World IP Review (WIPR) – OpenAI’s unconventional IP strategy for ChatGPT

Behind the ground- breaking artificial intelligence tool lies a meticulously crafted rights strategy that departs from the norm, according to research by Sagacious IP.

Generative AI has emerged as a transformative force in the technology sector, with OpenAI’s ChatGPT standing at the forefront of this revolution. Known for its remarkable ability to generate human-like text, ChatGPT represents a significant leap in natural language processing. However, behind its impressive capabilities lies a meticulously crafted patent strategy that reveals a deeper, more strategic approach than one might initially assume.

This article explores OpenAI’s patenting strategy, examining how it aligns with the organization’s mission while positioning it as a leader in the AI industry. We’ll delve into the journey of ChatGPT from its inception to its latest iteration, highlighting the profound contributions of OpenAI’s founders to the broader tech industry. By examining ChatGPT’s evolution alongside OpenAI’s patent strategy, this article will shed light on the interplay between innovation, intellectual property, and the pursuit of transformative AI technologies.

Evolution of ChatGPT

ChatGPT is built upon OpenAI’s series of Generative Pre-trained Transformers (GPT), starting from GPT-1 to the latest iterations. Each version has introduced significant advancements in natural language processing (NLP), enabling the model to understand and generate human-like text.

GPT-1: The Beginning

Released in 2018, GPT-1 introduced the concept of generative pre-training. With 117 million parameters, GPT-1 demonstrated the potential of using large-scale unsupervised learning to create powerful NLP models. Its ability to generate coherent text laid the groundwork for future developments.

GPT-2: Scaling Up

In 2019, GPT-2 significantly expanded the parameter count to 1.5 billion. This increase in model size enabled GPT-2 to generate more complex and contextually relevant text. However, due to concerns about misuse, OpenAI initially withheld the full model, releasing it gradually to study its impacts.

GPT-3: The Breakthrough

Launched in 2020, GPT-3 marked a substantial leap forward with 175 billion parameters. This vast increase in parameters allowed GPT-3 to achieve unprecedented levels of fluency and accuracy in text generation. GPT-3’s versatility has been demonstrated in a wide range of applications, from chatbots and virtual assistants to creative writing and code generation.

GPT-4: Advancing Capabilities

Released on March 14, 2023, GPT-4 continued the trend of scaling up with even more advanced capabilities. GPT-4 is a multimodal model, accepting both text and image inputs, and it exhibits human-level performance on various professional and academic benchmarks. It has been integrated into numerous applications, enhancing everything from accessibility tools to financial services.

The ability to attach files was introduced with GPT-4. This feature allows users to upload and work with various types of documents, including PDFs, Microsoft Word documents, and presentations. It enhances the model’s ability to analyze, summarize, and extract information from these files.

GPT-4o: The Latest Innovation

Announced on May 13, 2024, GPT-4o (GPT-4 Omni) is OpenAI’s latest flagship model. It is a multilingual, multimodal model that can reason across text, audio, and vision in real time. GPT-4o offers GPT-4-level intelligence but is much faster and more efficient, generating text twice as fast and at a lower cost. It is available in ChatGPT and the API, with enhanced capabilities for understanding and discussing images, translating text, and even engaging in real-time voice and video conversations. GPT-4o is designed to be more accessible, with improved language capabilities and support for over 50 languages.

GPT-5: The Future

OpenAI has announced that GPT-5 is in development and will be available to the U.S. government before its public release. This collaboration with the U.S. AI Safety Institute aims to ensure the model’s safety and reliability. GPT-5 is expected to bring even more advanced features and improvements, such as:

• Increased Parameter Size: Potentially around 17.5 trillion parameters, significantly enhancing its capabilities
• Improved Contextual Understanding: Better handling of complex and nuanced language, including sarcasm and irony.
• Multimodality: Enhanced ability to process and generate text, audio, and video content.
• Autonomous AI Agents: Capable of operating independently without human intervention.
• Cost-Effective Use: More efficient and affordable access to the OpenAI API.
• Long-Term Memory: Ability to retain and recall information over extended interactions.
• Reduced Hallucination: Improved accuracy and reliability in responses.

