DETAILED ACTION
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f), because the claim limitation uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation is: “processing unit to render an image …” in claims 24 and 35. Because this claim limitation is being interpreted under 35 U.S.C. 112(f), it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation to avoid it being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recites sufficient structure to perform the claimed function so as to avoid it being interpreted under 35 U.S.C. 112(f).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 24 and 35 are rejected under 35 U.S.C. 112(a), because the claim purports to invoke 35 U.S.C. 112(f), but fails to recite a combination of elements as required by that statutory provision and thus cannot rely on the specification to provide the structure, material or acts to support the claimed function.
As such, the claim recites a function that has no limits and covers every conceivable means for achieving the stated function, while the specification discloses at most only those means known to the inventor. Accordingly, the disclosure is not commensurate with the scope of the claim.
Particularly, they are considered as single means claims. See MPEP 2181(V).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless – (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 24-26 and 28-43 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Vogels (US 2020/0,184,313).
Referring to claims 24, 35 and 41, Vogels discloses a processor (fig. 25, system 2500) comprising a processing circuitry (fig. 14, adaptive sampling system 1400; paras.0195-0197) programmed to:
receive output (fig. 3, output 350) from a machine learning model (fig. 3, network 330; paras.0082-0083, machine learning CNN) jointly predicting (fig. 4A, predictor 440), for an individual pixel (para.0083, input pixels) of a denoised image (fig. 5A, denoised frame 590), a pixel value (paras.0085-86, Monte Carlo MC renderer output pixel) and an uncertainty value (fig. 16, respective error value 1606; para.0197, error value for each pixel; para.0294, uncertainty in relationship between input and reference; figs. 26A/26B, predicted value with high/low uncertainty) of the individual pixel; and
render an image (fig. 16, render a second input image 1608) using a ray-traced samples (fig. 16, first input image 1602/1606) distributed based on the predicted uncertainty value (fig. 16, distributed based on respective error value 1606).
As to claims 25, 36 and 42, Vogels discloses the apparatus of claim 24,
wherein the predicted pixel value output from the machine learning model represents a mean of a predicted distribution (para.0057, mean of samples from pixel’s sample distribution) of a possible pixel value (para.0057, pixel) of the individual pixel of the denoised image, and
the predicted uncertainty value output from the machine learning model (fig. 16, generate respective error value from first neural network 1606) represents uncertainty of the predicted distribution of the possible pixel value (fig. 16, respective error value represents difference between denoised data and input data for the pixel 1606).
As to claims 26 and 37, Vogels discloses the apparatus of claim 24,
wherein the predicted pixel value output from the machine learning model represents an expected value of a predicted distribution of possible pixel value of the individual pixel (para.0059, expected error of MC predicted distribution), and
the processing circuitry is to store (fig. 14, sampling map 1440) the predicted uncertainty value (fig. 16, sampling map 1606; para.0197, sampling map 1440 includes respective error values; para.0294, uncertainty) representing uncertainty of the predicted distribution in an uncertainty map corresponding to the denoised image (fig. 16, respective error value represents difference between denoised data and input data for the pixel 1606).
As to claim 28, Vogels discloses the apparatus of claim 24, wherein the processing circuitry is programmed to use machine learning model (fig. 3, network 330; paras.0082-0083, machine learning CNN) to predict an uncertainty map (fig. 16, sampling map 1606) comprising a channel (para.0197, respective error value of each pixel; fig. 3, output 350/360 RGB channels of pixel; para.0085) that represent the uncertainty value as: variance (para.0195, variance of each pixel in adaptive sampling) of a predicted distribution (para.0057, sample distribution) of the possible pixel value of the individual pixel of the denoised image (fig. 14, denoised image 1604).
As to claims 29 and 38, Vogels discloses the apparatus of claim 24,
wherein the processing circuitry is programmed to generate a sample distribution (para. 0239, sample distributions) that distributes the ray-traced samples (para.0055-0056, ray traced distributions) based on the predicted uncertainty value (fig. 16, distributed based on respective error value 1606) and a number of previously taken samples (para.0232, previous frame), and
allocate a sampling budget (para.0198, such as start with 16 samples per pixel) based on the sampling distribution (para.0195, adaptive sampling of noise sample distribution).
