CTNF 18/770,481 CTNF 96541 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 11/27/2024, 06/26/2025, 11/20/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Status Claims 1-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,079,954 B2. 07-21-aia AIA Claim (s) 1-4, 8-11 and 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schafer et al (U.S. 20200065945 A1; Schafer) , in view of Ramos et al (U.S. 10,628,931 B1; Ramos) . 07-21-aia AIA Claim (s) 5, 12 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schafer et al (U.S. 20200065945 A1; Schafer) , in view of Ramos et al (U.S. 10,628,931 B1; Ramos) , and in further view of Heller et al (U.S. 6,293,465 B1; Heller) . 07-21-aia AIA Claim (s) 6-7, 13-14 and 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schafer et al (U.S. 20200065945 A1; Schafer) , in view of Ramos et al (U.S. 10,628,931 B1; Ramos) , and in further view of Lai et al (U.S. 20190236759 B1; Lai) . Examiner Noted: Schafer et al (U.S. 20200065945 A1; Schafer) , which is prior art under 102(a)(2), AND Ramos et al (U.S. 10,628,931 B1; Ramos) , which is prior art under 102(a)(2), AND Lai et al (U.S. 20190236759 B1; Lai), which is prior art under 102(a)(2) . Double Patenting 08-33 AIA 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 § 2146 et seq. 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). The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp. Claims 1-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,079,954 B2. For Claim 1 , although this claim is not identical to Claim 1 of U.S. Patent No. 12,079,954 this claim is not patentably distinct from Claim 1 of U.S. Patent No. 12,079,954 because Claim 1 is broader than and fully encompassed by Claim 1 of U.S. Patent No. 12,079,954. Application 18/770,481 (U.S. 20240362746 A1) US Patent 12,079,954 B2 A computer-implemented method comprising: receiving a first set of sensor data generated by an input environmental sensor; determining one or more known defects with the input environmental sensor; providing the first set of sensor data generated to a trained generative adversarial model, wherein the trained generative adversarial model is trained to accept input sensor data from the input environment sensor and output a modified sensor data from a target environmental sensor having a target quality and not having the one or more known defects, wherein the target quality is a higher resolution than a first resolution of the input environmental sensor; and generating, by the trained generative adversarial model, a first modified sensor data having the target quality and not having the one or more known defects. A computer-implemented method comprising: receiving a set of first training data generated by a first environmental sensor having a first quality, the first set of training data being of the first quality and comprising one or more defects ; receiving a set of second training data generated by a target environmental sensor having a target quality higher than the first quality, the set of second training data being of the target quality and not having the one or more defects in the first set of training data, wherein the target environmental sensor generates data of a same type as the first environmental sensor; training, using the set of first training data and the set of second training data, a generative adversarial model to modify sensor data from the first environmental sensor by reducing a difference in quality associated with the one or more defects of the first set of training data between the sensor data generated by the first environmental sensor and sensor data generated by the target environmental sensor, wherein the training includes: obtaining, from a generator model of the generative adversarial model and using one or more data items in the set of first training data, a set of modified sensor data having a quality different from the first quality; inputting a set of data items comprising one or more data items in the set of second training data and the set of modified first sensor data into a discriminator model of the generative adversarial model; determining, by the discriminator model and using the set of data items, a classification for each of the data items, the classification indicative of whether a data item originates from the set of modified sensor data or the set of second training data; determining a classification error based on the classifications for each data item; and adjusting, based on the classification error, the discriminator model and the generator model; determining one or more known defects associated with an input environmental sensor; providing an input set of sensor data generated by the input environmental sensor to the trained generative adversarial model, the input set of sensor data having the first quality and comprising the one or more known defects, wherein the input environmental sensor has a first resolution; and generating, by the trained generative adversarial model, modified input sensor data having the target quality and not having the one or more known defects, wherein the target quality is a higher resolution than the first resolution of the input environmental sensor. For Claims 2-21 , although this claim is not identical to Claims 2-20 of U.S. Patent No. 12,079,954, this claim is not patentably distinct from Claims 2-20 of U.S. Patent No. 12,079,954 because Claims 2-21 are broader than and fully encompassed by Claims 2-20 of U.S. Patent No. 12,079,954. