 2024.[13]
• Q1 2026 Acceleration: $16.3 billion across 492 deals in Q1 2026 alone; autonomous machines delivered $29 billion across 118 transactions in Q1 2026, more than triple Q4 2025 (PitchBook).[14]
• Defense and Security Robotics: $8.0 billion across 234 deals in 2025, a 139% year-over-year increase (PitchBook).[15]
• Autonomous Drones: $6.2 billion across 169 deals on a trailing 12-month basis through December 2025 (PitchBook).
• Robot Foundation Models: More than $2.2 billion in dedicated funding in 2025.
• Seed-Stage Premium: AI startups enjoy a 42% premium over non-AI startups at the seed stage (2026).

Several individual transactions underscore the scale of capital at play. Skild AI raised close to $1.4 billion at a valuation above $14 billion for its robotics foundation model in January 2026.[16] Atoms, founded by Travis Kalanick, raised $1.7 billion for industrial AI and robotics. Helsing, a defense AI company, raised $1.8 billion in a Series E round at an $18 billion valuation in July 2026.[17] Safe Superintelligence secured $5 billion from NVIDIA in July 2026.

At the same time, the broader U.S. patent landscape is experiencing a notable shift. After seven years of growth, U.S. patent applications dropped 9% in 2025 from 2024, reaching their lowest level since 2019, according to IFI CLAIMS Patent Services.[18] U.S. patent grants slightly declined, down less than 1% from 324,064 in 2024 to 323,272 in 2025. However, AI patents continue to surge: in July 2026, IFI CLAIMS reported that AI patent grants broke the 100,000 milestone.[19] Half of the top ten fastest-growing patent technology areas relate to batteries and sustainability, areas closely linked to the electrification of autonomous platforms.

For physical AI companies navigating this environment, the question is not whether to pursue patents but how to build portfolios that signal value to the investors deploying these record sums.

4. What Physical AI Investors Should Evaluate

If patent portfolios correlate with valuations and exits for AI and AV companies, it follows that investors evaluating physical AI startups, whether in robotics, autonomous drones, surgical systems, agtech, warehouse automation, or defense, should incorporate patent portfolio assessment into their investment diligence. However, the inquiry should go beyond simply counting patent assets. Drawing on the taxonomy of patentable inventions we established in our first article in this series, and the IP strategy playbook outlined in our second article, investors should consider evaluating the following dimensions:

• Coverage Across the Technology Stack. Does the company’s patent portfolio address the key layers of its autonomous system? As discussed in our first article, the physical AI technology stack spans perception and sensing, AI/ML and decision-making, control and actuation, edge computing, simulation and digital twins, safety and redundancy, human-machine interface, fleet management, and data infrastructure. A portfolio concentrated in a single layer is not inherently deficient. Many successful companies build deep expertise in one domain. However, investors may view broader coverage as a sign of a more mature and resilient technology platform.
• Alignment of Filing Cadence with Milestones. Is the company’s patent filing cadence aligned with its product development and fundraising milestones? As discussed in our second article, the most effective IP strategies tie filing activity to the company’s stage: provisional filings at the pre-seed stage to establish priority, utility filings during seed and Series A rounds to build portfolio depth, and international filings as the company expands into new markets.
• Detectability and Enforceability. Are the patented claims directed to technology that can be detected in a competitor’s product or service? Patents on internal algorithms or embedded control systems may be difficult to enforce if infringement cannot be observed or reverse-engineered from external behavior.
• Trade Secret and Data Complement. Does the company maintain a complementary trade secret program for innovations that are better protected through secrecy? In physical AI, proprietary training datasets, sensor calibration parameters, fleet telemetry, simulation environments, and operational know-how may constitute significant value that patents alone cannot capture.
• Ownership and Chain of Title. Are all patent assets properly assigned, with clean inventor chains and no encumbrances from prior employers, universities, or collaboration partners? Gaps in ownership can undermine the entire portfolio’s value.
• Freedom to Operate. Has the company assessed third-party patent risk? Physical AI systems often integrate components from multiple suppliers and open-source projects, each of which may carry its own IP constraints.

Beyond these patent-specific considerations, investors evaluating physical AI companies should also diligence:

• The quality and provenance of proprietary datasets used for training and validation;
• The company’s regulatory exposure across the jurisdictions in which its systems will operate;
• Cybersecurity posture, particularly for connected and networked autonomous systems;
• The defensibility of the company’s rights to use third-party training data; and
• Whether the company’s AI capabilities are genuine and technically substantiated, recognizing that early-stage companies may have limited deployment data and that the line between forward-looking product vision and unsupported claims requires careful assessment.

Unsurprisingly, several of these additional diligence areas will be explored in the next article in this series, which addresses what buyers now diligence in AI and autonomy deals.

5. From Funding to Exit: Setting the Stage for Diligence

The correlation between patent portfolios and fundraising outcomes has immediate practical implications for physical AI companies at every stage. However, the significance of a deliberate, strategic patent portfolio extends well beyond the fundraising context. As companies mature and approach exit, whether through acquisition, merger, or IPO, the patent portfolio often becomes a central asset in the buyer’s or underwriter’s evaluation.

The Apple acquisitions data from our 2021 article illustrated this dynamic in the AV context: companies with approximately 20 patent assets not only commanded higher valuations but also reached exit faster. As physical AI companies move from prototype to production, from venture-backed growth to strategic exit, the IP portfolio that was built to support fundraising becomes the IP portfolio that must survive acquirer diligence.

Accordingly, the discipline of building a patent portfolio that maps to real, shipped technology, while not discounting the legitimate role of forward-looking or prophetic filings in a strategic portfolio, serves a dual purpose. It supports the fundraising narrative today and the diligence process tomorrow. Companies that treat patent filings as a strategic activity tied to product milestones, rather than an administrative afterthought, position themselves for both outcomes.

Indeed, the diligence inquiry for AI and autonomy transactions has expanded substantially since 2021. Buyers today evaluate not only patent assets but also proprietary data, patent characterization, technical authenticity, regulatory exposure, cybersecurity, and rights to use training data. In our next article in this series, we examine these six diligence pillars in detail, drawing on updated data from Apple’s AI acquisitions and the broader landscape of AI and autonomy deals.

Conclusion

The data assembled in this article points toward a consistent finding: patent portfolios are correlated with higher valuations, increased likelihood of securing venture capital, and faster paths to exit. This was true for Apple’s AV and AI acquisitions in 2021, and it appears to hold across a much broader set of startups and stages in 2025 and 2026. The emergence of the physical AI market, with record capital inflows exceeding $27.6 billion in 2025 and $16.3 billion in Q1 2026 alone, raises the stakes for IP strategy and makes the patent-funding correlation all the more consequential.

For physical AI companies, the lesson is not simply that patents are valuable, but that the cadence and substance of patent filings should map to real technology milestones, product shipments, and commercial deployments. A deliberate filing strategy tied to shipped technology creates a portfolio that supports the fundraising story today and withstands the rigor of acquirer diligence tomorrow. If the current trends in physical AI investment continue, companies that can demonstrate a strategic, data-aligned patent portfolio may be well positioned to capture a disproportionate share of the capital and exit opportunities ahead.

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Sources

[1] PitchBook, Q4 2025 Robotics & Physical AI VC Trends (2025), https://pitchbook.com/news/reports/q4-2025-robotics-physical-ai-vc-trends.

[2] PitchBook, Q1 2026 Robotics & Physical AI VC Trends (2026), https://pitchbook.com/news/reports/q1-2026-robotics-physical-ai-vc-trends.

[3] Chethan K. Srinivasa & Pakin Pongcheewin, Did AI Patents Help AV/EV-Related Companies Get Acquired by Apple?, Â鶹ֱ˛Ą & Lardner LLP Dashboard Insights (June 10, 2021).

[4] Chethan K. Srinivasa, From Self-Driving Vehicles to Physical AI: The Inventions Behind Autonomous Systems, Â鶹ֱ˛Ą & Lardner LLP Dashboard Insights (July 2026).

[5] Chethan K. Srinivasa, IP Strategy for Autonomous Systems Companies: A Playbook Beyond Autonomous Vehicles, Â鶹ֱ˛Ą & Lardner LLP Dashboard Insights (July 2026).

[6] Acquisition amounts are estimated based on publicly available records. See Srinivasa & Pongcheewin, supra note 3. Company founding dates per CrunchBase; acquisition dates per GlobalData; patent asset counts per USPTO public records.

[7] IFI CLAIMS Patent Servs., IFI Insights: Do Patent Applications of Electric Vehicle Startups Predict the Road Ahead in EV Industry Mergers and Acquisitions? (Mar. 16, 2022), https://www.ificlaims.com/news/ifi-insights-patent-applications-electric-vehicle-startups/.

[8] Andy White & James Ulan, Q1 2023 PitchBook Analyst Note: Introducing PitchBook Patent Research, PitchBook (Feb. 5, 2023), https://pitchbook.com/news/reports/q1-2023-pitchbook-analyst-note-introducing-pitchbook-patent-research.

[9] Tech Coast Angels, Do Patents Affect Outcomes in Early-Stage Investing?, Angel Capital Ass’n (2024), https://angelcapitalassociation.org/blog/do-patents-affect-outcomes-in-early-stage-investing/.

[10] Keegan Caldwell, Tips for Revamping Patent Portfolio Strategy in AI Deal Era, Law360 (Aug. 22, 2024) (citing PitchBook patent research data).

