DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 17 is objected to because of the following informalities:
Claim 17 is missing a period at the end. Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, and 4-20 with an earliest effective filing date of 12/22/23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khinvasara (U.S. Publication No. 2023/0281042 published on 9/7/23 as cited on IDS) in view of Krishnamurthy et al. (U.S. Publication No. 2022/0358718 published on 11/10/22 as cited on IDS).
With respect to claim 1, the Khinvasara reference teaches a storage device, comprising;
a storage controller ([0191] In at least one embodiment, one or more SoC of SoC(s) 1104 may further include dedicated high-performance mass storage controllers that may include their own DMA engines, and that may be used to free CPU(s) 1106 from routine data management tasks);
a flash memory communicatively coupled to the storage controller ([0202] In at least one embodiment, vehicle 1100 may further include data store(s) 1128 which may include, without limitation, off-chip (e.g., off SoC(s) 1104) storage. In at least one embodiment, data store(s) 1128 may include, without limitation, one or more storage elements including RAM, SRAM, dynamic random-access memory (“DRAM”), video random-access memory (“VRAM”), flash memory, hard disks, and/or other components and/or devices that may store at least one bit of data);
a neural processor communicatively coupled to the flash memory and the storage controller ([0199] In at least one embodiment, vehicle 1100 may include GPU(s) 1120 (e.g., discrete GPU(s), or dGPU(s)), that may be coupled to SoC(s) 1104 via a high-speed interconnect (e.g., NVIDIA's NVLINK channel). In at least one embodiment, GPU(s) 1120 may provide additional artificial intelligence functionality, such as by executing redundant and/or different neural networks, and may be used to train and/or update neural networks…)
The Khinvasara reference does not explicitly recite that the neural processor is configured to retrieve a base asset from memory and generate a derivative asset using the base asset. The Krishnamurthy reference teaches that the neural processor is configured to retrieve a base asset from memory and generate a derivative asset using the base asset ([0004] Accordingly, a method includes using at least one neural network, generating a three-dimensional (3D) object of a computer simulation asset. The method includes processing information associated with the asset using at least one physics engine to render an output. Further, the method includes feeding the output back to the neural network and receiving from the neural network a modified asset in response to feeding back the output of the physics engine to the neural network. [0040] FIGS. 2 and 3 illustrate techniques for allowing game designers to create and/or modify a three-dimensional (3D) asset for a computer simulation such as a computer game, typically a common non-character asset from scratch or by adapting an asset previously stored in an asset library). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara with the asset generation of Krishnamurthy as it would have made the system more desirable to users by enabling the generation of assets from user text.
With respect to claim 2, the Khinvasara and Krishnamurthy references teach all of the limitations of claim 1 as described above. In addition, the Krishnamurthy reference teaches that the neural processor is configured to generate the derivative asset with a neural network model trained with a machine learning algorithm ([0069] 2D generative models (such as generative adversarial networks (GAN)) are trained on respective asset classes such as tables and chairs to generate assets. Training may be supervised, semi-supervised, or unsupervised). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara with the asset generation of Krishnamurthy as it would have made the system more desirable to users by enabling the generation of assets from user text.
With respect to claim 4, the Khinvasara and Krishnamurthy references teach all of the limitations of claim 1 as described above. Additionally, the Krishnamurthy reference teaches that the neural processor further configured to generate two or more different derivative assets using the base asset ([0054] FIG. 6 illustrates example logic for specifying multiple assets and their desired relative locations to each other in a computer simulation. Commencing at block 600, text from direct text input or voice-to-text conversion is received describing the assets by name and their desired relative locations with respect to each other). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara with the asset generation of Krishnamurthy as it would have made the system more desirable to users by enabling the generation of assets from user text.
With respect to claim 5, the Khinvasara and Krishnamurthy references teach all of the limitations of claim 1 as described above. In addition, the Krishnamurthy reference teaches that the base asset is a text description of an image and wherein the derivative asset is an image derived from the text description of the image ([0042] FIG. 3 illustrates that the designer's ensuing speech (e.g., “brown chair with arms, 4-legs, cushioned surface and bannister back”) is received at block 300 and converted to text at block 302.[0048] The text may be input to an artificial intelligence (AI) engine such as one or more neural networks at block 304 to generate a 2D image of the requested asset.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara with the asset generation of Krishnamurthy as it would have made the system more desirable to users by enabling the generation of assets from user text.
