Behind generative AI’s push into mainstream use are thousands of inventions and applications, many protected by a growing number of patents. In 2024 alone, 51,000 generative AI patents were filed, and 24,880 were granted, up from fewer than 1,000 in 2014, according to patent data provider IFI CLAIMS.
That growth highlights the rapid expansion of generative AI use cases and may point to AI stocks or ETFs poised to deliver market-beating returns.
For investors, patent trends can serve as an additional data point in an investment thesis or provide clues about which direction a company is taking its own AI innovation. Who owns the most generative AI patents, where those patents are being applied, and which models they rely on all point to where generative AI is most likely to translate into durable revenue.
The companies filing for the most generative AI patents
While Chinese companies hold the most generative AI patents, U.S. companies are filing for patents at a faster pace, according to data from Harrity LLP, a law firm that specializes in patents.
Alphabet, Zhejiang University, Microsoft (MSFT +2.23%), University of Electronic Science and Technology of China, Baidu (BIDU +1.00%), and Nvidia (NVDA +1.15%) were the top six generative AI patent applicants in 2024. IBM (IBM -0.83%), Tsinghua University, Hangzhou Dianzi University, and State Grid Corporation of China round out the top 10, according to IFI.
Looking at AI patents generally, Alphabet ranked No. 1 globally in 2024 filings with 1,121, followed by Samsung and IBM. Microsoft, Amazon (AMZN +2.62%), Nvidia, Adobe (ADBE -2.40%), and several financial institutions also ranked highly. Eight of the top 10 AI patent filers and 13 of the top 20 in 2024 were American companies.
Tencent, while historically dominant in terms of total AI patents held, ranked 16th in 2024 filings but posted a 67% year-over-year increase, signaling renewed acceleration.
That data shows that top U.S. companies are driving current-generation AI development, likely in deployment, application infrastructure, and commercialization, but Chinese companies and institutions aren’t letting up.
A separate data set from IFI shows Google, Samsung, Huawei, Microsoft, State Grid, Bosch, Tencent, and IBM as the top AI patent applicants worldwide.
Most of the most aggressive AI patent applications are diversified technology companies, but two exceptions stand out:
- State Grid Corporation of China, the world’s largest state-owned utility, applies generative AI to grid optimization, predictive maintenance, and infrastructure planning.
- Ping An Insurance, a Chinese financial services firm, uses AI for underwriting, claims processing, and product personalization.
“Ping An epitomizes a broader trend within the global insurance industry to improve the ‘combined ratio,’ a measure of profitability that takes both insurance loss ratios and efficiency into account,” said Senior Investment Analyst and Lead Advisor at The Motley Fool Asit Sharma. “Machine learning and generative AI are opening up new opportunities for insurers to better assess risk in underwriting and automating areas like claims processing and customer service.
“More broadly, Ping An's patent portfolio also reflects its plan to expand beyond insurance into technology, digital health, and other areas where generative AI might give it a competitive springboard,” he added.
State Grid is the only utility company on the list.
Sharma sees two other factors behind State Grid’s generative AI push: Chinese industrial policy and geopolitical ambitions.
“State Grid's objectives are informed by China's industrial policy, which focuses on the development of exportable goods technology; for example, AI-capable sensors for Industrial Internet of Things (IIoT),” Sharma said.
“This focus is intertwined with geopolitics; for example, State Grid is tasked with supporting infrastructure for China's multinational Belt and Road initiative. So we can see that the company is likely active in genAI patents to ensure China's energy security while simultaneously innovating for industrial export and the export of energy technology wherever China seeks influence.”
Alphabet, Microsoft, and Adobe are integrating generative AI across their products. Alphabet and Microsoft have their own flagship generative AI tools in Google Gemini and Microsoft CoPilot, and each is augmenting its other well-known products and applications with generative AI.
Adobe is focused on using generative AI for image creation, content generation, and PDF analysis.
Patent count isn’t determinative for a company’s strength in generative AI. The quality of patents matters as well. Analysts suggest that despite the numerical lead Chinese companies hold in generative AI patents, they have struggled to build tools based on them, in part due to constraints on access to high-end, specialized AI semiconductors.