[11] Andrew Rapacke, Startup Patent Strategy: Guide to Protect IP & Raise Capital, Arapacke Law (Feb. 13, 2026), https://arapackelaw.com/patents/startup-patent/.

[12] Crunchbase News, Global Startup Funding and Venture Capital Data: 2025 Year in Review (2026), https://news.crunchbase.com/venture/funding-data-third-largest-year-2025/.

[13] PitchBook, Q4 2025 Robotics & Physical AI VC Trends, supra note 1.

[14] PitchBook, Q1 2026 Robotics & Physical AI VC Trends, supra note 2.

[15] PitchBook, Drone Deals Fueled VC’s 139% Surge into Defense & Robotics (2026), https://pitchbook.com/news/articles/drone-deals-fueled-vcs-139-surge-into-defense-robotics.

[16] Press Release, Skild AI, Skild AI Raises $1.4B, Now Valued Over $14B (Jan. 14, 2026), https://www.businesswire.com/news/home/20260114335623/en/.

[17] Intellizence, Startup Funding Trends in July 2026: AI, Robotics, Energy, & Healthcare (Aug. 5, 2026), https://intellizence.com/insights/startup-funding/startup-funding-trends-july-2026-ai-infrastructure-and-robotics/.

[18] Press Release, IFI CLAIMS Patent Servs., U.S. Patent Applications Decrease Dramatically One Year After Reaching Record High, Falling 9%; 2025 U.S. Patent Grants Also Down (Jan. 13, 2026), https://www.ificlaims.com/news/u-s-patent-applications-decrease-dramatically-one-year-after-reaching-record-high-falling-9-2025-u-s-patent-grants-also-down.

[19] Press Release, IFI CLAIMS Patent Servs., AI Patents Break 100,000 Grants Milestone (July 28, 2026), https://www.ificlaims.com/news/ifi-claims-ai-patents-break-100000-grants-milestone.

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DISCLAIMER: The information provided herein may not be applicable in all situations and should not be acted upon without specific legal advice based on particular situations.

More generative AI patent families were published in 2024 and 2025 than during the entire preceding decade, according to a recent analysis by the World Intellectual Property Organization (WIPO) and analytics firm, EconSight.¹ The numbers are striking: GenAI patent families surged from roughly 14,000 in 2023 to over 37,800 by 2025, nearly tripling in just two years.² But what may be most significant for companies building autonomous systems, robots, drones, and other physical AI solutions is not the volume itself. It is who is filing and what those filings are targeting.

As reported by Bloomberg Law, some of the fastest-growing patent activity is coming from companies better known for energy, infrastructure, manufacturing, and telecommunications than for developing the AI models that first captivated investors.³ Among the top GenAI patent holders through 2025 are SoftBank, State Grid Corp. of China, Bosch, and Inspur Group. Notably, many of these companies are not claiming the underlying AI models themselves; they are protecting specific applications within existing businesses. Bosch, for example, is staking out its position as a leader in AI automation tools for physical manufacturing, and State Grid’s filings are tied to grid optimization, predictive maintenance, and infrastructure planning.

This pattern should resonate with anyone tracking the autonomous systems patent landscape. The same dynamic is unfolding in physical AI.

The Physical AI Connection

The term “physical AI” has emerged as the unifying concept for AI systems that perceive, reason, and act in real-world environments, enabling machines to anticipate behavior rather than simply react to it. At CES 2026, NVIDIA CEO Jensen Huang described the moment as “the ChatGPT moment for physical AI, when machines begin to understand, reason and act in the real world.” At GTC 2026, NVIDIA announced major expansions to its physical AI platform spanning autonomous vehicles, industrial robots (through partnerships with FANUC and ABB), and humanoid robotics foundation models.

The underlying technical challenges across physical AI are structurally identical to those mapped by the European Patent Office’s original autonomous vehicle taxonomy.⁴ Perception and sensing, communication, data processing and AI, and vehicle control each have direct equivalents in robotics, drones, surgical systems, and defense platforms. The taxonomy that began with self-driving vehicles now applies to warehouse robots navigating dynamic environments, surgical systems performing precision manipulation, agricultural drones adapting to weather and terrain, and humanoid robots interacting with humans in unstructured settings.

Three Implications for Autonomous Systems Companies

The GenAI patent surge, combined with the broadening of patent activity beyond traditional AI platform developers, raises several strategic considerations for companies in the autonomous systems space:

Generalize The Playbook

The expansion of GenAI patent activity beyond traditional AI platform developers mirrors a trend that has been building for several years in the autonomous systems space. The playbook that AV startups developed for patent strategy between 2015 and 2022 must now be generalized. What to patent, where to file, and how to align IP activity with product development and funding milestones are questions that every autonomous systems company must answer deliberately and early.

For autonomous systems companies, patents are not merely legal instruments; they are strategic business assets that, when managed deliberately, support every phase of the company’s lifecycle from formation through exit. The GenAI patent surge underscores that point. Companies that are not traditionally associated with AI are now aggressively staking out IP territory in applied AI domains, and the line between GenAI and physical AI is becoming increasingly difficult to draw.

As WIPO has cautioned, a large portfolio is not necessarily the same as a strategically potent one, and the data do not allow us to readily distinguish between the two.² That is precisely why the first strategic question for autonomous systems companies is not “should we file patents?” It is “do we understand which layers of the autonomy stack contain our defensible innovation, and are we protecting them deliberately?”

The GenAI patent wave is being seeded now. For physical AI companies, the time to position is before that wave arrives.

DISCLAIMER: The information provided herein may not be applicable in all situations and should not be acted upon without specific legal advice based on particular situations.

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¹ World Intellectual Prop. Org. (WIPO), Patent Landscape Report: Generative Artificial Intelligence (2024), https://www.wipo.int/web-publications/patent-landscape-report-generative-artificial-intelligence-genai/en/index.html.

² WIPO, SPARK: Patent Trends Update in GenAI (July 2026), https://www.wipo.int/web-publications/spark-patent-trends-update-in-genai/en/index.html.

³ Christopher Yasiejko, GenAI Patent Surge Extends Well Beyond Familiar Platform Firms, Bloomberg Law (Aug. 10, 2026), .

⁴ European Patent Office, Patents and Self-Driving Vehicles: The Inventions Behind Automated Driving (Nov. 2018), https://www.epo.org/en/news-events/in-focus/ict/self-driving-vehicles.

August 2026

[Updated and Expanded from IP Considerations for Autonomous Vehicle Technology Startups and Automotive Suppliers (2019)]

For autonomous systems companies, patents are not just legal assets; they are business assets. A deliberate filing strategy can support fundraising, market positioning, partnerships, and exit planning, especially where the core technology spans AI, sensors, software, data, and physical control systems. Our latest article updates the AV patent playbook for the broader physical AI market.

Introduction

In 2019, we published an article outlining key intellectual property (IP) considerations for autonomous vehicle (AV) technology startups and their automotive supplier partners. At that time, the AV sector was the primary commercial context in which artificial intelligence, sensor systems, embedded software, and physical control algorithms converged in a single product. Since then, the same technological convergence has expanded well beyond automotive applications. Today, companies developing robotics platforms (industrial, surgical, and humanoid), unmanned aerial vehicles (UAVs/drones), warehouse and logistics automation, agricultural technology (agtech) automation, defense and unmanned systems, and other “physical AI” solutions face substantially the same IP challenges that AV startups faced in 2019, and often at an even greater scale and pace.

This article updates and broadens the original AV-focused IP playbook into a general framework for autonomous systems companies. It retains and generalizes the foundational concepts from the 2019 article, including the definition and purpose of an IP strategy, the protection of proprietary competitive information, and IP ownership considerations, while adding three new pillars that autonomous systems companies should address as part of a comprehensive IP strategy:

• What to Patent: a taxonomy of patentable subject matter across the autonomous systems landscape;
• Where to File: a jurisdictional filing strategy for global protection; and
• Aligning Filings with Product and Funding Milestones: a lifecycle-based framework tying IP activity to startup stage.

The principles discussed herein apply broadly to any company whose core value proposition rests on the integration of AI/ML models, sensor fusion, onboard or edge computing, control and actuation systems, and data pipelines into a physical system that operates with some degree of autonomy.

1. What Is an IP Strategy for Autonomous Systems Companies?

An IP strategy that aligns with a company’s short- and long-term business goals is essential to successfully bringing autonomous systems (whether robotic platforms, UAVs, automated warehouse systems, surgical devices, or defense platforms) and related components, software, and services to market. A well-executed IP strategy allows the owner of the IP assets to leverage those assets during negotiations with partners and acquirers, increase the company’s valuation, promote innovations in marketing and customer-facing materials, exclude competitors from practicing key technologies, and monetize IP through licensing or royalty-based revenue streams.

The IP strategy should address how and when the autonomous systems company and its commercial or industrial partners will pursue and protect their joint or respective IP in a manner consistent with their business goals, while accounting for ownership of the resulting IP. For startups, the IP strategy should support the company’s growth and exit strategy, whether that exit takes the form of an acquisition, an IPO, or a long-term licensing model. For established technology companies and industrial partners, the strategy should support the creation of barriers to entry and the maintenance of competitive positioning.

An ideal IP strategy creates value, reduces risk, and is realistically achievable for all parties, thereby facilitating the successful development and commercialization of autonomous systems.

