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 § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 10, 12, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sen et al. (US 2018/0114096) in view of Liu et al. (US Patent #10,496,104) in view of Huang et al. (US 2005/0147292) in view of Xu et al. (US 2018/0061058) in view of Reyneri et al. (US2008/0158400), and further in view of Masumoto et al. (US 2008/0259080). Regarding claims 1, 19, and 20, Sen teaches a method, comprising: filtering an input image with a set of filters identified using a machine learning framework (Paragraphs [0028-0029, 0036, 0044, 0053, and 0057 especially Paragraph [0036], …..the machine learning approach can include, for example, training a neural network with a filter to produce denoised or noise-free images, or training a model to directly output denoised or noise-free images);
outputting an output image comprising a denoised version of the input image (Paragraphs 0025-0027, 0033, and 0044, especially Paragraph 0025, …….machine learning to process a noisy image of a scene and produce a denoised output image. In one type of processing, a machine learning model that has been trained with ground truth images denoises the noisy image to produce an output image with noise removed), does not specifically teach wherein different filters are identified to denoise different portions of the input image, wherein the machine learning framework is trained at least in part on a set of training images comprising a prescribed scene type to which the input image belongs and wherein the set of training images on which the machine learning framework is at least in part trained comprises training images snapshots rendered from corresponding three-dimensional models at a plurality of different sampling intervals.
However, in related art, Liu wherein different filters are identified to denoise different portions of the input image (Col 10, lines 26-35). Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made to use (pre-AIA ) or before the effective filing date of the claimed invention (AIA ) to use Liu’s teaching about wherein different filters are identified for different portions of the input image with Sen’s invention in order to improve the appearance of the output image.
The combination of Sen and Liu fail to teach wherein the machine learning framework is trained at least in part on a set of training images comprising a prescribed scene type to which the input image belongs and wherein the set of training images on which the machine learning framework is at least in part trained comprises training images snapshots rendered from corresponding three-dimensional models at a plurality of different sampling intervals.
However, in related art, Huang teaches wherein the machine learning framework is trained at least in part on a set of training images comprising a prescribed scene type to which the input image belongs (creating a database of a plurality of model image characterizations, each of which represents the face of a known person that it is desired to identify in the input image as well as the person's face pose; see claim 1. wherein the process action for training the neural network ensemble comprises an action of preparing each model image characterization from a model image depicting the face of a known person that it is desired to identify in the input image by, extracting the portion of the model image depicting said face, normalizing the extracted portion of the model image by resizing it to a prescribed scale if not already at the prescribed scale and adjusting the region so that the eye locations of the depicted subject fall within a prescribed area; see claim 5). Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made to use (pre-AIA ) or before the effective filing date of the claimed invention (AIA ) to use Huang’s teaching about wherein the machine learning framework is trained at least in part on a set of training images comprising a prescribed scene type to which the input image belongs with Sen’s and Liu’s invention in order to adjust the region of the input image so that the locations of the depicted subject fall within a prescribed area (See Huang, claim 5). The combination of Sen, Liu, and Huang fail to teach wherein the set of training images on which the machine learning framework is at least in part trained comprises training images snapshots rendered from corresponding three-dimensional models at a plurality of different sampling intervals.
However, in related art, Xu teaches wherein the set of training images on which the machine learning framework is at least in part trained comprises training images (Paragraph 0026, Xu teaches Convolutional neural network (CNN) is a type of machine learning algorithm that can be trained by supervised learning….Paragraph 0076….. CNN model training unit 102 may use the training images received from training image database 101 to train a CNN model for performing image segmentation of new 3D images…… The user interface may be used for selecting sets of training images, adjusting one or more parameters of the training process (e.g., the number of adjacent image slices in each stack), selecting or modifying a framework of a CNN model, and/or manually or semi-automatically segmenting an image for training). Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made to use (pre-AIA ) or before the effective filing date of the claimed invention (AIA ) to use Xu’s teaching about wherein the set of training images on which the machine learning framework is at least in part trained comprises at least one subset of training images with Sen’s, Liu’s, and Huang’s invention in order to generate images that clearly show relevant information.