OpenAI’s Unconventional Patent Strategy: Speed, Focus, and Competitive Edge

OpenAI’s approach to intellectual property has undergone a significant evolution, mirroring the organization’s growth and the increasing complexity of the AI landscape. This shift in strategy reveals a nuanced balance between open collaboration and proprietary protection.

Not too long ago, a search for patents by OpenAI would have yielded no results. This aligned with the organization’s initial commitment to open-source principles and the free dissemination of AI research. However, the landscape has changed dramatically. As of now (9^th August, 2024), OpenAI has 9 granted US patents and 1 published US application.

OpenAI has adopted a patenting strategy that defies convention. While tech giants typically amass vast patent portfolios, OpenAI has charted a different course — one that prioritizes speed and strategic focus over sheer volume.

• The Speed Advantage: At the heart of OpenAI’s strategy lies its masterful use of the USPTO’s Track One program. This move has slashed patent approval times from years to mere months, with OpenAI securing patents in an average of just 11 months. In an industry where yesterday’s cutting-edge is today’s old news, this speed is nothing short of revolutionary.

• Quality Over Quantity: OpenAI’s patent portfolio might seem modest at first glance—9 granted US patents and 1 published US application. However, this lean approach belies a laser-focused strategy. Each patent represents a carefully chosen, high-impact innovation, forming a concentrated arsenal of intellectual property.

• Strategic Implications: This approach isn’t just about bureaucratic efficiency—it’s a chess move in the GenAI arms race. By rapidly securing protection for key innovations, OpenAI can:
  • Outmanoeuvre competitors in critical AI domains.
  • Accelerate the commercialization of groundbreaking technologies.
  • Create a nimble, adaptive R&D process that quickly translates breakthroughs into protected assets.

As the AI landscape continues to evolve at breakneck speed, OpenAI’s unconventional patenting strategy may prove to be a key differentiator.

Prioritizing the U.S. Market: A Strategic Edge for OpenAI

OpenAI’s focus on the U.S. market for its patent filings is a well-calculated strategy that offers multiple advantages, solidifying the company’s competitive position in the rapidly evolving tech industry. This approach reflects a broader trend among high-tech companies that recognize the value of securing intellectual property in the world’s largest and most influential market.

Prioritizing the U.S. market offers several additional benefits:

• Enhanced Competitive Edge: OpenAI’s use of the U.S. Patent and Trademark Office’s Track One program, which offers expedited patent approvals, is a key element of its strategy. By securing patents more quickly, OpenAI can protect its innovations and bring them to market with less delay, reducing the window of opportunity for competitors.
• Focused Innovation Protection: By concentrating its patent filings in the U.S., OpenAI ensures that its most critical technologies are protected in a market that not only offers substantial value but also sets global standards for innovation. This approach is reminiscent of Apple’s strategy, where the company focuses on key markets to protect its innovations, knowing that success in these markets can drive global influence. Apple’s early and concentrated patenting in the U.S. has been instrumental in defending its intellectual property against numerous challenges.
• Significant Tech Hub Presence: The U.S. is home to Silicon Valley, the world’s premier hub for technological innovation. For a company like OpenAI, securing patents in the U.S. means embedding itself within this critical ecosystem. Just as Google has done by amassing a significant number of patents in the U.S., OpenAI’s strategy reinforces its position as a leader in AI innovation. The presence of major tech companies, venture capital, and top talent in the U.S. makes it the ideal market for establishing strong intellectual property protections.
• Optimal Resource Allocation: By focusing its patenting efforts in the U.S., OpenAI can allocate its resources more effectively. This allows the company to align its patent strategy with its broader business objectives, ensuring that its investments in intellectual property protection are both strategic and impactful. By concentrating on the U.S., OpenAI supports its long-term growth and innovation goals, maximizing the return on its intellectual property investments.