Critically, IP strategy in the autonomous systems space is not limited to patent filings. A comprehensive strategy should integrate:

• Patents: for novel methods, systems, and architectures;
• Trade secrets: for proprietary algorithms, training data, and internal processes;
• Copyrights: for software code, documentation, and training materials;
• Trademarks: for brand identity and market positioning; and
• Contractual protections: including NDAs, invention assignment agreements, and collaboration agreements.

The interplay among these tools is particularly important for autonomous systems companies, where a single product may embody patentable hardware inventions alongside trade-secret-protected AI models and copyrighted firmware.

2. Protecting Proprietary Competitive Information

An IP strategy leverages legal tools to identify and capture a company’s proprietary competitive information, while accounting for inherent challenges raised by company culture, expedited development and deployment roadmaps, and strategic goals. These challenges are not unique to AV companies; they recur across the full spectrum of autonomous systems ventures.

Cultural challenges. Early-stage technology startups, whether in robotics, drones, agtech, or defense, often exhibit an openness with which founders discuss their innovations with third parties. This can occur in collaborative shared workspace environments (particularly common in the hardware startup ecosystem), through initial discussions when recruiting new talent or potential partners, or during informal exchanges at industry events and accelerator programs. The high velocity of hiring and talent mobility in the AI and robotics sectors compounds this risk: engineers may move between competing companies rapidly, carrying institutional knowledge with them.

Maturing-stage challenges. As the company matures, additional challenges arise from its need to make public disclosures to persuade customers, inform regulatory entities, demonstrate product capabilities to potential acquirers, or satisfy government contracting requirements. In the autonomous systems context, common scenarios for inadvertent public disclosure include:

• Demonstrating a new perception system, manipulation algorithm, or sensor fusion approach to a potential OEM, integration partner, or customer;
• Disclosing deployment plans or technical architectures to regulators (e.g., FAA for drones, FDA for surgical robotics, NHTSA for AVs, DoD for defense systems);
• Testing autonomous systems in public or semi-public environments, such as flying drones over agricultural fields, operating warehouse robots in customer facilities, or running AVs on public roads;
• Discussions that occur during capital raising or recruiting processes;
• Publishing white papers, academic theses, or presenting at conferences and trade shows;
• Selling or offering to sell products that embed new sensors, AI models, or data-collection processes without considering that the act of sale may constitute a public disclosure or trigger the one-year grace period for patent filing; and
• Open-sourcing software components or publishing APIs that reveal underlying architectural choices.

Companies frequently overlook protecting their IP around software-related innovations, such as AI models, reinforcement learning policies, or algorithms used to enhance safety, accuracy, reliability, robustness, efficiency, or user experience. In many autonomous systems, the most competitively significant innovations reside in software and data rather than in hardware, making this oversight particularly costly.

Actionable framework. Establishing and executing an effective IP strategy early, before any public disclosure takes place, is essential. To protect proprietary competitive information, a company should:

• Establish a plan and process for identifying the functional features of its solution that differentiate it from the competition, and the innovations that enable those features;
• Identify which individuals or parties contributed to each innovation;
• Determine which tools in the IP protection toolbox (trade secret, copyright, trademark, patent, or contractual) to deploy for each innovation; and
• Take action: file patent applications, execute NDAs and confidentiality agreements, register trademarks and copyrights, implement trade secret protocols, and ensure documents are appropriately marked and kept confidential.

3. IP Ownership Considerations

The ultimate goal of an IP strategy is to obtain and retain ownership so that IP can be monetized through exclusivity, licensing, valuation enhancement, or strategic leverage. It is not sufficient to merely create an IP asset without also having a plan to retain the appropriate ownership or rights.

In the absence of a formal agreement governing IP ownership, rights generated or derived from an individual’s employment or consulting relationship may inadvertently be waived or may vest in another company. This risk is acute in the autonomous systems space, where development frequently involves:

• Cross-functional teams spanning hardware, software, AI/ML, and systems engineering;
• Collaboration with university labs, government research institutions, or open-source communities;
• Engagement of specialized consultants (e.g., for sensor design, simulation environments, or safety certification);
• Joint development agreements with OEM partners, system integrators, or contract manufacturers; and
• Use of pre-existing IP contributed by founders from prior employers or academic positions.

As part of an IP strategy, companies should take steps to ensure they retain ownership in their IP prior to collaboration with other parties. This includes:

• Invention assignment agreements: All employees and contractors should execute agreements that clearly assign to the company all IP created within the scope of their engagement. These should be tailored to the jurisdiction and should be specific enough to survive challenge.
• Contributor IP audits: Before filing patent applications, companies should conduct an audit to confirm that all named inventors have valid assignment agreements in place and that no third party has a claim to the underlying technology.
• Non-compete and non-solicit considerations: Technology companies, and the autonomous systems sector in particular, are characterized by frequent employee mobility. Many states (and countries) limit or prohibit non-compete clauses, making trade secret protection and robust assignment agreements all the more important.
• Joint development IP ownership: When collaborating with partners (whether OEMs, integrators, or research institutions), ownership of resulting IP should be allocated by written agreement before development begins. Absent such an agreement, default rules (which vary by jurisdiction) may produce unintended consequences.

Failure to take steps to retain ownership of IP may prove costly and may impede both capital formation and product development. Technology startup investors typically perform IP ownership due diligence as a condition of financing. If a company has not taken steps to protect and retain ownership of its IP, or if there are unresolved questions about who contributed to core inventions, it may be inhibited from commercializing its innovations with partners or closing a financing round.

4. What to Patent: A Taxonomy of Patentable Subject Matter

For autonomous systems companies, the question of what to patent requires a systematic assessment of the technology stack. Unlike a single-domain product, an autonomous system typically integrates innovations across multiple layers, from sensing and perception through decision-making and control to physical actuation. Each layer presents distinct patenting opportunities, and each raises different considerations regarding patent eligibility, claim drafting, and the trade-off between patent protection and trade secrecy.

The following taxonomy identifies the principal categories of patentable subject matter for autonomous systems companies:

4.1 Perception and Sensor Fusion Systems

Autonomous systems rely on one or more sensing modalities, including lidar, radar, cameras (visible and infrared), ultrasonic sensors, inertial measurement units (IMUs), tactile/haptic sensors, and GPS/GNSS. Innovations in how these sensors are configured, calibrated, and fused to generate a unified environmental model are highly patentable. Key areas include:

• Novel sensor hardware configurations and form factors;
• Sensor calibration methods (including cross-modality alignment);
• Multi-sensor fusion architectures (early fusion, late fusion, hybrid approaches);
• Object detection, classification, and tracking algorithms tied to sensor data;
• Point cloud processing and 3D reconstruction techniques; and
• Degraded-mode perception (e.g., handling sensor failures or adverse environmental conditions).

4.2 AI/ML Models and Training Methods

AI and machine learning sit at the core of most autonomous systems. While the underlying mathematical algorithms themselves are generally not patentable as abstract ideas, specific technical applications of AI/ML, particularly those that produce a concrete technical improvement, are often patentable when properly claimed. Patentable innovations include:

• Novel neural network architectures designed for specific perception, prediction, or planning tasks;
• Training methodologies (e.g., curriculum learning, sim-to-real transfer, domain randomization);
• Data augmentation and synthetic data generation techniques;
• Model compression, quantization, and pruning for deployment on edge hardware;
• Reinforcement learning reward-shaping and policy optimization for physical control; and
• Continual/online learning systems that adapt models after deployment.

Patent vs. trade secret considerations: Companies should carefully evaluate whether certain AI innovations are better protected as trade secrets. Training data sets, proprietary labeling taxonomies, hyperparameter configurations, and internal model architectures that are not discernible from the shipped product may be more effectively protected through trade secrecy than through patent disclosure. However, innovations that are detectable in the product’s behavior or outputs, or that are likely to be independently discovered by competitors, are generally better served by patent protection.

Post-Alice eligibility considerations: Under U.S. law, claims directed to AI/ML innovations must be drafted with care to satisfy the two-step patent eligibility framework of Alice Corp. v. CLS Bank (2014). Claims should be tied to a specific technical improvement (e.g., improved accuracy in a defined sensor context, reduced computational load, faster inference for real-time control) rather than reciting an abstract mathematical concept. Many jurisdictions outside the United States, including the EPO, China, Japan, and South Korea, apply different (and in some cases more permissive) eligibility frameworks for AI-related inventions.

4.3 Control and Actuation Systems

The physical control layer, translating perception and planning outputs into motor commands, joint movements, or vehicle steering inputs, presents rich patenting opportunities:

• Motion planning algorithms (path planning, trajectory optimization, obstacle avoidance);
• Control policies for manipulation (grasping, assembly, surgical instrument articulation);
• Adaptive control systems that adjust to changing environmental or load conditions;
• Multi-degree-of-freedom actuator coordination; and
• Hardware-software co-design for low-latency control loops.

4.4 Edge Computing and Onboard Compute Architectures

Autonomous systems frequently require real-time or near-real-time processing at the edge, creating opportunities for architectural patents:

• Custom SoC or FPGA designs optimized for inference workloads;
• Heterogeneous compute scheduling (CPU/GPU/TPU/FPGA task allocation);
• Power management and thermal regulation for compute-intensive mobile systems;
• Distributed compute architectures across multiple onboard processors; and
• Hardware-aware model optimization (co-designed inference engines).