The combination of Sen, Liu, Huang, and Xu fail to teach training images snapshots rendered from corresponding three-dimensional models at a plurality of different sampling intervals.
However, in related art, Reyneri teaches training images snapshots rendered at a plurality of different sampling intervals (See abstract and claims 1, 16, and 18, especially claim 1….. A method in an image sensor for digitally recording an image of a scene within a snapshot of said scene, said image sensor comprising a two-dimensional array of pixel elements, said method comprising: after an initial exposure period within said snapshot, sampling pixel intensity values at said pixel elements at a plurality of sampling intervals within said snapshot). Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made to use (pre-AIA ) or before the effective filing date of the claimed invention (AIA ) to use Reyneri’s teaching about training images snapshots rendered from corresponding three-dimensional models at a plurality of different sampling intervals with Sen’s, Liu’s, Huang’s, and Xu’s invention in order to reduce the load of the training process.
The combination of Sen, Liu, Huang, Xu, and Reyneri fail to teach snapshots rendered from corresponding three-dimensional models.
However, in related art, Masumoto teaches snapshots rendered from corresponding three-dimensional models (Paragraph [0034]….. the image processing workstation 3 includes an image obtaining means 10 for obtaining a three-dimensional medical image V of a target patient for radiological reading from the modality 1 or image storage server 2 in response to a request from a radiological reader, a ray casting means 60 for determining a pixel value of each pixel on a projection plane using brightness values and opacity levels of a plurality of examination points, which are points on the three-dimensional medical image V sampled at a predetermined interval along each of a plurality of visual lines connecting an arbitrary viewpoint and each pixel on the projection plane and generating a volume rendering image (pseudo three-dimensional image), and an image display means 70 for displaying the generated volume rendering image). Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made to use (pre-AIA ) or before the effective filing date of the claimed invention (AIA ) to use Masumoto’s teaching about snapshots rendered from corresponding three-dimensional models with Sen’s, Liu’s, Huang’s, Xu’s, and Reyneri’s invention so that all sides of the image are visible.
Regarding claim 2, the combination of Sen, Liu, Huang, Xu, Reyneri, and Masumoto teach all the claimed elements in claim 1. In addition, Sen teaches the method of claim 1, wherein the set of filters comprises denoising filters (Paragraphs 0025-0027, 0033, and 0044, especially Paragraph 0025, …….machine learning to process a noisy image of a scene and produce a denoised output image. In one type of processing, a machine learning model that has been trained with ground truth images denoises the noisy image to produce an output image with noise removed)
Regarding claim 3, the combination of S Sen, Liu, Huang, Xu, Reyneri, and Masumoto teach all the claimed elements in claim 1. In addition, Sen teaches the method of claim 1, wherein the set of filters comprises spatial filters (Paragraph 0012). Regarding claim 4, the combination of Sen, Liu, Huang, Xu, Reyneri, and Masumoto teach all the claimed elements in claim 1. In addition, Sen teaches the method of claim 1, wherein the input image comprises a sparsely ray traced image (Paragraph 0004). Regarding claim 10, the combination of Sen, Liu, Huang, Xu, Reyneri, and Masumoto teach all the claimed elements in claim 1. In addition, Sen teaches the method of claim 1, wherein image render time is substantially reduced by generating the noisy input image and then denoising the noisy input image using the set of filters to generate the output image (Paragraphs [0028-0029, 0036, 0044, 0053, and 0057). Regarding claim 12, the combination of Sen, Liu, Huang, Xu, Reyneri, and Masumoto teach all the claimed elements in claim 1. In addition, Sen teaches the method of claim 1, wherein identifying the set of filters using the machine learning framework comprises identifying a set of one or more filter parameters (Paragraphs 0006, 0012, 0028-0031, 0037, 0043-0044, 0046, and 0051-0053).
Regarding claim 17, the combination of Sen, Liu, Huang, Xu, Reyneri, and Masumoto teach all the claimed elements in claim 1. In addition, Sen teaches the method of claim 1, wherein the output image comprises a photorealistic rendering (Paragraphs 0003, 0004, and 0024).