Weighing the Trade-Offs: Limited International Reach

While OpenAI’s U.S.-centric strategy offers clear benefits, it also presents potential risks, particularly as the company looks to expand its influence on a global scale. The limited international patent activity could expose OpenAI to competitive pressures and missed opportunities in key global markets.

• Global Competition and Vulnerability: By not expanding its patent portfolio internationally, OpenAI may leave its innovations vulnerable to competitors in other regions. This could lead to challenges in enforcing IP rights abroad, particularly in markets with less robust patent protections.
• Market Expansion Limitations: As OpenAI considers expanding into new markets, particularly those in Europe and Asia where technological advancements are rapidly accelerating, the absence of a robust international patent portfolio could hinder its ability to compete. Companies like Samsung, which hold extensive patent portfolios across multiple regions, have leveraged their global IP protection to dominate in various markets. OpenAI may face significant challenges if it attempts to enter these markets without similar protections.
• Strategic Adjustments for Future Growth: To mitigate these risks, OpenAI might consider selective international expansion of its patent portfolio or explore partnerships and licensing agreements with international firms. This would allow the company to leverage existing IP protections in new markets without the need for extensive international filings.

In conclusion, OpenAI’s focus on the U.S. market offers significant advantages, accelerating the protection and commercialization of its technologies. However, balancing this strategy with selective international expansion could further strengthen its global presence, safeguarding its innovations and supporting its long-term vision of global leadership in AI technology.

What’s Inside OpenAI’s Patents?

Here is a detailed look at some of OpenAI’s most significant patents, showcasing the breadth and impact of their innovations:

US11886826B1 – Systems and Methods for Language model-based Text Insertion

The technology described in US11886826B1 relates to systems and methods for language model-based text insertion. In simple terms, this system is designed to automatically generate and insert text based on a given input text prompt by leveraging language models to automatically generate and insert text into input prompts, with a focus on optimizing the language model through iterative training cycles based on user feedback and labeled data.

US11983488B1 – Systems and Methods for Language Model-based Text Editing

The technology described in US11983488B1 relates to systems and methods for language model-based text editing. This system involves utilizing a language model to automatically generate and edit text based on user instructions and input prompts. The language model is optimized through iterative cycles of training based on outcome metrics associated with the output text and datasets. The datasets used for training the language model include annotated, labeled, enriched, or demonstration data based on the output text. The system continuously optimizes the language model through training cycles using outcome metrics and various datasets to enhance the text generation and editing capabilities.

US11922144B1 – Schema-based Integration of External APIs with Natural Language Applications

The technology described in US11922144B1 involves a computer-implemented method for integrating a specific external Application Programming Interface (API) with a natural language model user interface. Based on the access to the API, a response message is transmitted to the natural language model user interface. This response message includes the result of the access to the API. The response message is summarized to provide natural language text to the user via the interface. The technology enables users to interact with external services using natural language inputs and receive responses in a user-friendly format.

US11922550B1 – Systems and Methods for Hierarchical Text-conditional Image Generation

The technology described in US11922550B1 involves systems and methods for hierarchical text-conditional image generation. The system comprises at least one memory storing instructions and at least one processor that executes these instructions to generate an image corresponding to a text input. The operations performed by the system include accessing a text description and inputting it into a text encoder to receive a text embedding. This text embedding is then inputted into a first sub-model, which generates a corresponding image embedding based on the text description or the text embedding. The system further inputs either the text description or the corresponding image embedding into a second sub-model, which generates an output image based on this input. The second sub-model includes a first upsampler model and a second upsampler model, which are used for upsampling before generating the output image. Notably, the second upsampler model is trained on images corrupted with blind super resolution (BSR) degradation. It is important to highlight that the second sub-model is different from the first sub-model. Finally, the output image generated by the second sub-model is made accessible to a device. This device can be configured to train an image generation model using the output image or be associated with an image generation request. This technology allows for the generation of high-quality images based on text inputs, utilizing a hierarchical approach involving multiple sub-models.