4.5 Simulation and Testing Methodologies

Given the difficulty and expense of physical testing for autonomous systems, simulation is a critical development tool and a rich source of patentable innovation:

• Digital twin architectures for physical system modeling;
• Scenario generation and coverage-driven testing methodologies;
• Sim-to-real transfer techniques and domain adaptation;
• Hardware-in-the-loop (HIL) and software-in-the-loop (SIL) simulation frameworks; and
• Automated validation and regression testing for safety-critical systems.

4.6 Safety, Fail-Safe, and Redundancy Systems

Safety architecture is a highly patentable and competitively differentiating area:

• Fault detection, isolation, and recovery (FDIR) systems;
• Redundant sensor and compute architectures with voting or arbitration logic;
• Safe degradation and minimal-risk-condition strategies;
• Runtime monitoring and anomaly detection for ML model outputs; and
• Formal verification methods applied to autonomous decision-making.

4.7 Human-Machine Interface and Teleoperation

As autonomous systems increasingly operate alongside humans or under human supervision, innovations at the human-machine boundary are patentable:

• Teleoperation interfaces and latency-compensation techniques;
• Shared autonomy (blending human and machine control);
• Intent prediction and handoff protocols between human and autonomous control;
• Augmented reality overlays for operator situational awareness; and
• Multi-robot supervisory control interfaces.

4.8 Fleet and Swarm Management

For companies deploying multiple autonomous agents, coordination and fleet management represent a significant patent opportunity:

• Multi-agent task allocation and scheduling algorithms;
• Decentralized coordination and consensus protocols;
• Communication protocols for low-latency inter-agent messaging;
• Fleet-level health monitoring and predictive maintenance; and
• Swarm behavior policies and emergent coordination strategies.

4.9 Data Pipeline, Labeling, and Annotation Techniques

The data infrastructure underlying autonomous systems development can yield patentable innovations:

• Automated or semi-automated data labeling pipelines;
• Active learning and intelligent data selection for model retraining;
• Data compression and efficient storage for high-bandwidth sensor streams;
• Privacy-preserving data collection and anonymization techniques; and
• Federated learning architectures for distributed fleet data.

Trade secret note: Certain data pipeline innovations, particularly those relating to proprietary labeling taxonomies, data curation heuristics, and internal tooling, may be better protected as trade secrets if they are not detectable from the product’s external behavior and provide sustained competitive advantage through secrecy.

5. Where to File: Jurisdictional Filing Strategy

A patent provides protection only in the jurisdiction where it is granted. For autonomous systems companies, whose technologies are typically manufactured, deployed, and competed over on a global basis, a thoughtful jurisdictional filing strategy is essential to maximizing the return on IP investment while managing costs.

5.1 United States as the Anchor Filing

For most autonomous systems companies headquartered in the United States, the U.S. provisional patent application serves as the initial filing vehicle. A provisional application:

• Establishes a priority date at relatively low cost;
• Provides a 12-month window to refine the invention, conduct further testing, and assess commercial viability before committing to the expense of a non-provisional (utility) filing;
• Allows the company to use “Patent Pending” in marketing and investor materials; and
• Preserves international filing options via the Paris Convention or the Patent Cooperation Treaty (PCT).

Within 12 months of the provisional filing date, the company must file a non-provisional U.S. utility application (or a PCT application claiming priority to the provisional) to maintain the benefit of the priority date. Companies should plan for this deadline in their budgeting and IP management processes.

5.2 The PCT System for International Rights Preservation

The Patent Cooperation Treaty (PCT) provides a mechanism to preserve patent rights in over 150 member countries through a single international application. Filing a PCT application within 12 months of the earliest priority date (typically the U.S. provisional) provides:

• An additional period, generally 30 or 31 months from the priority date, before the company must commit to filing in individual national or regional offices;
• An International Search Report (ISR) and Written Opinion providing an early assessment of patentability, which can inform strategic decisions about which markets to pursue;
• Flexibility to defer significant national-phase filing costs until the company has greater clarity on market potential and competitive landscape; and
• A credible demonstration of global IP intent for investors and partners.

The PCT system is particularly valuable for autonomous systems startups, which frequently face budget constraints early in their lifecycle and benefit from the ability to defer costly national-phase decisions until later funding rounds.

5.3 Strategic National-Phase Entry

When the PCT national-phase deadline approaches (typically 30-31 months from the priority date), companies must make strategic decisions about where to pursue patent protection. Key factors include:

Manufacturing hubs. Patents in jurisdictions where the technology or its components are manufactured provide leverage against competitors producing infringing products and supply chain protection. For autonomous systems, key manufacturing jurisdictions typically include:

• China (CNIPA): the world’s largest manufacturing base for electronics, sensors, and an increasingly important hub for robotics and drone production;
• Japan (JPO): a critical market for industrial robotics, automotive components, and precision actuators;
• South Korea (KIPO): a leader in semiconductor fabrication, display technology, and industrial automation; and
• Germany/EPO: a hub for automotive technology, industrial automation, and precision manufacturing.

Target markets. Patents should be obtained in jurisdictions where the company’s products or services will be sold, deployed, or offered. For autonomous systems, this may include large addressable markets such as the United States, European Union, China, Japan, and emerging markets where the technology is being deployed (e.g., Australia, India, Brazil for agtech or mining automation).

Competitor locations. Patents in jurisdictions where key competitors are headquartered or operate can provide offensive and defensive leverage, enabling the company to bring infringement actions or negotiate cross-licenses. Companies should monitor competitor filing activity (which is publicly available after 18 months) to inform their own geographic filing strategy.

Freedom-to-operate and defensive considerations. In fast-moving competitive markets, filing in jurisdictions where blocking patents may be asserted against the company can create defensive value, either by building a portfolio for cross-licensing or by establishing prior art that limits competitors’ ability to obtain broad claims in those markets.

5.4 Regional and National Considerations

Certain jurisdictions present unique strategic considerations for autonomous systems companies:

• European Patent Office (EPO): The EPO’s examination of AI-related inventions follows the “technical character” framework, which generally requires that the AI contribute to a technical solution to a technical problem. Claims directed to autonomous system control, sensor processing, or physical-world interaction typically satisfy this standard.
• China (CNIPA): China has rapidly expanded its patent system and is now the world’s largest patent-filing jurisdiction by volume. For autonomous systems companies, China presents both offensive opportunity (protecting innovations from copying by domestic competitors) and defensive necessity (ensuring freedom to operate in the Chinese market). China’s guidelines on AI patentability are evolving and generally practical.
• Japan (JPO): Japan has well-developed examination guidelines for AI and IoT-related inventions, and Japanese patents are enforceable and respected. Japan is a critical jurisdiction for companies in the robotics, automotive, and industrial automation spaces.
• South Korea (KIPO): South Korea’s AI and Fourth Industrial Revolution examination guidelines are relatively permissive. Given the concentration of electronics manufacturing and the presence of major autonomous systems competitors, Korean filings are strategically important for many companies.

6. Aligning IP Filings with Product and Funding Milestones

One of the most impactful yet frequently overlooked aspects of IP strategy for autonomous systems companies is the alignment of filing activity with the company’s product development trajectory and fundraising lifecycle. Patent filings are expensive and time-sensitive; filing too early may result in premature disclosure of immature technology, while filing too late may result in loss of rights due to prior public disclosure or competitors’ intervening filings. The following lifecycle framework maps IP activities to the typical startup funding stages.

6.1 Pre-Seed / Seed Stage

At this stage, the core technical innovation is being defined, and initial prototypes may be under development. IP priorities include:

• Provisional patent applications on core innovations: File provisional applications before any public disclosure, including demo days, accelerator pitch events, investor meetings, and conference presentations. The goal is to secure a priority date on the fundamental inventive concepts at minimal cost.
• Founder IP assignment agreements: Ensure that all founders have executed written assignments transferring ownership of their pre-company inventions (to the extent they relate to the company’s business) and all future inventions to the company. This is a threshold requirement for institutional investors.
• Trade secret hygiene: Establish basic trade secret protocols, including marking confidential documents, restricting access to proprietary code repositories, executing NDAs with anyone who will receive confidential technical information, and maintaining records of what constitutes proprietary information.
• Employee/contractor assignment agreements: Even before hiring full-time employees, any contractor or technical consultant should sign an invention assignment and confidentiality agreement.

Investor signaling: A provisional patent application filed before a seed round signals to investors that the founders are sophisticated about IP protection. At due diligence, investors will ask whether the company has filed patent applications and whether all IP is properly assigned.

6.2 Series A / Series B

At this stage, the company has typically demonstrated product-market fit (or is approaching it), has a growing engineering team, and is developing the technology in multiple directions. IP priorities shift toward building portfolio depth:

• Converting provisionals to utility applications: Before the 12-month provisional deadline, convert (or refile) each provisional as a non-provisional U.S. utility application or PCT international application. This is a critical deadline that requires advance planning and budgeting.
• PCT filings: File PCT applications to preserve international rights. The PCT system’s 30/31-month national-phase deadline aligns well with the typical 18-24-month period between a Series A and Series B, allowing the company to defer costly national-phase decisions to a point when it has more capital and market clarity.
• Building portfolio depth around product-market-fit features: As the product crystallizes, identify and file on the specific innovations that differentiate the company’s offering from competitors, including novel perception approaches, control algorithms, safety features, user interface innovations, and system architecture choices that are core to the product.
• Continuation and divisional strategy: For foundational inventions, consider filing continuation applications to pursue additional claim sets covering different aspects of the technology or different embodiments (e.g., claims directed to the method, the system, the training process, or the data pipeline). This builds claim density around the core innovations.
• Trade secret program maturation: As the team grows, formalize the trade secret program with access controls, departure protocols, and regular audits of what is being disclosed externally versus what should remain confidential.

