Skip to content
PatentWorld
Chapter 25

Artificial Intelligence

The growth of AI-related patenting activity in the United States

Having examined agricultural technology and the transformation of food production through precision agriculture, this chapter turns to artificial intelligence, one of the largest technology domains by patent volume in this study and one whose cross-domain reach extends into virtually every other field examined in ACT 6.

Artificial intelligence has evolved from a specialized academic pursuit to one of the most active domains in the United States patent system. This chapter examines the trajectory of AI-related patents — from early expert systems and symbolic reasoning through the machine learning transformation to the current era of deep learning and generative models.

Growth Trajectory

Figure 1

AI Patent Filings Grew From 5,201 in 2012 to 29,624 in 2023, Consistent With Deep Learning Advances

Annual count of utility patents classified under AI-related CPC codes (G06N, G06F18, G06V, G10L15, G06F40), tracking the growth trajectory of AI patenting.

Annual count and share of utility patents classified under AI-related CPC codes (G06N, G06F18, G06V, G10L15, G06F40), 1976–2025. The most prominent pattern is the sharp increase beginning around 2012, coinciding with advances in deep learning frameworks and GPU computing. Grant year shown. Application dates are typically 2–3 years earlier.
The rapid growth in AI patents is consistent with the broader expansion of AI capabilities, including advances in deep learning frameworks, GPU computing, and large-scale data availability.
Figure 2

AI's Share of Total Patents Rose From 0.15% in 1976 to 9.4% in 2023, Indicating a Reallocation of Inventive Effort

AI patents as a percentage of all utility patents, showing the growing reallocation of inventive effort toward AI technologies.

Percentage of all utility patents classified under AI-related CPC codes. The upward trend indicates that AI patenting growth is not merely tracking overall patent growth but represents a disproportionate concentration of inventive effort.
The growing share of AI patents among all patents demonstrates that AI growth is not merely tracking overall patent expansion; rather, it suggests a genuine reallocation of inventive effort toward AI technologies.
Figure 3

AI Patent Growth Is Dominated by Incumbents, Though Entrant Growth Accelerated After 2015

Annual patent counts decomposed by entrants (first patent in domain that year) versus incumbents.

Entrants are assignees filing their first AI patent in a given year. Incumbents had at least one prior-year patent. Grant year shown.

AI Subfields

Figure 4

Neural Networks / Deep Learning Has Emerged as One of the Largest AI Subfields Since 2012

Patent counts by AI subfield (neural networks, machine learning, natural language processing, and related subfields) over time, based on specific CPC group codes across the AI classification set.

Patent counts by AI subfield over time, based on CPC classifications. The data reveal that neural networks/deep learning and machine learning account for the majority of recent growth, surpassing earlier subfields including computer vision, knowledge-based systems, and other classical approaches.
The shift from expert systems to deep learning reflects fundamental changes in AI methodology, moving from hand-crafted rules to data-driven pattern recognition.

Leading Organizations

Figure 5

IBM (16,781), Google (7,775), and Samsung (6,195) Lead in Total AI Patent Volume, Consistent With the Resource-Intensive Nature of AI R&D

Organizations ranked by total AI-related patent count From 1976 to 2025 (Through September), showing concentration among large technology firms.

Organizations ranked by total AI-related patents, 1976–2025. The data indicate a concentration among large technology firms with substantial computational infrastructure and data assets.
The dominance of large technology firms in AI patenting reflects the resource-intensive nature of AI R&D, which requires large-scale datasets, computing infrastructure, and specialized talent.

Top Inventors

Figure 6

Charles Howard Cella Leads With 317 AI Patents; a Small Cohort of Prolific Inventors Dominates Output

Primary inventors ranked by total AI-related patent count From 1976 to 2025 (Through September), illustrating the skewed distribution of individual output.

Primary inventors ranked by total AI-related patents, 1976–2025. The distribution exhibits pronounced skewness, with a small number of highly productive individuals accounting for a disproportionate share of total AI patent output.
The concentration of AI patenting among a small cohort of prolific inventors mirrors the broader superstar pattern in innovation, where a few highly productive individuals account for a disproportionate share of output.

Geographic Distribution

Figure 7

The United States Leads With 145,763 AI Patents, Followed by Japan (30,150) and China (15,596)

Countries ranked by total AI-related patents based on primary inventor location, showing geographic distribution of AI innovation.

