DETAILED ACTION
Claims 1-20 are currently pending.
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 § 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the mathematical steps of training a machine learning model without significantly more.
Regarding claim 1, under step 2A prong 1, the claim recites the mathematical limitations:
obtaining a target image by upscaling a downscaled image obtained by downscaling the reference image;
obtaining an adversarial image by upscaling an optimized distorted image obtained by distorting the downscaled image;
obtaining, as a second parameter values, a result of subtracting a scaled gradient from the first parameter values, wherein the scaled gradient is a result of product of a defined learning rate value and a gradient of a loss function with respect to the first parameter values for the reference image, wherein the loss function is a maximum among:
zero; and
a result of adding a define hinge loss gap hyper-parameter value to a result of subtracting the first result value from the second result value;
including the second parameter values in the machine learning model.
Under step 2A prong 2, the claim recites additional limitations
obtaining a trained image quality assessment machine learning model by training a machine learning model using first training data including a reference image, wherein the machine learning model includes:
obtaining, from the machine learning model with first parameter values, a first result value for the target image relative to the reference image;
obtaining from the machine learning model with the first parameter values, a second result value for the adversarial image relative to the reference image.
This judicial exception is not integrated into a practical application because these limitations amount to data gathering steps.
Under step 2B, The claim fails to recite any additional elements which would amount to significantly more than the abstract idea.
Regarding claim 10, the claim follows the same logic as claim 1 above with additional elements of a non-transitory computer readable medium and a processor configured to execute instructions stored on the non-transitory computer readable medium. These amount to generic computer processing components and fail to remedy the abstract idea.
Regarding claim 17, the claim follows the same logic as claim 1 above with additional elements of a non-transitory computer readable medium, comprising executable instructions that, when executed by a processor, facilitate performance operations. These amount to generic computer processing components and fail to remedy the abstract idea.
Regarding claims 2, 11, and 18, the claims add additional mathematical steps for calculating an optimized perturbation. These fail to remedy the abstract idea of the independent claims.
Regarding claims 3 and 12, the claims add additional mathematical steps for calculating an optimized perturbation. These fail to remedy the abstract idea of the independent claims.
Regarding 4, 13, and 19the claims add additional mathematical steps for calculating an optimized perturbation. These fail to remedy the abstract idea of the independent claims.
Regarding claim 5, the claim adds the limitation that subsequent to training the machine learning model is the trained image quality assessment machine learning model. This amount to data gathering and fails to remedy the abstract idea of the independent claim.
Regarding claims 6, 14, and 20, the claim adds limitations that the machine learning model is a classification model, includes a backbone model, and subsequent to training the machine learning model is the trained image quality assessment machine learning model. These amount to data gathering and fails to remedy the abstract idea of the independent claims.
Regarding claim 7, the claim adds a second iteration of the steps outlined in claim 1. The claim follows the same logic as claim 1 and therefore fails to remedy the abstract idea of claim 1.
Regarding claims 8 and 15, the claims add limitations of a plurality of reference images. These amounts to data gathering and fails to remedy the abstract idea of the independent claims.
Regarding claims 9 and 16, the claims add the limitation of reference videos. These amounts to data gathering and fails to remedy the abstract idea of the independent claims
Allowable Subject Matter
Claims 1-20 are not rejected under the prior art and would be in condition for allowance if the above rejection under 35 U.S.C. 101 were overcome.
Regarding claim 1, neither the closest known prior art, nor any reasonable combination thereof, teaches:
obtaining, as second parameter values, a result of subtracting a scaled gradient from the first parameter values, wherein the scaled gradient is a result of a product of a defined learning rate value and a gradient of a loss function with respect to the first parameter values for the reference image, wherein the loss function is a maximum among:
zero; and
a result of adding a defined hinge loss gap hyper-parameter value to a result of subtracting the first result value from the second result value; and
including the second parameter values in the machine learning model.
Claims 2-9 depend from claim 1 and would therefore also be allowed.
Claims 10 and 17 would be allowed for similar reasons discussed above, claims 11-16 and 18-20 depend from claims 10 and 17 and would therefore also be allowable.
Jiang (US 2022/0351403) teaches a GAN network which receives a downsampled image and outputs a distorted upscale image from the generator. See [0018]. However, Jiang fails to teach the parameter values as claimed.
Ahn (US 2021/0142440) teaches the use of a hinge GAN loss to optimize the discriminator of a GAN. See [0499]. However Ahn fails to teach the parameters as claimed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Refer to PTO-892, Notice of References Cited for a listing of analogous art.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Molly K Wilburn whose telephone number is (571)272-3589. The examiner can normally be reached Monday-Friday 8am-4pm.
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/Molly Wilburn/Primary Examiner, Art Unit 2666