19Notice 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 .
Double Patenting
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 conflicting claims 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined 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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Claim(s) 1-19 is/are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-41 of U.S. Patent No. 12322069. Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is fully disclosed in the patent and thus is anticipated by the patent. For example, note the following relationship between the instant application claim 1 and patented claim 1 (examiner note: similarities to the patented claims have been underlined.).
Instant 19193881
Patent 12322069
Claim 1
An imaging support apparatus comprising:
a processor; and a memory connected to or built into the processor,
wherein the memory stores a first trained model,
the first trained model is a trained model used for control related to imaging performed by an imaging apparatus,
and the processor is configured to generate a second trained model used for the control by performing learning processing in which a first image, which is acquired by being captured by the imaging apparatus, and a set value, which is applied to the imaging apparatus in a case where the first image is acquired, are used as teacher data,
perform specific processing based on a first set value, which is output from the first trained model in a case where a second image is input to the first trained model, and a second set value, which is output from the second trained model in a case where the second image is input to the second trained model,
and display, on a display, information related to the second trained model,
and wherein the type of the first set value and the type of the second set value are the same.
Claim 1
An imaging support apparatus comprising:
a processor; and a memory connected to or built into the processor,
wherein the memory stores a first trained model,
the first trained model is a trained model used for control related to imaging performed by an imaging apparatus,
and the processor is configured to generate a second trained model used for the control by performing learning processing in which a first image, which is acquired by being captured by the imaging apparatus, and a set value, which is applied to the imaging apparatus in a case where the first image is acquired, are used as teacher data,
perform specific processing based on a first set value, which is output from the first trained model in a case where a second image is input to the first trained model, and a second set value, which is output from the second trained model in a case where the second image is input to the second trained model,
Claim 17
The imaging support apparatus according to claim 1, wherein the specific processing is processing that includes fourth processing of outputting first data for displaying a fourth image corresponding to an image obtained by applying a first output result, which is output from the first trained model by inputting a third image to the first trained model, to the third image, and a sixth image corresponding to an image obtained by applying a second output result, which is output from the second trained model by inputting a fifth image to the second trained model, to the fifth image, on a first display.
and wherein the type of the first set value and the type of the second set value are the same, and the learning processing is performed in a case where the number of the first images reaches a first threshold.
Using a similar analysis as above claims 2-19 of the instant application can be found to recite similar subject matter to the following claims respectively of patent 12322069. Thus, claims 1-19 of the instant application are respectively anticipated by claims 1-49 of patent 12322069.
Claim 2
Claim 21
Claim 3
Claim 22
Claim 4
Claim 22
Claim 5
Claim 2
Claim 6
Claim 14
Claim 7
Claim 15
Claim 8
Claim 16
Claim 9
Claim 17
Claim 10
Claim 32
Claim 11
Claim 34
Claim 12
Claim 36
Claim 13
Claim 38
Claim 14
Claim 40
Claim 15
Claim 33
Claim 16
Claim 35
Claim 17
Claim 37
Claim 18
Claim 39
Claim 19
Claim 41
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shcherbinin et al. (US 20200387750) - (¶0011) a neural network model training apparatus for enhancing image detail is provided. The neural network model training apparatus includes a memory configured to store one or more instructions, and at least one processor configured to execute the one or more instructions stored in the memory to obtain a low quality input image patch and a high quality input image patch, obtain a low quality output image patch by inputting the low quality input image patch to a first neural network model, obtain a high quality output image patch by inputting the high quality input image patch to a second neural network model, and train the first neural network model based on a loss function set to reduce a difference between the low quality output image patch and the high quality input image patch, and a difference between the high quality output image patch and the high quality input image patch, wherein the second neural network model is identical to the first neural network model.
Sun et al. (US 20250363590) – (¶0012 and ¶0090) a first noisy input with a recursively-cascading machine-learned denoising diffusion model to generate a first predicted image, wherein the first predicted image is associated with a third time that is temporally between the first time and the second time, and wherein the first predicted image has the second image resolution. The operations include upsampling the first predicted image to generate a first upsampled predicted image that has the first resolution. The operations include processing at least the first upsampled predicted image and a second noisy input with the recursively-cascading machine-learned denoising diffusion model to generate a second predicted image, wherein the second predicted image is associated with the third time that is temporally between the first time and the second time, and wherein the second predicted image has the first image resolution.
Kang et al. (US 20190295261) - (¶0005) a processor-implemented learning method for an image segmentation, including: training first duplicate layers, as duplications of trained first layers of a pre-trained model, so that a second feature extracted from a target image by the trained first duplicate layers is matched to a first feature extracted from a training image by the trained first layers; regularizing the trained first duplicate layers so that a similarity between the first feature and a third feature extracted from the training image by the regularized first duplicate layers meets a threshold; and training second duplicate layers, as duplications of trained second layers of the pre-trained model, to be configured to segment the target image based on the regularized first duplicate layers, the trained second layer being configured to segment the training image.
Baek et al. (US 20210166369) – first artificial intelligence model trained to reduce noise and the artificial intelligence model may include a third intelligence model and a fourth intelligence model trained to reduce noise (either of which can be considered second values) of different intensities. Accordingly, the first set value and the type of the second set value are of the same type (noise reductions.); (¶0068) additionally the artificial intelligence model may be divided to a plurality of artificial intelligence models according to image sharpness, and the processor 120 may process the image with the artificial intelligence model that corresponds to the image sharpness; (¶0069-¶0070) the processor may apply the plurality of artificial intelligence models sequentially
Hiasa (US 20210319537) – (¶0069-¶0070) e white balance correction may be performed on the captured image, the first model output 202, and the second model output 203 to be compared. the captured image, the first model output 202 and the second model output 203 are the undeveloped RAW images, the first map 205 may be generated based on gamma correction. The image viewed by the user is in a gamma-corrected state. The predetermined threshold value in step S203 may be changed based on the magnitude of the signal value of a pixel of any of the captured image, the first model output 202, or the second model output 203, and the gamma correction.
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/Frank Johnson/Primary Examiner, Art Unit 2425
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