US11887367B1 – Using Machine Learning to Train and Use a Model to Perform Automatic Interface Actions based on Video and Input Datasets

The technology described in US11887367B1 involves using machine learning to train a model that can perform automated actions based on video and input datasets. This technology enables the training of a machine learning model that can automatically perform actions based on video data, even when that data is initially unlabeled. The use of pseudo-labels and the inverse dynamics model are key components of this approach.

US11983806B1 – Systems and Methods for Image Generation with Machine Learning Models

The technology described in the patent US11983806B1 involves systems and methods for image generation using machine learning models based on text input. The system includes at least one memory storing instructions and at least one processor executing these instructions to perform various operations. The machine learning model generates an enhanced image based on the input image, the masked region, or the text input. This involves replicating pixel values from the input image or the masked image to the enhanced image. The generated image segment is inserted into the enhanced image by replacing the masked region. This technology utilizes machine learning models, specifically a text-to-image model, to enhance images based on text input. It involves generating an enhanced image by replicating pixel values and inserting image segments based on the input provided.

US12039431B1 – Systems and Methods for Interacting with a Multimodal Machine Learning Model

This recently granted patent covers systems and methods for interacting with a pre-trained multimodal machine learning model through a graphical user interface. Users can interact with an image to create a contextual prompt that highlights specific areas of the image. This prompt is then used, along with the image, to generate input data for the model, which tailors the textual response based on the prompt. The system provides the user with a textual response that includes a prompt suggestion and a selectable control in the interface to choose the suggestion. Essentially, this technology enables users to create contextual prompts from images and generates a relevant textual response using the model. It also offers interaction suggestions for the user to select.

US12008341B2 – Systems and Methods for Generating Natural Language using Language Models Trained on Computer Code

This recently granted patent involves generating natural language docstrings from computer code using machine learning. It trains a model to create docstrings that describe code samples. The model generates possible docstrings, selects the one that best represents the code’s purpose, and outputs it with the corresponding code segment. It also accepts code samples, describes them, and creates a template for building other models. This process automates code documentation, making it easier for developers to understand and document their code.

US12051205B1 – Systems and Methods for Interacting with a Large Language Model

The invention relates to systems and methods for interacting with a large language model, specifically a multimodal machine learning model, using both a textual prompt and an image. It starts with providing a graphical user interface linked to the multimodal model. An image is displayed to the user within this interface, and the user submits a textual prompt. The system then generates input data from the image and the prompt, which is processed by the model. The model, configured with prompt engineering, identifies a specific location in the image based on the input. The output includes an indication of this location and highlights it within the graphical user interface by placing a cursor at the identified spot.

US20240249186A1 – Systems and Methods for Using Contrastive Pre-Training to Generate Text and Code Embeddings

This recently published application describes a method for generating semantic similarity using vector representations. It begins by receiving a training dataset from unlabeled data, which includes paired samples representing positive examples. These samples are converted into vectors for numerical processing by machine learning algorithms. Negative example pairs are also included in the dataset for contrastive learning. Both positive and negative pairs are converted into vectors, ensuring comprehensive representation in the vector space. A machine learning model, initialized with pre-trained generative language models, is then trained using contrastive methods. After training, the model can process queries involving natural language inputs to generate semantic similarity results, reflecting the relationships between data units in the embedding space.

OpenAI’s patent portfolio showcases their vision for the future. Each patent is a piece of their larger plan, hinting at innovative AI technologies that will soon impact the market. These patents highlight OpenAI’s strategic thinking and suggest that the future success of businesses will depend on harnessing the transformative power of AI.

The Role of Trade Secrets in OpenAI’s IP Strategy

While the name “OpenAI” suggests a commitment to transparency, the company’s actual approach involves a sophisticated use of trade secrets to protect proprietary information. Trade secrets complement the patent strategy by safeguarding innovations that may not be suitable for patenting or where the company prefers to keep details confidential.

