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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/23/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 06/27/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – 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.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) 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 limitations are: “differentiable module”, “base logic” and “refinement function” in claims 1-9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are 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/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-9 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: “a differentiable module of a differentiable model of an image signal processor” seems to be an image processing software module, which does not fall within any of the four categories without embedding on a “non-transitory computer-readable medium”.
Claim Rejections - 35 USC § 102
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 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 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Yao et al (“Yao” hereinafter, U.S. Publication No. 2022/0207656 A1).
As per claim 1, Yao discloses a differentiable module of a differentiable model of an image signal processor, the image signal processor (paragraph [0043]: graphical processing unit) comprising a pipeline of functional blocks, wherein the differentiable module is configured to implement a single functional block of the pipeline (paragraph [0184] and figures 9A & 9B for a pipeline of functional blocks), the differentiable module comprising: base logic configured to receive an input image signal and to process the received input image signal by performing a base image processing function that represents a task of the functional block of the pipeline implemented by the module (figure 12: layer 2 of node 2 is the claimed “base logic” which inputs an image and outputs an image through the pipeline); a refinement function configured to receive the input image signal and to process the received input image signal in parallel to the processing of the received input image signal by the base logic (figure 12: layer 3 of node 3 is the claimed “refinement function”); and combining logic configured to combine the processed image signal from the base logic and the processed image signal from the refinement function to determine an output image signal to be outputted from the differentiable module (figure 12: node 1 is the claimed “combining logic” that combines the outputs from the node 2 and node 3).
As per claim 2, Yao teaches wherein the differentiable module is representable with a command stream as a combination of operations from a set of elementary neural network operations which are available on an inference device, for implementation on the inference device (figure 9 is an example of deep neural networks).
As per claim 3, Yao discloses wherein the set of elementary neural network operations consists of one or more of: a convolutional operation; a pooling operation; an element-wise operation; an activation operation; a local response normalisation operation; a tensor rescale operation; a channel permutation operation; and a reshaping operation (figure 31 is an example of convolutional neural network).
As per claim 4, Yao discloses wherein the inference device is a neural network accelerator (figure 16 is an example of accelerator architectures).
As per claim 5, Yao discloses wherein the base image processing function that the logic of the differentiable module is configured to perform is a function that, when applied to an image signal, refines the image signal in one regard (paragraph [0200] the layer 3 in node 3 is an additional neural network to perform unsupervised training of large neural network, which helps refine the image signal output as explained in paragraph [0195]).
As per claim 6, Yao discloses wherein the refinement function is configured to supplement the refinement performed by the base image processing function (as explained above, the parallel processing of layer 3 in node 3 supplements the processing of layer 2 in node 2).
As per claim 7, Yao discloses wherein in supplementing the refinement performed by the base image processing function the refinement function is configured to process the input image signal in a manner such that the combining logic corrects an error remaining in the processed image signal after the base logic has processed the received input image signal (paragraph [0195]: the errors are propagated back through the system in neural network training).
As per claim 8, Yao teaches wherein a refinement of the image signal performed by the refinement function is small in comparison to a refinement of the image signal performed by the base logic (the size of the layers in node 2 and node 3 can be adjusted in accordance to a system design).
As per claim 9, Yao teaches wherein the differentiable module is any of a demosaicing module, a sharpener module, a black-level subtraction module, a spatial denoiser module, a global tone mapping module, a channel gain module, an automatic white balance, or a colour correction module (paragraph [0096]: deep learning neural network is implemented to perform denoising).
As per claim 10, see figures 6, 9 and 12 and paragraph [0207] & [0347] for inference algorithm on a logic device as shown in figure 27.
As per claim 11, see explanation in claim 1.
As per claim 12, see explanation in claim 2.
As per claim 13, see explanation in claim 3.
As per claim 14, see explanation in claim 4.
As per claim 15, see explanation in claim 5.
As per claim 16, see explanation in claim 6.
As per claim 17, see explanation in claim 7.
A per claim 18, see explanation in claim 8.
As per claim 19, see explanation in claim 9.
As per claim 20, see explanation in claim 1 and 10, the examiner notes Yao’s system is a computer-like system, which inherently includes a non-transitory computer readable storage medium.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sharma et al, U.S. Publication No. 2020/0389588 A1, see Image Signal Processing optimization framework for computer vision application in figures 1-3.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TOM Y LU whose telephone number is (571)272-7393. The examiner can normally be reached Monday - Friday, 9AM - 5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella can be reached at (571) 272 - 7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TOM Y LU/Primary Examiner, Art Unit 2667
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