With respect to claim 6, the Khinvasara and Krishnamurthy references teach all of the limitations of claim 1 as described above. Additionally, the Krishnamurthy reference teaches that the base asset is text description of a digital object and wherein the derivative asset is a digital representation of a three dimensional of the digital object, wherein the neural processor is configure to generate the derivate three dimensional digital object from the text description of the digital object ([0077] Turning to FIG. 15 for an alternate approach to generating 3D assets, at block 1500 a 3D GAN model is trained to generate 3D object. The part encodings for each part of the asset, e.g., for a chair the encodings for the arms, legs, back, etc., are extracted at block 1502. Moving to block 1504, the part encodings are transformed based on the shape description 1506 of the desired asset. Proceeding to block 1508, the 3D asset generation is conditioned based on appearance descriptions 1510, such as non-shape descriptions such as style or size or color. The reconstructed mesh 1512 of the 3D asset is output with or without texturing, as desired). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara with the asset generation of Krishnamurthy as it would have made the system more desirable to users by enabling the generation of assets from user text.
With respect to claim 7, the Khinvasara and Krishnamurthy references teach all of the limitations of claim 1 as described above. In addition, the Krishnamurthy reference teaches that the base asset is an image of a digital object and the derivative asset is three dimensional representation of the image of the digital object wherein the neural processor is configure to generate the derivate three dimensional digital object from the image of the digital object ([0049] Proceeding from block 304 to block 306, the 2D image is converted to a 3D asset of the asset using a 2D-to-3D conversion system that uses, e.g., layer stacking or other technique such as creating 3D anaglyph stereograms, false height relief, etc. A 2D to 3D reconstruction model may be used. An encoder-decoder neural architecture may be included, where the encoder takes as input a 2D image and generates an encoding and the 3D decoder generates a 3D object based on the encoding. A 3D object or asset thus can be generated using 2D to 3D reconstruction, generating a 3D object using a generative neural model and then transforming it to meet the specs, or transforming an existing 3D model as per the desired specs.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara with the asset generation of Krishnamurthy as it would have made the system more desirable to users by enabling the generation of assets from user text.
With respect to claim 8, the Khinvasara and Krishnamurthy references teach all of the limitations of claim 1 as described above. Additionally, the Krishnamurthy reference teaches that the derivative asset is different from the base asset ([0050] The 3D asset may be presented on, e.g., the display shown in FIG. 2 and at block 308 artist modifications to the asset may be received using voice or other input such as point-and-click device graphical manipulation input. The modifications may include changes to size, shape, color, style of certain parts of the asset (but not to all parts of the asset), texture of the surface of the asset, etc.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara with the asset generation of Krishnamurthy as it would have made the system more desirable to users by enabling the generation of assets from user text.
With respect to claim 9, the Khinvasara and Krishnamurthy references teach all of the limitations of claim 1 as described above. In addition, the Khinvasara reference teaches that the flash memory further includes neural network model data and the neural processor is configured to use the neural network model data ([0069] In at least one embodiment, data center 112 includes one or more servers used, at least in part, to perform neural network training or inferencing. Data center 112 can include one or more embodiments of a data center as described herein, including in conjunction with at least FIG. 10. In at least one embodiment, data center 112 includes GPUs used to accelerate deep learning, machine learning, and high-performance computing (HPC) workloads. In at least one embodiment, data center 112 includes computing systems purpose-built to perform artificial-intelligence (AI) tasks such as neural network inferencing. In at least one embodiment, data center 112 includes a server platform to accelerate deep learning, machine learning, and HPC.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara with the asset generation of Krishnamurthy as it would have made the system more desirable to users by enabling the generation of assets from user text.
With respect to claim 10, the Khinvasara and Krishnamurthy references teach all of the limitations of claim 1 as described above. Additionally, the Krishnamurthy reference teaches that the flash memory includes a script file including instructions for generation of the derivative asset and the neural processor is further configured to use the script file with the base asset to generate the derivative asset ([0006] In another aspect, a device includes at least one processor programmed with instructions to identify at least one three-dimensional (3D) asset for a computer simulation. [0005] If desired, the method may include inputting at least one rule to the neural network for use in generating the 3D object.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara with the asset generation of Krishnamurthy as it would have made the system more desirable to users by enabling the generation of assets from user text.