But that gap in computing might not last forever, Sharma said. “A GPU accelerator deficit is likely hampering the rate of progress that Chinese tech firms can make in LLM development. However, restriction reliably births innovation over the long run, so we can expect to see a growing portion of Chinese patents aimed at workarounds for semiconductor compute power, including alternatives to GPU-based training and inference.”
Applications and use cases most generative AI patents are built for, by company
The most common application cited in generative AI patents is software or an otherwise generally defined use case. That’s the case overall and among the 10 companies that hold the most generative AI patents. Life sciences are the second-most-common application cited in generative AI patents, followed by document management and publishing, business solutions, and industry and manufacturing, according to WIPO.
Examining the use case for generative AI patents held by companies provides insights into which parts of their businesses or products may be improved by AI and, more broadly, which sectors may see the most AI-driven innovation.
Among the 10 most prolific generative AI patent holders, all but 4 have at least 10% of their generative AI patents in document management and publishing. Those six companies are Baidu (14%), Microsoft (14%), Alibaba (BABA +0.82%) (12%), Ping An Insurance Group (11%), Tencent (11%), and Bytedance (10%).
Some companies are patenting generative AI applications that are well within their areas of expertise. For example, Ping An Insurance Group holds 101 generative AI patents with banking and finance use cases, which account for 6% of its generative AI patents. No other company in the top 10 had more than 0.85% of its generative AI patents in the banking and finance area.
Applying generative AI to business solutions is a focus for both Alibaba and Microsoft. Alibaba has 91 generative AI patents in that area, accounting for some 16% of its total generative AI patents, while Microsoft holds 39, equal to 9% of its total. Looking purely at the number of patents, Ping An Insurance Group has 124 generative AI patents related to business solutions, and Tencent has 119.
Tencent holds 90 generative AI patents with entertainment applications, accounting for 4% of its total and the most in that category among the top patent holders. Tencent is the largest video game company by some measures and is among the largest multimedia companies in the world, with music and video streaming services among its offerings.
Life and medical sciences are another category in which a handful of companies hold a decent number of generative AI patents. Ping An Insurance Group leads the way with 227 generative AI patents in this area, accounting for roughly 13% of its total generative AI patents. Tencent has 73, roughly 3% of its generative AI patents, while IBM has 59, about 9% of its own generative AI patents.
Alphabet and Microsoft stand out for having 6% and 5% of their generative AI patents, respectively, in the field of life and medical sciences. For Alphabet, life and medical sciences are the third-most-popular use case for its generative AI patents behind telecommunications and software and other applications.
Use cases involving personal devices, computing, and human-computer interfaces account for 8% of Samsung’s generative AI patents, aligning with the company’s long-standing focus on hardware. Microsoft is another company innovating in the hardware and generative AI space -- 6% of its generative AI patents have a use case in that area.
Samsung also leads the way in generative AI patents with a telecommunications application, followed by Alphabet. Both are seeking to better integrate generative AI into their smartphones.
The top generative AI patent applications overall
Among all generative AI patents, nearly half, some 48%, have a use case in software or are more nebulously defined, according to WIPO.
The next-most-common applications of generative AI patents are life sciences (9% of all generative AI patents), document management and publishing (8%), and business solutions (8%).
The applications cited in generative AI patents that have seen the most growth since 2018 are energy management (81% average annual increase), transportation (66%), agriculture (60%), and security (59%). Those applications still make up a fraction of generative AI patents, however.
Which generative AI models are most common in patents, by company
Different generative AI models have distinct strengths that inform how companies use them. The models companies cite in their patents can shed light on how they’re seeking to deploy generative AI.
WIPO categorizes generative AI into the following models:
- GAN: Generative adversarial networks (GANs) comprise a machine learning framework that pits two neural networks -- a generator and a discriminator -- against each other in a game of true-or-false, improving the quality of generated data over time. Main applications include image and video production. Beyond restoring old pictures and improving AI-generated art, GANs have a range of uses in medical and scientific fields, including modeling and imaging the cosmos, analyzing medical imaging, and reproducing medical imaging to protect patient privacy.
- LLM: Large language models (LLMs) specialize in natural language processing and allow for processing and generating significant amounts of text. Applications range from text generation for a variety of tasks to customer service chatbots, coding, translation, education, and summarizing text.
