Case 7:26-mc-00318-LS Document 6-2 Filed 08/18/26 Page 1 of 95 EXHIBIT 1 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 2 of1 95 of 94 UNITED STATES DISTRICT COURT FOR THE WESTERN DISTRICT OF TEXAS MIDLAND-ODESSA DIVISION NEURAL AI, LLC ) ) ) Plaintiff, ) v. ) Civil Action No. 7:24-cv-00221 ) NVIDIA CORPORATION ) ) JURY TRIAL DEMANDED ) Defendant. ) ) FIRST AMENDED COMPLAINT FOR PATENT INFRINGEMENT Neural AI, LLC (“Neural AI” or “Plaintiff”) alleges against Defendant Nvidia Corporation (“Nvidia” or “Defendant”) the following: 1. This case involves patented technologies that revolutionized, and have become widely adopted in, the field of graphical processor unit (“GPU”)-accelerated computing for artificial intelligence, machine learning, and complex numerical simulations. GPU-accelerated computing powers many of the most advanced and powerful forms of artificial intelligence that have exploded over the past decade. 2. Highly complex numerical simulations, such as the prediction of protein chains, genetic sequences and cryptographic sequences, and advanced machine learning techniques such as deep learning neural networks, require hardware capable of a high degree of parallel processing for efficient computation. GPUs, which generally have hundreds to thousands more computational processors or “cores” than central processing units (“CPUs”), are the preferred hardware for executing such simulations and machine learning techniques. Indeed, the parallel operation of thousands of high-performance GPUs have become a basic necessity for the execution and training 1 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 3 of2 95 of 94 of complex natural language and image-generation models, such as ChatGPT’s GPT-4 and Sora.AI. (See https://www.fierceelectronics.com/sensors/chatgpt-runs-10k-nvidia-training-gpus- potential-thousands-more.) 3. Before Plaintiff’s innovations, the conventional wisdom in the field of GPU- accelerated computing was that the exchange of intermediate outputs between a GPU and a CPU was too computationally expensive. This was so because the GPU, adapted for highly parallel processing tasks (e.g., graphically modeling a physics engine or rendering complex moving images), was ill-suited for handling operations better left to the CPU, like interacting with a user’s mouse and keyboard or sending and receiving simple datasets. Plaintiff’s foundational technology changed this by inventing techniques that leveraged the unique advantages of both the CPU and the GPU to enable their efficient interplay in hardware-accelerated computing. 4. Plaintiff’s patented technologies are enshrined in U.S. Patent Nos. 8,648,867 (“the ’867 Patent”), RE49,461 (“the ’461 Patent”), and RE48,438 (“the ’438 Patent”) (collectively, “the Asserted Patents” or “The GPU-Based Acceleration Patents”). NATURE OF THE CASE 5. Plaintiff brings claims under the patent laws of the United States, 35 U.S.C. § 1, et seq., for infringement of the Asserted Patents. Defendant has infringed and continues to infringe each of the Asserted Patents under at least 35 U.S.C. §§271(a), 271(b) and 271(c). THE PARTIES 6. Plaintiff Neural AI, LLC, is the owner by assignment of each of the Asserted Patents. 7. The technology of the Asserted Patents underpins multiple artificial intelligence and accelerated computing products that incorporate the patented technology, such as Neurala, 2 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 4 of3 95 of 94 Inc.’s Vision Inspection Automation (VIA), Vision AI software, and Brain Builder platform. 8. Neural AI is a Texas limited liability company and is a registered business in Texas. Neural AI maintains its principal office in this District, at 510 Austin Avenue, Suite 2554, Waco, TX 76701. 9. Defendant Nvidia Corporation is a Delaware corporation with its headquarters and principal place of business in Santa Clara, California. (See https://investor.nvidia.com/financial- info/sec-filings/sec-filings-details/default.aspx?FilingId=17293267, U.S. Securities and Exchange Commission Form 10-K for Fiscal Year Ended January 28, 2024; https://nvidianews.nvidia.com/multimedia/santa-clara-headquarters.) Defendant Nvidia Corporation is registered with the Secretary of State to conduct business in Texas. Nvidia has an office in this District located in Austin, Texas. (See https://www.nvidia.com/en-us/contac.) JURISDICTION & VENUE 10. This action arises under the Patent Laws of the United States, 35 U.S.C. § 1, et seq. The Court has subject matter jurisdiction pursuant to 28 U.S.C. §§ 1331 and 1338(a). 11. This Court has personal jurisdiction over Defendant because it regularly conducts business in the State of Texas and in this District. This business includes operating systems, using and/or providing computer hardware, software, firmware, and platforms, and/or providing services and/or engaging in activities in Texas and in this District that infringe one or more claims of the Asserted Patents, as well as inducing and contributing to the direct infringement of others through acts in this District. 12. Nvidia has also, directly and through its extensive network of partnerships, including with local IT service providers, purposefully and voluntarily placed products and/or provided services that practice and/or implement the methods, systems, and apparatuses claimed 3 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 5 of4 95 of 94 in the Asserted Patents into the stream of commerce with the intention and expectation that they will be purchased and used by customers in this District, as detailed below. (See https://www.nvidia.com/en-us/about-nvidia/partners/.) 13. Defendant has also acknowledged that this Court has personal jurisdiction over it in cases filed against it in this District. (See, e.g., Vantage Micro LLC v. NVIDIA Corporation, Case No. 6:19-cv-00582-RP, ECF 22 (W.D. Tex., Jan. 4, 2020) (admitting to personal jurisdiction); Ocean Semiconductor LLC v. NVIDIA Corporation, Case No. 6:20-cv-01211-ADA, ECF 14 (W.D. Tex., Mar. 12, 2021) (same).) Defendant has admitted “it is subject to this Court’s general personal jurisdiction.” (Id.) 14. Venue is proper in this District pursuant to 28 U.S.C. §§ 1391(b) and (c) and 28 U.S.C. § 1400(b) because Defendant Nvidia Corporation has regular and systematic contacts within this District and has committed acts of infringement within this District. 15. Defendant Nvidia Corporation is a registered business in Texas and has regular and established places of business in this District. Nvidia has an office in this District located at 11001 Lakeline Blvd, Suite 100 Bldg. 2, Austin, Texas 78717. (See https://craft.co/nvidia.) Nvidia’s Austin office has “54,000 SF of new shell office and DVS labs” and “35,000 SF of offices, testing and software labs.” (See https://kiddgrp.com/project/nvidia-corporation/.) 16. Defendant Nvidia Corporation has hundreds of employees in this District— including positions in engineering, sales, marketing, and finance. LinkedIn lists approximately 792 persons associated with Nvidia and identified as being located in the Austin or Austin metropolitan area. (See https://www.linkedin.com/company/nvidia/people/?facetGeoRegion=104472865%2C90000064.) LinkedIn also lists approximately 1,158 persons associated with Nvidia and identified as being 4 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 6 of5 95 of 94 located in the State of Texas. (See id.) 17. In addition, Defendant Nvidia Corporation has over 100 jobs posted for the State of Texas on its affiliated Workday page with approximately 93 of those jobs—the vast majority of which are engineering jobs—listed for Austin, Texas. (See https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite?locations=91336993fab910 af6d702939a7fcc2d9&locations=91336993fab910af6d702b631b94c2de (approximately 111 Nvidia job postings for Texas).) These jobs are particularly relevant to the Asserted Patents and Accused Products, as defined below, because they pertain to artificial intelligence, machine learning, deep learning, data centers, accelerated computing, high performance computing (“HPC”), and related hardware, software, and/or firmware—including Nvidia’s GPUs, CPUs, systems-on-a-chip (“SoCs”), platforms, and application programming interfaces. 18. Nvidia’s operations in this District include client outreach and sales for each of the Accused Products and related or supporting services. As detailed above, Nvidia has customer- facing personnel and operations in this District. Nvidia also provides technical support to partners and customers for its products in the District. 19. Nvidia has committed acts of infringement within this District. Nvidia uses the Accused Products in this District in manners that practice the Asserted Patents, including by testing the Accused Products and by using the Accused Products at its offices and premises in this District. 20. Defendant makes, uses, advertises, offers for sale, and/or sells hardware for accelerated computing, including GPUs, CPUs, and SoCs; computers for accelerated computing (e.g., supercomputers, servers, and data centers for high performance computing); and computer platform software-as-a-service (“SaaS”) that implements accelerated computing (including the Accused Products) in the State of Texas and in this District directly and/or through its partnerships 5 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 7 of6 95 of 94 with businesses in the State of Texas and in this District. Defendant also provides data center and HPC services that practice the Asserted Patents in the State of Texas and in this District directly and/or through its partnerships with businesses in the State of Texas and in this District. 21. Nvidia sells, offers for sale, advertises, makes, installs, and/or otherwise provides hardware, software, firmware, and/or computer platforms for accelerated computing and data center and HPC services, including the Accused Products, the use of which infringes the Asserted Patents in this District and the State of Texas. (See https://www.nvidia.com/en-us/data- center/solutions/accelerated-computing/.) Nvidia performs these acts directly and/or through its partnerships with other entities. (See id. (“NVIDIA has defined a range of accelerated platforms that each consist of hardware systems designed according to the needs of the use case as well as the software stack that enables the operation and management of the business applications. These hardware systems and software are available from NVIDIA and our partners.”).) 22. Nvidia also uses a network of partners, which comprise re-sellers, managed service providers, and product and solution experts, to provide the Accused Products and implementation services for the Accused Products to customers in this District. Each of these partners sells, offers for sale, installs, and/or implements Nvidia’s accelerated computing hardware, software, and/or computer platform services. (See https://www.nvidia.com/en-us/about-nvidia/partners/.) 23. Nvidia’s partners include “Data Center Provider[s].” (See https://www.nvidia.com/en-us/about-nvidia/partners/.) Nvidia’s Data Center Provider partners “offer colocation services such as high-density data center facilities, interconnected infrastructure, and state-of-art cooling technologies for hosting NVIDIA DGX™ servers globally.” (See id.) Nvidia’s Data Center Provider partners in the “NVIDIA DGX-Ready Data Center program, built on the NVIDIA DGX™ platform and delivered by NVIDIA partners,” help “accelerate the scaling 6 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 8 of7 95 of 94 of AI across [a customer’s] organization.” (See https://www.nvidia.com/en-us/data- center/colocation-partners/#aligned-energy.) 24. As further detailed below, Nvidia engages in activities that directly infringe the Asserted Patents within this District. For example, Nvidia’s operation and use of its accelerated computing hardware, software, and/or computer platform services, including its data center-scale accelerated computing platforms, within this District infringe the Asserted Patents. 25. Nvidia also infringes (directly or indirectly) the Asserted Patents by providing services in connection with the Accused Products including installing, maintaining, supporting, operating, providing instructions, and/or advertising Nvidia’s computer platform, data center, and HPC services within this District. For example, under Nvidia’s cloud and data center line of products and services, the Nvidia DGX platform is a “a fully integrated hardware and software AI platform” and “combines the best of NVIDIA software, infrastructure, and expertise in a modern, unified AI development solution.” (See https://www.nvidia.com/en-us/data-center/dgx-platform/.) Indeed, “DGX infrastructure is a complete AI solution, and includes NVIDIA AI Enterprise software to accelerate data science pipelines and streamline development and deployment of production-grade AI applications.” (See id.) Nvidia platform user and partner customers infringe the Asserted Patents by installing and operating Nvidia’s computer platform software, which performs the claimed methods in the Asserted Patents within this District. (See also, e.g., https://www.nvidia.com/en-us/data-center/products/ai-enterprise/ (Nvidia AI Enterprise); https://developer.nvidia.com/cuda-zone (Nvidia CUDA Toolkit); https://www.nvidia.com/en- us/data-center/gpu-cloud-computing/ (GPU Cloud Computing).) 26. Defendant encourages and induces its customers of the Accused Products to perform the methods claimed in the Asserted Patents. For example, Nvidia makes its accelerated 7 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 9 of8 95 of 94 computing platforms and services available on its website, widely advertises those platforms and services, provides applications that allow partners and users to access those platforms and services, provides instructions for installing, and maintaining those platforms and services and supporting software and/or firmware, and provides technical support to users. (See https://www.nvidia.com/en-us/data-center/dgx-support/.) 