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 .
Status of Claims
This communication is in response to the Application Filed on 03/24/2026
Claims 1–12 are pending in this application.
Drawings
The drawing(s) filed on 09/25/2025 are accepted by the Examiner.
Response to Amendment
Applicant’s Amendments filed on 03/24/2026 has been entered and made of record.
Currently pending Claim(s):
Independent Claim(s):
Amended Claim(s):
1–12
1, 11 and 12
1, 11 and 12
Response to Applicant’s Arguments
This office action is responsive to Applicant’s Arguments/Remarks Made in an Amendment received on 03/24/2026.
In view of the amendments filed on 03/24/2026 to the specification, the specification objections is withdrawn.
In view of applicant Arguments/Remarks and amendment filed on 03/24/2026 with respect to independent claims 1, 11 and 12 under 35 U.S.C 103, claim rejection has been fully considered and the arguments are found to be persuasive (See Pages 8–10), therefore the claim rejection with respect to 35 U.S.C. 103 is withdrawn.
DOUBLE PATENTING REJECTION
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1, 8, 11 and 12 are provisionally rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 7 and 17–20 of copending Application No. 18/600,679.
Regarding Claims 1, 8, 11 and 12: The following table illustrates the correspondence between the claimed limitation of 1, 8, 11 and 12 of the current application and the claimed limitation of 7 and 17–20 of co-pending Application No. 18/600,679.
19/339,822 (instant app)
18/600,679 (copending Application)
Claim 1
A computer-implemented method for generating image pairs for training a machine learning model for image processing, the method including:
receiving a set of training images, comprising a first training image and a second training image, and scene information for the set of training images, the scene information indicating a first class of image for the first training image and a second class of image, different to the first class of image for the second training image;
selecting and applying one of a plurality of degradation models to training images in the set of training images to form a set of degraded images corresponding to the set of training images, wherein the selecting is based on the scene information and comprises:
selecting a first degradation model of the plurality of degradation models for applying to the first training image based on the scene information indicating the first class of image for the first training image; and
selecting a second degradation model of the plurality of degradation models, different to the first degradation model, for applying to the second training image based on the scene information indicating the second class of image for the second training image;
wherein each degraded image and corresponding training image forms an image pair for training a machine learning model
Claim 18
a first image pair of the plurality of image pairs is associated with a first class and the degraded image of the first image pair was generated by applying a first degradation model to the target training image of the first image pair; a second image pair of the plurality of image pairs is associated with a second class and the degraded image of the second image pair was generated by applying a second degradation model to the target training image of the second image pair;
Claim 17
wherein the machine learning was trained based on a plurality of image pairs, each image pair comprising a target training image and a degraded image
Claim 18
a first image pair of the plurality of image pairs is associated with a first class and the degraded image of the first image pair was generated by applying a first degradation model to the target training image of the first image pair; a second image pair of the plurality of image pairs is associated with a second class and the degraded image of the second image pair was generated by applying a second degradation model to the target training image of the second image pair; the first image pair is different to the second image pair and the first degradation model is different to the second degradation model
Claim 18
a first image pair of the plurality of image pairs is associated with a first class and the degraded image of the first image pair was generated by applying a first degradation model to the target training image of the first image pair; a second image pair of the plurality of image pairs is associated with a second class and the degraded image of the second image pair was generated by applying a second degradation model to the target training image of the second image pair;
Claim 19
wherein the first degradation model and not the second degradation model was selected for the first image pair due to the association of the first image pair with the first class and not the second class and the second degradation model and not the first degradation model was selected for the second image pair due to the association of the second image pair with the second class and not the first class
Claim 19
wherein the first degradation model and not the second degradation model was selected for the first image pair due to the association of the first image pair with the first class and not the second class and the second degradation model and not the first degradation model was selected for the second image pair due to the association of the second image pair with the second class and not the first class
Claim 18
the first image pair is different to the second image pair and the first degradation model is different to the second degradation model
Claim 19
wherein the first degradation model and not the second degradation model was selected for the first image pair due to the association of the first image pair with the first class and not the second class and the second degradation model and not the first degradation model was selected for the second image pair due to the association of the second image pair with the second class and not the first class
Claim 17
wherein the machine learning was trained based on a plurality of image pairs
Claim 8
The method of claim 2, wherein the at least one visual parameter includes at least one of: (i) brightness, (ii) contrast, (iii) saturation, (iv) vibrance,(v) whites, (vi) blacks, (vii) shadows and (viii) highlights.