Regarding claim 18, the combination of Sen, Liu, Huang, Xu, Reyneri, and Masumoto teach all the claimed elements in claim 1. In addition, Sen teaches the method of claim 1, wherein the output image comprises a frame of an animation or a video sequence (Paragraphs 0003, 0014, 0040, and 0085, especially Paragraph [0085], Sen teaches Although described herein regarding scene images, the method may be applied to frames of video, including photorealistic frames, graphic frames, cartoon frames, etc. To handle video sequences, the existing neural network described herein may be used without retraining and the cross-bilateral filter can be extended to operate on 3-D spatio-temporal volumes).
Claims 5 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Sen et al. (US 2018/0114096) in view of Liu et al. (US Patent #10,496,104) in view of Huang et al. (US 2005/0147292) in view of Xu et al. (US 2018/0061058) in view of in view of Reyneri et al. (US2008/0158400) in view of Masumoto et al. (US 2008/0259080), and further in view of Meyer et al. (US 2018/0293710).
Regarding claim 5, the combination of Sen, Liu, Huang, Xu, Reyneri, and Masumoto fail to teach the method of claim 1, wherein the output image has a quality or resolution comparable to that achievable by ray tracing with large numbers of samples.
However, in related art, Meyer teaches the method of claim 1, wherein the output image has a quality or resolution comparable to that achievable by ray tracing with large numbers of samples (Paragraphs 0055 and 0062). Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made to use (pre-AIA ) or before the effective filing date of the claimed invention (AIA ) to use Meyer’s teaching about wherein the output image has a quality or resolution comparable to that achievable by ray tracing with large numbers of samples with Sen’s, Liu’s, Huang’s, Xu’s, Reyneri, and Masumoto’s invention in order to producing high-quality images (See Meyer, Paragraph [0005]).
Regarding claim 8, the combination of Sen, Liu, Huang, Xu, Reyneri, and Masumoto fail to teach the method of claim 1, wherein the output image has a quality or resolution comparable to ray tracing with number of samples needed for convergence.
However, in related art, Meyer teaches the method of claim 1, wherein the output image has a quality or resolution comparable to ray tracing with number of samples needed for convergence (Paragraph [0062]). Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made to use (pre-AIA ) or before the effective filing date of the claimed invention (AIA ) to use Meyer’s teaching about wherein the output image is comparable to ray tracing with a number of samples needed for convergence with Sen’s, Liu’s, Huang’s, Xu’s, Reyneri’s, and Masumoto’s invention in order to producing high-quality images (See Meyer, Paragraph [0005]).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Sen et al. (US 2018/0114096) in view of Liu et al. (US Patent #10,496,104) in view of Huang et al. (US 2005/0147292) in view of Xu et al. (US 2018/0061058) in view of Reyneri et al. (US2008/0158400) in view of Masumoto et al. (US 2008/0259080), and further in view of Shohara (US 2010/0066868).
Regarding claim 6, the combination of Sen, Liu, Huang, Xu, Reyneri, and Masumoto fail to teach the method of claim 1, wherein the input image violates a noise threshold and the output image satisfies the noise threshold.
However, in related art, Shohara teaches the method of claim 1, wherein the input image violates a noise threshold and the output image satisfies the noise threshold (claim 4). Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made to use (pre-AIA ) or before the effective filing date of the claimed invention (AIA ) to use Shohara’s teaching about wherein the input image violates a noise threshold and the output image satisfies the noise threshold with Sen’s, Liu’s, Huang’s, Xu’s, Reyneri, and Masumoto’s invention in order to reduce an impulse noise of the input image (See Shohara, Abstract).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Sen et al. (US 2018/0114096) in view of Liu et al. (US Patent #10,496,104) in view of Huang et al. (US 2005/0147292) in view of Xu et al. (US 2018/0061058) in view of Reyneri et al. (US2008/0158400) in view of Masumoto et al. (US 2008/0259080), and further in view of Vogels et al. (US 2018/0293496). Regarding claim 7, the combination of Sen, Liu, Huang, Xu, Reyneri, and Masumoto fail to teach the method of claim 1, wherein the set of filters transforms the noisy input image into denoised output image.