27. Nvidia further encourages and induces its customers to operate Nvidia’s hardware and software in an infringing manner, and to use Nvidia’s infringing computer platforms, by providing directions for and encouraging customers to install software, such as software for NVIDIA AI Enterprise and CUDA, (see https://docs.nvidia.com/ai-enterprise/deployment-guide- vmware/0.1.0/software.html; https://developer.nvidia.com/cuda-downloads), which offers evaluation, installation, configuration, customization, and development of Nvidia’s infringing software products and services. 28. Defendant also contributes to the infringement of its customers and end users of the Accused Products by offering within the United States or importing into the United States the Accused Products, which are for use in practicing, and under normal operation practice, one or more of the methods claimed in the Asserted Patents, constituting a material part of the inventions claimed, and not a staple article or commodity of commerce suitable for substantial non-infringing uses. Indeed, as shown herein, the Accused Products and the example functionality described below have no substantial non-infringing uses and are specifically designed to practice the methods claimed in the Asserted Patents. 29. On information and belief, Defendant has not disputed that venue is proper in this District in cases filed against it in this District. (See, e.g., Vantage Micro LLC v. NVIDIA Corp., No. 6:19-cv-00582, ECF 22; Polaris Innovations Ltd. v. Dell Inc. et al., No. 5:16-cv-00451, ECF 8 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30Filed Filed 08/18/26 12/12/24Page Page 10 of 9 of 9594 19; Cirrus Logic, Inc. v. ATI Techs., et al., No. 1:03-cv-00302, ECF 6.) 30. Defendant’s infringement adversely impacts Plaintiff in this District. PLAINTIFF’S PATENTED INNOVATIONS 31. The Asserted Patents pioneered the adaptation of GPU-acceleration technology to the supervised execution of complex artificial intelligence algorithms and numerical simulations, such that it became possible for the first time to dynamically supervise, review, and correct intermediate “solutions” that were produced by these accelerated algorithms and simulations without performance loss. The GPU-Based Acceleration Patents U.S. Patent Nos. 8,648,867, RE49,461, and RE48,438 32. The ’867, ’461, and ’438 Patents are part of the same patent family and generally disclose and claim systems and methods related to the accelerated execution of numerical simulations and neural networks such that the intermediate outputs of a given execution “step” can be dynamically transferred from the GPU to the CPU, reviewed, and corrected within the same computational cycle before being fed as inputs to the next execution step. 33. The ’867 Patent is entitled “Graphic Processor Based Accelerator System and Method,” was filed on September 24, 2007, and was duly and legally issued by the United States Patent and Trademark Office (“USPTO”) on February 11, 2014. The ’867 Patent claims priority to Provisional Application No. 60/826,892, filed on September 25, 2006. A true and correct copy of the ’867 Patent is attached as Exhibit 1. 34. The ’438 Patent is entitled “Graphic Processor Based Accelerator System and Method,” was filed on November 9, 2017, and was duly and legally issued by the USPTO on February 16, 2021. The ’438 Patent is a re-issue of the ’867 Patent and claims priority to Provisional Application No. 60/826,892, filed on September 25, 2006. A true and correct copy of 9 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 11 10 of 95 of 94 the ’438 Patent is attached as Exhibit 2. 35. The ’461 Patent is also entitled “Graphic Processor Based Accelerator System and Method,” was filed on December 29, 2020, and was duly and legally issued by the USPTO on March 14, 2023. The ’461 Patent is a re-issue of the ’867 Patent and claims priority to Provisional Application No. 60/826,892, filed on September 25, 2006. A true and correct copy of the ’461 Patent is attached as Exhibit 3. 36. The ’867 Patent improves upon prior GPU acceleration technology by disclosing and claiming a novel hardware and firmware system for performing a numerical simulation that permits dynamic editing of the outputs that flow from intermediate “steps” of that simulation, before they become inputs to the next “step.” In particular, the ’867 patent discloses a CPU tethered to a GPU-based accelerator, each with their own corresponding memories, and an accelerator “controller” that coordinates transfers of data between the CPU and the GPU-based accelerator, such that the intermediate results from one step can be transferred from the GPU-based accelerator to the CPU, reviewed and corrected by the CPU, and transferred back to the GPU-based accelerator before the next computational cycle begins. 37. The ’867 Patent explains that performing the numerical computation in this stepwise fashion enables the system to eliminate “race conditions,” i.e., conflicts that occur when two programmatic “threads” attempt to change the same shared data at the same time, which would otherwise occur when other system elements attempt to access intermediate outputs of the numerical computation. (See ’867 Patent, 5:60-6:31.) This avoids the computational overhead prevalent in conventional GPU-based accelerator architectures when transferring data from the accelerator to the CPU. 10 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 12 11 of 95 of 94 38. By enabling such “controller-driven data exchange” between the GPU-based accelerator and the CPU, the system described in the ’867 Patent allows for an “input parser” executing on a CPU core to “change input…on the fly during the simulation,” thus enabling automatic review and dynamic error correction of the numerical simulations or neural networks that are being executed on the claimed system. (See id., 9:8-17.) Such dynamic, in-execution review and error correction of whether each intermediate “step” of a simulation or neural network is generating correct results is essential to the performance and reliability of large language models, image classification, and image generation models that have become prevalent today. Because of the scale to which such simulations and models have grown, it is no longer feasible to “restart” them from scratch, only to correct them as they execute. 39. The ’461 and ’438 Patents disclose hardware and firmware configurations similar to those of the ’867 Patent, but are directed to using those configurations to process the layers of an artificial neural network (“ANN”). The ’461 Patent is directed to further interplay between the CPU and the GPU-based accelerator: separating the CPU and GPU-based accelerator into separate “streams,” whereby the CPU executes a “user interaction stream” (e.g., enabling the parsing and dynamic editing of intermediate outputs, or for the ANN to be paused and resumed), while the accelerator executes a “computational stream” that executes the layers of the artificial neural network. When the ANN is initialized, control over the generation of outputs shifts to the computational stream. However, once a pre-defined layer of the ANN has completed execution, or is interrupted, control over the generation of outputs and feeding of inputs is shifted back to the CPU’s user interaction stream. 40. The Asserted Patents describe this “shift of priorities” as “[t]he crucial feature of the interaction between the User Interaction Stream and the Computational Stream.” (’867 Patent, 11 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 13 12 of 95 of 94 7:45-47.) Even though the computational stream is in control during the ANN computation, priority shifting enables “[t]he user [to] retain[] the ability to interrupt the simulation, change the input, or to change the display properties of the framework” because the user’s “interactions are queued to be performed at times determined by the controller-driven data exchange to avoid corruption of the data.” (Id., 8:47-57.) ACCUSED PRODUCTS 41. Nvidia offers, sells, and uses several products that provide and implement GPU- acceleration hardware, software, platforms, and services for individuals and enterprises and incorporate Plaintiff’s patented technologies. (See https://www.nvidia.com/en- us/solutions/ai/inference/; https://marketplace.nvidia.com/en-us/data-center/?page=4; https://marketplace.nvidia.com/en-us/laptops-workstations/?page=9; https://marketplace.nvidia.com/en-us/software/?page=3.) 42. The Accused Products include Nvidia’s GPU accelerators and superchips. (See https://resources.nvidia.com/l/en-us-gpu.) Nvidia’s GPU accelerators include Nvidia’s GPUs with Nvidia’s “Hopper,” “Ada Lovelace,” “Ampere,” “Turing,” “Volta,” “Pascal,” and “Maxwell” GPU architectures. (See https://docs.nvidia.com/deeplearning/cudnn/archives/cudnn-896/support- matrix/index.html.) These GPUs are specifically designed to run and implement GPU-based hardware acceleration using Nvidia’s proprietary CUDA (Compute Unified Device Architecture) platform and CUDA libraries for GPU acceleration. (See id. (Nvidia GPU architectures implementing Nvidia’s cuDNN (CUDA Deep Neural Network) library for GPU acceleration.); https://developer.nvidia.com/cuda-gpus.) 43. Nvidia’s Hopper GPUs include the H100 and H200 GPUs. (See https://www.nvidia.com/en-us/data-center/technologies/hopper-architecture/ (Hopper 12 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 14 13 of 95 of 94 architecture); https://www.nvidia.com/en-us/data-center/h100/ (H100); https://www.nvidia.com/en-us/data-center/h200/ (H200).) In addition, Nvidia’s superchips that implement GPU accelerators include the GH200, or Grace Hopper Superchip, which implements the Hopper-GPU architecture. (See https://www.nvidia.com/en-us/data-center/grace-hopper- superchip/ (GH200).) 44. Nvidia’s Ada Lovelace (or Lovelace) GPUs include Nvidia Data Center GPUs, including L40, L40S, and L4 GPUs; Nvidia Workstation and Professional Laptop GPUs, including RTX Ada Generations series GPUs and Laptop GPUs; and GeForce RTX 40 series GPUs and Laptop GPUs. (See https://www.nvidia.com/en-us/technologies/ada-architecture/ (Ada Lovelace architecture). See https://www.nvidia.com/en-us/data-center/l40/ (L40); https://www.nvidia.com/en-us/data-center/l40s/ (L40S); https://www.nvidia.com/en-us/data- center/l4/ (L4). See https://resources.nvidia.com/en-us-design-viz-stories-ep/l40-linecard (Nvidia Professional GPUs); https://www.nvidia.com/en-us/ai-on-rtx/ (RTX GPUs featuring “Accelerated Development”); https://www.nvidia.com/en-us/design-visualization/desktop-graphics/ (RTX Ada Generation GPUs); https://www.nvidia.com/en-us/design-visualization/rtx-professional- laptops/compare-table/ (RTX Ada Generation Laptop GPUs). See https://www.nvidia.com/en- us/geforce/graphics-cards/40-series/ (GeForce RTX 40 GPUs); https://www.nvidia.com/en- us/geforce/graphics-cards/compare/ (GeForce RTX 40 GPUs); https://www.nvidia.com/en- us/geforce/laptops/compare/ (GeForce RTX 40 Laptop GPUs).) 45. Nvidia’s Ampere GPUs include Nvidia Data Center GPUs, including A100, A40, A30, A16, A10, and A2 GPUs; Nvidia Workstation and Professional Laptop GPUs, including RTX A series GPUs and Laptop GPUs; GeForce RTX 30 series GPUs and Laptop GPUs; and GeForce MX570 Laptop GPU. (See https://www.nvidia.com/en-us/data-center/ampere- 13 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 15 14 of 95 of 94 architecture/ (Ampere architecture). See https://www.nvidia.com/en-us/data-center/a100/ (A100); https://www.nvidia.com/en-us/data-center/a40/ (A40); https://www.nvidia.com/en-us/data- center/a30/ (A30); https://www.nvidia.com/en-us/data-center/a16/ (A16); https://www.nvidia.com/en-us/data-center/a10/ (A10); https://www.nvidia.com/en-us/data- center/a2/ (A2). See https://www.nvidia.com/en-us/design-visualization/desktop-graphics/ (RTX A GPUs); https://www.nvidia.com/en-us/design-visualization/rtx-professional-laptops/compare- table/ (RTX A Laptop GPUs). See https://www.nvidia.com/en-us/geforce/graphics-cards/30- series/ (GeForce RTX 30 GPUs); https://www.nvidia.com/en-us/geforce/graphics-cards/compare/ (GeForce RTX 30 GPUs); https://www.nvidia.com/en-us/geforce/laptops/compare/30-series/ (GeForce RTX 30 Laptop GPUs); https://www.nvidia.com/en-us/geforce/gaming-laptops/mx- 570/ (GeForce MX570 Laptop GPU).) 46. Nvidia’s Turing GPUs include Nvidia Data Center GPUs, including Tesla T4 GPUs and Quadro RTX 8000 (passive) and Quadro RTX 6000 (passive) GPUs; Nvidia Workstation and Professional Laptop GPUs, including T series GPUs and Laptop GPUs, Quadro T series Laptop GPUs, and Quadro RTX series GPUs and Laptop GPUs; Titan series Titan RTX GPU; GeForce RTX 20 series and GeForce GTX 16 series GPUs and Laptop GPUs; and GeForce MX550, MX450, and MX430 Laptop GPUs. (See https://www.nvidia.com/en-us/geforce/turing/ (Turing architecture). See https://www.nvidia.com/en-us/data-center/tesla-t4/ (Tesla T4); https://www.nvidia.com/en-gb/design-visualization/quadro-data-center/ (Quadro RTX 8000 (passive) and Quadro RTX 6000 (passive). See https://www.nvidia.com/en-us/design- visualization/quadro/ (T series GPUs/Laptop GPUs, Quadro T series Laptop GPUs, and Quadro RTX GPUs/Laptop GPUs); https://www.nvidia.com/en-us/design-visualization/desktop-graphics (T series GPUs/Laptop GPUs); https://www.nvidia.com/content/dam/en- 14 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 16 15 of 95 of 94 zz/Solutions/titan/documents/titan-rtx-for-creators-us-nvidia-1011126-r6-web.pdf (Titan RTX); https://www.nvidia.com/en-us/geforce/20-series/ (GeForce RTX 20 GPUs); https://www.nvidia.com/en-us/geforce/graphics-cards/compare/ (GeForce RTX 20 GPUs and GeForce GTX 16 GPUs); https://www.nvidia.com/en-us/geforce/gaming-laptops/compare-20- series/ (GeForce RTX 20 Laptop GPUs); https://www.nvidia.com/en-us/geforce/gaming- laptops/compare-16-series/ (GeForce GTX 16 Laptop GPUs); https://www.nvidia.com/en- us/geforce/gaming-laptops/mx-550/ (GeForce MX550 Laptop GPU); https://www.nvidia.com/en- us/geforce/gaming-laptops/mx-450/ (GeForce MX450 Laptop GPU); https://wccftech.com/nvidia-geforce-mx450-turing-discrete-notebook-gpu-gddr6-pcie-4/ (GeForce M Laptop GPUs).) 