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 .
Priority
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on November 7, 2024 complies with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
35 USC § 101 Statutory Analysis
The claims do not recite any of the judicial exceptions enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance. Further, the claims do not recite any method of organizing human activity, such as a fundamental economic concept or managing interactions between people. Finally, the claims do not recite a mathematical relationship, formula, or calculation. Thus, the claims are eligible because they do not recite a judicial exception.
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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 and 5-16 are rejected under 35 U.S.C. §102(a)(1) as being anticipated by Kim et al. (U.S. Patent Application Publication No. US 2021/00739745 A1) (hereafter referred to as “Kim”).
With regard to claim 1, Kim describes generating a first image by inputting an input image or an image based on the input image into a first machine learning model (see Figure 5, element S110, the low-resolution image is inputted into the first machine learning model corresponds to applicant’s first image); generating a second image by inputting the generated first image into a second machine learning model different from the first machine learning model (see Figure 5, elements S130 and S140, the recognized object image output from the second machine learning model corresponds to applicant’s second image); and generating a third image using the first image and the second image, wherein each of the first image, the second image, and the third image has a larger number of pixels than those of the input image (see Figure 5, element S160, the output final image corresponds to applicant’s third image, and see Figure 6, which illustrates that the first image, the second image, and the third image has a larger number of pixels than those of the input image), and wherein the second image has fewer high-frequency components than those of the first image (see Figure 5, the recognized object image has fewer high-frequency components than those of the first image, e.g. the table and window in the low resolution image).
As to claim 5, Kim describes wherein the high-frequency components are frequency components higher than frequency components of the input image (see Figures 5 and 6).
In regard to claim 6, Kim describes wherein the number of pixels of the first image, the number of pixels of the second image, and the number of pixels of the third image are equal to one another (see Figure 6).
With regard to claim 7, Kim describes wherein the third image is generated by calculating a weighted average of the first image and the second image by using a first weight of the first image and a second weight of the second image (refer for example to paragraphs [0035], [0043], [0047] and [0048]).
As to claim 8, Kim describes a processor (see Figure 1 and refer for example to paragraphs [0071] through [0074]) configured to generate a first image by inputting an input image or an image based on the input image into a first machine learning model (see Figure 5, element S110, the low-resolution image is inputted into the first machine learning model corresponds to applicant’s first image); generate a second image by inputting the first image into a second machine learning model different from the first machine learning model (see Figure 5, elements S130 and S140, the recognized object image output from the second machine learning model corresponds to applicant’s second image); and generate a third image by using the first image and the second image, wherein each of the first image, the second image, and the third image has a larger number of pixels than those of the input image (see Figure 5, element S160, the output final image corresponds to applicant’s third image, and see Figure 6, which illustrates that the first image, the second image, and the third image has a larger number of pixels than those of the input image), and wherein the second image has fewer high-frequency components than those of the first image (see Figure 5, the recognized object image has fewer high-frequency components than those of the first image, e.g. the table and window in the low resolution image).
In regard to claim 9, Kim describes an image processing apparatus according to claim 8 and an image sensor (see Figure 1 and refer for example to paragraphs [0071] through [0075]).
With regard to claim 10, Kim describes a non-transitory computer-readable memory storing a program that causes a computer to execute the image processing method according to claim 1 (see Figure 1 and refer for example to paragraphs [0071] through [0074]).
As to claim 11, Kim describes acquiring a first training image having a low resolution or an image based on the first training image, and a second training image having a high resolution corresponding to the first training image (see Figure 5, element S110, the low-resolution image is inputted into the first machine learning model corresponds to applicant’s first image); and training a first machine learning model and a second machine learning model based on the first training image or the image based on the first training image and the second training image (see Figure 5, elements S130 and S140, the recognized object image output from the second machine learning model corresponds to applicant’s second image), wherein a calculating method of a loss during training of the first machine learning model and a training method of a loss during learning of the second machine learning model are different from each other (refer for example to paragraphs [0130] through [0135]).
In regard to claim 12, Kim describes wherein the loss during the training of the first machine learning model is calculated using an adversarial loss based on a first upscaled patch generated by inputting the first training image to the first machine learning model, and the second training image, and wherein the loss during the training of the second machine learning model is calculated using a mean squared error based on a second upscaled patch generated by inputting the first upscaled patch to the second machine learning model (refer to paragraphs [0130] through [0135]).
With regard to claim 13, Kim describes wherein the first machine learning model and the second machine learning model are simultaneously trained (refer for example to paragraphs [0148] through [0149]).
As to claim 14, Kim describes a processor (see Figure 1 and refer for example to paragraphs [0071] through [0074]) configured to acquire a first training image having a low resolution or an image based on the first training image, and a second training image having a high resolution corresponding to the first training image (see Figure 5, element S110, the low-resolution image is inputted into the first machine learning model corresponds to applicant’s first image); and train a first machine learning model and a second machine learning model based on the first training image or the image based on the first training image and the second training image (see Figure 5, elements S130 and S140, the recognized object image output from the second machine learning model corresponds to applicant’s second image), wherein a calculating method of a loss during learning of the first machine learning model and a calculating method of a loss during learning of the second machine learning model are different from each other (refer for example to paragraphs [0130] through [0135]).
