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 Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claim 1 is directed to an efficient rendering method comprising the steps of: constructing, selecting, generating, determining, determining, determining, inputting and controlling which are nothing more than softare instructions. Software instructions are non-statutory under 35 U.S.S. 101.
Claims 2-8 depend from claim 1 and comprise further steps, for example claim 2 comprises the steps of adjusting, selecting and carying out, therefore claims 2-8 have the same problem as claim 1 and are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
GUENTER (US2014/0247277A1) discloses a method and system for foveated image rendering are provided herein. The method includes tracking a gaze point of a user on a display device and generating a specified number of eccentricity layers based on the gaze point of the user. The method also includes antialiasing the eccentricity layers to remove artifacts, rendering a foveated image based on the eccentricity layers, and displaying the foveated image to the user via the display device; CUI (CN115170741A) discloses a fast radiation field reconstruction method under sparse visual angle input, by calculating the position of the edge pixel of the outline image in the space, calculating to obtain the axis alignment surrounding box of the target to be reconstructed; aligning the shaft alignment surrounding box; at the same time, distributing the voxel confidence level to be optimized and the learable voxel characteristic vector as the three-dimensional representation of the local area for each voxel vertex; voxel confidence of each voxel vertex, by projecting the voxel vertex to the outline image of each input image to perform the calculation of the visual angle number, initializing the voxel confidence, performing volume rendering to the input RGB image, optimizing voxel confidence and voxel feature vector and multilayer sensing machine of voxel vertex by reconstructing the luminosity error loss and full differential error loss, obtaining the radiation field representation of the scene; for the optimization process, by periodically performing voxel cutting to continuously refine the geometric estimation of the radiation field, obtaining a more refined radiation field representation; JIANG (CN116452758A) discloses a neural radiation field model acceleration training method, device, device and medium, relating to the technical field of computer vision. The method comprises: performing multi-view image collection on the scene to be rendered by the camera; obtaining the camera pose parameter according to the collected image, and obtaining the characteristic point cloud of the image under the relative coordinate system; calculating the distance between each characteristic point in the characteristic point cloud and the camera, taking the distance as the pseudo-depth value of the pixel corresponding to the characteristic point; calculating the depth loss according to the predicted depth value and the pseudo-depth value, and calculating the colour loss according to the predicted pixel colour and the real colour of the image; the depth loss and the colour loss are input to the neural network, the neural network parameter is transmitted in reverse direction, and the neural network parameter is updated. The invention can improve the rendering speed of the nerve radiation field model, reduce or even eliminate the floater in the nerve radiation field model and improve the effect of the scene reconstruction; YOUNG (US2021/0366079A1) discloses methods and systems are provided for using temporal supersampling to increase a displayed resolution associated with peripheral region of a foveated rendering view. A method for enabling reconstitution of higher resolution pixels from a low resolution sampling region for fragment data is provided. The method includes an operation for receiving a fragment from a rasterizer of a GPU and for applying temporal supersampling to the fragment with the low resolution sampling region over a plurality of prior frames to obtain a plurality of color values. The method further includes an operation for reconstituting a plurality of high resolution pixels in a buffer that is based on the plurality of color values obtained via the temporal supersampling. Moreover, the method includes an operation for sending the plurality of high resolution pixels for display; DENG et. al., “FoV-NeRF: Foveated Neural Radiance Fields for Virtual Reality,” IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 28, NO. 11, NOVEMBER 2022; discloses Virtual Reality (VR) is becoming ubiquitous with the rise of consumer displays and commercial VR platforms. Such displays require low latency and high quality rendering of synthetic imagery with reduced compute overheads. Recent advances in neural rendering showed promise of unlocking new possibilities in 3D computer graphics via image-based representations of virtual or physical environments. Specifically, the neural radiance fields (NeRF) demonstrated that photo-realistic quality and continuous view changes of 3D scenes can be achieved without loss of view-dependent effects. While NeRF can significantly benefit rendering for VR applications, it faces unique challenges posed by high field-of-view, high resolution, and stereoscopic/egocentric viewing, typically causing low quality and high latency of the rendered images. In VR, this not only harms the interaction experience but may also cause sickness. To tackle these problems toward six-degrees-of-freedom, egocentric, and stereo NeRF in VR, we present the first gaze-contingent 3D neural representation and view synthesis method. We incorporate the human psychophysics of visual- and stereo-acuity into an egocentric neural representation of 3D scenery. We then jointly optimize the latency/performance and visual quality while mutually bridging human perception and neural scene synthesis to achieve perceptually high-quality immersive interaction. We conducted both objective analysis and subjective studies to evaluate the effectiveness of our approach. We find that our method significantly reduces latency (up to 99% time reduction compared with NeRF) without loss of high-fidelity rendering (perceptually identical to full-resolution ground truth). The presented approach may serve as the first step toward future VR/AR systems that capture, teleport, and visualize remote environments in real-time.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAURICE L MCDOWELL, JR whose telephone number is (571)270-3707. The examiner can normally be reached Mon-Fri: 2pm-10pm.
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/MAURICE L. MCDOWELL, JR/Primary Examiner, Art Unit 2612