47. Nvidia’s Volta GPUs include Nvidia Data Center GPUs, including the Tesla V100 GPU; Nvidia Workstation GPUs, including Quadro GV100; and Titan series Titan V GPU. (See https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/ (Volta architecture); https://www.nvidia.com/en-us/data-center/v100/ (Tesla V100); https://www.nvidia.com/content/dam/en-zz/Solutions/design- visualization/productspage/quadro/quadro-desktop/quadro-volta-gv100-data-sheet-us-nvidia- 704619-r3-web.pdf (Quadro GV100); https://nvidianews.nvidia.com/news/nvidia-titan-v- transforms-the-pc-into-ai-supercomputer (Titan V).) 48. Nvidia’s Pascal GPUs include Nvidia Data Center GPUs, including Tesla P100, P40, and P4 GPUs; Nvidia Workstation and Professional Laptop GPUs, including the Quadro GP100 GPU and Quadro P series GPUs and Laptop GPUs; Titan series Titan Xp and Titan X GPUs; GeForce GTX 10 series GPUs and Laptop GPUs; and GeForce MX300 series, MX200 series, and MX150 Laptop GPUs. (See https://developer.nvidia.com/pascal; 15 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 17 16 of 95 of 94 https://www.nvidia.com/en-us/data-center/pascal-gpu-architecture/ (Pascal architecture). See https://www.nvidia.com/en-us/data-center/tesla-p100 (Tesla P100); https://developer.nvidia.com/cuda-gpus (Tesla P40 and P4); https://www.nvidia.com/content/dam/en-zz/Solutions/design- visualization/productspage/quadro/quadro-desktop/quadro-pascal-gp100-data-sheet-us-nv- 704562-r1.pdf (Quadro GP100); https://www.nvidia.com/en-us/design-visualization/quadro/ (Quadro P series GPUs/Laptop GPUs). See https://www.nvidia.com/content/geforce- gtx/NVIDIA_TITAN_X_USER_GUIDE_v02.pdf (Titan X); https://www.nvidia.com/content/geforce-gtx/NVIDIA_TITAN_Xp_USER_GUIDE_v02.pdf (Titan Xp); https://www.nvidia.com/en-us/geforce/10-series/ (GeForce GTX 10); https://www.nvidia.com/en-us/geforce/graphics-cards/compare/ (GeForce GTX 10 GPUs); https://www.nvidia.com/en-us/geforce/news/gfecnt/nvidia-geforce-gtx-10-series-laptops/ (GeForce GTX 10 Laptop GPUs); https://www.nvidia.com/en-us/geforce/gaming-laptops/mx- 350/ (GeForce MX350 Laptop GPU); https://www.nvidia.com/en-us/geforce/gaming-laptops/mx- 330/ (GeForce MX330 Laptop GPU); https://wccftech.com/nvidia-geforce-mx450-turing- discrete-notebook-gpu-gddr6-pcie-4/ (GeForce M Laptop GPUs).) 49. Nvidia’s Maxwell GPUs include Nvidia Data Center GPUs, including Tesla M60, M40, and M10 GPUs; Nvidia Workstation and Professional Laptop GPUs, including Quadro M series GPUs and Laptop GPUs, the NVS 810 GPU, and Tesla M6 series Laptop GPUs; Titan series GTX Titan X GPU; GeForce GTX 900 series and GeForce GTX 700 series GPUs and Laptop GPUs; and GeForce MX130 series and MX110 Laptop GPUs. (See https://developer.nvidia.com/blog/maxwell-most-advanced-cuda-gpu-ever-made/ (Maxwell architecture); https://www.nvidia.com/content/dam/en-zz/Solutions/design- 16 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 18 17 of 95 of 94 visualization/solutions/resources/documents1/nvidia-m60-datasheet.pdf (M60); https://images.nvidia.com/content/tesla/pdf/78071_Tesla_M40_24GB_Print_Datasheet_LR.PDF (M40); https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/tesla- m10/pdf/188359-Tesla-M10-DS-NV-Aug19-A4-fnl-Web.pdf (M10); https://www.nvidia.com/en-us/design-visualization/quadro/ (Quadro M GPUs/Laptop GPUs); https://www.nvidia.com/docs/IO/146527/nvs-810-datasheet.pdf (NVS 810); https://images.nvidia.com/content/tesla/pdf/188300-Tesla-M6-DS-Aug19-A4-fnl-Web.pdf (Tesla M6); https://www.nvidia.com/content/geforce-gtx/GTX_TITAN_X_User_Guide.pdf (GTX Titan X); https://developer.nvidia.com/maxwell-compute-architecture (GeForce GTX 900 and 700 GPUs/Laptop GPUs); https://wccftech.com/nvidia-geforce-mx450-turing-discrete-notebook-gpu- gddr6-pcie-4/ (GeForce M Laptop GPUs).) 50. These GPUs and superchips implement, and are specifically designed for, GPU- acceleration for artificial intelligence and neural networks. Nvidia’s proprietary CUDA platform for parallel computing, which includes GPU-acceleration libraries such as cuDNN (CUDA Deep Neural Network), is implemented in the Nvidia Hopper, Ada Lovelace, Ampere, Turing, Volta, Pascal, and Maxwell GPU architectures. 17 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 19 18 of 95 of 94 (See https://docs.nvidia.com/deeplearning/cudnn/archives/cudnn-896/support-matrix/index.html (emphasis added).) 51. The Accused Products further include Nvidia’s supercomputers and servers that implement its GPU accelerators and superchips. These supercomputers and servers include: the EGX line of servers for data centers and edge devices, the HGX line of supercomputers, the DGX line of supercomputers, and the OVX line of supercomputers. (See https://www.nvidia.com/en- us/data-center/solutions/accelerated-computing/.) 52. Nvidia’s “EGX hardware portfolio” includes “accelerators [that] combine the performance of NVIDIA Ampere GPUs.” (See https://www.nvidia.com/en-us/data- center/products/egx/; see https://www.nvidia.com/en-us/design-visualization/egx-graphics/.) Nvidia’s HGX “AI supercomputing platform brings together the full power of NVIDIA GPUs, NVIDIA NVLink™, NVIDIA networking, and fully optimized AI and high-performance computing (HPC) software stacks.” (See https://www.nvidia.com/en-us/data-center/hgx/; https://nvdam.widen.net/s/5kgbjq2v2t/hpc-hgx-h100-datasheet-nvidia-web.) One example 18 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 20 19 of 95 of 94 configuration includes “four or eight H200 or H100 GPUs.” (See id.; see https://nvdam.widen.net/s/5kgbjq2v2t/hpc-hgx-h100-datasheet-nvidia-web.) Nvidia’s DGX supercomputers include the DGX H200, DGX BasePOD, and DGX SuperPOD with DGX GB200. (See https://www.nvidia.com/en-us/data-center/dgx-platform/; see also https://www.nvidia.com/en-us/data-center/base-command/; https://resources.nvidia.com/en-us- dgx-software/nvidia-base-command (DGX Base Command operating system for DGX data centers.) And Nvidia’s OVX supercomputers implement “L40S GPUs . . . for both complex AI and graphics-intensive workloads.” (See https://www.nvidia.com/en-us/data-center/products/ovx/; see https://resources.nvidia.com/en-us-ovx/ovx-datasheet.) 53. The Accused Products further include Nvidia’s software, platforms, and services for accelerated computing. These include CUDA, Nvidia AI Enterprise, the DGX Platform, Nvidia Omniverse, Nvidia Drive, Nvidia Isaac Sim, and Nvidia NGC. 54. CUDA is Nvidia’s proprietary “parallel computing platform and programming model.” (See https://developer.nvidia.com/cuda-zone.) CUDA is designed to support Nvidia’s GPU accelerators and superchips and includes software specifically for GPU-acceleration such as the cuDNN “GPU-accelerated library.” (See id.; https://developer.nvidia.com/cudnn.) In addition, Nvidia’s CUDA-X, built on top of CUDA, is a collection of “GPU-accelerated microservices and libraries for AI.” (See https://www.nvidia.com/en-us/technologies/cuda-x/.) Nvidia also offers the CUDA Toolkit and SDK Manager for developing GPU-accelerated applications. (See https://developer.nvidia.com/cuda-toolkit; https://developer.nvidia.com/sdk-manager.) 55. In addition, Nvidia AI Enterprise is Nvidia’s “end-to-end, cloud-native software platform” for “accelerat[ing] data science pipelines . . . and other generative AI applications.” (See https://www.nvidia.com/en-us/data-center/products/ai-enterprise/.) It is Nvidia’s “‘operating 19 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 21 20 of 95 of 94 system’ for enterprise AI.” (See id.) 56. In addition, Nvidia’s DGX platform is “is a complete AI solution, and includes NVIDIA AI Enterprise software.” (See https://www.nvidia.com/en-us/data-center/dgx-platform/.) Nvidia DGX Cloud is “an AI-training-as-a-service platform which includes cloud-based infrastructure and software for AI, customizable pretrained AI models, and access to NVIDIA experts.” (See https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3- 8dcc-b429fc06c1a4.pdf, Nvidia U.S. Securities and Exchange Commission Form 10-K for Fiscal Year Ended January 28, 2024 at 6.) 57. In addition, Nvidia Omniverse is “a development platform and operating system for building virtual world simulation applications, available as a software subscription.” (See https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3-8dcc- b429fc06c1a4.pdf, Nvidia U.S. Securities and Exchange Commission Form 10-K for Fiscal Year Ended January 28, 2024 at 6.) Nvidia Omniverse implements software and services “into existing software tools and simulation workflows for building AI systems.” (See https://www.nvidia.com/en-us/omniverse/.) 58. In addition, Nvidia Drive is a platform that “consists of both the AI infrastructure and in-vehicle hardware and software” for autonomous vehicles. (See https://www.nvidia.com/en- us/self-driving-cars/.) “NVIDIA DRIVE Infrastructure encompasses data center hardware, software, and workflows—both on premises and in NVIDIA DGX Cloud & Omniverse.” (See id.) 59. In addition, Nvidia Isaac Sim is a platform that enables “developers to design, simulate, test, and train AI-based robots and autonomous machines in a physically-based virtual environment.” (See https://developer.nvidia.com/isaac/sim.) It is built on Nvidia Omniverse. (See id.) 20 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 22 21 of 95 of 94 60. In addition, Nvidia NGC is a collection of software services and tools that support “end-to-end AI and digital twin workflows” that runs on “NVIDIA GPU-accelerated platforms.” (See https://www.nvidia.com/en-us/gpu-cloud/.) NGC “offers a collection of cloud services . . . for generative AI, drug discovery, and speech AI solutions, and the NGC Private Registry for securely sharing proprietary AI software.” (See id.) FIRST CAUSE OF ACTION (INFRINGEMENT OF THE ’867 PATENT) 61. Plaintiff realleges and incorporates by reference the allegations of the preceding paragraphs of this Complaint. 62. Defendant has infringed and continues to infringe one or more claims of the ’867 Patent in violation of 35 U.S.C. § 271 in this District and elsewhere in the United States and will continue to do so. The Accused Products, including features of, e.g., the Grace Hopper Superchip (GH200), at least when used for their ordinary and customary purposes, practice each element of at least claim 16 of the ’867 Patent as demonstrated below. 63. For example, claim 16 of the ’867 Patent recites: 16. A method for performing a numerical simulation on input data in a computer system including a central processing unit and an accelerator, the method comprising: receiving, by an accelerator, first input data from the central processing unit; transferring, by an accelerator controller, the first input data into a first partition, referenced by first pointer, of an accelerator memory before a first computational cycle of the numerical simulation; performing, by at least one graphics processing unit during the first computational cycle, at least one calculation on the first portion of the input data as to generate first output data; 21 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 23 22 of 95 of 94 storing, by the accelerator controller, the first output data into a second partition, referenced by a second pointer, of the accelerator memory; and swapping the first pointer with the second pointer at the end of the first computational cycle, such that the first output data becomes an input for a second computational cycle of the numerical simulation. 64. The Accused Products perform each step of the method of claim 16 of the ’867 Patent. To the extent the preamble is construed to be limiting, the Accused Products perform a method for performing a numerical simulation on input data in a computer system including a central processing unit and an accelerator, as further explained below. For instance, the Grace Hopper Superchip (GH200) “brings together the groundbreaking performance of the NVIDIA Hopper GPU with the versatility of the NVIDIA Grace™ CPU . . . in a single Superchip.” 22 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 24 23 of 95 of 94 (See https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper (emphasis added).) 65. The “Grace Hopper Superchip is the first true heterogeneous accelerated platform for high-performance computing (HPC) and AI workloads. It accelerates applications with the strengths of both GPUs and CPUs while providing the simplest and most productive heterogeneous programming model to date.” (See https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper (emphasis added).) 66. In addition, the Accused Products, including the Grace Hopper Superchip, implement CUDA, Nvidia’s proprietary “parallel computing platform and programming model.” CUDA enables NVIDIA GPUs to be used for general purpose computing tasks. CUDA further includes the CUDA Toolkit, which “includes GPU-accelerated libraries, a compiler, development tools and the CUDA runtime.” As an example, the “CUDA® Deep Neural Network library (cuDNN) is a GPU-acceleration library of primitives for deep neural networks.” It “provides highly tuned implementations for standard routines” for GPU-based acceleration. 23 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 25 24 of 95 of 94 (See https://developer.nvidia.com/cuda-zone (emphasis added).) (See https://developer.nvidia.com/cudnn (emphasis added).) 67. Nvidia GPU architectures that implement CUDA and cuDNN include the Hopper (e.g., Grace Hopper Superchip (GH200), H100), Ada Lovelace, Ampere, Turing, Volta, Pascal, and Maxwell GPU architectures of the Accused Products. 24 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 26 25 of 95 of 94 (See https://docs.nvidia.com/deeplearning/cudnn/archives/cudnn-896/support-matrix/index.html (emphasis added).) 68. The Accused Products perform a method that includes receiving, by an accelerator, first input data from the central processing unit. For instance, the “CUDA programming model” implements programming functions and instructions for CPUs and GPUs. “The host is the CPU available in the system” and “system memory associated with the CPU is called host memory.” “The GPU is called a device and GPU memory likewise called device memory.” As an example, the first main CUDA program execution step is “[c]opy[ing] the input data from host [CPU] memory to device [GPU] memory, also known as host-to-device transfer.” 