6.3 Growth Stage / Series C and Beyond

At this stage, the company is scaling commercially, entering new markets, and may be considering international expansion. IP activities should reflect the broadening business:

• National-phase entry in target markets: Enter national phase in jurisdictions aligned with the company’s go-to-market strategy, manufacturing partnerships, and competitive landscape. Prioritize jurisdictions where the company will sell products, where competitors manufacture, and where enforcement is practical.
• Continuation strategy for key patent families: Continue to build claim density around the company’s most valuable patent families through continuations, continuations-in-part, and divisional applications. Adapt claims to cover the specific embodiments that competitors are likely to practice.
• Licensing and monetization: As the portfolio matures, evaluate opportunities for out-licensing to non-competing entities, cross-licensing with partners or competitors, or standards-essential patent (SEP) declarations if the company’s innovations are adopted into industry standards.
• Design patents: For autonomous systems with distinctive physical form factors (e.g., drones, humanoid robots, surgical instruments), consider design patent filings to protect the ornamental appearance alongside utility patents covering the functional innovations.
• Freedom-to-operate (FTO) clearance: Conduct FTO analyses before entering new markets or launching new products. Identify third-party patents that may pose infringement risks and develop strategies for designing around, licensing, challenging, or accepting the risk.

6.4 Pre-Exit: IPO or M&A

When a liquidity event is on the horizon, IP strategy shifts toward due diligence readiness and portfolio presentation:

• Portfolio completeness review: Conduct a comprehensive review of whether the patent portfolio adequately covers the company’s current product lines, planned products, and key competitive differentiators. Identify gaps and file additional applications where warranted.
• IP due diligence readiness: Prepare for acquirer or underwriter due diligence by ensuring that all patent assignments are recorded, all inventor declarations are complete, all maintenance fees are current, and all ownership chains are clean. Organize the portfolio into a clear presentation showing coverage by product line, technology area, and geography.
• Freedom-to-operate clearance: Conduct or update FTO analyses for the company’s core products and target markets. Acquirers and IPO underwriters will examine whether the company faces material infringement risk.
• Valuation support: A well-organized patent portfolio with clear coverage of revenue-generating products can materially support the company’s valuation in an acquisition or IPO.
• Defensive publications: For innovations that the company does not intend to patent but wishes to preserve as prior art (to prevent competitors from patenting the same technology), consider publishing defensive disclosures to establish a public record of the innovation.

7. Conclusion

The autonomous systems landscape has expanded dramatically since the early days of the AV patent race. Today, companies building robots, drones, automated warehouses, surgical devices, agricultural platforms, and defense systems face the same fundamental IP challenges, but at greater scale, across more technology layers, and in a more crowded competitive environment.

A comprehensive IP strategy, one that addresses what to patent, where to file, and how to align filings with product and funding milestones, plays a pivotal role for both autonomous systems startups and their commercial and industrial partners in successfully developing and deploying advanced technology solutions while avoiding the pitfalls that come with IP protection and ownership.

The key principles for autonomous systems companies can be summarized as follows:

• Start early. File provisional applications before public disclosure. Establish trade secret protocols and assignment agreements from day one.
• Be deliberate about what to patent versus what to protect as a trade secret. Not every innovation is best served by patent disclosure; proprietary training data, internal algorithms, and operational know-how may be better protected through secrecy.
• Think globally from the start. Use the PCT system to preserve international options while managing costs. Make national-phase entry decisions based on where the technology will be manufactured, sold, and competed.
• Align filing cadence with business milestones. Let the fundraising lifecycle inform when to invest in building portfolio depth, entering national phase, and conducting FTO clearance.
• Maintain clean ownership. Ensure all IP is properly assigned, all inventor chains are documented, and all collaborative relationships are governed by agreements that allocate IP rights clearly.
• Prepare for the exit from day one. Build the portfolio and maintain records as though a due diligence review could begin tomorrow.

For autonomous systems companies, patents are not merely legal instruments; they are strategic business assets that, when managed deliberately, support every phase of the company’s lifecycle from formation through exit.

DISCLAIMER: The information provided herein may not be applicable in all situations and should not be acted upon without specific legal advice based on particular situations.

Autonomy started with cars. The same perception, decision, simulation, and control challenges now apply across robots, drones, and AI-enabled machines operating in the physical world. Understanding the taxonomy of these inventions is the first step toward protecting them.

The Original AV Patent Taxonomy: Where It Started

The self-driving vehicle industry has always been, at its core, a patent story. Every autonomous vehicle on the road today represents the convergence of hundreds (sometimes thousands) of patented inventions working together. In 2019, the European Patent Office mapped this innovation landscape in a comprehensive study, Patents and Self-Driving Vehicles, identifying and categorizing every major technology subsector required to make a car drive itself. While the study focused on European filings, its taxonomy has become a globally recognized framework, and one that is directly relevant to U.S. patent practitioners and companies building patent portfolios at the USPTO.

The taxonomy broke the self-driving technology stack into four principal layers:

1. Perception & Sensing: lidar, radar, cameras, and ultrasonic sensors (the system’s “eyes”) that detect and interpret the physical environment;
2. Communication: vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) data exchange that allows the system to “see” beyond its own sensors;
3. Data Processing & AI: the onboard computing and machine learning that make sense of all that sensor data in real time; and
4. Vehicle Control: the path planning, steering, and braking systems that translate decisions into physical action.

For U.S. companies, these layers map directly to the types of utility patent applications filed daily at the USPTO, and to the claim drafting challenges that patent practitioners navigate under Alice and Section 101.

That technology taxonomy is complemented by the SAE J3016 standard, the most widely cited classification system in the industry and one routinely referenced by U.S. regulators, courts, and patent examiners. SAE J3016 defines six levels of driving automation, from Level 0 (no automation; a conventional car) through Level 5 (full automation, where no human driver is needed, anywhere, anytime). The critical dividing line sits between Level 2 and Level 3. At Levels 1 and 2 (think adaptive cruise control, lane-keeping assist), the human driver remains in charge of monitoring the road. At Levels 3 through 5, the automated driving system itself takes over the entire driving task (perceiving, deciding, and acting) within its defined operating conditions.

Why does this matter for patent strategy? Because the level of automation directly correlates with the density and complexity of patentable innovation. The jump from Level 2 to Level 3 is where the truly novel inventions live, and where U.S. patent filings have surged. This layered classification gave patent practitioners, investors, and business leaders a shared vocabulary for identifying where innovation was occurring and where patent protection could be most strategically deployed.

What the Patent Landscape Says Now

The patent landscape for transportation innovation has changed dramatically since 2019, and the data are striking. WIPO’s Technology Trends 2025: Future of Transportation report found that over 1.1 million patent families related to the future of transportation were published between 2000 and 2023, growing at a compound annual growth rate of 11%. To put that in perspective, traditional transportation technologies grew at only 4% over the same period. The report identified two driving megatrends (Sustainability and Digitalization) and organized the patent landscape into four technology trend clusters: Sustainable Propulsion, Automation and Circularity, Communication and Security, and Human-Machine Interface.

Notably, land transport patents dominate, with over 3.5 times the number of patents compared to sea, air, and space transport modalities combined. The top five inventor locations (China, Japan, the United States, the Republic of Korea, and Germany) account for over 90% of all inventions. These data points confirm that the original AV innovation ecosystem has matured, but they also reveal something equally important: the same digitalization technologies (communication, security, automation) that drive autonomous vehicle innovation are now being deployed across every mode of transportation and every category of autonomous physical system.

This trend is global. At the EPO, AI-related inventions and battery technologies were identified as key growth areas in 2024. At the USPTO, autonomous driving and AI patent filings have grown exponentially, with top assignees including not only traditional automakers (Ford, GM, Toyota) but also technology companies (Waymo, NVIDIA, Baidu). The convergence of AI, sensor fusion, and physical control systems is not slowing; if anything, the patent data suggest it is accelerating and broadening. The United States remains one of the most active and strategically important filing jurisdictions.

The Physical AI Expansion

The term “physical AI” has emerged as the unifying concept for this broadening. At CES 2026, NVIDIA CEO Jensen Huang described the moment as “the ChatGPT moment for physical AI—when machines begin to understand, reason and act in the real world.” Physical AI in this context refers to AI systems that perceive, reason, and act in real-world environments, enabling machines to anticipate behavior rather than simply react to it. At GTC 2026, NVIDIA announced major expansions to its physical AI platform spanning autonomous vehicles, industrial robots (through partnerships with FANUC and ABB), and humanoid robotics foundation models.

This is not merely a rebranding exercise. The underlying technical challenges are structurally identical to those mapped by the EPO’s original AV taxonomy. Consider the parallels:

AV Technology Layer (EPO 2019)	Physical AI Equivalent (2026)
Perception & Sensing (lidar, radar, cameras)	Multimodal perception for robots, drones, surgical systems
Communication (V2V, V2X)	Multi-agent coordination, swarm protocols, fleet messaging
Data Processing & AI (onboard ML, decision-making)	Edge inference, world models, sim-to-real transfer
Vehicle Control (path planning, actuation)	Manipulation, locomotion, multi-DOF control loops
Simulation & Testing (scenario generation)	Digital twins, hardware-in-the-loop, synthetic data
Safety & Redundancy (fault detection, fail-safe)	Runtime monitoring, formal verification, FDIR systems

The taxonomy that began with self-driving vehicles now applies to warehouse robots navigating dynamic environments, surgical systems performing precision manipulation, agricultural drones adapting to weather and terrain, defense platforms coordinating in contested airspace, and humanoid robots interacting with humans in unstructured settings. In each case, the same fundamental engineering questions arise: how does the system perceive its environment, how does it decide what to do, how does it execute that decision in the physical world, and how does it fail safely when something goes wrong?