OpenAI’s trade secrets likely cover several critical areas:

• Training Sets and Data Output: The data used to train models and the resulting data outputs are crucial for developing competitive AI technologies. Trade secrets help protect these proprietary datasets and their associated processing techniques.
• Neural Networks: This includes modular network structures and individual modules that contribute to the model’s unique capabilities. Trade secrets can shield these architectural details from competitors.
• Learning Algorithms: Algorithms involved in learning, backpropagation, and other processes are vital for the model’s performance. By keeping these algorithms confidential, OpenAI maintains a competitive edge over other companies in the field.

Why Trade Secrets Matter?

For AI companies like OpenAI, trade secrets are vital for protecting critical technologies such as algorithms, training data, and proprietary processes. Unlike patents, which require full public disclosure, trade secrets allow these innovations to remain confidential indefinitely, as long as secrecy is maintained. This strategy is exemplified by Coca-Cola, which has kept its formula secret for over a century.

Trade secrets offer several key benefits:

• Flexibility in Protection: Trade secrets do not require public disclosure, allowing OpenAI to protect aspects of their technology that are difficult to patent or where public knowledge could erode their competitive edge.
• Cost-Efficiency: Managing trade secrets can be more cost-effective than patenting, particularly when the competitive advantage lies in the implementation rather than the idea itself.
• Long-Term Protection: Unlike patents, which expire after 20 years, trade secrets can be protected indefinitely, providing a long-term strategy for safeguarding valuable technological know-how.

OpenAI’s Battle for Brand Protection: Trademarking “GPT”

OpenAI’s bid to trademark “ChatGPT” and “GPT” encountered resistance from the USPTO, which deemed these terms too generic and descriptive to qualify for exclusive registration. The central issue is whether these terms can be seen as distinctive trademarks or if they simply describe a category of technology.

OpenAI’s Arguments

OpenAI’s case for trademark protection was based on several key points. The organization argued that securing trademarks for “ChatGPT” and “GPT” was vital for preserving their brand identity as the creators of these terms. They aimed to prevent unauthorized use and maintain the distinctiveness of their innovations. Additionally, OpenAI contended that trademarks would help avoid consumer confusion, ensuring that similar names used by other companies would not mislead the public about the origin and quality of AI products. The organization also highlighted the substantial investment made in developing ChatGPT and related technologies, asserting that trademarks were crucial to protecting this investment and preventing competitors from unfairly leveraging their advancements.

USPTO’s Concerns

In contrast, the USPTO’s decision was based on the view that “ChatGPT” and “GPT” are generic terms within the AI sector, representing a broad category of software rather than specific brands. The USPTO’s concerns included the risk of monopolizing essential industry terminology if these terms were trademarked. There was also apprehension that granting these trademarks might inadvertently hinder competition and innovation by restricting other companies from using these well-understood terms to describe their own AI technologies.

Furthermore, approving such trademarks could set a precedent that complicates the trademarking of generic technological terms, leading to a proliferation of claims and potentially stifling industry development.

Despite OpenAI’s attempts to broaden its brand with names like Sora for its text-to-video generation model, “GPT” continues to be strongly associated with OpenAI. This connection is largely due to the widespread recognition of ChatGPT and models such as GPT-3 and GPT-4, which have solidified “GPT” as a term intrinsically linked to OpenAI in the public’s mind.

The USPTO’s decision underscores the complexities of trademarking generic terms in an increasingly crowded field, setting a precedent for how such terms are treated in the future. As generative AI continues to advance, the ongoing interplay between brand protection and industry-wide terminology will likely remain a focal point for both legal and technological discussions.