In regard to claim 15, Kim describes a non-transitory computer-readable memory storing a program that causes a computer to execute the learning method according to claim 11 (see Figure 1 and refer for example to paragraphs [0071] through [0074]).
With regard to claim 16, Kim describes an image processing apparatus according to claim 8 (see Figure 1 and refer for example to paragraphs [0071] through [0074]); and a control apparatus communicable with the image processing apparatus (see Figure 1 and refer for example to paragraphs [0071] through [0074]), wherein the control apparatus includes a transmitter configured to transmit a request regarding execution of processing to an input image or an image based on the input image (see Figure 1, element 140 and refer for example to paragraphs [0075] and [0076]), to the image processing apparatus, and wherein the image processing apparatus generates the third image by executing the processing to the input image according to the request (see Figure 5, element S160, the output final image corresponds to applicant’s third image, and also see Figure 6, which illustrates the output final image corresponds to applicant’s third image).
Claim Rejections - 35 USC § 103
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 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 35 U.S.C. §103(a) 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 sKimll 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 2-4 are rejected under 35 U.S.C. §103(a) as being unpatentable over Kim et al. (U.S. Patent Application Publication No. US 2021/00739745 A1) in view of Karras (U.S. Patent Application Publication No. US 2019/0171936 A1) (hereafter referred to as “Karras”), Khan (U.S. Patent Application Publication No. US 2024/0062333 A1) (hereafter referred to as “Khan”), or Ki (U.S. Patent Application Publication No. US 2024/0223917 A1) (hereafter referred to as “Ki”).
The arguments advanced in section 7 above, as to the applicability of Kim, are incorporated herein.
With regard to claims 2-4, Kim describes that “In machine learning or deep learning, learning optimization algorithms may be deployed to minimize a cost function”, “In machine learning or deep learning, learning optimization algorithms may be deployed to minimize a cost function”, that “the step size and may also include methods that increase optimization accuracy … and increases optimization accuracy by adjusting the step size and step direction”, and that the “Learning rate and accuracy of an artificial neural network rely not only on the structure and learning optimization algorithms of the artificial neural network but also on the hyperparameters thereof. Therefore, in order to obtain a good learning model, it is important to choose a proper structure and learning algorithms for the artificial neural network, but also to choose proper hyperparameters” (see paragraphs [0137] through [0145]). Although Kim does not explicitly describe wherein a scale of the second machine learning model is smaller than that of an output layer of the first machine learning model, wherein a scale of the second machine learning model is smaller than half of an overall scale of the first machine learning model and wherein the output layer of the first machine learning model includes a single convolution layer, and wherein the scale of each of the output layer of the first machine learning model and the second machine learning model is expressed by the following equation:
∑
l
=
1
L
k
l
×
k
l
×
c
l
×
n
l
where L is the number of convolution layers, kl is a kernel size of a convolution filter in an 1-th layer (l=1 to L), cl is the number of channels in the convolution filter in the l-th layer, and
n
l
is the number of convolution filters in the l-th layer, the variation of sizes of machine learning models well known and widely utilized in the prior art.
Karras discloses a progressive modification of neural networks system (see Figure 1A and refer to the abstract) which describes using multiple neural networks to obtain details in video image (refer for example to paragraph [0004]) and utilizes variations of the sizes of machine learning models (refer for example to paragraphs [0031] and [0092]).
Khan discloses an upscaling of image data system (see Figure 2 and refer to the abstract) which describes using multiple neural networks to obtain details in video image (refer for example to paragraph [0014]) and utilizes variations of the sizes of machine learning models (refer for example to paragraphs [0014] and [0067]).
Ki discloses a system for high-resolution image zooming (see Figure 1 and refer to the abstract) which describes using multiple neural networks to obtain details in video image (refer for example to paragraph [0017]) and utilizes variations of the sizes of machine learning models (refer for example to paragraph [0105], and to paragraphs [0111] through [0114]).
Given the teachings of the references and the same environment of operation, namely that of image processing using multiple machine learning models, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Kim system in the manner described by Ki, Khan or according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested by Kim (see paragraphs [0137] through [0145]), Karras (refer for example to paragraph [0004]), Khan (refer for example to paragraph [0014]), Ki (refer for example to paragraph [0017]) which fails to patentably distinguish over the prior art absent some novel and unexpected result.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Choi, Elron, Levinshtein, Jung, Van Beek and Wang all disclose systems similar to applicant’s claimed invention.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jose L. Couso whose telephone number is (571) 272-7388. The examiner can normally be reached on Monday through Friday from 5:30am to 1:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella, can be reached on 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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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.
/JOSE L COUSO/Primary Examiner, Art Unit 2667
June 25, 2026
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