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 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 skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-23-aia AIA The factual inquiries 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. 07-21-aia AIA Claim (s) 1-4, 8-11 and 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schafer et al (U.S. 20200065945 A1; Schafer) , in view of Ramos et al (U.S. 10,628,931 B1; Ramos) . Regarding claim 1, Schafer discloses a computer-implemented method (Figs. 3 and Paragraph 5: “a framework for pixel identification using adversarial networks”) comprising: receiving a first set of sensor data (Fig. 1: Input image 108) generated by an input environmental sensor; (Paragraph 26: “Imaging device 202 acquires medical image data 220 associated with at least one patient. Such medical image data 220 may be processed and stored in database 209”; Fig.3 and Paragraph 30: “The images in first and second training image datasets may be acquired, directly or indirectly, by using techniques such as high-resolution computed tomography (HRCT), magnetic resonance (MR) imaging, computed tomography (CT), helical CT, X-ray, angiography, positron emission tomography (PET), fluoroscopy, ultrasound, single photon emission computed tomography (SPECT), or a combination thereof.”) determining one or more known defects with the input environmental sensor; (Fig.3 and Paragraph 31: “The images in the first training dataset may be associated with their segmentation masks delineating defective pixels and corrected images.”) providing the first set of sensor data (Fig. 1: Input image 108) generated to a trained generative adversarial model, (Fig.1: an image corrector 102 ) wherein the trained generative adversarial model is trained to accept input sensor data (Fig. 1: Input image 108) from the input environment sensor and output a modified sensor data (Fig.1: Corrected Image) from a target environmental sensor (Fig.1: Real image (without defective pixel) having a target quality and not having the one or more known defects, (Paragraph 31: “ The real images are original images that contain no defective pixels and are acquired directly from an imaging device 202 without further processing.”; Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels”; Paragraph 36: “Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels”) ; and generating, by the trained generative adversarial model, (Fig.1 image corrector 102) a first modified sensor data (Fig.1: Corrected Image) having the target quality and not having the one or more known defects. (Paragraph 19: “image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels.”; Paragraph 32: “At 304, correction module 206 performs adversarial training of corrector 102 and classifier 104 with first and second training image datasets respectively. In some implementations, the corrector 102 includes a deep convolutional network. FIG. 4 shows an exemplary corrector 102. The corrector 102 is trained to take as input an image 402 with defective pixels and generate a corrected image 412. The exemplary corrector 102 may include an encoder subnetwork 404, a defective pixel detector subnetwork 406, a raw correction subnetwork 408 and a fusion subnetwork 410.”) However, Schafer does not disclose wherein the target quality is a higher resolution than a first resolution of the input environmental sensor Ramos discloses receiving a first set of sensor data generated by an input environmental sensor; (Fig.4: camera 302; Digital image 316, Generated image 324; Col 4- lines 32-38: “the method includes receiving, at a control system 70, the digital image 316 (see FIG. 4) of the first portion of the user's face in real time, as in block 108. The camera is unable to view the second portion of the user's face, therefore a generated image 324 of the digital image is incomplete and/or lacking in resolution for the second portion of the user's face.”) providing the first set of sensor data generated to a trained generative adversarial model, (Fig.4: GAN 310) wherein the trained generative adversarial model is trained to accept input sensor data (Fig.4: Generated image 324) from the input environment sensor (Fig.4: Camera 302) and output a modified sensor data (Fig.4: Complete enhanced digital facial image 330) from a target environmental sensor (Fig.4: Real Image 318, Image Example 314) having a target quality, (Figs 3-4; Col 7- lines 25-29: “ using a GAN 310 (Generative Adversarial Network), imperfections due to low resolution or even missing data can be generated. The GAN can be trained with images 318 of the user. These images can be retrieved from the user or from user devises with the user's permission.”; Col 5- lines 51-56: “Paragraph 26: “ the method 100 includes generating, in real time, a complete enhanced digital facial image 330 of the user's face, using the GAN, which includes the digital image of the first portion of the user's face 324, the