From Taxonomy to Strategy

Understanding this expanded taxonomy is not an academic exercise. It is the predicate for any meaningful IP strategy in the autonomous systems space. A company developing autonomous warehouse robots, for example, must identify which layers of the technology stack contain its most competitively differentiated innovations. Is the company’s advantage in its perception pipeline (sensor fusion, object detection)? In its control algorithms (grasping, navigation in dynamic environments)? In its simulation infrastructure (digital twins, scenario generation for safety validation)? Or in its data pipeline (proprietary labeling, active learning, federated fleet data)?

Each layer presents distinct considerations for patentability, for the trade-off between patent disclosure and trade secrecy, and for where and when to file. In the United States, AI-related claims must be drafted with particular care to satisfy the post-Alice eligibility framework under 35 U.S.C. § 101. The good news for autonomous systems companies is that claims tying AI innovations to concrete physical-world improvements (such as better sensor accuracy, faster real-time control, or reduced computational load for edge processing) tend to fare well under the USPTO’s 2024 guidance on AI subject matter eligibility. The key is connecting the software innovation to a specific, technical improvement in how the machine operates in the real world. Internationally, the EPO, China, Japan, and South Korea each apply different (and in some cases more permissive) eligibility frameworks for AI-related inventions, which creates opportunities for companies building global portfolios.

The taxonomy also informs defensive strategy. As WIPO’s data confirm, the transportation patent landscape is heavily concentrated among a small number of jurisdictions and companies. Companies entering adjacent physical AI markets (robotics, drones, agtech, defense) should anticipate that incumbent AV patent holders may assert their portfolios broadly, particularly where claim language is drafted at a level of abstraction that captures perception, planning, and control innovations regardless of the specific physical platform.

Setting the Stage

The expansion of the autonomy taxonomy from vehicles to physical AI systems has a straightforward implication for companies, investors, and IP leaders: the playbook that AV startups developed for patent strategy between 2015 and 2022 must now be generalized. What to patent, where to file, and how to align IP activity with product development and funding milestones are questions that every autonomous systems company must answer deliberately and early.

In our next article, we will address exactly that: a comprehensive IP strategy framework for autonomous systems companies, covering the protection of proprietary competitive information, IP ownership considerations, a detailed taxonomy of patentable subject matter across the full autonomous systems stack, jurisdictional filing strategy, and the alignment of patent filings with product and funding milestones. The taxonomy established here (perception, AI/ML, control, edge computing, simulation, safety, human-machine interface, fleet management, and data infrastructure) provides the foundation on which that strategy is built.

For autonomous systems companies, the first strategic question is not “should we file patents?” It is “do we understand which layers of the autonomy stack contain our defensible innovation, and are we protecting them deliberately?” The answer requires a taxonomy. The rest requires a strategy.

DISCLAIMER: The information provided herein may not be applicable in all situations and should not be acted upon without specific legal advice based on particular situations.

Chicago has become the latest city to push back against new data center development. On August 11, 2026, Chicago Mayor Brandon Johnson signed Executive Order 2026-5, titled “Protecting Chicagoans’ Clean Water, Clean Air, Energy Affordability, and Quality of Life.” The Order establishes an enhanced regulatory framework for data center development in Chicago. In the Order, the Mayor calls on the City Council to enact a temporary moratorium on new data center construction and material expansions of existing facilities.

Key Takeaways from the Order

• Immediate Regulatory Action: Within 90 days of the Order’s effective date (approximately November 9, 2026, subject to potential extension by the Mayor), the Chief Sustainability Officer (CSO) must publish data center air pollution rules and noise rules under Section 8-32-090 of the Municipal Code. New air permit conditions will be addressed through annual certificates of operation.
• Moratorium Request: The Mayor has called on City Council to enact a temporary moratorium on new data center development and material expansion.
• Task Force Recommendations Due March 2027: The Data Center Task Force will develop comprehensive regulatory recommendations, including proposed Municipal Code amendments, by March 15, 2027.

Provisions of Executive Order 2026-5

• Sections 1 and 2 (Air Pollution and Noise Control): The CSO may impose conditions on air pollution control permits for regulated data center equipment and areas, and must review the noise framework under Section 8-32-090 of the Municipal Code. Within 90 days of the Order’s effective date (approximately November 9, 2026, subject to extension by the Mayor), the CSO must publish air pollution and noise rules through the applicable public notice process. Compliance with new air permit conditions will be addressed in annual certificate of operation applications under Section 11-4-660 of the Municipal Code.

• Section 3 (Enhanced Planned Development Review): For every proposed Planned Development that includes a data center, the Department of Planning and Development (DPD) must notify the Commissioners of Buildings, Business Affairs and Consumer Protection, Environment, and Water Management. Each has 60 days to provide written recommendations and proposed conditions, which DPD must include in any proposed Planned Development ordinance submitted to City Council. The review must consider projected electricity demand, water consumption, emergency and backup power generation, air emissions, noise impacts, cumulative environmental impacts, transportation impacts, emergency response capacity, and impacts on neighboring communities. Section 3 automatically rescinds when a City Council ordinance establishing a temporary moratorium on new data centers or material expansions becomes effective.

• Section 4 (Data Center Task Force): The Interdepartmental Data Center Environment and Community Protection Task Force (Data Center Task Force) is co-chaired by the CSO and the Mayor’s Chief of Police and includes representatives from Building, Environment, Planning and Development, Technology and Innovation, and Water Management, plus other designated City personnel. It will evaluate and, where appropriate, implement the Sustainable Data Centers Working Group’s recommendations, engage residents, subject-matter experts, and stakeholders, and provide recommendations and proposed Municipal Code amendments and rules to the Mayor by March 15, 2027 (or another date approved by the Mayor consistent with any moratorium timing), addressing permitting, impact controls, licensing, zoning, and reporting.

What does this mean to you?

Existing operators: Review current air pollution permits and prepare for new conditions at the next annual certificate of operation renewal.

If you have pending PD projects: Projects awaiting final City Council approval should expect enhanced cross-departmental review and possible delays if a moratorium is enacted.

As demand for artificial intelligence infrastructure continues to accelerate, policymakers are increasingly grappling with the impact of large-scale data center development on local communities, energy resources, and economic policy. New York has now become the first state in the nation to enact a statewide moratorium on certain AI data center development projects—marking a significant departure from the local and municipal restrictions that have characterized regulatory responses to date.

While the immediate impact on nationwide data center growth may be limited, the move could signal a broader shift in how states approach the rapid expansion of AI infrastructure.

A First-of-Its-Kind Statewide Action

Historically, efforts to slow or pause data center growth have occurred at the municipal or county level, often driven by local concerns surrounding energy consumption, land use, water resources, and community impact. New York’s action is notable because it represents the first statewide moratorium of its kind.

The development bears close watching as several other states—including Minnesota, Michigan, Pennsylvania, South Carolina, New Hampshire, and Virginia—are considering legislation that could impose similar restrictions on future data center projects. If additional states follow suit, New York’s decision could become the beginning of a broader regulatory trend rather than an isolated event.

Limited Immediate Impact, Significant Long-Term Implications

Despite the attention surrounding the moratorium, New York has not been among the most active markets for new hyperscale data center development in recent years. As a result, the direct effect on overall U.S. data center growth may be relatively modest.

Nevertheless, industry participants—from hyperscalers and AI firms to developers and investors—are likely viewing the development as an important signal. The greater concern may not be the New York market itself, but whether other jurisdictions adopt similar policies that could influence future site selection decisions and infrastructure planning.

In this sense, New York’s moratorium may represent the first domino in a broader conversation about how states regulate the rapidly expanding AI economy.

State Politics Are Becoming Increasingly Important

The data center industry’s site selection process has evolved considerably over the past several years. Beyond traditional considerations such as power availability, fiber connectivity, tax incentives, and workforce access, developers are now paying closer attention to political and community dynamics.

Growing local opposition to data center projects, combined with increased legislative activity at the state level, has elevated regulatory risk as a key factor in development planning. Industry participants—including data center developers, hyperscalers, and emerging AI infrastructure providers—are closely monitoring state legislative developments as they evaluate where to deploy capital and build new facilities.

Tax Incentives Also Face Greater Scrutiny

In addition to the moratorium, New York Governor Kathy Hochul has indicated plans to pursue repeal of the state’s sales tax exemption for large data centers. Such a change could significantly affect project economics.

For facilities currently under development—or those in the planning stages—the loss of these exemptions could increase costs associated with purchasing critical equipment and construction materials. This includes major infrastructure investments such as long-lead electrical equipment, as well as high-value computing hardware like GPUs and CPUs that are central to AI workloads.

Developers with projects in New York may therefore face both regulatory uncertainty and increased capital expenditures, potentially reshaping investment decisions in the state.