With respect to claim 11, the Khinvasara and Krishnamurthy references teach all of the limitations of claim 1 as described above. In addition, the Khinvasara reference teaches that the storage controller, the flash memory and the neural processor are located on the same circuit board ([0649] In at least one embodiment, architecture and/or functionality of various previous figures are implemented in context of a general computer system, a circuit board system, a game console system dedicated for entertainment purposes, an application-specific system, and more. In at least one embodiment, computer system 1400 may take form of a desktop computer, a laptop computer, a tablet computer, servers, supercomputers, a smart-phone (e.g., a wireless, hand-held device), personal digital assistant (“PDA”), a digital camera, a vehicle, a head mounted display, a hand-held electronic device, a mobile phone device, a television, workstation, game consoles, embedded system, and/or any other type of logic. In at least one embodiment, a computer system 1400 comprises or refers to any devices in FIGS. 8A-41B.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara with the asset generation of Krishnamurthy as it would have made the system more desirable to users by enabling the generation of assets from user text.
With respect to claim 12, the Khinvasara and Krishnamurthy references teach all of the limitations of claim 1 as described above. Additionally, the Khinvasara reference teaches a central processing unit communicatively coupled with the neural processor, flash memory and storage controller ([0158] In at least one embodiment, vehicle 1100 may include any number of SoCs 1104. In at least one embodiment, each of SoCs 1104 may include, without limitation, central processing units (“CPU(s)”) 1106, graphics processing units (“GPU(s)”) 1108, processor(s) 1110, cache(s) 1112, accelerator(s) 1114, data store(s) 1116, and/or other components and features not illustrated. [0202] In at least one embodiment, vehicle 1100 may further include data store(s) 1128 which may include, without limitation, off-chip (e.g., off SoC(s) 1104) storage. In at least one embodiment, data store(s) 1128 may include, without limitation, one or more storage elements including RAM, SRAM, dynamic random-access memory (“DRAM”), video random-access memory (“VRAM”), flash memory, hard disks, and/or other components and/or devices that may store at least one bit of data.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara with the asset generation of Krishnamurthy as it would have made the system more desirable to users by enabling the generation of assets from user text.
With respect to claim 13, the Khinvasara and Krishnamurthy references teach all of the limitations of claim 1 as described above. Additionally, the Khinvasara reference teaches a physical interface communicatively coupled with the storage controller and the neural processor ([0270] As described herein, although various multi-core processors 1605 and GPUs 1610 may be physically coupled to a particular memory 1601, 1620, respectively, and/or a unified memory architecture may be implemented in which a virtual system address space (also referred to as “effective address” space) is distributed among various physical memories. [0227] For example, in at least one embodiment, diverse implementation and intentional non-identity makes an overall system more fault-tolerant, especially to faults caused by software (or software-hardware interface) functionality.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara with the asset generation of Krishnamurthy as it would have made the system more desirable to users by enabling the generation of assets from user text.
With respect to claim 14, the Khinvasara and Krishnamurthy references teach all of the limitations of claim 13 as described above. Additionally, the Khinvasara reference teaches that the physical interface is an M.2 connector ([0254] In at least one embodiment, components such as WLAN unit 1350 and Bluetooth unit 1352, as well as WWAN unit 1356 may be implemented in a Next Generation Form Factor (“NGFF”).). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara with the asset generation of Krishnamurthy as it would have made the system more desirable to users by enabling the generation of assets from user text.
With respect to claim 15, the Khinvasara and Krishnamurthy references teach all of the limitations of claim 13 as described above. In addition, the Khinvasara reference teaches that the physical interface is a Universal Serial Bus connector ([0264] In at least one embodiment, USB interface 1540 may be any type of USB connector or USB socket. For instance, in at least one embodiment, USB interface 1540 is a USB 3.0 Type-C socket for data and power.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara with the asset generation of Krishnamurthy as it would have made the system more desirable to users by enabling the generation of assets from user text.