Claim 7
The method of claim 1, wherein the at least one visual parameter comprises one or more of: (i) brightness, (ii) contrast, (iii) saturation, (iv) vibrance, (v) whites, (vi) blacks, (vii) shadows and (viii) highlights.
Claim 11
A computer processing system including one or more computer processors and computer-readable storage, the computer processing system configured to perform a method comprising:
receiving a set of training images, comprising a first training image and a second training image, and scene information for the set of training images, the scene information indicating a first class of image for the first training image and a second class of image, different to the first class of image for the second training image;
selecting and applying one of a plurality of degradation models to training images in the set of training images to form a set of degraded images corresponding to the set of training images, wherein the selecting is based on the scene information and comprises:
selecting a first degradation model of the plurality of degradation models for applying to the first training image based on the scene information indicating the first class of image for the first training image; and
selecting a second degradation model of the plurality of degradation models, different to the first degradation model, for applying to the second training image based on the scene information indicating the second class of image for the second training image;
wherein each degraded image and corresponding training image forms an image pair for training a machine learning model.
Claim 20
Non-transitory computer-readable storage storing instructions for a computer processing system, wherein the instructions, when executed by the computer processing system cause the computer processing system to perform a method comprising
Claim 17
wherein the machine learning was trained based on a plurality of image pairs, each image pair comprising a target training image and a degraded image
Claim 18
a first image pair of the plurality of image pairs is associated with a first class and the degraded image of the first image pair was generated by applying a first degradation model to the target training image of the first image pair; a second image pair of the plurality of image pairs is associated with a second class and the degraded image of the second image pair was generated by applying a second degradation model to the target training image of the second image pair; the first image pair is different to the second image pair and the first degradation model is different to the second degradation model
Claim 18
a first image pair of the plurality of image pairs is associated with a first class and the degraded image of the first image pair was generated by applying a first degradation model to the target training image of the first image pair; a second image pair of the plurality of image pairs is associated with a second class and the degraded image of the second image pair was generated by applying a second degradation model to the target training image of the second image pair;
Claim 19
wherein the first degradation model and not the second degradation model was selected for the first image pair due to the association of the first image pair with the first class and not the second class and the second degradation model and not the first degradation model was selected for the second image pair due to the association of the second image pair with the second class and not the first class
Claim 19
wherein the first degradation model and not the second degradation model was selected for the first image pair due to the association of the first image pair with the first class and not the second class and the second degradation model and not the first degradation model was selected for the second image pair due to the association of the second image pair with the second class and not the first class
Claim 18
the first image pair is different to the second image pair and the first degradation model is different to the second degradation model
Claim 19
wherein the first degradation model and not the second degradation model was selected for the first image pair due to the association of the first image pair with the first class and not the second class and the second degradation model and not the first degradation model was selected for the second image pair due to the association of the second image pair with the second class and not the first class
Claim 17
wherein the machine learning was trained based on a plurality of image pairs
Claim 12
Non-transitory computer-readable storage storing instructions for a computer processing system, wherein the instructions, when executed by the computer processing system cause the computer processing system to perform a method comprising:
receiving a set of training images, comprising a first training image and a second training image, and scene information for the set of training images, the scene information indicating a first class of image for the first training image and a second class of image, different to the first class of image for the second training image;
selecting and applying one of a plurality of degradation models to training image in the set of training images to form a set of degraded images corresponding to the set of training images, wherein the selecting is based on the scene information and comprises:
selecting a first degradation model of the plurality of degradation models for applying to the first training image based on the scene information indicating the first class of image for the first training image; and
selecting a second degradation model of the plurality of degradation models, different to the first degradation model, for applying to the second training image based on the scene information indicating the second class of image for the second training image;
wherein each degraded image and corresponding training image forms an image pair for training a machine learning model.