Looking Ahead

New York’s moratorium may not dramatically alter the trajectory of U.S. data center growth on its own, but it represents an important milestone in the evolving regulatory landscape surrounding AI infrastructure. As states balance economic development opportunities with energy, environmental, and community concerns, industry stakeholders should expect heightened scrutiny of future data center projects.

For developers, investors, hyperscalers, and AI companies, monitoring legislative activity at both the state and local level will remain critical as the race to build next-generation digital infrastructure continues.

Want to learn more about Â鶹ֱ˛Ąâ€™s Data Centers and Digital Infrastructure team? Click here.

Connecticut has significantly expanded and toughened its consumer privacy law. Under (Public Act 25-113), sweeping amendments to the Connecticut Data Privacy Act (CTDPA) took effect on July 1, 2026, with one key obligation – a new profiling impact assessment – following on August 1, 2026. The amendments widen who must comply, broaden the data the law protects, and add new obligations around profiling, artificial intelligence, minors’ data, and privacy disclosures. For many businesses, the takeaway is simple: you may now be covered even if you never were before.

Who Is Now Covered?

As of July 1, 2026, the CTDPA applies to any entity that conducts business in Connecticut, or targets products or services to Connecticut residents, and that during the preceding calendar year satisfied any one of the following:

• Controlled or processed the personal data of at least 35,000 consumers (reduced from 100,000), excluding data processed solely to complete a payment transaction;
• Controlled or processed consumers’ sensitive data, regardless of volume; or
• Offered consumers’ personal data for sale in trade or commerce, regardless of volume.

The prior threshold tied to deriving 25% or more of gross revenue from the sale of personal data has been removed. Because the second and third triggers contain no numerical threshold, any business that processes sensitive data or offers personal data for sale may now fall within the scope of the CTDPA, even a very small one.

What Counts as “Sensitive Data” under the CTDPA?

Your company is swept in if, during the past year, it collected or processed any of the following, no matter how few Connecticut residents were involved:

– Data revealing racial or ethnic origin – Data revealing religious beliefs – Data revealing a mental or physical health condition, diagnosis, disability, or treatment – Data revealing sex life, sexual orientation, or status as nonbinary or transgender – Data revealing citizenship or immigration status – Consumer health data – Genetic or biometric data, or information derived therefrom – Precise geolocation data

– Personal data collected from a known or willfully disregarded child – Data concerning an individual’s status as a victim of a crime – Neural data – Financial account numbers, log-in credentials, or credit/debit card numbers that, combined with any required access code or password, would allow access to a financial account – Government-issued identification numbers (e.g., Social Security numbers, passport numbers, state ID or driver’s license numbers)

What Counts as a “Sale”?

The “sale” trigger is broader than many businesses assume. Under the CTDPA, a “sale” includes sharing, transferring, or disclosing personal data to a third party for valuable consideration (in addition to money), so many everyday practices can qualify, including:

• deploying tracking cookies from ad networks to serve targeted ads later;
• embedding third-party tracking pixels on a checkout page, which transfers unique device IDs and browsing history to an ad network in exchange for targeted audience insights and optimized ad campaigns;
• sending customer data to a vendor whose terms let it use your data to improve its own algorithms or train its AI models; and
• swapping loyalty data or email marketing lists with a partner brand to cross-promote, even with no money changing hands (among other examples).

What’s Changed and Why It Matters

Below are the key operational implications for covered businesses under the latest amendments to the CTDPA.

Stronger Profiling and Automated Decision-Making Rules

The opt-out right now covers any automated decision that produces a legal or similarly significant effect (not just those made without human involvement) and expressly includes decisions made “on behalf of” a controller by third parties or service providers. Where feasible, consumers may question outcomes, receive an explanation of how a decision was reached, review the data used, and – in housing-related contexts – correct inaccurate data and request re-evaluation.

Expanded Consumer Rights

Consumers gain new rights tied to covered profiling decisions and a new right to obtain a list of third parties to whom their personal data was sold. The rights to know and access now expressly include inferences and profiling information. Notably, controllers may not hand over certain higher-risk categories (such as Social Security numbers, certain financial data, and biometric elements) in response to a request; only confirmation that the data is held is required.

Heightened Protections for Individuals Under 18

The protected age range rises from 13-16 to 13-17, and there is now a blanket prohibition on targeted advertising to, and the sale of personal data for this age group, regardless of consent, where the controller has actual knowledge of or willfully disregards that a consumer is between 13 and 17 years of age. Controllers are also barred from using design features that increase, sustain, or extend a minor’s use of an online service, and must conduct heightened impact assessments for minors’ data.

Updated Privacy Notice Requirements

Privacy notices must now disclose whether personal data is used to train large language models (LLMs), along with profiling and targeted advertising practices. Notices must be reachable through a conspicuous homepage hyperlink containing the word “privacy,” provided in each language the controller uses, and accessible to individuals with disabilities. Material retroactive changes to data practices require notice and an opportunity to withdraw consent.

Tighter Data Minimization and New Impact Assessments

Data collection must now be reasonably necessary and proportionate to the disclosed purposes, and material new secondary uses require fresh consent. Controllers that profile consumers to make legally significant decisions must conduct a dedicated impact assessment (separate from the existing data protection assessment) for processing activities created or generated on or after August 1, 2026.

Updated Controller Duties

Processing sensitive data now requires both a “reasonably necessary” basis and consent, and antidiscrimination obligations are reinforced to guard against unlawful disparate treatment.

The Bottom Line

Connecticut has shifted from a threshold-based law that mostly affected large companies to an expansive framework that can capture smaller organizations – particularly those that touch sensitive data, rely on ad-tech and targeted advertising, share data in ways that may count as a “sale,” or offer online features used by minors. With the Connecticut Attorney General’s cure period gone and enforcement already active, the cost of waiting has risen. Companies with any nexus to Connecticut should consider the following steps:

• Reassess whether you are in scope. Given that the sensitive data and sale triggers under the amended CTDPA have no volume threshold, assume you may be in scope until confirmed otherwise.
• Map your data. Inventory what personal and sensitive data you collect, how it is used, and with whom it is shared – including via tracking pixels and list exchanges.
• Refresh privacy notices and consent flows. Disclose profiling, targeted advertising, and any use of personal data to train LLMs, and ensure notices are conspicuous, multilingual, and accessible.
• Review profiling and automated decision-making. Build compliant processes to honor opt-out requests, provide explanations for how decisions were reached, and complete the new impact assessment for activities on or after August 1, 2026.
• Strengthen protections for minors. Stop targeted advertising to, and sales of personal data of, individuals aged 13-17, and remove engagement-maximizing design features.

If you are unsure whether the amended CTDPA now applies to your business – or how to close the gaps – our Data Privacy and Cybersecurity attorneys can help you assess your compliance obligations and build a practical path forward. Please reach out to a member of our team to start the conversation.

How AI is modernizing investigations for high-stakes matters

As scrutiny intensifies, leaders must understand how AI can strengthen investigations without compromising reliability, privilege or trust.

In brief

• AI is transforming investigations by accelerating analysis of communications, transactions and evidence while improving accuracy and early decision‑making.
• These benefits depend on disciplined governance, transparency and human judgment to ensure defensibility under regulatory and legal scrutiny.
• Leaders should expect advisors to deploy AI responsibly, validate outputs and protect privilege throughout the investigative lifecycle.

As organizations expand their use of AI across the enterprise, even the most complex and non‑routine activities are being reshaped — including corporate and government investigations. These matters often represent a company’s most consequential and urgent challenges, requiring the expertise of legal advisors and forensic specialists who have traditionally relied on labor-intensive methods. AI is transforming both the speed and scale of these efforts, enabling teams to work more efficiently and with far greater accuracy and insight. Leadership teams overseeing such matters will increasingly depend on professionals who can integrate AI appropriately to realize these benefits.

Even highly complex, bespoke investigations can gain significant advantages from AI. Generative AI (GenAI), agentic tools, predictive analytics, and advanced legal technology platforms enable investigators to rapidly analyse vast data sets, detect patterns of misconduct, and build case narratives like never before. AI not only reduces professional hours and associated costs but also accelerates the ability to pinpoint and intervene — helping organizations “stop the bleeding” sooner and return to normal operations more quickly. In critical matters where days, not weeks, count, these capabilities are transformative.

But the benefits of AI do not diminish the need for professional judgment and responsible application. High-stakes investigations face intense scrutiny from regulators, auditors, boards and litigators. Heightened transparency and defensibility remain key considerations in deploying AI. AI should therefore serve as a powerful accelerant — not a replacement — for the expertise required to navigate sensitive and nuanced matters.

Boards, committees, executives and legal counsel who oversee investigations — and rely heavily on external advisors — should expect their advisors to answer two essential questions:

• What benefits can we realize on this matter through the use of AI?
• How will AI be applied responsibly to ensure the work withstands scrutiny?

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Where AI delivers tangible investigative value

From communications review to financial analysis and drafting, AI enables faster, more accurate investigations with earlier clarity for leadership.

AI is already strengthening investigations and the specific value these capabilities deliver to leadership teams, allowing teams to work faster, more accurately and with greater precision. For example, investigative teams currently employ AI tools to review large data sets of electronic communications to quickly identify critical discussions and focus on key issues, participants and timeframes. AI is also being used to analyze certain transactions by comparing separate datasets such as accounting data, supporting business records and related contemporaneous communications. Although each investigation is unique, several consistent use cases are emerging across legal, forensic and discovery domains. These capabilities are no longer experimental — they are being deployed today to enhance investigative judgment and accelerate outcomes in matters that carry significant legal, regulatory and reputational stakes.