first additional user facial images 318, and the AI generated enhanced additional facial images 312, as in block 132.) wherein the target quality is a higher resolution than a first resolution of the input environmental sensor; (Col 5- lines 25-36: “the improving of the resolution includes receiving the digital image at the AI system 308 which includes a Generative Adversarial Network (GAN) 310 (see FIG. 4). The GAN uses first additional user facial images of the user to generate enhanced second additional facial images using a training method by the GAN. … The method can check for more first additional user facial images at block 120, depicted as real images 318 which are generated from image examples 314. If there are more images (e.g., image examples 314), the method uses the images (real images 318) to improve resolution in the enhanced second additional facial images , as in block 116.”) and generating, by the trained generative adversarial model, a first modified sensor data having the target quality (Figs 3-4 and Col 5- lines 51-61: “ the method 100 includes generating, in real time, a complete enhanced digital facial image 330 of the user's face, using the GAN, which includes the digital image of the first portion of the user's face 324, the first additional user facial images 318, and the AI generated enhanced additional facial images 312, as in block 132. The AI generated enhanced additional facial images and the first additional user facial images correspond to the second portions of the user's facial image, for the generation, in real time, of the complete enhanced digital facial image of the user's face 330 (see FIG. 4).”) Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Schafer by including the AI system which includes a Generative Adversarial Network (GAN) that is taught by Ramos, to make the invention that using artificial intelligence (AI) to enhance a digital image; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving resolution in the generated images using Generative Adversarial Network. (Ramos: Paragraph 23) Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention. Regarding claim 2, Schafer, as modified by Ramos, discloses all the claims invention. Schafer further discloses comprising: obtaining, from a generator model of the generative adversarial model (Fig.1: The corrector 102) and using one or more data items in the first set of sensor data (Fig.1: Input image 108) , a set of modified sensor data having a quality different from a first quality of the first set of sensor data; (Fig.1: Corrected Image 106) (Figs.1-3 and Paragraph 32: “At 304, correction module 206 performs adversarial training of corrector 102 and classifier 104 with first and second training image datasets respectively. In some implementations, the corrector 102 includes a deep convolutional network. FIG. 4 shows an exemplary corrector 102. The corrector 102 is trained to take as input an image 402 with defective pixels and generate a corrected image 412. The exemplary corrector 102 may include an encoder subnetwork 404, a defective pixel detector subnetwork 406, a raw correction subnetwork 408 and a fusion subnetwork 410.”) inputting a set of data items comprising one or more data items in a second set of sensor data and the set of modified sensor data into a discriminator model of the generative adversarial model, wherein the second set of sensor data is of the target quality and does not have the one or more defects in the first set of sensor data; (Fig.1 and Paragraph 38-39: “Classifier 104 is trained to classify input images as being a real image without any defects or a synthetic or corrected image generated by corrector 102. … Corrector 102 and classifier 104 may be trained by using an adversarial training strategy. For example, both the corrector 102 and classifier 104 may be trained simultaneously, with the goal of the corrector 102 being to produce a corrected image that looks as real as possible, and the goal of the classifier 104 being to recognize whether an input image is real or generated by the classifier 102 (i.e., synthetic). The goal of the adversarial training is to minimize the overall loss functions for both the corrector 102 and the classifier 104”) determining, by the discriminator model and using the set of data items, a classification for each of the data items, the classification indicative of whether a data item originates from the set of modified sensor data or the second set of sensor data; (Fig.1 and Paragraph 38: “ Classifier 104 is trained to classify input images as being a real image without any defects or a synthetic or corrected image generated by corrector 102. FIG. 5 shows an exemplary classifier 104. Classifier 104 may include a deep convolutional network 504. … The input image 502 may be a corrected image generated by corrector 102 or a real image. Classifier may output a probability 506 of the image 502 being a real image.”) determining a classification error based on the classification for each data item; ( Paragraph 44: “the adversarial term L.sub.adversarial drives the optimization so that the image generated by the corrector 102 fools the classifier 104 and gets classified as “real”. The