Claim 20
Non-transitory computer-readable storage storing instructions for a computer processing system, wherein the instructions, when executed by the computer processing system cause the computer processing system to perform a method comprising
Claim 17
wherein the machine learning was trained based on a plurality of image pairs, each image pair comprising a target training image and a degraded image
Claim 18
a first image pair of the plurality of image pairs is associated with a first class and the degraded image of the first image pair was generated by applying a first degradation model to the target training image of the first image pair; a second image pair of the plurality of image pairs is associated with a second class and the degraded image of the second image pair was generated by applying a second degradation model to the target training image of the second image pair; the first image pair is different to the second image pair and the first degradation model is different to the second degradation model
Claim 18
a first image pair of the plurality of image pairs is associated with a first class and the degraded image of the first image pair was generated by applying a first degradation model to the target training image of the first image pair; a second image pair of the plurality of image pairs is associated with a second class and the degraded image of the second image pair was generated by applying a second degradation model to the target training image of the second image pair;
Claim 19
wherein the first degradation model and not the second degradation model was selected for the first image pair due to the association of the first image pair with the first class and not the second class and the second degradation model and not the first degradation model was selected for the second image pair due to the association of the second image pair with the second class and not the first class
Claim 19
wherein the first degradation model and not the second degradation model was selected for the first image pair due to the association of the first image pair with the first class and not the second class and the second degradation model and not the first degradation model was selected for the second image pair due to the association of the second image pair with the second class and not the first class
Claim 18
the first image pair is different to the second image pair and the first degradation model is different to the second degradation model
Claim 19
wherein the first degradation model and not the second degradation model was selected for the first image pair due to the association of the first image pair with the first class and not the second class and the second degradation model and not the first degradation model was selected for the second image pair due to the association of the second image pair with the second class and not the first class
Claim 17
wherein the machine learning was trained based on a plurality of image pairs
Table 1
The table (Table 1) above shows that independent claims 1, 11 and 12 of this Application is not identical to the claims of copending Application No. 18/600,679. However, the claims are not patentably distinct. The copending Application is narrower than claims 1, 11 and 12 since it includes several additional limitations not found in claims 1, 11 and 12 of the instant Application.
With respect to claims 2–7, 9 and 10, claims 2–7, 9 and 10 do not correspond to any claims of copending Application No. 18/600,679.
Allowable Subject Matter
Objection
Claims 2–7, 9 and 10 are objected to as being dependent upon a rejected base claim, claim 1, respectively but would be allowable if rewritten in independent form including all of the limitations of the base claims and any intervening claims, once the nonstatutory double patenting rejection is overcome.
Claims 1, 8, 11 and 12 would be allowable provided the nonstatutory double patenting rejection is overcome.
Examiner’s Statements of Reasons for Allowance
Claims 1, 11 and 12 are allowed as applicant’s Arguments/Remarks filed on 03/24/2026 are persuasive on pages 8–10. Further Applicant’s Reply has been found to be persuasive and have shown that the claims have differentiated the claimed invention from the cited prior art. Upon completing an updated prior art search and considering the combination of limitations as presented as a whole for the claim, the feature highlighted below are considered an improvement over the prior art and have not been found to be anticipated or rendered obvious by a combination of prior art.
Independent Claim(s) 1, 11 and 12 recites, inter alia the uniquely distinct features as shown in the excerpt below:
[1] “A computer-implemented method for generating image pairs for training a machine learning model for image processing, the method including:
receiving a set of training images, comprising a first training image and a second training image, and scene information for the set of training images, the scene information indicating a first class of image for the first training image and a second class of image, different to the first class of image for the second training image;
selecting and applying one of a plurality of degradation models to training images in the set of training images to form a set of degraded images corresponding to the set of training images, wherein the selecting is based on the scene information and comprises:
selecting a first degradation model of the plurality of degradation models for applying to the first training image based on the scene information indicating the first class of image for the first training image; and
selecting a second degradation model of the plurality of degradation models, different to the first degradation model, for applying to the second training image based on the scene information indicating the second class of image for the second training image;
wherein each degraded image and corresponding training image forms an image pair for training a machine learning model”, as recited by independent claim 1, in combination with the other elements/steps of the claim.
