DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending.
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 1-5, 6-12, 15-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claims 1, 8 and 15, the following 2-step analysis is applied for analyzing the 35 U.S.C. § 101 subject matter eligibility of the claims.
Step 1: The Statutory Categories
Claim(s) 1, 8, and 15 recite(s) a "processor," a "system," and a "method," which fall under the statutory categories of a machine and a process.
Step 2A: The Judicial Exceptions
Prong 1: do the claims recite an exception?
Claim(s) 1, 8, and 15 is/are directed to the abstract idea of a mathematical concept and/or a mental process. Specifically, the claims recite using "one or more neural networks" to perform mathematical data manipulation, namely, extracting, denoising, and combining texture and pixel data to generate an upsampled image. These limitations represent mathematical algorithms and the abstract processing of information.
Prong 2: is the exception integrated into a practical application?
The claim(s) does/do not integrate the abstract idea into a practical application. The claims recite generating an upsampled image using the abstract idea, but do so at a high level of functional generality ("based, at least in part, on: denoising... and combining"). The recitation of generic hardware ("a processor," "one or more circuits," "a system") simply instructs the practitioner to apply the abstract mathematical idea on a generic computer. It does not provide a specific, technical improvement to the functioning of the computer itself or an otherwise eligible, specific technological process.
Step 2B: The Inventive Concept
Do the claims amount to "significantly more" than the exception?
The additional elements in the claim(s), whether considered individually or as an ordered combination, do not amount to significantly more than the abstract idea. The generic processors, circuits, and systems merely provide a conventional technological environment to execute the neural network's mathematical data processing. This amounts to no more than well-understood, routine, and conventional computer functions in the field.
Conclusion: Claim(s) 1, 8, and 15 is/are directed to an abstract idea and lacks an inventive concept. Claim(s) 1, 8, and 15 is/are rejected as ineligible subject matter under 35 U.S.C. § 101.
Regarding dependent claims 2-5, 6-7, 9-12 and 16-19: limitations in these dependent claims have been examined in a similar way as to the above independent claims. It was found that claims 2-5, 6-7, 9-12 and 16-19 are ineligible subject matter under 35 U.S.C. § 101:
Claims 2, 9 and 16: Ineligible. Merely identifies an additional data input (a noisy version); insignificant extra-solution activity.
Claims 3, 10 and 17: Ineligible. Merely specifies the source of the data; mere data gathering.
Claims 4, 11 and 18: Ineligible. Simply defines the data types (high vs. low resolution); acts as a generic field-of-use limitation.
Claims 5, 12 and 19: Ineligible. Adds another generic, functional recitation of a "neural network"; still abstract mathematical data processing.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vogels et al (US20180293711A1) in view of Ma et al (US20210027426A1).
Regarding claims 1, 8 and 15, Vogels teaches a processor comprising: one or more circuits to cause one or more neural networks to generate an upsampled version of one or more images based, at least in part, on:
(Vogels, " FIG. 3 illustrates an exemplary denoising pipeline according to some embodiments of the present invention. The denoising method may include inputting raw image data (310) from a renderer 302, preprocessing (320) the input data, and transforming the preprocessed input data through a neural network 330", [0085]; Ma, "the image to be processed is processed by a target neural network model to obtain a target image, the target image being a denoised image with a second resolution,", [0006]; "the second resolution being higher than the first resolution,", [0007]; "A super-resolution technology is used to process a low-resolution image to further improve a resolution and sharpness of the image and thus to achieve a better sensory effect.", [0027]; Vogels teaches a image denoising pipeline; Ma expressly teaches using a neural network to process an image to obtain an upsampled version with a higher resolution)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate Ma into Vogels in order to improve image resolution and sensory effect via a neural network upsampling model. The combination of Vogels and Ma also teaches other enhanced capabilities.
The combination of Vogels and Ma further teaches:
denoising, separately from denoising pixel data, texture data extracted from the one or more images; and
(Vogels, "it may be advantageous to factor out the noisy albedo from the diffuse color in a preprocessing step. Albedo, also referred to as texture, is a measure of local diffuse reflecting power of a surface", "It has been demonstrated that denoising the albedo and irradiance separately can improve the performance", [0114]; extracting and denoising texture data (albedo) separately from pixel data (irradiance/diffuse color) to improve overall performance)
combining the denoised texture data with the separately denoised pixel data.
(Vogels, "The method may further include multiplying back the albedo to the denoised effective irradiance to obtain a denoised irradiance", [0119]; combining the separately denoised texture data (albedo) with the denoised pixel data (irradiance))
Regarding claims 2, 9 and 16, the combination of Vogels and Ma teaches its/their respective base claim(s).
The combination further teaches the processor of claim 1, wherein the one or more circuits are to generate the upsampled version of one or more images further based on a noisy version of the one or more images.
(Vogels, "a supervised machine learning approach to estimate g using a dataset D of N example pairs of noisy image patches", [0080]; Ma, "the acquired image to be processed has a low-resolution, and is processed by a target neural network to obtain the denoised high-resolution image.", [0040]; Vogels teaches using noisy image inputs; Ma teaches generating an upsampled high-resolution version from the initial image; together Vogels and Ma teach generating the upsampled version based on a noisy version of the image)
Regarding claims 3, 10 and 17, the combination of Vogels and Ma teaches its/their respective base claim(s).
The combination further teaches the processor of claim 1, wherein the texture data is extracted from a noisy version of the one or more images.