With respect to claim 16, the Khinvasara and Krishnamurthy references teach all of the limitations of claim 13 as described above. Additionally, the Krishnamurthy reference teaches that the derivative asset is sent to a computer system ([0053]-[0054] Proceeding to block 504, the 3D asset may be modified as described herein by an artist or other user for use in a computer simulation. Additional details of 3D asset generation are illustrated in FIGS. 12-15 discussed below. FIG. 6 illustrates example logic for specifying multiple assets and their desired relative locations to each other in a computer simulation.) and the Khinvasara reference teaches that the system is communicatively coupled to the storage device through the physical interface ([0245] In at least one embodiment, computer system 1200 may include, without limitation, a memory 1220. In at least one embodiment, memory 1220 may be a Dynamic Random Access Memory (“DRAM”) device, a Static Random Access Memory (“SRAM”) device, a flash memory device, or another memory device. In at least one embodiment, memory 1220 may store instruction(s) 1219 and/or data 1221 represented by data signals that may be executed by processor 1202.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara with the asset generation of Krishnamurthy as it would have made the system more desirable to users by enabling the generation of assets from user text.
With respect to claim 17, the Khinvasara and Krishnamurthy references teach all of the limitations of claim 16 as described above. Additionally, the Krishnamurthy reference teaches that the derivative asset is communicated to the storage controller and the storage controller sends the derivative asset through the physical interface to the computer system ([0067] The 2D transformation model 1206 outputs transformed synthetic representations 1208, in the example shown, of chairs in 2D. The representations 1208 may be included in an asset library, used for artist input, and used for 3D reconstruction. [0037] Now in reference to the afore-mentioned at least one server 52, it includes at least one server processor 54, at least one tangible computer readable storage medium 56 such as disk-based or solid-state storage, and at least one network interface 58 that, under control of the server processor 54, allows for communication with the other devices of FIG. 1 over the network 22, and indeed may facilitate communication between servers and client devices in accordance with present principles. [0032] In addition to the foregoing, the AVD 12 may also include one or more input ports 26 such as a high-definition multimedia interface (HDMI) port or a USB port to physically connect to another CE device…). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara with the asset generation of Krishnamurthy as it would have made the system more desirable to users by enabling the generation of assets from user text.
With respect to claim 18, the Khinvasara and Krishnamurthy references teach all of the limitations of claim 13 as described above. In addition, the Krishnamurthy reference teaches that the neural processor is further configured to generate the derivative asset in response to a request for the derivative asset from a computer system coupled to the storage device through the physical interface ([0070] When an asset is requested, the appropriate trained model is selected for the specified asset in the description. For example, if there are separate models to generate chairs, tables, etc., then the model is selected based on the specified asset. [0037] Now in reference to the afore-mentioned at least one server 52, it includes at least one server processor 54, at least one tangible computer readable storage medium 56 such as disk-based or solid-state storage, and at least one network interface 58 that, under control of the server processor 54, allows for communication with the other devices of FIG. 1 over the network 22, and indeed may facilitate communication between servers and client devices in accordance with present principles.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara with the asset generation of Krishnamurthy as it would have made the system more desirable to users by enabling the generation of assets from user text.
With respect to claim 19, the Khinvasara and Krishnamurthy references teach all of the limitations of claim 1 as described above. In addition, the Khinvasara reference teaches that the storage controller is an NVME controller or a USB controller ([0071] In at least one embodiment, data such as compressed data files, bit streams, binary code, or some combination thereof are located on a data sources such as main memories, nonvolatile memory express hardware (NVMe), hard disk drives (HDDs), solid state drives (SSDs), network hardware, network interface controllers (NICs), GPU device memories, or some combination thereof. In at least one embodiment, data are found on data sinks such as main memories, NVMe, HDDs, SSDs, network hardware, NICs, GPU device memories, or some combination thereof. [0312] In at least one embodiment, integrated circuit 1700 includes peripheral or bus logic including a USB controller 1725…). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara with the asset generation of Krishnamurthy as it would have made the system more desirable to users by enabling the generation of assets from user text.