[11] “A computer processing system including one or more computer processors and computer-readable storage, the computer processing system configured to perform a method comprising:
receiving a set of training images, comprising a first training image and a second training image, and scene information for the set of training images, the scene information indicating a first class of image for the first training image and a second class of image, different to the first class of image for the second training image;
selecting and applying one of a plurality of degradation models to training images in the set of training images to form a set of degraded images corresponding to the set of training images, wherein the selecting is based on the scene information and comprises:
selecting a first degradation model of the plurality of degradation models for applying to the first training image based on the scene information indicating the first class of image for the first training image; and
selecting a second degradation model of the plurality of degradation models, different to the first degradation model, for applying to the second training image based on the scene information indicating the second class of image for the second training image;
wherein each degraded image and corresponding training image forms an image pair for training a machine learning model”, as recited by independent claim 11, in combination with the other elements/steps of the claim.
[12] “Non-transitory computer-readable storage storing instructions for a computer processing system, wherein the instructions, when executed by the computer processing system cause the computer processing system to perform a method comprising:
receiving a set of training images, comprising a first training image and a second training image, and scene information for the set of training images, the scene information indicating a first class of image for the first training image and a second class of image, different to the first class of image for the second training image;
selecting and applying one of a plurality of degradation models to training image in the set of training images to form a set of degraded images corresponding to the set of training images, wherein the selecting is based on the scene information and comprises:
selecting a first degradation model of the plurality of degradation models for applying to the first training image based on the scene information indicating the first class of image for the first training image; and
selecting a second degradation model of the plurality of degradation models, different to the first degradation model, for applying to the second training image based on the scene information indicating the second class of image for the second training image;
wherein each degraded image and corresponding training image forms an image pair for training a machine learning model”, as recited by independent claim 12, in combination with the other elements/steps of the claim.
These features, considered in combination with the remainder of the claim’s limitations are not fairly disclosed, thought or suggested by the cited prior art. Specifically, the closest prior art (Previously cited), Lin et al. (US 20230298142 A1), Zhu et al. (US 11983853 B1), and Goto et al. (US 20200110994 A1), fails to either anticipate or render obvious the above underlined limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bobovich et al. (US 20180268584 A1) teaches thus, the training engine 110 may be configured to generate, based on a plurality of initialization images, a feature set and an augmented initialization image set. The neural network engine 140 may process the augmented initialization image set by at least applying, to each image in the augmented initialization image set, the weights (e.g., weight matrices) for detecting the features in the feature set. But Bobovich does not teach augmenting the data set based on the scene class.
Wrenninge et al. (US 10235601 B1) teaches For example, synthetic images can be augmented (e.g., scaled, translated, brightened, and/or darkened, etc.) by a parameterized factor, for which the value (e.g., for each image, for each sample, etc.) is determined by sampling a low-discrepancy sequence (LDS). But Bobovich does not teach augmenting the data set based on the scene class.
Almazan et al. (US 20200226421 A1) teaches by the data processor in the first server, the pre-trained convolutional neural network by sequentially processing a plurality of triplet of images and allowing a different size input for each image, each triplet containing a query image degraded by adding random noise to a region of the query image, a positive image corresponding to an image of a same subject as in the query image, and a negative image corresponding to an image of a different subject as in the query image. But Bobovich does not teach degrading the data set based on the scene class.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DION J SATCHER whose telephone number is (703)756-5849. The examiner can normally be reached Monday - Thursday 5:30 am - 2:30 pm, Friday 5:30 am - 9:30 am PST.
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/DION J SATCHER/Patent Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676
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