25 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 27 26 of 95 of 94 (See https://developer.nvidia.com/blog/cuda-refresher-cuda-programming-model/ (emphasis added).) 69. The Accused Products practice a method that includes transferring, by an accelerator controller, the first input data into a first partition, referenced by first pointer, of an accelerator memory before a first computational cycle of the numerical simulation. For instance, the GPU architecture of the Accused Products implements a controller. As an example, the Hopper-GPU architecture implements “HBM3 memory controllers” including “12 512-bit memory controllers” coupled GPU memory including “6 HBM3 or HBM2e stacks,” “80 GB HBM3, 5 HBM3 stacks,” and “80 GB HBM2e, 5 HBM2e stacks.” 26 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 28 27 of 95 of 94 (See https://developer.nvidia.com/blog/nvidia-hopper-architecture-in-depth/ (emphasis added).) 70. The Accused Products implement CUDA, Nvidia’s parallel computing platform. CUDA enables NVIDIA GPUs to be used for general purpose computing tasks and includes specialized GPU-acceleration libraries such as cuDNN. Examples of parameters used in CUDA include pointers “dst” (“Destination memory address”) and “src” (“Source memory address”). For instance, exemplary CUDA function “cudaMemcpy” copies “bytes [data] from the memory area pointed to by src [source memory address pointer] to the memory area pointed to by dst 27 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 29 28 of 95 of 94 [destination memory address pointer], where kind [type of transfer] specifies the direction of the copy.” One of the destinations is “cudaMemcpyHostToDevice,” or host (CPU) to device (GPU). (See https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__MEMORY.html (emphasis added).) 71. The Accused Products practice a method that includes performing, by at least one graphics processing unit during the first computational cycle, at least one calculation on the first portion of the input data as to generate first output data. For instance, CUDA uses “streams” to execute a sequence of commands in order. As shown below, an exemplary CUDA function “cudaMemcpyAsync” is used to copy data between a host (CPU) and a device (GPU). “Each stream copies its portion of input array hostPtr [pointer for CPU] to array inputDevPtr in device [GPU] memory.” The stream then “processes inputDevPtr on the device [GPU] by calling MyKernel(), and copies the result outputDevPtr back to the same portion of hostPtr.” 28 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 30 29 of 95 of 94 (See https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#creation-and- destruction-of-streams (emphasis added).) 72. The Accused Products practice a method that includes storing, by the accelerator controller, the first output data into a second partition, referenced by a second pointer, of the accelerator memory. For instance, exemplary excerpts of CUDA code shown below demonstrate CUDA being used to calculate a square sub-matrix Csub of matrix C using the function MatMul. An exemplary CUDA stream “allocate[s] [matrix] C in device memory.” After Matrices A and B are synchronized and multiplied, the exemplary CUDA stream “[w]rite[s] Csub to device [GPU] memory.” 29 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 31 30 of 95 of 94 ***** (See https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#shared-memory (emphasis added).) 73. In addition, exemplary CUDA function “cudaMalloc” is used to “allocate weight, work, and reserve space buffer sizes in the GPU memory.” “The work-space buffer is used for temporary storage” and the “ content can be discarded or modified after all GPU kernels launched by the corresponding API complete.” The “reserve-space buffer” used to transfer intermediate results is used for transferring “intermediate results” as used in the cuDNN GPU-acceleration library for CUDA. 30 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 32 31 of 95 of 94 (See https://docs.nvidia.com/deeplearning/cudnn/archives/cudnn- 893/api/index.html#cudnnGetMultiHeadAttnBuffers (emphasis added).) 74. The Accused Products practice a method that includes swapping the first pointer with the second pointer at the end of the first computational cycle, such that the first output data becomes an input for a second computational cycle of the numerical simulation. For example, the cuDNN GPU-acceleration library of the Accused Products implement operations that “take tensors as input and produce tensors as output.” (See https://docs.nvidia.com/deeplearning/cudnn/archives/cudnn-881/developer- guide/index.html#tensors-layouts (emphasis added).) 31 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 33 32 of 95 of 94 75. Nvidia confirms that CUDA implements pointer swapping for device (GPU) pointers. ***** (See https://forums.developer.nvidia.com/t/swap-device-pointers/38964 (emphasis added).) 32 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 34 33 of 95 of 94 (See https://docs.nvidia.com/cuda/cuda-c-programming-guide/#creation-and-destruction-of- streams (emphasis added).) 76. Each claim in the ’867 Patent recites an independent invention. Neither claim 16, described above, nor any other individual claim is representative of all claims in the ’867 Patent. 77. Defendant has been aware of the technology patented by the ’867 Patent since at least 2007, when the inventors of the Asserted Patents first discussed their patented technologies 33 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 35 34 of 95 of 94 with Mr. Sanford Russell, then the CTO of Nvidia. At the time, the inventors asked Defendant to collaborate with them on training neural networks using Nvidia’s GPUs. Defendant informed the inventors, through Mr. Russell, that it was not interested in the collaboration. Defendant has also cited the application for the ’867 Patent in its own patent portfolio since at least June 28, 2010. ***** (See https://patents.google.com/patent/US8648867B2/en?oq=8648867#citedBy (emphasis added).) ***** 34 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 36 35 of 95 of 94 (See https://patentimages.storage.googleapis.com/ee/13/e9/61df149c3fddc7/US8922566.pdf (Nvidia U.S. Patent No. 8,922,566) (emphasis added).) (See https://patentcenter.uspto.gov/applications/13335850/displayReferences/referenceForms?applicat ion= (Nvidia U.S. Appl. No. 13/335,850 August 12, 2014, List of References Cited by Examiner) (emphasis added).) 78. Starting in or around 2016, the inventors of the Asserted Patents held multiple discussions with Nvidia to invest in or purchase their AI company, Neurala, Inc., and all its assets, including the ’867 Patent and its related patents and applications. These discussions included at least Mr. Alvin Lin, an Nvidia Senior Director of Business Development, and Mr. Jeff Herbst, then an Nvidia Vice President of Business Development and head of Nvidia’s Inception GPU Ventures, in or around September 6, 2016. In or around October 2016, Nvidia, through its representatives, 35 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 37 36 of 95 of 94 initiated discussions with the inventors to invest in Neurala, Inc. for approximately $10 million. 79. The inventors also discussed their patented technology, in addition to the ’867 Patent and its family, with Defendant’s representatives at Nvidia’s artificial intelligence conference in or around June 2017. On or about June 26, 2017, Defendant received materials from the inventors, in lieu of a meeting on or about June 29, that identified the ’867 Patent and its family and described the technology in detail. Defendant had previously stated it was interested in the inventors’ solutions. Defendant also featured the inventors on its website as members of Defendant’s start-up incubator on or about September 25, 2019. ***** (See https://developer.nvidia.com/blog/inception-spotlight-ai-startup-neurala-sees-7x-speedup- with-ngc/ (September 25, 2019); see also https://www.youtube.com/watch?v=-WBtxGLoQNs (“Neurala Accelerating AI Video Annotation with NGC Containers” posted by Defendant’s YouTube account).) 80. Neural AI and/or its predecessors-in-interest have satisfied all statutory obligations required to collect pre-filing damages for the full period allowed by law for infringement of the 36 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 38 37 of 95 of 94 ’867 Patent. 81. Defendant directly infringes at least claim 16 of the ’867 Patent, either literally or under the doctrine of equivalents, by performing the steps described above. For example, Defendant performs the claimed method in an infringing manner as described above by implementing the Accused Products as part of its accelerated computing operations and running corresponding software that implements the infringing performance. Defendant also performs the claimed method in an infringing manner when testing the operation of the Accused Products and corresponding systems. As another example, Defendant performs the claimed method when providing or administering services to third parties, customers, and partners using the Accused Products. 82. Defendant’s partners, customers, and users of its Accused Products and corresponding systems and services directly infringe at least claim 16 of the ’867 Patent, literally or under the doctrine of equivalents, at least by using the Accused Products and corresponding systems and services, as described above. 83. Defendant has actively induced and is actively inducing infringement of at least claim 16 of the ’867 Patent with specific intent to induce infringement, and/or willful blindness to the possibility that its acts induce infringement, in violation of 35 U.S.C. § 271(b). For example, Defendant encourages and induces customers to use Nvidia’s CUDA platform in a manner that infringes claim 16 of the ’867 Patent at least by offering and providing software that performs a method that infringes claim 16 when installed and operated by the customer using the Accused Products, and by engaging in activities relating to selling, marketing, advertising, promotion, installation, support, and distribution of the Accused Products. 84. Defendant encourages, instructs, directs, and/or requires third parties—including 37 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 39 38 of 95 of 94 its certified partners and/or customers—to perform the claimed method using the software, platform, services, and systems in infringing ways, as described above. 85. Defendant further encourages and induces its customers to infringe claim 16 of the ’867 Patent: 1) by making its accelerated computing and data center services available on its website, providing applications that allow users to access those services, widely advertising those services, and providing technical support and instructions to users (see https://www.nvidia.com/en-us/data-center/data-center-gpus/gpu-test-drive/); and 2) through activities relating to marketing, advertising, promotion, installation, support, and distribution of the Accused Products, including its CUDA platform, and services in the United States. (See https://www.nvidia.com/en-us/; see https://www.nvidia.com/en-us/about-nvidia/partners/; https://www.nvidia.com/en-us/data-center/where-to-buy/; https://www.nvidia.com/en-us/data- center/where-to-buy-tesla/.) 86. For example, Defendant shares instructions, guides, and manuals, which advertise and instruct third parties on how to use its hardware and platform as described above, including at least customers and partners. (See https://docs.nvidia.com/cuda/cuda-c-programming-guide/.) Defendant also provides customer service and technical support to purchasers of the Accused Products and corresponding systems and services, which directs and encourages customers to perform certain actions that use the Accused Products in an infringing manner. (See https://www.nvidia.com/en-us/support/; https://www.nvidia.com/en- us/support/enterprise/services/.) 87. Defendant and/or Defendant’s partners recommend and sell the Accused Products and provide technical support for the installation, implementation, integration, and ongoing operation of the Accused Products for each individual customer. On information and belief, each 38 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 40 39 of 95 of 94 customer enters into a contractual relationship with Defendant and/or one of Defendant’s partners, which obligates each customer to perform certain actions in order to use the Accused Products. (See https://www.nvidia.com/en-us/agreements/; https://www.nvidia.com/en- us/agreements/cloud-services/nvidia-cloud-agreement/; https://www.nvidia.com/en- us/agreements/cloud-services/service-specific-terms-for-nvidia-dgx-cloud/.) Further, in order to receive the benefit of Defendant’s and/or its partner’s continued technical support and their specialized knowledge and guidance of the operability of the Accused Products, each customer must continue to use the Accused Products in a way that infringes the ’867 Patent. (See https://www.nvidia.com/en-us/support/.) 88. Further, as the entity that provides installation, implementation, and integration of the Accused Products in addition to ensuring the Accused Product remains operational for each customer through ongoing technical support, on information and belief, Defendant and/or Defendant’s partners affirmatively aid and abet each customer’s use of the Accused Products in a manner that performs the claimed method of, and infringes, the ’867 Patent. 89. Defendant also contributes to the infringement of its partners, customers, and users of the Accused Products by providing within the United States or importing into the United States the Accused Products, which are for use in practicing, and under normal operation practice, the methods, systems, and devices claimed in the Asserted Patents, constituting a material part of the inventions claimed, and not a staple article or commodity of commerce suitable for substantial non-infringing uses. Indeed, as shown above, the Accused Products and the example functionality have no substantial non-infringing uses but are specifically designed to practice the ’867 Patent. 