With respect to claim 20, the Khinvasara and Krishnamurthy references teach all of the limitations of claim 1 as described above. Additionally, the Krishnamurthy reference teaches that the derivative asset includes derivative scripting for behavior of a derivative three dimensional model and the base asset includes a base script for behavior of a three dimensional model ([0006] In another aspect, a device includes at least one processor programmed with instructions to identify at least one three-dimensional (3D) asset for a computer simulation. The instructions are executable to input the 3D asset to at least one physics engine, and using at least one neural network, modify at least one property of the asset geometry to maintain constant inertial tensors calculated by the physics engine to tend to move or deform the asset. [0066]-[0068] Commencing at block 1200, representations 1202 such as photographs of real 2D objects such as chairs, to carry on the examples above, are input to a conditional generative neural model for 2D synthesis. The resulting output 1204 are representations of synthetic chairs in 2D. The output 1204 is sent to an optional 2D transformation model 1206 for interpolation and feature editing. The model 1206 may be entirely AI-based or it may be interactive between an AI model and a human operator. The 2D transformation model 1206 outputs transformed synthetic representations 1208, in the example shown, of chairs in 2D. The representations 1208 may be included in an asset library, used for artist input, and used for 3D reconstruction. Indeed, the transformed synthetic representations 1208 and/or representations 1202 of real assets in 2D such as chairs may be input to a neural model 1210. The neural model 1210 transforms the 2D representations into 3D shapes to output reconstructed meshes 1212 of the assets. The neural model 1210 involves an implicit function and mesh deformation as appropriate. If desired, the reconstructed meshes 1212 may be input to a texture transformation model 1214 for neural rendering of textures of the 3D asset.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara with the asset generation of Krishnamurthy as it would have made the system more desirable to users by enabling the generation of assets from user text.
Claim(s) 3 with an earliest effective filing date of 12/22/23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khinvasara (U.S. Publication No. 2023/0281042 published on 9/7/23 as cited on IDS) in view of Krishnamurthy et al. (U.S. Publication No. 2022/0358718 published on 11/10/22 as cited on IDS) and further in view of Saharia et al. (U.S. Publication No. 2023/0377226 published on 11/23/23 as cited on IDS).
With respect to claim 3, the Khinvasara and Krishnamurthy references teach all of the limitations of claim 2 as described above. They do not explicitly recite that the neural network model is trained with a diffusion type model for generation of the derivative asset. The Saharia reference teaches that the neural network model is trained with a diffusion type model for generation of the derivative asset ([0004] This specification describes an image generation system implemented as computer programs on one or more computers in one or more locations that generates an image from a conditioning input using a text encoder neural network and a sequence of generative neural networks. [0008]-[0010] In some implementations of the method, each generative neural network in the sequence is a diffusion-based generative neural network. In some implementations of the method, the diffusion-based generative neural networks use classifier-free guidance. In some implementations of the method, for each subsequent diffusion-based generative neural network, processing the respective input to generate, as output, the respective output image includes: sampling a latent image having the respective output resolution; and denoising the latent image over a sequence of steps into the respective output image.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Khinvasara and Krishnamurthy with the diffusion models of Saharia. Such a modification would have made the system more efficient (Saharia [0031]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kim et al. U.S. Publication No. 2025/0053796
A system and method for in-storage machine learning operations. In some embodiments, a system includes a first persistent memory, and a control and inference circuit. The first persistent memory may be connected to the control and inference circuit by a wideband data connection, and the control and inference circuit may be configured to perform arithmetic operations.
Navon et al. U.S. Publication No. 2023/0114005
Recurrent Neural Networks (RNNs) wherein a non-volatile memory (NVM) array provides a memory bank for the RNN. The RNN may include a Neural Turning Machine (NTM) and the memory bank may be an NTM matrix stored in the NVM array. In some examples, a data storage device (DSD) that controls the NVM array includes both a data storage controller and a separate NTM controller. The separate NTM controller accesses the NTM matrix of the NVM array directly while bypassing flash translation layer (FTL) components of the data storage controller. Additionally, various majority wins error detection and correction procedures are described, as well as various disparity count-based procedures.
Kachare et al. U.S. Publication No. 2019/0317901
A controller of a data storage device includes: a host interface providing an interface to a host computer; a flash translation layer (FTL) translating a logical block address (LBA) to a physical block address (PBA) associated with an input/output (I/O) request; a flash interface providing an interface to flash media to access data stored on the flash media; and one or more deep neural network (DNN) modules for predicting an I/O access pattern of the host computer. The one or more DNN modules provide one or more prediction outputs to the FTL that are associated with one or more past I/O requests and a current I/O request received from the host computer, and the one or more prediction outputs include at least one predicted I/O request following the current I/O request. The FTL prefetches data stored in the flash media that is associated with the at least one predicted I/O request.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KRIS E MACKES whose telephone number is (571)270-3554. The examiner can normally be reached Monday-Friday 9:00-4:00 EST.
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/KRIS E MACKES/Primary Examiner, Art Unit 2153
Prosecution Insights