OpenAI Founders’ and Key Members’ Contributions to Tech Giants

OpenAI’s founders and key members have profoundly impacted technology through their extensive patent portfolios, many of which are assigned to prominent companies beyond OpenAI. Their broad collaboration within the tech industry reflects their significant influence and diverse expertise. Here are some notable contributions:

Samuel Harris Altman: 7 patents — 3, assigned to Flipt, 3 to Green Dot Corporation, and 1 to Loopt.

His inventions reflect his entrepreneurial spirit and innovation across various sectors. His patents highlight advancements in mobile communication and location-aware technologies, including social networking, secure location sharing, and transaction systems. These innovations enhance user experience and security in mobile environments.

Andrej Karpathy: 12 patents — 1, assigned to Google and 11 to Tesla.

His patents cover a wide range of innovative technologies, from machine learning and autonomous driving to data processing. Notable contributions include advancements in generating training data, predicting three-dimensional features, and automating video annotation. These innovations highlight Karpathy’s versatility and deep expertise in pushing the frontiers of AI and autonomous systems.

Ilya Sutskever: 22 patents — 21, assigned to Google and 1 to Microsoft Corporation.

Sutskever’s work spans a broad spectrum of neural network advancements. His patents include improvements in neural network architectures, methods for parallelizing convolutional networks, and techniques for tackling overfitting in RNNs. Additionally, his innovations extend to processing and classifying image and text data, generating target phoneme sequences from speech, and addressing advanced concepts like neural random-access machines. Sutskever’s collaborative efforts with Wojciech Zaremba on 2 patents assigned to Google further emphasize his extensive contributions to both theoretical and practical aspects of machine learning.

Wojciech Zaremba: 6 patents — 4, assigned to Google, 1 to the National University of Ireland, and 1 to Meta.

His diverse patent portfolio underscores his broad expertise and collaborative work in machine learning technologies. Key areas include advancements in neural networks, natural language processing, user authentication, and machine translation, reflecting his impact on developing more powerful and versatile AI systems.

Pamela Shen Vagata: 3 patents, assigned to Meta.

His patents focus on social media and related technologies. Her patents highlight her expertise in developing tools and interfaces that enhance the usability, efficiency, and effectiveness of machine learning systems, playing a crucial role in advancing the capabilities and applications of these technologies.

Trevor Blackwell: 7 patents, assigned to Anybots.

Blackwell’s contributions to robotics are notable. His patents include innovations in self-balancing capabilities, control mechanisms, and design elements for various robotic systems. These advancements span both functional and aesthetic improvements, significantly impacting the field of robotics.

Diederik Kingma: 2 patents, assigned to Google.

His patents emphasize his work in enhancing the efficiency and effectiveness of diffusion models. His contributions are crucial for developing scalable and high-performing AI systems, addressing computational challenges and advancing the field.

Greg D. Brockman: 1 patent, assigned to Stripe.

It addresses network communication protocols, particularly in transactions involving multiple origins. This patent aims to improve transaction efficiency and security in networked environments.

In total, OpenAI’s key members hold 58 patents outside of the organization, demonstrating their extensive contributions to the tech industry. This diverse portfolio highlights their broad impact and collaborative efforts in advancing technology across multiple domains.

Summation

OpenAI’s patent strategy reveals a thoughtful balance between speed and strategic focus, positioning the organization as a leader in the rapidly evolving AI landscape. By leveraging expedited patent approvals and concentrating efforts on critical U.S. innovations, OpenAI is effectively safeguarding its breakthroughs while accelerating their market introduction. However, the company’s limited international patent activity presents potential challenges as it seeks to expand its global influence. To enhance its competitive edge further, OpenAI may need to consider selective international expansion and strategic partnerships. As the AI sector continues to advance, OpenAI’s approach to intellectual property will play a pivotal role in shaping its ongoing success and leadership in the field.

Authored by: Vaibhav Henry, CGO, Sagacious IP and Mitthatmeer Kaur, Assistant Manager (Content Creation and Strategy), Sagacious IP. Views expressed are authors own.

Originally published on – World IP Review (WIPR) OpenAI’s unconventional IP strategy for ChatGPT