Countries ranked by total AI-related patents based on primary inventor location. The United States maintains a substantial lead, while the strong presence of Japan, China, and South Korea indicates significant Asian investment in AI-driven electronics and consumer technology.
The United States lead in AI patenting reflects its concentration of major AI research laboratories and technology firms, while the strong presence of Japan, China, and South Korea indicates substantial Asian investment in AI-driven electronics and consumer technology.
Figure 8

California Leads US AI Patenting With 54,763 Patents, 4.1x Second-Place Washington (13,271)

US states ranked by total AI-related patents based on primary inventor location, highlighting geographic clustering within the United States.

US states ranked by total AI-related patents based on primary inventor location. California's substantial lead is consistent with agglomeration effects, where proximity to talent pools, venture capital, and established AI research communities may foster a self-reinforcing concentration of innovation.
California's dominance in AI patents is consistent with strong agglomeration effects: proximity to talent pools, venture capital, and established AI research communities may foster a self-reinforcing concentration of innovation.

Quality Indicators

Figure 9

AI Patent Technology Scope Nearly Doubled From 1.69 to 3.19 CPC Subclasses, 1990–2024

Average claims, backward citations, and technology scope (CPC subclasses) for AI patents by year, measuring quality trends.

Average claims, backward citations, and technology scope for AI-related patents by year. Backward citations peaked around 2014 before declining in recent years, while technology scope has risen, suggesting that AI patents are becoming increasingly interdisciplinary even as citation patterns have shifted.
Backward citations peaked around 2014 and have since declined, possibly reflecting changes in examiner citation practices or the rapid expansion of the field. Rising technology scope suggests that AI patents are becoming more interdisciplinary, consistent with the expanding role of AI as a general-purpose technology.
Figure 10

AI Patent Top-Decile Citation Share Fell From 23.2% in 1990 to 18.8% in 2020 as Volume Expanded

Share of domain patents in the top decile of system-wide forward citations by grant year × CPC section.

Top decile computed relative to all utility patents in the same grant year and primary CPC section. Rising share indicates domain quality outpacing the system; falling share indicates dilution.

AI Patenting Strategies

The leading AI patent holders pursue markedly different strategies. Some firms concentrate on neural networks and deep learning, while others distribute their portfolios across computer vision, natural language processing, and other subfields. A comparison of AI subfield portfolios across major holders reveals where each organization concentrates its inventive effort and identifies areas that remain relatively underexplored.

AI as a General Purpose Technology

A defining characteristic of general-purpose technologies (GPTs) is their diffusion into multiple sectors of the economy. By tracking how frequently AI-classified patents also carry CPC codes from non-AI technology areas (excluding Section G, which contains AI), it is possible to measure the spread of AI into healthcare, manufacturing, chemistry, and other domains.

Figure 11

AI-Electricity Co-Occurrence Rose From 11.0% to 30.0% and AI-Healthcare From 4.5% to 8.2%, 2000–2024

Percentage of AI patents co-classified with non-AI CPC sections, measuring AI's diffusion into healthcare, manufacturing, and other domains.

Percentage of AI patents that also carry CPC codes from each non-AI section (G excluded). Rising lines indicate AI diffusing into that sector. The most notable pattern is the increasing co-occurrence with Human Necessities (Section A, encompassing healthcare) and Performing Operations (Section B, encompassing manufacturing).
The presence of AI across multiple CPC sections is consistent with its characterization as a general-purpose technology, comparable to electricity or computing in earlier eras. The rising co-occurrence with healthcare and manufacturing CPC codes suggests expanding real-world applications of AI.
Figure 12

AI Patents Reach 7 Distinct CPC Sections With HHI Declining to 0.41, Confirming GPT Status

Distinct non-AI CPC sections co-occurring with AI patents, and Herfindahl-Hirschman Index (HHI) of their distribution, by year.

Distinct sections measures breadth of AI application; falling HHI indicates increasingly even distribution across technology domains. Both trends are consistent with AI functioning as a general-purpose technology.

The Attribution Challenge in AI Patenting

AI patents increasingly involve larger inventor teams and corporate assignees, reflecting the capital-intensive nature of modern AI research. The average AI patent lists more inventors than non-AI patents, and this disparity has widened over time, raising questions about individual attribution in an era of large-scale collaborative AI development.