However, in related art, Vogels teaches the method of claim 1, wherein the set of filters transforms the noisy input image into denoised output image (Paragraph [0190]). Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made to use (pre-AIA ) or before the effective filing date of the claimed invention (AIA ) to use Vogels’s teaching about wherein the set of filters transforms the noisy input image into denoised output image with Sen’s, Liu’s, Huang’s, Xu’s, Reyneri, and Masumoto invention in order to capable of producing high quality images that are nearly indistinguishable from photographs (See Vogels, Paragraph 0006).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Sen et al. (US 2018/0114096) in view of Liu et al. (US Patent #10,496,104) in view of Huang et al. (US 2005/0147292) in view of Xu et al. (US 2018/0061058) in view of Reyneri et al. (US2008/0158400) in view of Masumoto et al. (US 2008/0259080), and further in view of Hecht (US 2015/0228110). Regarding claim 9, the combination of Sen, Liu, Huang, Xu, Reyneri, and Masumoto fail to teach the method of claim 1, wherein the set of filters predicts pixel values that would result from ray tracing with a larger number of samples.
However, in related art, Hecht teaches the method of claim 1, wherein the set of filters predicts pixel values that would result from ray tracing with a larger number of samples (Paragraphs 0020, 0029, and 0034). Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made to use (pre-AIA ) or before the effective filing date of the claimed invention (AIA ) to use Hecht’s teaching about wherein the set of one or more filters predicts pixel values that would result from ray tracing with a larger number of samples with Sen’s, Liu’s, Huang’s, Xu’s, Reyneri’s, and Masumoto’s invention in order to determine how many samples are needed (See Hecht, Paragraph 0029).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Sen et al. (US 2018/0114096) in view of Liu et al. (US Patent #10,496,104) in view of Huang et al. (US 2005/0147292) in view of Xu et al. (US 2018/0061058) in view of Reyneri et al. (US2008/0158400) in view of Masumoto et al. (US 2008/0259080), and further in view of Luebke et al. (US Patent #10,388,059). Regarding claim 11, the combination of Sen, Liu, Huang, Xu, Reyneri, and Masumoto fail to teach the method of claim 1, wherein the method effectively eliminates using a large number of samples during ray tracing while still generating an image that has a noise profile that ray tracing with a large number of samples provides.
However, in related art, Luebke teaches the method of claim 1, wherein the method effectively eliminates using a large number of samples during ray tracing while still generating an image that has a noise profile that ray tracing with a large number of samples provides (Claims 1, 14, and 18). Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made to use (pre-AIA ) or before the effective filing date of the claimed invention (AIA ) to use Luebke’s teaching about wherein the method effectively eliminates using a large number of samples during ray tracing while still generating an image that has a noise profile that ray tracing with a large number of samples provides with Sen’s, Liu’s, Huang’s, Xu’s, Reyneri’s, and Masumoto’s invention for striking a balance between temporal stability and image sharpness in interactive ray tracing applications (See Luebke, Col 28, lines 54-57).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Sen et al. (US 2018/0114096) in view of Liu et al. (US Patent #10,496,104) in view of Huang et al. (US 2005/0147292) in view of Xu et al. (US 2018/0061058) in view of Reyneri et al. (US2008/0158400) in view of Masumoto et al. (US 2008/0259080), and further in view of Vogels et al. (US 2018/0293496).
Regarding claim 13, the combination of Sen, Liu, Huang, Xu, Reyneri, and Masumoto fail to teach the method of claim 1, wherein the output image has a higher quality or resolution than the input images.
However, in related art, Vogels teaches the method of claim 1, wherein the output image has a higher quality or resolution than the input images (Paragraph [0190]). Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made to use (pre-AIA ) or before the effective filing date of the claimed invention (AIA ) to use Vogels’s teaching about wherein the output image has a higher quality or resolution than the input images with Sen’s, Liu’s, Huang’s, Xu’s, Reyneri’s, and Masumoto’s invention in order to capable of producing high quality images that are nearly indistinguishable from photographs (See Vogels, Paragraph 0006).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Sen et al. (US 2018/0114096) in view of Liu et al. (US Patent #10,496,104) in view of Huang et al. (US 2005/0147292) in view of Xu et al. (US 2018/0061058) in view of Reyneri et al. (US2008/0158400) in view of Masumoto et al. (US 2008/0259080), and further in view of Chuang et al. (US 2013/0128056).