As to claim 30, Vogels discloses the apparatus of claim 24, wherein the processing circuitry is programmed to render the image based on combining an input image (fig. 14, input to denoised image 1422) used by the machine learning model (fig. 3, network 330; paras.0082-0083, machine learning CNN) to generate the denoised image with the ray-traced samples (fig. 14, input to noisy image 1412) using a tracked number of rendered samples per pixel (para.0198, such as start with 16 samples per pixel).
As to claim 31, Vogels discloses the processor of claim 24, wherein the processing circuitry is programmed to apply the image in a subsequent pass (para.0198, such as start with 16 samples per pixel, then doubled in next iteration) through the machine learning model (para.0083, machine learning).
As to claim 32, Vogels discloses the processor of claim 24, wherein the processing circuitry is programmed to operate an adaptive rendering loop (para.0198, such as start with 16 samples per pixel, then doubled in next iteration) to render images with successively higher ray-traced sample counts in successive iteration until a completion criterion is satisfied (para.0198, such as tripled or quadrupled).
As to claim 33, Vogels discloses the processor of claim 24, wherein the processing circuitry is programmed to operate an adaptive rendering loop to render images with successively higher ray-traced sample counts in successive iteration until a completion criterion is satisfied (para.0198, such as start with 16 samples per pixel, then doubled in next iteration until quadrupled), wherein the completion criterion comprises: an expiration of a sampling budget (para.0198, tripled/quadrupled).
As to claims 34, 40 and 43, Vogels discloses the processor of claim 24, wherein the processor is comprised in: a computer graphics system (fig. 25, system 2500, graphics processor 2510).
As to claim 39, Vogels discloses the apparatus of claim 35, wherein the hardware processors are programmed to track a number of rendered samples per pixel (para. 0083, input to Monte Carlo path tracing rendering) and generate the image based on averaging the ray-traced samples (para. 0085, averaged per-pixel data) into a corresponding input image (para.0086, Monte Carlo denoising output image) associated with the denoised image using the number of rendered samples per pixel.
Claims 24 and 35 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Pohl (US 2020/0,211,157).
Referring to claims 24 and 35, Pohl discloses a processor (fig. 1, system 100; paras. 0146-0148, adaptive ray tracing system with machine learning) comprising a processing unit (fig. 16, GPU 1604; para.0049, machine learning logic) programmed to: [functional languages ignored].
See MPEP 2114(II). “Apparatus claim cover what a device is, not what a device does.” The only structure component for the claims is “a processor comprising processing circuitry”. The claimed invention appeared to be software program executing on general purpose computer systems, which might be better suitable for CRM or method claims.
Allowable Subject Matter
Claim 27 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Applicant’s argument (pp.11-12) filed on May 5, 2026 persuasive.
Response to Arguments
Applicant’s arguments have been fully considered, but they are not deemed to be persuasive.
Applicant argues that Vogels does not disclose jointly predicting a pixel value at an uncertainty value (pp. 9-11).
Vogels discloses the uncertainty value with respective error value of each respective pixel. The respective error value is a difference between denoised pixel data and input pixel data. The uncertainty is related to each pixel in a given image would have different error value. Vogels discloses a convolutional neural network performs machine learning to jointly generate the respective error value of each pixel (fig. 16, 1606). For example, obtaining the predicted uncertainty value of an individual pixel through obtaining the difference between denoised data and input data of the pixel. The sampling map includes a distribution of the error value of each pixel for an image. Fig. 16 describes an operation rather than a theoretic concept behavior.
Applicant argues that the functional language of claims 24 and 35 are proper as relied on “features of an apparatus may be recited either structurally or functionally” in MPEP 2114 (p.12).
Citing In re Schreiber, Examiner reminds applicant to provide proof that the claimed functional language limitations do not find to be inherent capable in the prior art processor structure. In Schreiber, the conical top of a popcorn machine is structurally anticipated by the prior art - Harz, the functional language does not limit Schreiber’s claim patentable over Harz.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner Cheng-Yuan Tseng whose telephone number is (571)272-9772, and fax number is (571)273-9772. The examiner can normally be reached on Monday through Friday from 09:00 to 17:30 Eastern Time. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alicia Harrington can be reached on (571)272-2330. The fax phone number for the organization where this application or proceeding is assigned is (571)273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at (866)217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call (800)786-9199 (IN USA OR CANADA) or (571)272-1000.
/CHENG YUAN TSENG/Primary Examiner, Art Unit 2615
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