90. On information and belief, the infringing actions of each partner, customer, and/or user of the Accused Products are attributable to Defendant. For example, on information and belief, 39 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 41 40 of 95 of 94 Defendant directs and controls the activities or actions of its partners or others in connection with the Accused Products by contractual agreement or otherwise requiring partners or others to provide information and instructions to customers who acquire the Accused Products which, when followed, results in infringement. Defendant further directs and controls the operation of devices executing the Accused Products by programming the software which, when executed by a customer or user, performs the claimed method of at least claim 16 of the ’867 Patent. 91. Plaintiff has suffered and continue to suffer damages as a result of Defendant’s infringement of the ’867 Patent. Defendant is therefore liable to Plaintiff under 35 U.S.C. § 284 for damages in an amount that adequately compensates Plaintiff for Defendant’s infringement, but no less than a reasonable royalty. 92. Defendant’s infringement of the ’867 Patent is knowing and willful. Defendant had actual knowledge of the ’867 Patent application since at least 2010 and actual knowledge of the ’867 Patent, and its family, since at least 2017. 93. On information and belief, despite Defendant’s knowledge of the Asserted Patents and Plaintiff’s patented technology, Defendant made the deliberate decision to sell products and services that it knew infringe these patents. Defendant’s continued infringement of the ’867 Patent with knowledge of the ’867 Patent constitutes willful infringement. SECOND CAUSE OF ACTION (INFRINGEMENT OF THE ’438 PATENT) 94. Plaintiff realleges and incorporates by reference the allegations of the preceding paragraphs of this Complaint. 95. Defendant has infringed and continues to infringe one or more claims of the ’438 Patent in violation of 35 U.S.C. § 271 in this District and elsewhere in the United States and will continue to do so. The Accused Products, including features of, e.g., the Grace Hopper Superchip (GH200), at least 40 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 42 41 of 95 of 94 when used for their ordinary and customary purposes, practice each element of at least claim 21 of the ’438 Patent as demonstrated below. 96. For example, claim 21 of the ’438 Patent recites: 21. A method of performing a sequence of computations representing an artificial neural network, the method comprising: receiving, at a central processing unit (CPU), first input data acquired from an external system in real time; initializing, by a controller operably coupled to a graphics processing unit (GPU), textures and shaders in a memory operably coupled to the GPU; transferring the first input data received by the CPU to the memory operably coupled to the GPU; performing, by the graphics processing unit (GPU), a first computation in the sequence of computations on the first input data based on the textures and shaders to generate first output data, computations in the sequence of computations representing respective layers of neurons in the artificial neural network, an output of the first computation in the sequence of computations representing an output of a first neuron in a first layer in the artificial neural network; storing, in the memory operably coupled to the GPU, the first input data and the first output data; and transferring second input data acquired from the external system in real time into the memory operably coupled to the GPU after the GPU starts the first computation and before the GPU starts a second computation of the sequence of computations, an output of the second computation in the sequence of computations representing an output of a second neuron in a second layer in the artificial neural network. 97. The Accused Products perform each step of the method of claim 21 of the ’438 Patent. To the extent the preamble is construed to be limiting, the Accused Products perform a method of performing a sequence of computations representing an artificial neural network, as further explained below. For instance, the Grace Hopper Superchip (GH200) “brings together the 41 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 43 42 of 95 of 94 groundbreaking performance of the NVIDIA Hopper GPU with the versatility of the NVIDIA Grace™ CPU . . . in a single Superchip.” It includes the cuDNN (CUDA Deep Neural Network) library for “[d]eep neural networks.” ***** 42 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 44 43 of 95 of 94 (See https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper (emphasis added).) 98. The “Grace Hopper Superchip is the first true heterogeneous accelerated platform for high-performance computing (HPC) and AI workloads. It accelerates applications with the strengths of both GPUs and CPUs while providing the simplest and most productive heterogeneous programming model to date.” (See https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper (emphasis added).) 99. In addition, the Accused Products, including the Grace Hopper Superchip, implement CUDA, Nvidia’s proprietary “parallel computing platform and programming model.” CUDA further includes the CUDA Toolkit, which “includes GPU-accelerated libraries, a compiler, development tools and the CUDA runtime.” As an example, the “CUDA® Deep Neural Network library (cuDNN) is a GPU-acceleration library of primitives for deep neural networks.” 43 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 45 44 of 95 of 94 It “provides highly tuned implementations for standard routines” for GPU-based acceleration. (See https://developer.nvidia.com/cuda-zone (emphasis added).) (See https://developer.nvidia.com/cudnn (emphasis added).) 100. Nvidia GPU architectures that implement CUDA and cuDNN include the Hopper (e.g., Grace Hopper Superchip (GH200), H100), Ada Lovelace, Ampere, Turing, Volta, Pascal, and Maxwell GPU architectures of the Accused Products. 44 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 46 45 of 95 of 94 (See https://docs.nvidia.com/deeplearning/cudnn/archives/cudnn-896/support-matrix/index.html (emphasis added).) 101. The Accused Products perform a method that includes receiving, at a central processing unit (CPU), first input data acquired from an external system in real time. For instance, as illustrated below, a diagram describing the architecture of the Grace Hopper Superchip depicts a CPU (“Grace CPU”) with “[u]p to 72 cores” that receives and sends input and output data via “High-Speed IO” (“PCIe-5”). The Grace CPU is further depicted as being coupled to memory “CPU LPDDR5X” up to 480GB via a link up to “500 GB/s.” 45 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 47 46 of 95 of 94 ***** 46 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 48 47 of 95 of 94 (See https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper (emphasis added).) 102. In a related diagram example, “CPU PHYSICAL MEMORY” (LPDDR5X) is illustrated as being accessed by a CPU (“CPU-resident access”). (See https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper (emphasis added).) 103. As previously stated, the Accused Products implement CUDA and specialized GPU-acceleration libraries such as cuDNN. “CUDA® is a parallel computing platform and 47 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 49 48 of 95 of 94 programming model developed by NVIDIA for general computing on graphical processing units (GPUs)” including “GPU-accelerated applications.” In GPU-accelerated applications, “the sequential part of the workload runs on the CPU – which is optimized for single-threaded performance – while the compute intensive portion of the application runs on thousands of GPU cores in parallel.” (See https://developer.nvidia. com/cuda-zone (emphasis added).) 104. The “CUDA programming model” implements programming functions and instructions for CPUs and GPUs. “The host is the CPU available in the system” and “system memory associated with the CPU is called host memory.” As an example, the first main CUDA program execution step is “[c]opy[ing] the input data from host [CPU] memory.” 48 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 50 49 of 95 of 94 (See https://developer.nvidia.com/blog/cuda-refresher-cuda-programming-model/ (emphasis added).) 105. The Accused Products perform a method that includes initializing, by a controller operably coupled to a graphics processing unit (GPU), textures and shaders in a memory operably coupled to the GPU. For instance, as illustrated below, a GPU (“Hopper GPU”) for the Grace Hopper Superchip is depicted as accessing both CPU and GPU memory using “NVLINK” for both accessing and storing data. Indeed, “NVIDIA GH200 is designed to accelerate applications with exceptionally large memory footprints.” As illustrated, the Hopper GPUs are illustrated coupled to “GPU HBM3” high bandwidth memory or “GPU HBM3e” high bandwidth memory. (See https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper (emphasis added).) 106. The GPU architecture of the Accused Products implements a controller. As an example, the Hopper-GPU architecture includes “GPU processing clusters” and “texture processing clusters” and implements “HBM3 memory controllers” including “12 512-bit memory controllers” coupled GPU memory including “6 HBM3 or HBM2e stacks,” “80 GB HBM3, 5 HBM3 stacks,” and “80 GB HBM2e, 5 HBM2e stacks.” 49 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 51 50 of 95 of 94 (See https://developer.nvidia.com/blog/nvidia-hopper-architecture-in-depth/ (emphasis added).) 107. In addition, CUDA includes the exemplary NPP (Nvidia Performance Primitives) library “for performing CUDA accelerated processing” and “performing CUDA accelerated processing for 2D image and signal processing.” 50 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 52 51 of 95 of 94 ***** (See https://docs.nvidia.com/cuda/index.html (emphasis added).) (See https://docs.nvidia.com/cuda/npp/introduction.html (emphasis added).) 108. As an example, the exemplary CUDA NPP library passes image data using “[a] pointer to the image’s underlying data type” and “[a] line step in bytes.” In this example, the pointer is passed “to the underlying pixel data type” and the pointer and line step are passed individually for processing involving “image data.” 51 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 53 52 of 95 of 94 (See https://docs.nvidia.com/cuda/npp/introduction.html#nppi_conventions_lb_1passing_image_data (emphasis added).) 109. As another example, the exemplary CUDA NPP library implements function for image color conversion. These functions “manipulat[e] an image’s color model and sampling format” and “can be found in the nppicc [NVIDIA Performance Primitives Image Color Conversion] library.” As shown, these functions save “application load time” and “CUDA runtime.” (See https://docs.nvidia.com/cuda/npp/image_color_conversion.html#image-color-model- conversion-functions (emphasis added).) 110. The Accused Products perform a method that includes transferring the first input 52 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 54 53 of 95 of 94 data received by the CPU to the memory operably coupled to the GPU. For instance, the “CUDA programming model” implements programming functions and instructions for CPUs and GPUs. “The host is the CPU available in the system” and “system memory associated with the CPU is called host memory.” “The GPU is called a device and GPU memory likewise called device memory.” As an example, the first main CUDA program execution step is “[c]opy[ing] the input data from host [CPU] memory to device [GPU] memory, also known as host-to-device transfer.” (See https://developer.nvidia.com/blog/cuda-refresher-cuda-programming-model/ (emphasis added).) 111. The Accused Products perform a method that includes performing, by the graphics processing unit (GPU), a first computation in the sequence of computations on the first input data based on the textures and shaders to generate first output data, computations in the sequence of computations representing respective layers of neurons in the artificial neural network, an output of the first computation in the sequence of computations representing an output of a first neuron in a first layer in the artificial neural network. For instance, the CUDA platform programming implemented in the Accused Products utilizes the GPU and GPU memory. As an example, after the “host-to-device transfer” (host (CPU) memory to device (GPU) memory), the second main 53 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 55 54 of 95 of 94 step is “[l]oad the GPU program and execute, caching data on-chip for performance.” (See https://developer.nvidia.com/blog/cuda-refresher-cuda-programming-model/ (emphasis added).) 112. In addition, as previously stated, cuDNN is a CUDA “GPU-acceleration library of primitives for deep neural networks.” (See https://developer.nvidia.com/cudnn.) The exemplary cuDNN release notes below demonstrate computations implemented for RNNs and related data being transferred to GPU memory. As shown, users do “not need to transfer [an] array [from RNN data descriptors] to device memory; the operation will be performed automatically by RNN APIs.” ***** 54 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 56 55 of 95 of 94 ***** (See https://docs.nvidia.com/deeplearning/cudnn/archives/cudnn-893/release- notes/index.html#abstract (emphasis added).) 113. Relatedly, cuDNN operations below exemplify tensors being used as inputs and outputs (e.g., Tmp0). Exemplary “cuDNN operations take tensors as input and produce tensors as output.” As part of CUDA, these cuDNN operations implement computer tasks performed by a GPU. 55 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 57 56 of 95 of 94 ***** (See https://docs.nvidia.com/deeplearning/cudnn/archives/cudnn-881/developer- guide/index.html#tensors-layouts (emphasis added).) 