- VAE: Variational autoencoders (VAEs) compress data and use it to generate new, similar, realistic data. Applications include image generation and reconstruction, detection of anomalous data, medical imaging, and drug research.
- Diffusion models: Diffusion models are trained to generate new data by removing noise from existing images or data sets. Image generation is the most common use of diffusion models.
- Autoregressive models: Autoregressive models predict and generate the next value, such as a word, based on previous inputs. These models are foundational to natural language processing and LLMs.
Here’s how frequently each model shows up in the patents held by the top generative AI patent holders.
Most companies use GAN primarily in their generative AI patents, but a lack of specificity in many of their patent applications creates data gaps that make it difficult to draw more concrete conclusions.
The type of data companies use most in generative AI patents
The type of data used to develop patented generative AI inventions is another signpost that investors can use to see what types of solutions companies are building.
Images and video are the most common forms of data used, showing up in 17,996 generative AI patents across all filers from 2014 to 2023, per WIPO. That’s followed by other types of data (14,270 patents), text (13,494 patents), and speech and voice (13,480 patents).
3D image models as a data source appeared in 3,145 generative AI patents from 2014 to 2023. Molecules, genes, and proteins appeared in 1,494 patents, and code appeared in 1,340.
The fastest-growing type of data used in generative AI patents is from molecules, genes, and proteins, followed by images and video, then code.
Generative AI patent trends
Generative AI patenting has accelerated sharply over the past decade, moving from an academic niche to the mainstream. According to IFI, 51,000 generative AI patents were filed in 2024, and just under 25,000 were granted. In 2014, a total of 621 generative AI patents were filed, and 214 were granted. In 2014, generative AI accounted for 6% of all AI patents granted. That share jumped to 26% in 2024.
The number of generative AI patents filed and granted roughly doubled in 2018 and again in 2019 then climbed rapidly until 2024, when they increased by more than 10,000. This coincides with the introduction of transformer architecture and subsequent breakthroughs in large language models in the late 2010s, as well as massive competition among mainstream technology companies to capture LLM market share in the following years.
Two-thirds of inventors holding generative AI patents are based in China, according to WIPO. The United States is home to the second-largest share, with 11%, followed by South Korea, Japan, and India.
The percentage of generative AI patents won by U.S. companies has steadily declined each year, from 20% in 2014 to 8% in 2023. Meanwhile, the share of generative AI patents awarded to inventors in China has grown from 33% in 2014 to 72% in 2023. The share of Japanese inventors' share of generative AI patents won annually declined from 18% to 3% over the same period.
Why does data on generative AI patents matter for investors?
Generative AI patents are one indicator of how companies and AI stocks may evolve over time. Companies with competitive generative AI patent portfolios signal strong prospects for innovation, new product development, and durable advantages that can support shareholder returns.
While the data suggests that Chinese companies dominate the generative AI patent landscape, top U.S. companies are also applying for AI patents at a growing clip. That may better position major U.S. AI businesses, like Alphabet, Microsoft, Nvidia, IBM, and Adobe, to translate innovations into commercial products that drive revenue growth and, potentially, long-term stock performance.
For investors evaluating AI stocks, patents are just one data point among many that can offer insight into a company’s strength and direction in a fast-moving field.
“To an extent, the quantity of filed patents does matter, as it gives us a sense of a tech leader's activity in AI ecosystem innovation and its ability to turn R&D into practicable and potentially protected (upon patent grant) ideas,” Sharma said. “Beyond this, it's a little harder for investors to gauge the impact of a generative AI patent portfolio.”
Sources
- Centre for International Governance Innovation (2024). “China Leads on Generative AI Patents, but What Does that Mean?”
- Centre for International Governance Innovation (2021). “What Do China’s High Patent Numbers Really Mean?”
- HarrityLLP (2025). “AI Patent 100.”
- IFI CLAIMS Patent Services (2025). “IFI Insights: Tracking the evolution of AI with patents.”
- Stanford University (2025). “The 2025 AI Index Report.”
- WIPO (2024). “Patent Landscape Report - Generative Artificial Intelligence (GenAI).”
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