Regarding claim 14, the combination of Sen, Liu, Huang, Xu, Reyneri, and Masumoto fail to teach the method of claim 1, wherein the different portions of the input image have different noise signature.
However, in related art, Chuang teaches the method of claim 1, wherein the different portions of the input image have different noise signature ((Paragraph 0017… the noise signature may comprise a set of discrete noise values or descriptors correlated to one or more image attributes or parameters, e.g., intensity level, color channel, and/or texture, and thus may be or include a (possibly multi-dimensional) noise profile with respect to these parameters. Paragraph 0018…. An estimate of sensor sensitivity associated with the image may be automatically determined based on the determined noise signature. For example, the noise signature may be provided as in input to a trained classifier, e.g., a support vector machine, neural network, etc., which may then operate to estimate the sensor sensitivity. In other words, a trained classifier may take the noise signature as input, e.g., as input features, and may generate a corresponding sensor sensitivity value, where the sensor sensitivity value is a "prediction" or estimate of the sensor sensitivity of the camera that produced the image). Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made to use (pre-AIA ) or before the effective filing date of the claimed invention (AIA ) to use Chuang’s teaching about wherein the different portions of the input image have different noise signature with Sen’s, Liu’s, Huang’s, Xu’s, Reyneri’s, and Masumoto’s invention in order to efficiently and effectively reduces noise and enhance the look of the image.
Claims 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Sen et al. (US 2018/0114096) in view of Liu et al. (US Patent #10,496,104) in view of Huang et al. (US 2005/0147292) in view of Xu et al. (US 2018/0061058) in view of Reyneri et al. (US2008/0158400) in view of Masumoto et al. (US 2008/0259080), and further in view of Rust et al. (US 2003/0187354).
Regarding claim 15, the combination of Sen, Liu, Huang, Xu, Reyneri, and Masumoto fail to teach the method of claim 1, wherein the machine learning framework is trained to learn filters for attribute combinations having different noise signatures.
However, in related art, Rust teaches the method of claim 1, wherein the machine learning framework is trained to learn filters for attribute combinations having different noise signatures (Paragraph 0032). Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made to use (pre-AIA ) or before the effective filing date of the claimed invention (AIA ) to use Rust’s teaching about wherein the machine learning framework is trained to learn filters for attribute combinations having different noise signatures with Sen’s, Liu’s, Huang’s, Xu’s, Reyneri’s, and Masumoto’s invention in order to maximize available dynamic range to create the highest resolution scan images possible while rejecting noise. (See Rust, Abstract).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Sen et al. (US 2018/0114096) in view of Liu et al. (US Patent #10,496,104) in view of Huang et al. (US 2005/0147292) in view of Xu et al. (US 2018/0061058) in view of Reyneri et al. (US2008/0158400) in view of Masumoto et al. (US 2008/0259080) in view of Rust et al. (US 2003/0187354), and further in view of Chuang et al. (US 2013/0128056).
Regarding claim 16, the combination of Sen, Liu, Huang, Xu, Reyneri, Masumoto, and Rust fail to teach the method of claim 15, wherein attribute combinations comprise one or more attributes associated with object/scene types, geometries, placements, materials, textures, camera characteristics, lighting characteristics, numbers of samples, and contrast.
However, in related art, Chuang teaches the method of claim 15, wherein attribute combinations comprise one or more attributes associated with object/scene types, geometries, placements, materials, textures, camera characteristics, lighting characteristics, numbers of samples, and contrast (Paragraphs 0017, 0061, and 0102).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made to use (pre-AIA ) or before the effective filing date of the claimed invention (AIA ) to use Chuang’s teaching about wherein attribute combinations comprise one or more attributes associated with object/scene types, geometries, placements, materials, textures, camera characteristics, lighting characteristics, numbers of samples, and contrast with Sen’s, Liu’s, Huang’s, Xu’s, Reyneri’s, Masumoto’s, and Rust’s invention in order to enable the device to clearly display the content.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DOMINIC E REGO/Primary Examiner, Art Unit 2648 Tel 571-272-8132
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