114. The Accused Products perform a method that includes storing, in the memory operably coupled to the GPU, the first input data and the first output data. For instance, as illustrated below, a diagram describing the architecture of the Grace Hopper Superchip depicts a GPU (“Hopper GPU”) in communication with GPU memory (“GPUHBM3 or HBm3e” high bandwidth memory). 56 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 58 57 of 95 of 94 (See https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper (emphasis added).) 115. As previously stated, the “CUDA programming model” implements programming functions and instructions for CPUs (host) and GPUs (device). For example, after the “host-to- device transfer” (CPU to GPU) first main step and “[l]oad[ing] the GPU program and execut[ing]” 57 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 59 58 of 95 of 94 and “caching data on-chip for performance” for the second main step, the “results” are stored on GPU “device memory.” (See https://developer.nvidia.com/blog/cuda-refresher-cuda-programming-model/ (emphasis added.) 116. The Accused Products perform a method that includes transferring second input data acquired from the external system in real time into the memory operably coupled to the GPU after the GPU starts the first computation and before the GPU starts a second computation of the sequence of computations, an output of the second computation in the sequence of computations representing an output of a second neuron in a second layer in the artificial neural network. For instance, as illustrated below, a diagram describing the architecture of the Grace Hopper Superchip depicts a CPU (“Grace CPU”) in communication with a GPU (“Hopper GPU”) via “NVLink-C2C” (chip-to-chip). “High-Speed IO” input and output data is received by the CPU via “PCIe-5.” 58 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 60 59 of 95 of 94 (See https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper (emphasis added).) 117. Furthermore, the Accused Products, including the Grace Hopper Superchip, implement libraries and SDKs designed for neural networks that “are created from large numbers of identical neurons [that] are highly parallel by nature.” The Accused Products implement 59 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 61 60 of 95 of 94 cuDNN, a library “makes it easy to obtain state-of-the-art performance with Deep Neural Networks,” and TensorRT, a platform accelerator and runtime for optimizing, validating, and deploying neural networks for inference (e.g., applying knowledge from a trained neural network model and inferring a result). (See https://developer.nvidia.com/discover/artificial-neural-network (emphasis added).) 118. An example below illustrates an exemplary neural network the Accused Products are designed to accelerate using parallel computations. “Input” (four) and “Output” (eight) neurons are depicted below in a full-connected or linear layer structure in which all of the input neurons depicted in a first layer are connected to all of the output neurons depicted in a second layer. Computations for the neural network are performed using, for example, “NVIDIA Matrix Multiplication.” Examples of inputs and outputs for forward propagation, activation gradient computation, and weight gradient computation (as matrix by matrix multiplications) are shown below. 60 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 62 61 of 95 of 94 61 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 63 62 of 95 of 94 ***** (See https://docs.nvidia.com/deeplearning/performance/dl-performance-fully- connected/index.html#performance (annotations added).) 62 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 64 63 of 95 of 94 119. Indeed, the cuDNN GPU-acceleration library of the Accused Products implement operations that “take tensors as input and produce tensors as output.” (See https://docs.nvidia.com/deeplearning/cudnn/archives/cudnn-881/developer- guide/index.html#tensors-layouts (emphasis added).) 120. Each claim in the ’438 Patent recites an independent invention. Neither claim 21, described above, nor any other individual claim is representative of all claims in the ’438 Patent. 121. Defendant has been aware of the ’438 Patent since at least the filing of the original Complaint. Defendant has been aware of the technology patented by the ’438 Patent since at least 2007, when the inventors of the Asserted Patents first discussed their patented technologies with Mr. Sanford Russell, then the CTO of Nvidia. At the time, the inventors asked Defendant to collaborate with them on training neural networks using Nvidia’s GPUs. Defendant informed the inventors, through Mr. Russell, that it was not interested in the collaboration. Defendant has also cited an ancestor of the ’438 Patent in its own patent portfolio since at least June 28, 2010 (See https://patents.google.com/patent/US8648867B2/en?oq=8648867#citedBy; https://patentimages.storage.googleapis.com/ee/13/e9/61df149c3fddc7/US8922566.pdf; https://patentcenter.uspto.gov/applications/13335850/displayReferences/referenceForms?applicat ion= (Nvidia U.S. Appl. No. 13/335,850 August 12, 2014, List of References Cited by Examiner).) 122. Starting in or around 2016, the inventors of the Asserted Patents held multiple discussions with Nvidia to invest in or purchase their AI company, Neurala, Inc., and all its assets, including the ’438 Patent family. These discussions included at least Mr. Alvin Lin, an Nvidia Senior Director of Business Development, and Mr. Jeff Herbst, then an Nvidia Vice President of Business Development and head of Nvidia’s Inception GPU Ventures, in or around September 6, 63 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 65 64 of 95 of 94 2016. In or around October 2016, Nvidia, through its representatives, initiated discussions with the inventors to invest in Neurala, Inc. for approximately $10 million. 123. The inventors also discussed their patented technology, including the underlying technology and family to the ’438 Patent (including U.S. Patent No. 9,189,828, the patent the ’438 Patent reissued from), with Defendant’s representatives at Nvidia’s artificial intelligence conference in or around June 2017. On or about June 26, 2017, Defendant received materials from the inventors, in lieu of a meeting on or about June 29, that identified patents related to the ’438 Patent and described the technology in detail. Defendant had previously stated it was interested in the inventors’ solutions. Defendant also featured the inventors on its website as members of Defendant’s start-up incubator on or about September 25, 2019. ***** (See https://developer.nvidia.com/blog/inception-spotlight-ai-startup-neurala-sees-7x-speedup- with-ngc/ (September 25, 2019); see also https://www.youtube.com/watch?v=-WBtxGLoQNs (“Neurala Accelerating AI Video Annotation with NGC Containers” posted by Defendant’s YouTube account).) 64 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 66 65 of 95 of 94 124. Neural AI and/or its predecessors-in-interest have satisfied all statutory obligations required to collect pre-filing damages for the full period allowed by law for infringement of the ’438 Patent. 125. Defendant directly infringes at least claim 21 of the ’438 Patent, either literally or under the doctrine of equivalents, by performing the steps described above. For example, Defendant performs the claimed method in an infringing manner as described above by implementing the Accused Products as part of its accelerated computing operations and running corresponding software that implements the infringing performance. Defendant also performs the claimed method in an infringing manner when testing the operation of the Accused Products and corresponding systems. As another example, Defendant performs the claimed method when providing or administering services to third parties, customers, and partners using the Accused Products. 126. Defendant’s partners, customers, and users of its Accused Products and corresponding systems and services directly infringe at least claim 21 of the ’438 Patent, literally or under the doctrine of equivalents, at least by using the Accused Products and corresponding systems and services, as described above. 127. Defendant has actively induced and is actively inducing infringement of at least claim 21 of the ’438 Patent with specific intent to induce infringement, and/or willful blindness to the possibility that its acts induce infringement, in violation of 35 U.S.C. § 271(b). For example, Defendant encourages and induces customers to use Nvidia’s CUDA platform in a manner that infringes claim 21 of the ’438 Patent at least by offering and providing software that performs a method that infringes claim 21 when installed and operated by the customer using the Accused Products, and by engaging in activities relating to selling, marketing, advertising, promotion, 65 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 67 66 of 95 of 94 installation, support, and distribution of the Accused Products. 128. Defendant encourages, instructs, directs, and/or requires third parties—including its certified partners and/or customers—to perform the claimed method using the software, platform, services, and systems in infringing ways, as described above. 129. Defendant further encourages and induces its customers to infringe claim 21 of the ’438 Patent: 1) by making its accelerated computing and data center services available on its website, providing applications that allow users to access those services, widely advertising those services, and providing technical support and instructions to users (see https://www.nvidia.com/en-us/data-center/data-center-gpus/gpu-test-drive/); and 2) through activities relating to marketing, advertising, promotion, installation, support, and distribution of the Accused Products, including its CUDA platform, and services in the United States. (See https://www.nvidia.com/en-us/; see https://www.nvidia.com/en-us/about-nvidia/partners/; https://www.nvidia.com/en-us/data-center/where-to-buy/; https://www.nvidia.com/en-us/data- center/where-to-buy-tesla/.) 130. For example, Defendant shares instructions, guides, and manuals, which advertise and instruct third parties on how to use its hardware and platform as described above, including at least customers and partners. (See https://docs.nvidia.com/cuda/cuda-c-programming-guide/.) Defendant also provides customer service and technical support to purchasers of the Accused Products and corresponding systems and services, which directs and encourages customers to perform certain actions that use the Accused Products in an infringing manner. (See https://www.nvidia.com/en-us/support/; https://www.nvidia.com/en- us/support/enterprise/services/.) 131. Defendant and/or Defendant’s partners recommend and sell the Accused Products 66 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 68 67 of 95 of 94 and provide technical support for the installation, implementation, integration, and ongoing operation of the Accused Products for each individual customer. On information and belief, each customer enters into a contractual relationship with Defendant and/or one of Defendant’s partners, which obligates each customer to perform certain actions in order to use the Accused Products. (See https://www.nvidia.com/en-us/agreements/; https://www.nvidia.com/en- us/agreements/cloud-services/nvidia-cloud-agreement/; https://www.nvidia.com/en- us/agreements/cloud-services/service-specific-terms-for-nvidia-dgx-cloud/.) Further, in order to receive the benefit of Defendant’s and/or its partner’s continued technical support and their specialized knowledge and guidance of the operability of the Accused Products, each customer must continue to use the Accused Products in a way that infringes the ’438 Patent. (See https://www.nvidia.com/en-us/support/.) 132. Further, as the entity that provides installation, implementation, and integration of the Accused Products in addition to ensuring the Accused Product remains operational for each customer through ongoing technical support, on information and belief, Defendant and/or Defendant’s partners affirmatively aid and abet each customer’s use of the Accused Products in a manner that performs the claimed method of, and infringes, the ’438 Patent. 133. Defendant also contributes to the infringement of its partners, customers, and users of the Accused Products by providing within the United States or importing into the United States the Accused Products, which are for use in practicing, and under normal operation practice, the methods, systems, and devices claimed in the Asserted Patents, constituting a material part of the inventions claimed, and not a staple article or commodity of commerce suitable for substantial non-infringing uses. Indeed, as shown above, the Accused Products and the example functionality have no substantial non-infringing uses but are specifically designed to practice the ’438 Patent. 67 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 69 68 of 95 of 94 134. On information and belief, the infringing actions of each partner, customer, and/or user of the Accused Products are attributable to Defendant. For example, on information and belief, Defendant directs and controls the activities or actions of its partners or others in connection with the Accused Products by contractual agreement or otherwise requiring partners or others to provide information and instructions to customers who acquire the Accused Products which, when followed, results in infringement. Defendant further directs and controls the operation of devices executing the Accused Products by programming the software which, when executed by a customer or user, performs the claimed method of at least claim 21 of the ’438 Patent. 135. Plaintiff has suffered and continues to suffer damages as a result of Defendant’s infringement of the ’438 Patent. Defendant is therefore liable to Plaintiff under 35 U.S.C. § 284 for damages in an amount that adequately compensates Plaintiff for Defendant’s infringement, but no less than a reasonable royalty. 