(Vogels, "it may be advantageous to factor out the noisy albedo from the diffuse color in a preprocessing step. Albedo, also referred to as texture, is a measure of local diffuse reflecting power of a surface.", [0114]; extracting the texture data (albedo) from the noisy version of the image)
Regarding claims 4, 11 and 18, the combination of Vogels and Ma teaches its/their respective base claim(s).
The combination further teaches the processor of claim 1, wherein the upsampled version of the one or more images is a high-resolution image and the one or more images are one or more low-resolution images.
(Ma, "That is, the acquired image to be processed has a low-resolution, and is processed by a target neural network to obtain the denoised high-resolution image.", [0040]; the upsampled version is a high-resolution image and the initial one or more images are low-resolution images)
Regarding claims 5, 12 and 19, the combination of Vogels and Ma teaches its/their respective base claim(s).
The combination further teaches the processor of claim 1, wherein the one or more circuits are to generate the denoised pixel data of the one or more images using a neural network to denoise a noisy version of the one or more images.
(Vogels, "a supervised machine learning approach to estimate g using a dataset D of N example pairs of noisy image patches and their corresponding reference color information", [0080]; "model the denoising function gin Eq. (14) with a deep convolutional neural network (CNN).", [0084]; generating denoised pixel data using a convolutional neural network to denoise a noisy version of the images)
Regarding claims 6, 13 and 20, the combination of Vogels and Ma teaches its/their respective base claim(s).
The combination further teaches the processor of claim 1, wherein the one or more circuits are to generate the denoised pixel data of the one or more images by separately denoising a diffuse light version of a noisy one or more images and a specular light version of the noisy one or more images.
(Vogels, "the input image can be decomposed into diffuse and specular components ... The diffuse and specular components are then independently preprocessed, filtered, and postprocessed, before recombining them to obtain the final image.", [0111]; generating denoised pixel data by separately denoising the diffuse and specular components of the noisy image)
Regarding claims 7 and 14, the combination of Vogels and Ma teaches its/their respective base claim(s).
The combination further teaches the processor of claim 1,
wherein the one or more circuits are to generate the denoised pixel data of the one or more images by separately denoising a diffuse light version of a noisy one or more images and a specular light version of the noisy one or more images,
(Vogels, "the input image can be decomposed into diffuse and specular components ... The diffuse and specular components are then independently preprocessed, filtered, and postprocessed", [0111]; separately denoising the diffuse light version and specular light version)
wherein the one or more neural networks are to use different neural networks to denoise the diffuse light version and the specular light version.
(Vogels, "transforming the preprocessed diffused component through a diffuse network 824", "transforming the preprocessed specular component through a specular network 834", [0112]; "the diffuse network 924 and the specular network 1134 are pre-trained separately on the diffuse references and specular references, respectively.", [0130]; using different neural networks to separately denoise the diffuse and specular light versions)
Response to Arguments
Applicant's arguments filed on 6/29/2026 with respect to the 35 U.S.C. § 103 rejection to one or more of the pending claims have been fully considered but are moot in view of the new ground(s) of rejection.
Regarding claim(s) 1-5, 6-12, 15-19, Applicant, in the remarks, argues that the 35 U.S.C. § 101 rejection on claim eligibility is improper.
The Examiner respectfully disagreed.
Applicant's arguments have been fully considered but are not persuasive. The rejection of claims 1-5, 6-12, and 15-19 under 35 U.S.C. § 101 is maintained.
Applicant contends the claims integrate the abstract idea into a practical application under Step 2A, Prong Two, because they improve computer functioning or another technology. While MPEP 2106.04(d)(1) and 2106.05(a) recognize such improvements, Applicant overlooks the controlling requirement in the very sections it quotes: the specification must set forth an improvement and the claim itself must reflect the disclosed improvement. The claims here do not.
The independent claims recite, at a high level of generality, causing neural networks to generate an upsampled image "based, at least in part, on" denoising texture data separately from pixel data and combining the two. This is a purely functional, result-oriented recitation of a mathematical data-manipulation process. The claims specify no particular network architecture and no concrete technique by which the asserted benefit is achieved. The open-ended "based, at least in part, on" language confirms no specific mechanism is required. A claim reciting the desired outcome of an improvement, without the technical means producing it, does not reflect that improvement as MPEP 2106.05(a) requires.
Applicant's reliance on paragraphs [0002] and [0097] fails for the same reason. Those passages describe generating high-quality video quickly with fewer resources, for use in a video or video game, yet none of that appears in the claims. The claims recite no video, no speed or latency constraint, and no reduction in computational resources. The alleged improvement resides in the disclosure, not the claim scope.
Applicant's analogy to Claim 3 of Example 48 is unpersuasive. That claim's eligibility rested on a specific ordered combination of concrete operations tying abstract steps to a defined improvement in speech-to-text technology. Applicant's claims contain no comparable ordered combination, only generic "denoising" and "combining" by unspecified neural networks.
Under Step 2B, the additional elements, "a processor", "one or more circuits", "a system" and generic "neural networks", remain instructions to apply the abstract idea using well-understood, routine, and conventional components. The claims do not amount to significantly more, and the § 101 rejection is maintained.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIANXUN YANG whose telephone number is (571)272-9874. The examiner can normally be reached on MON-FRI: 8AM-5PM Pacific Time.
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/JIANXUN YANG/
Primary Examiner, Art Unit 2662 8/8/2026
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