Across industries, several use cases consistently demonstrate the tangible benefits that AI provides.

1. Rapid analysis of large volumes of communications

Organizations continue to generate immense amounts of email, chat, messages and collaborative‑platform content. Traditional review approaches — manual sampling, search terms and linear review — struggle to keep up with the volume and complexity.

What AI enables

• Rapid ingestion and review of communications at scale
• Identification of themes, sentiment shifts, behavioral anomalies and emerging issues
• Prioritization of key custodians, conversations and time periods for human review

Value to leadership

• Meaningfully reduces time by compressing weeks or months of manual review into hours or days
• Reduces cost by minimizing attorney and specialist review time
• Improves decision-making by surfacing critical issues earlier, enabling faster triage and early case assessment

2. Detection of financial and transactional irregularities

Investigations often require analyzing structured data—such as accounting records, payment files, procurement activity, HR data and transaction logs. These datasets are often too large and too complex for traditional manual forensic techniques to fully evaluate and, before AI, have relied upon judgmental targeting and sampling.

What AI enables

• Detection of anomalous transactions, unusual patterns or inconsistent accounting entries
• Linking of transactional spikes or anomalies to related communications or documents
• Cross-dataset analytics to highlight high-risk processes, vendors or individuals

Value to leadership

• Accelerates insight by scanning millions of records rapidly
• Reduces cost by streamlining forensic accounting procedures
• Improves risk focus by identifying the highest-risk activity early and guiding investigative resources

3. Accelerated drafting of investigative materials

Significant investigative time is typically spent creating chronologies, witness outlines, workplans, memoranda and interim reports.

What AI enables

• First-draft creation of workplans, timelines, interview outlines and narrative summaries
• Synthesis of large sets of documents into clear, defensible narratives
• Consistent organization of facts aligned to investigative logic

Value to leadership

• Faster output that compresses drafting cycles
• Lower cost by reducing hours spent by senior investigators and counsel
• More informed decision-making through clear, structured updates earlier in the lifecycle

4. Enhanced interview preparation and post‑interview analysis

Interviews remain one of the most judgment-led phases of an investigation. AI does not replace investigative skill — but it significantly enhances preparation and follow‑up.

What AI enables

• Summaries of key documents and communications for each witness
• Identification of inconsistencies, corroborating statements, or new leads from transcripts
• Suggestions for follow-up questions based on cross‑referencing of structured and unstructured data

Value to leadership

• Improves quality of interviews by grounding them in comprehensive, data‑driven insight
• Reduces manual prep time and accelerates post‑interview analysis
• Enhances defensibility through more consistent, better‑documented evaluative processes

Across all investigative use cases, AI delivers cross‑cutting advantages that significantly enhance the efficiency and strategic value of the work. By surfacing key documents and early indicators sooner, AI accelerates initial case understanding and shortens the path to clarity. It unifies evidence by integrating structured and unstructured data into a more complete fact pattern, while reducing reliance on manual review — lowering cost, minimizing sampling risks and improving overall accuracy. Together, these collective benefits enable investigative teams to operate with greater speed, insight and confidence.

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Why responsible AI use determines investigative defensibility

Governance, transparency, verification and human oversight are essential to ensure AI enabled investigations withstand external scrutiny.

Investigations — particularly those involving potential misconduct, regulatory scrutiny or significant organizational impact — nearly always face some form of external review. Boards, auditors, regulators and government agencies routinely evaluate not only the findings of an investigation but also the sufficiency of the process used to reach them. As AI becomes more deeply embedded in investigative workflows, leadership should expect its use to be evaluated with the same rigor applied to traditional forensic and legal procedures.

Defensibility depends on the professionals guiding the work — their judgment in selecting AI tools built on responsible governance principles, their decisions about how those tools will be deployed, their oversight of AI‑generated outputs throughout the matter and their deployment in a manner to uphold privilege protections. A sound investigation begins with clarity about who the key stakeholders are and what information they will require, as this understanding shapes the investigative workplan, including when and how AI should be incorporated. Ensuring defensibility requires thoughtful decisions at every stage — beginning with this upfront planning to inform selection of the right tools and their application.

Governance and transparency of AI tools

Corporate boards and leadership will rely on the expertise of its legal advisors and forensic specialists to select and deploy AI tools that are both appropriate and advantageous to the unique nature of the matter. This professional judgment is applied at the very outset — specifically in selecting AI tools that are built on responsible governance principles. AI models vary widely in their approaches to data security, model training, transparency and auditability. Professionals must therefore prioritize tools that provide explainability, maintain clear documentation of how outputs were generated, and align with established governance principles. These features help ensure reliability before any analysis begins.

That same judgment is essential in determining how and when to deploy AI. The decision to apply AI cannot be based on convenience alone; it must reflect the size and complexity of the data, the nature of the allegations, and the likely scrutiny the investigation will face. Whether AI is used to review communications, identify anomalies or synthesize documents, its application must be demonstrably reliable, reasonable and sufficient. By grounding both tool selection and deployment in expertise and responsible governance, professionals ensure that AI enhances investigative rigor rather than undermining it.

Testing and verification of AI outputs

Two core concepts underpin the credibility of AI-generated insights: accuracy and explainability.

• Accuracy reflects AI’s ability to produce consistent and correct results. Investigators should apply reperformance techniques to verify that the system arrives at the same outcome under similar conditions. This not only validates the tool’s effectiveness but also demonstrates to external stakeholders that results were not arbitrary or the product of a “black box.”
• Explainability applies to subjective or judgment‑oriented assessments — particularly with decision-making often seen in Agentic AI — such as prioritizing documents, identifying thematic patterns, or suggesting relationships between data points. Here, investigators must be able to follow the model’s rationale, understand why it reached a conclusion and assess whether the logic is reasonable within the broader fact pattern. This ability to test and challenge the AI’s reasoning is essential to establishing that human judgment, not automation, guided the investigation and interpretation of results.

AI should enhance professional expertise — not replace it. Investigators remain responsible for interpreting outputs, assessing their reasonableness, adjusting inputs or parameters, and documenting key decisions. They also oversee and verify the accuracy and explainability of AI‑generated results. Throughout the process, investigators must stay actively engaged: testing outputs, validating that they align with the broader fact pattern, and ensuring no model operates with unchecked autonomy. Maintaining clear records of how AI supported the investigation, how outputs were validated, and where human judgment guided the analysis is essential to demonstrating reliability and sufficiency during external review.

Consistent with these principles, courts have made clear that uncritical or unchecked reliance on AI — particularly without independent verification, transparency, and documented human judgment — can expose organizations to serious legal and evidentiary consequences. Recent federal decisions demonstrate that AI‑related failures are treated no differently than other lapses in professional responsibility.[1] Where parties have relied on AI-generated outputs without meaningful review, courts have imposed significant sanctions, awarded attorneys’ fees and costs, and warned that such conduct can undermine the integrity of proceedings and jeopardize case outcomes. These decisions underscore a central expectation: AI may assist professional work, but responsibility for accuracy, reasonableness and reliability remains firmly with the humans deploying it.

Protecting privilege

Further, investigators must exercise caution both in the degree to which they rely on AI and in the nature of the information they disclose to generative AI tools. Courts have clarified that the use of AI tools does not, standing alone, create or preserve attorney‑client privilege or work‑product protection. In particular, disclosures to publicly available AI platforms may undermine confidentiality and result in the waiver of otherwise applicable protections. Recent decisions emphasize that recognized privileges depend on a confidential, fiduciary relationship with a licensed professional and do not extend to interactions with non‑human, publicly accessible systems. [2] Accordingly, investigators and counsel must exercise care in determining what information is shared with AI tools and must ensure that AI is used in a manner consistent with privilege, confidentiality obligations and professional oversight.

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[1] Representative decisions include: ByoPlanet Int’l, LLC v. Johansson, 2025 WL 201025 (S.D. Fla. July 17, 2025); Versant Funding LLC v. Teras Breakbulk Ocean Navigation Enters., LLC, 2025 WL 1440351 (S.D. Fla. May 20, 2025); Huntington Nat’l Bank v. M/Y [redacted], 2025 WL 1684109 (S.D. Fla. June 11, 2025), report and recommendation approved, 2025 WL 1684136 (S.D. Fla. June 16, 2025).

[2] See United States v. Heppner, No. 25 CR. 503 (JSR), 2026 WL 436479 (S.D.N.Y. Feb. 17, 2026).

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What human-led, AI accelerated investigations enable

Organizations that pair AI’s analytical power with professional judgment are better positioned to manage risk and respond decisively.

AI is reshaping how investigations are conducted, enabling teams to work faster, more accurately and with greater precision, when paired with experienced professional judgment. The most effective investigative teams embrace AI for the acceleration and analytical power it brings, while maintaining the governance, oversight and disciplined reasoning required to withstand scrutiny.

Organizations that modernize their investigative approaches — by empowering legal advisors and forensic specialists to deploy AI wisely — will be better equipped to manage risk, uncover facts quickly, respond decisively in high‑stakes environments, and reduce financial and operational disruption. As the landscape of data, regulation and corporate exposure continues to evolve, shifting from traditional methods to human-led, AI-accelerated investigations is not simply an enhancement. It is a strategic imperative.

This article was co-authored by Jeff Ferguson and Katie Kyle, partners for Forensic & Integrity Services at Ernst & Young LLP.