136. Defendant’s infringement of the ’438 Patent is knowing and willful. Defendant acquired actual knowledge of the patent that the ’438 Patent reissued from, and its family, since at least 2017 and has acquired additional knowledge of the ’438 Patent since at least the filing of this lawsuit. 137. On information and belief, despite Defendant’s knowledge of the Asserted Patents and Plaintiff’s patented technology, Defendant made the deliberate decision to sell products and services that it knew infringe these patents. Defendant’s continued infringement of the ’438 Patent with knowledge of the ’438 Patent constitutes willful infringement. THIRD CAUSE OF ACTION (INFRINGEMENT OF THE ’461 PATENT) 138. Plaintiff realleges and incorporates by reference the allegations of the preceding paragraphs of this Complaint. 68 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 70 69 of 95 of 94 139. Defendant has infringed and continues to infringe one or more claims of the ’461 Patent in violation of 35 U.S.C. § 271 in this District and elsewhere in the United States and will continue to do so. The Accused Products, including features of, e.g., the Grace Hopper Superchip (GH200), at least when used for their ordinary and customary purposes, practice each element of at least claim 21 of the ’461 Patent as demonstrated below. 140. For example, claim 21 of the ’461 Patent recites: 21. A method of executing computations representing an artificial neural network on a computer system comprising at least one central processing unit (CPU), a processing unit, a first memory partition, and a second memory partition, the method comprising: executing, by the at least one CPU, a user interaction stream, the user interaction stream controlling transfer of inputs to the artificial neural network to the first memory partition and the second memory partition; executing, by the processing unit, a computational stream, the computational stream controlling data exchange between the user interaction stream and the computational stream during execution of the computations representing the artificial neural network; shifting control of a data exchange between the user interaction stream and the computational stream to the computational stream in response to starting execution of the computations representing the artificial neural network; shifting control of the data exchange between the user interaction stream and the computational stream to the user interaction stream in response to completion or interruption of the computations representing the artificial neural network; queueing a user command received by the user interaction stream during execution of the computations representing the artificial neural network; and executing the user command during execution of the computations representing the artificial neural network at times determined by the computational stream. 141. The Accused Products perform each step of the method of claim 21 of the ’461 69 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 71 70 of 95 of 94 Patent. To the extent the preamble is construed to be limiting, the Accused Products perform a method of executing computations representing an artificial neural network on a computer system comprising at least one central processing unit (CPU), a processing unit, a first memory partition, and a second memory partition, as further explained below. For instance, the Grace Hopper Superchip (GH200) “brings together the groundbreaking performance of the NVIDIA Hopper GPU with the versatility of the NVIDIA Grace™ CPU . . . in a single Superchip.” It includes the cuDNN (CUDA Deep Neural Network) library for “[d]eep neural networks.” ***** 70 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 72 71 of 95 of 94 (See https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper (emphasis added).) 142. As illustrated below, a diagram describing the architecture of the Grace Hopper Superchip depicts a CPU (“Grace CPU”) with “[u]p to 72 cores” and CPU memory (“CPU LPDDR5X”) and a GPU (“Hopper GPU”) and GPU memory (“GPUHBM3 or HBm3e”). 71 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 73 72 of 95 of 94 (See https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper (emphasis added).) 143. The “Grace Hopper Superchip is the first true heterogeneous accelerated platform for high-performance computing (HPC) and AI workloads. It accelerates applications with the strengths of both GPUs and CPUs while providing the simplest and most productive heterogeneous programming model to date.” (See https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper (emphasis added).) 144. In addition, the Accused Products, including the Grace Hopper Superchip, implement CUDA, Nvidia’s proprietary “parallel computing platform and programming model.” CUDA further includes the CUDA Toolkit, which “includes GPU-accelerated libraries, a compiler, development tools and the CUDA runtime.” As an example, the “CUDA® Deep Neural 72 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 74 73 of 95 of 94 Network library (cuDNN) is a GPU-acceleration library of primitives for deep neural networks.” It “provides highly tuned implementations for standard routines” for GPU-based acceleration. (See https://developer.nvidia.com/cuda-zone (emphasis added).) (See https://developer.nvidia.com/cudnn (emphasis added).) 145. Nvidia GPU architectures that implement CUDA and cuDNN include the Hopper (e.g., Grace Hopper Superchip (GH200), H100), Ada Lovelace, Ampere, Turing, Volta, Pascal, and Maxwell GPU architectures of the Accused Products. 73 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 75 74 of 95 of 94 (See https://docs.nvidia.com/deeplearning/cudnn/archives/cudnn-896/support-matrix/index.html (emphasis added).) 146. The Accused Products perform a method that includes executing, by the at least one CPU, a user interaction stream, the user interaction stream controlling transfer of inputs to the artificial neural network to the first memory partition and the second memory partition. For instance, as shown in the Grace Hopper Superchip architecture diagram below, the Grace Hopper Superchip is illustrated below with a CPU (“GRACE CPU”). The CPU “share[s] a single per- process page table” with a GPU (“Hopper GPU”), “enabling all CPU and GPU threads to access all system-allocated memory.” The CPU is depicted as coupled to the GPU via “NVLINK C2C [chip-to-chip],” and can access the “System Page Table” and “CPU PHYSICAL MEMORY” via “CPU-resident access” and “GPU PHYSICAL MEMORY” via “[r]emote access” and “PTE [page table entry] B.” The GPU can also access the System Page Table, and it can access “GPU PHYSICAL MEMORY” via “GPU-resident access” and “CPU PHYSICAL MEMORY” via “[r]emote access” and “PTE A.” Moreover, the “System Page Table” “[t]ranslates CPU malloc() 74 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 76 75 of 95 of 94 [memory allocation] to CPU or GPU.” “The CPU heap, CPU thread stack, global variables memory-mapped files, and inter-process memory are accessible to all CPU and GPU threads.” (See https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper (emphasis added).) 147. The “CUDA programming model” implements programming functions and instructions for CPUs and GPUs. “The host is the CPU available in the system” and “system memory associated with the CPU is called host memory.” “The GPU is called a device and GPU memory likewise called device memory.” As an example, the first main CUDA program execution step is “[c]opy[ing] the input data from host [CPU] memory to device [GPU] memory, also known as host-to-device transfer.” 75 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 77 76 of 95 of 94 (See https://developer.nvidia.com/blog/cuda-refresher-cuda-programming-model/ (emphasis added).) 148. The Accused Products perform a method that includes executing, by the processing unit, a computational stream, the computational stream controlling data exchange between the user interaction stream and the computational stream during execution of the computations representing the artificial neural network. For instance, the “CUDA programming model” implements programming functions and instructions for CPUs and GPUs. As previously stated, the host is the CPU and the device is the GPU. After “[c]opy[ing] the input data from host [CPU] memory to device [GPU] memory,” the second main CUDA program execution step is “[l]oad[ing] the GPU program and execut[ing].” 76 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 78 77 of 95 of 94 (See https://developer.nvidia.com/blog/cuda-refresher-cuda-programming-model/ (emphasis added).) 149. Indeed, the Grace Hopper Superchip “is designed to accelerate applications” using “Extended GPU Memory.” As depicted in the gram of the Grace Hopper architecture below, a GPU (“HOPPER GPU”) can access “Local CPU,” “Peer CPU,” and “Peer GPU” memory via “NVLink.” 77 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 79 78 of 95 of 94 (See https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper (emphasis added).) 150. For instance, exemplary CUDA library cuDNN function “cudnnSetRNNDescriptor_v8” “initializes a previously created RNN [recurrent neural network] descriptor object.” This function “store[s] all information needed to compute the total number of adjustable weights/biases in the RNN model.” In addition, the parameters “dirMode,” “inputMode,” and “datatype” confirm the exchange of calculations and values between the hidden layers of an RNN. ***** 78 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 80 79 of 95 of 94 (See https://docs.nvidia.com/deeplearning/cudnn/archives/cudnn-891/pdf/cuDNN-API.pdf (emphasis added).) 151. The Accused Products perform a method that includes shifting control of a data exchange between the user interaction stream and the computational stream to the computational stream in response to starting execution of the computations representing the artificial neural network. For instance, the “CUDA programming model” implements programming functions and instructions for CPUs (host) and GPUs (device). As an example, the “host-to-device transfer” (CPU to GPU) first main step, the second main step is “[l]oad the GPU program and execute” and the third main step is “[c]opy the results from device [GPU] memory to host [CPU] memory, also known as device-to-host transfer” (GPU to CPU). 79 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 81 80 of 95 of 94 (See https://developer.nvidia.com/blog/cuda-refresher-cuda-programming-model/ (emphasis added.) 152. As previously stated, the Grace Hopper Superchip “is designed to accelerate applications” using “Extended GPU Memory” and the GPU can access local/peer CPU and peer GPU memory via “NVLink.” The Grace Hopper Superchip’s Extended GPU Memory feature “enables GPUs to access all the system memory efficiently” and “physical memory in the system can be allocated to be accessible from any GPU thread.” 80 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 82 81 of 95 of 94 (See https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper (emphasis added).) 153. The Accused Products perform a method that includes shifting control of the data exchange between the user interaction stream and the computational stream to the user interaction stream in response to completion or interruption of the computations representing the artificial neural network. For instance, after the “CUDA programming model” “host-to-device transfer” (CPU to GPU) and GPU program load and execution steps, the third main step is “[c]opy the results from device [GPU] memory to host [CPU] memory, also known as device-to-host transfer” (GPU to CPU). The “host-to-device transfer” (CPU to GPU) first main step can be reintroduced for additional computations. (See https://developer.nvidia.com/blog/cuda-refresher-cuda-programming-model/ (emphasis 81 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 83 82 of 95 of 94 added.) 154. In addition, as shown in the Grace Hopper Superchip architecture diagram below, the CPU (“GRACE CPU”) “share[s] a single per-process page table” with a GPU (“Hopper GPU”), “enabling all CPU and GPU threads to access all system-allocated memory.” “The CPU heap, CPU thread stack, global variables memory-mapped files, and inter-process memory are accessible to all CPU and GPU threads.” (See https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper (emphasis added).) 155. The Accused Products perform a method that includes queueing a user command received by the user interaction stream during execution of the computations representing the artificial neural network. For instance, as shown by publicly available CUDA toolkit 82 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 84 83 of 95 of 94 documentation, CUDA implements exemplary “memory management functions” that “[c]op[y] data between host [CPU] and device [GPU].” This includes CUDA functions “cudaMemcpy” and “cudaMemcpyAsync.” ***** ***** (See https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__MEMORY.html (emphasis added).) 156. As an example, exemplary CUDA memory management function “cudaMemcpyAsync” “[c]opies count bytes [data] from the memory area pointed to by src [source memory address pointer] to the memory area pointed to by dst [destination memory address pointer], where kind [type of transfer] specifies the direction of the copy.” Destinations includes “cudaMemcpyHostToDevice [CPU to device GPU], cudaMemcpyDeviceToHost [GPU to CPU], cudaMemcpyDeviceToDevice [GPU to GPU]. Because the function “cudaMemcpyAsync() is asynchronous with respect to the host, [] the call may return before the copy is complete. The copy can optionally be associated to a stream [identified stream] by passing a non-zero stream argument.” 83 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 85 84 of 95 of 94 (See https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__MEMORY.html (emphasis added).) 157. The Accused Products perform a method that includes executing the user command during execution of the computations representing the artificial neural network at times determined by the computational stream. For instance, as shown by exemplary and publicly available CUDA toolkit documentation, CUDA implements “memory management functions” that “[c]op[y] data between host [CPU] and device [GPU].” 