Figure 13

AI Patents Average 3.5 Inventors versus 3.2 for Non-AI in 2024, With the Gap Widening Since 2010

Average inventors per patent for AI versus non-AI utility patents by year, showing the widening complexity gap between the two categories.

Average number of inventors per patent for AI-related versus non-AI utility patents, 1976–2025. The data indicate that AI patents consistently involve larger teams, and the gap has widened since 2010, reflecting the increasing complexity of AI systems.
AI patents consistently involve larger teams than non-AI patents, and the gap has widened since 2010. This pattern reflects the increasing complexity of AI systems, which require expertise spanning machine learning, domain knowledge, hardware, and software engineering.
Figure 14

Corporate Assignees Dominate AI Patenting, With Intensifying Concentration Since 2010

Distribution of AI patents by assignee type (corporate, university, government, individual) over time, showing the intensifying corporate share.

Distribution of AI patent assignees by type (corporate, university, government, individual) over time. The data reveal that the corporate share has intensified since 2010 as large technology firms expanded their AI research divisions, while university AI patenting has grown in absolute terms but declined as a proportion.
Corporate assignees have dominated AI patenting throughout its history, and the corporate share has intensified since 2010 as large technology companies expanded their AI research divisions. University AI patenting has grown in absolute terms but has declined as a share.

Analytical Deep Dives

For metric definitions and cross-domain comparisons, see the ACT 6 Overview.

Figure 15

Top-4 Concentration in AI Patents Declined Steadily From 25.2% in 1984 to 10.9% by 2025 (Through September)

Share of annual domain patents held by the four largest organizations, measuring organizational concentration in AI patenting.

CR4 (four-firm concentration ratio) computed as the sum of the top 4 organizations' annual AI patent counts divided by total AI patents. The steady decline from 25% to 11% reflects the democratization of AI research, with an accelerating drop after 2015 consistent with cloud computing and open-source frameworks lowering barriers to AI innovation.
AI exhibits a substantial concentration decline among ACT 6 domains, consistent with the technology's transition from specialized research labs to a general-purpose capability accessible to organizations across all sectors.
Figure 16

AI Subfield Diversity More Than Doubled From 0.40 in 1976 to 0.84 by 2025 (Through September)

Normalized Shannon entropy of subfield patent distributions, measuring how evenly inventive activity is spread across AI subfields.

Normalized Shannon entropy of AI subfield patent distributions. The increase from 0.40 (highly concentrated in symbolic AI) to 0.84 (broadly distributed across machine learning, computer vision, NLP, robotics, and other subfields) represents one of the largest diversification trajectories among all technology domains studied.
The entropy trajectory mirrors AI's intellectual evolution: from narrow expert systems in the 1970s-80s through the statistical learning revolution of the 2000s to the current era of deep learning, generative AI, and domain-specific applications spanning virtually every CPC section.
Figure 17

2010s AI Entrants Patent at 134 Patents per Year, Nearly Double the 68 per Year of 1970s Entrants

Mean patents per active year for top organizations grouped by the decade in which they first filed an AI patent.

Mean patents per active year for top AI organizations grouped by entry decade. The 2.0x velocity increase from 1970s to 2010s cohorts reflects the acceleration of AI patenting, coinciding with the rise of cloud infrastructure, pre-trained models, and large-scale data availability.
The velocity acceleration is concentrated in the 2000s and 2010s cohorts, coinciding with the deep learning revolution and the entry of technology firms that rapidly scaled AI patent portfolios from near-zero to thousands of annual filings.

Having documented the growth of artificial intelligence in the patent system, the organizational strategies behind AI patenting are explored further in Assignee Composition.

Figure 18

AI Filings Peaked at 25,853 in 2020 While Grants Reached 26,001 in 2023 — a 3-Year Examination Lag

Annual patent filings versus grants for artificial intelligence, revealing the USPTO examination pipeline.

The substantial gap between AI filing and grant curves during 2015–2020 reflects the rapid acceleration of AI patent applications that overwhelmed examination capacity. Grants continued climbing through 2023 as the backlog was processed.

Data coverage: January 1976 through September 2025. All 2025 figures reflect partial-year data.