84 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 86 85 of 95 of 94 ***** ***** (See https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__MEMORY.html (emphasis added).) 158. As an example, exemplary CUDA memory management function “cudaMemcpyAsync” “[c]opies count bytes [data] from the memory area pointed to by src [source memory address pointer] to the memory area pointed to by dst [destination memory address pointer], where kind [type of transfer] specifies the direction of the copy.” Because the function “cudaMemcpyAsync() is asynchronous with respect to the host, [] the call may return before the copy is complete. The copy can optionally be associated to a stream [identified stream].” 85 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 87 86 of 95 of 94 (See https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__MEMORY.html (emphasis added).) 159. In another example, CUDA implements “CUDA-specific memory APIs [that] provide users with guarantees about where the memory resides, which threads can access it, whether it is migratable, and many other features that enable users to extract all the performance the hardware has to offer.” (See https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper (emphasis added).) 86 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 88 87 of 95 of 94 160. Each claim in the ’461 Patent recites an independent invention. Neither claim 21, described above, nor any other individual claim is representative of all claims in the ’461 Patent. 161. Defendant has been aware of the ’461 Patent since at least the filing of the original Complaint. Defendant has been aware of the technology patented by the ’461 Patent since at least 2007, when the inventors of the Asserted Patents first discussed their patented technologies with Mr. Sanford Russell, then the CTO of Nvidia. At the time, the inventors asked Defendant to collaborate with them on training neural networks using Nvidia’s GPUs. Defendant informed the inventors, through Mr. Russell, that it was not interested in the collaboration. Defendant has also cited an ancestor of the ’461 Patent in its own patent portfolio since at least June 28, 2010 (See https://patents.google.com/patent/US8648867B2/en?oq=8648867#citedBy; https://patentimages.storage.googleapis.com/ee/13/e9/61df149c3fddc7/US8922566.pdf; https://patentcenter.uspto.gov/applications/13335850/displayReferences/referenceForms?applicat ion= (Nvidia U.S. Appl. No. 13/335,850 August 12, 2014, List of References Cited by Examiner).) 162. Starting in or around 2016, the inventors of the Asserted Patents held multiple discussions with Nvidia to invest in or purchase their AI company, Neurala, Inc., and all its assets, including the ’461 Patent family. These discussions included at least Mr. Alvin Lin, an Nvidia Senior Director of Business Development, and Mr. Jeff Herbst, then an Nvidia Vice President of Business Development and head of Nvidia’s Inception GPU Ventures, in or around September 6, 2016. In or around October 2016, Nvidia, through its representatives, initiated discussions with the inventors to invest in Neurala, Inc. for approximately $10 million. 163. The inventors also discussed their patented technology, including the underlying technology and family to the ’461 Patent, with Defendant’s representatives at Nvidia’s artificial intelligence conference in or around June 2017. On or about June 26, 2017, Defendant received 87 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 89 88 of 95 of 94 materials from the inventors, in lieu of a meeting on or about June 29, that identified patents related to the ’461 Patent and described the technology in detail. Defendant had previously stated it was interested in the inventors’ solutions. Defendant also featured the inventors on its website as members of Defendant’s start-up incubator on or about September 25, 2019. ***** (See https://developer.nvidia.com/blog/inception-spotlight-ai-startup-neurala-sees-7x-speedup- with-ngc/ (September 25, 2019); see also https://www.youtube.com/watch?v=-WBtxGLoQNs (“Neurala Accelerating AI Video Annotation with NGC Containers” posted by Defendant’s YouTube account).) 164. Neural AI and/or its predecessors-in-interest have satisfied all statutory obligations required to collect pre-filing damages for the full period allowed by law for infringement of the ’461 Patent. 165. Defendant directly infringes at least claim 21 of the ’461 Patent, either literally or under the doctrine of equivalents, by performing the steps described above. For example, Defendant performs the claimed method in an infringing manner as described above by 88 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 90 89 of 95 of 94 implementing the Accused Products as part of its accelerated computing operations and running corresponding software that implements the infringing performance. Defendant also performs the claimed method in an infringing manner when testing the operation of the Accused Products and corresponding systems. As another example, Defendant performs the claimed method when providing or administering services to third parties, customers, and partners using the Accused Products. 166. Defendant’s partners, customers, and users of its Accused Products and corresponding systems and services directly infringe at least claim 21 of the ’461 Patent, literally or under the doctrine of equivalents, at least by using the Accused Products and corresponding systems and services, as described above. 167. Defendant has actively induced and is actively inducing infringement of at least claim 21 of the ’461 Patent with specific intent to induce infringement, and/or willful blindness to the possibility that its acts induce infringement, in violation of 35 U.S.C. § 271(b). For example, Defendant encourages and induces customers to use Nvidia’s CUDA platform in a manner that infringes claim 21 of the ’461 Patent at least by offering and providing software that performs a method that infringes claim 21 when installed and operated by the customer using the Accused Products, and by engaging in activities relating to selling, marketing, advertising, promotion, installation, support, and distribution of the Accused Products. 168. Defendant encourages, instructs, directs, and/or requires third parties—including its certified partners and/or customers—to perform the claimed method using the software, platform, services, and systems in infringing ways, as described above. 169. Defendant further encourages and induces its customers to infringe claim 21 of the ’461 Patent: 1) by making its accelerated computing and data center services available on its 89 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 91 90 of 95 of 94 website, providing applications that allow users to access those services, widely advertising those services, and providing technical support and instructions to users (see https://www.nvidia.com/en-us/data-center/data-center-gpus/gpu-test-drive/); and 2) through activities relating to marketing, advertising, promotion, installation, support, and distribution of the Accused Products, including its CUDA platform, and services in the United States. (See https://www.nvidia.com/en-us/; see https://www.nvidia.com/en-us/about-nvidia/partners/; https://www.nvidia.com/en-us/data-center/where-to-buy/; https://www.nvidia.com/en-us/data- center/where-to-buy-tesla/.) 170. For example, Defendant shares instructions, guides, and manuals, which advertise and instruct third parties on how to use its hardware and platform as described above, including at least customers and partners. (See https://docs.nvidia.com/cuda/cuda-c-programming-guide/.) Defendant also provides customer service and technical support to purchasers of the Accused Products and corresponding systems and services, which directs and encourages customers to perform certain actions that use the Accused Products in an infringing manner. (See https://www.nvidia.com/en-us/support/; https://www.nvidia.com/en- us/support/enterprise/services/.) 171. Defendant and/or Defendant’s partners recommend and sell the Accused Products and provide technical support for the installation, implementation, integration, and ongoing operation of the Accused Products for each individual customer. On information and belief, each customer enters into a contractual relationship with Defendant and/or one of Defendant’s partners, which obligates each customer to perform certain actions in order to use the Accused Products. (See https://www.nvidia.com/en-us/agreements/; https://www.nvidia.com/en- us/agreements/cloud-services/nvidia-cloud-agreement/; https://www.nvidia.com/en- 90 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 92 91 of 95 of 94 us/agreements/cloud-services/service-specific-terms-for-nvidia-dgx-cloud/.) Further, in order to receive the benefit of Defendant’s and/or its partner’s continued technical support and their specialized knowledge and guidance of the operability of the Accused Products, each customer must continue to use the Accused Products in a way that infringes the ’461 Patent. (See https://www.nvidia.com/en-us/support/.) 172. Further, as the entity that provides installation, implementation, and integration of the Accused Products in addition to ensuring the Accused Product remains operational for each customer through ongoing technical support, on information and belief, Defendant and/or Defendant’s partners affirmatively aid and abet each customer’s use of the Accused Products in a manner that performs the claimed method of, and infringes, the ’461 Patent. 173. Defendant also contributes to the infringement of its partners, customers, and users of the Accused Products by providing within the United States or importing into the United States the Accused Products, which are for use in practicing, and under normal operation practice, the methods, systems, and devices claimed in the Asserted Patents, constituting a material part of the inventions claimed, and not a staple article or commodity of commerce suitable for substantial non-infringing uses. Indeed, as shown above, the Accused Products and the example functionality have no substantial non-infringing uses but are specifically designed to practice the ’461 Patent. 174. On information and belief, the infringing actions of each partner, customer, and/or user of the Accused Products are attributable to Defendant. For example, on information and belief, Defendant directs and controls the activities or actions of its partners or others in connection with the Accused Products by contractual agreement or otherwise requiring partners or others to provide information and instructions to customers who acquire the Accused Products which, when followed, results in infringement. Defendant further directs and controls the operation of devices 91 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 93 92 of 95 of 94 executing the Accused Products by programming the software which, when executed by a customer or user, performs the claimed method of at least claim 21 of the ’461 Patent. 175. Plaintiff has suffered and continues to suffer damages as a result of Defendant’s infringement of the ’461 Patent. Defendant is therefore liable to Plaintiff under 35 U.S.C. § 284 for damages in an amount that adequately compensates Plaintiff for Defendant’s infringement, but no less than a reasonable royalty. 176. Defendant’s infringement of the ’461 Patent is knowing and willful. Defendant acquired actual knowledge of the family of the ’461 Patent since at least 2017 and has acquired additional knowledge of the ’461 Patent since at least the filing of this lawsuit. 177. On information and belief, despite Defendant’s knowledge of the Asserted Patents and Plaintiff’s patented technology, Defendant made the deliberate decision to sell products and services that it knew infringe these patents. Defendant’s continued infringement of the ’461 Patent with knowledge of the ’461 Patent constitutes willful infringement. 92 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 94 93 of 95 of 94 PRAYER FOR RELIEF WHEREFORE, Plaintiff respectfully requests the following relief: a) That this Court adjudge and decree that Defendant has been, and is currently, infringing each of the Asserted Patents; b) That this Court award Plaintiff damages to compensate for Defendant’s past and future infringement of the Asserted Patents, through the life of the Asserted Patents; c) That this Court award Plaintiff pre- and post-judgment interest on such; d) That this Court order an accounting of damages incurred by Plaintiff from six years prior to the date this lawsuit was filed through entry of a final, non-appealable judgment; e) That this Court determine that this patent infringement case is exceptional and award Plaintiff its costs and attorneys’ fees incurred in this action; f) That this Court award increased damages under 35 U.S.C. § 284; and g) That this Court award such other relief as the Court deems just and proper. DEMAND FOR JURY TRIAL Plaintiff respectfully requests a trial by jury on all issues triable thereby. 93 Case Case 7:24-cv-00221-ADA-DTG 7:26-mc-00318-LS Document Document 6-2 30 Filed Filed 08/18/26 12/12/24 Page Page 95 94 of 95 of 94 DATED: December 12, 2024 By:/s/ Mark D. Siegmund Mark D. Siegmund Texas Bar No. 24117055 CHERRY JOHNSON SIEGMUND JAMES PLLC Bridgeview Center 7901 Fish Pond Road, 2nd Floor Waco, Texas 76710 Telephone: (254) 732-2242 Facsimile: (866) 627-3509 msiegmund@cjsjlaw.com Christopher C. Campbell KING & SPALDING LLP 1700 Pennsylvania Avenue, NW Suite 900 Washington, DC 20006 Telephone: (202) 626-5578 Facsimile: (202) 626-3737 ccampbell@kslaw.com Britton F. Davis Brian Eutermoser (pro hac vice to be filed) KING & SPALDING LLP 1401 Lawrence Street Suite 1900 Denver, CO 80202 Telephone: (720) 535-2300 Facsimile: (720) 535-2400 bfdavis@kslaw.com beutermoser@kslaw.com Attorneys for Plaintiff Neural AI, LLC CERTIFICATE OF SERVICE The undersigned does hereby certify that a true and correct copy of the foregoing document was served on all counsel of record via the Court’s electronic filing system on this 12th day of December 2024. By:/s/ Mark D. Siegmund Mark D. Siegmund 94