adversarial term L.sub.adversarial may be defined as follows: (Equation 5) wherein Class( ) is the classifier function.”) and adjusting, based on the classification error, the discriminator model and the generator model. (Paragraph 45: “pseudo-code 602 for training the corrector 102 and the classifier 104. For a given number of epochs (nbEpochs), the classifier 104 and the corrector 102 may be trained simultaneously. During one epoch, the classifier 104 (θ.sup.class) may be trained given a batch of real and corrected (or synthetic) images for a certain number of iterations (nbIters). Afterwards, the corrector 102 (θ.sup.corr) may be trained for a certain number of iterations by computing the predictions of the classifier 104 using the current state of the classifier 104. Classical optimizers, such as Stochastic Gradient Descent (SGD) or Adam, may be used to train the corrector 102 and the classifier 104. ”) Regarding claim 3, Schafer, as modified by Ramos, discloses all the claims invention. Schafer further discloses wherein each of the input environmental sensor and the target environmental sensor acquires one or more of (i) sounds, (ii) images, or (iii) video. (Paragraph 46: “ The current image may be a radiographic image (2D X-ray image) of a patient. The current image may be acquired by, for example, imaging device 202, using the same modality (e.g., flat panel detector) as that used to acquire the first and second training image datasets.”; Paragraph 26: “Imaging device 202 acquires medical image data 220 associated with at least one patient. Such medical image data 220 may be processed and stored in database 209. Imaging device 202 may be a radiology scanner (e.g., X-ray, MR or a CT scanner)”) Regarding claim 4, Schafer, as modified by Ramos, discloses all the claims invention. Schafer further discloses further comprising: inputting the first set of sensor data into the generative adversarial model, wherein the first set of sensor data has a first quality; (Fig.1 and Paragraph 46: “Returning to FIG. 3, at 306, correction module 206 applies the trained corrector 102 to a current image to correct defective pixels. The trained classifier 104 may be discarded once training is completed. The current image may be a radiographic image (2D X-ray image) of a patient. The current image may be acquired by, for example, imaging device 202, using the same modality (e.g., flat panel detector) as that used to acquire the first and second training image datasets”; Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels .”) and obtaining, using the generator model of the generative adversarial model, the modified sensor data based on the input first sensor data, the modified sensor data having a quality higher than the first quality. (Paragraph 47: “at 308, correction module 206 presents the corrected image. The corrected image may be displayed to the user at, for example, workstation 203. The probabilistic map may also be displayed; Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels.”) Regarding claim 8, Schafer discloses a system (Fig.2: computer system 201) comprising: an input environmental sensor; (Fig.2: Image device 202; Workstation 203) a target environmental sensor; (Fig.2: Image device 202;Workstation 203) a trained generative adversarial model; (Fig.2: Correction module 206; image corrector 102 and a classifier 104) one or more memory devices storing instructions; and one or more data processing apparatus that are configured to interact with the one or more memory devices, and upon execution of the instructions, perform operations (Paragraphs 22-23: “computer system 201 comprises a processor or central processing unit (CPU) 204 coupled to one or more non-transitory computer-readable media 205 (e.g., computer storage or memory), display device 210 (e.g., monitor) and various input devices 211 (e.g., mouse or keyboard) via an input-output interface 221. … The present technology may be implemented in various forms of hardware, software, firmware, special purpose processors, or a combination thereof, either as part of the microinstruction code or as part of an application program or software product, or a combination thereof, which is executed via the operating system”) including: receiving a first set of sensor data (Fig. 1: Input image 108) generated by an input environmental sensor; (Paragraph 26: “Imaging device 202 acquires medical image data 220 associated with at least one patient. Such medical image data 220 may be processed and stored in database 209”; Fig.3 and Paragraph 30: “The images in first and second training image datasets may be acquired, directly or indirectly, by using techniques such as high-resolution computed tomography (HRCT), magnetic resonance (MR) imaging, computed tomography (CT), helical CT, X-ray, angiography, positron emission tomography (PET), fluoroscopy, ultrasound, single photon emission computed tomography (SPECT), or a combination thereof.”) determining one or more known defects with the input environmental sensor; (Fig.3 and Paragraph 31: “The images in the first training dataset may be associated with their segmentation masks delineating defective pixels and corrected images.”) providing the first set of sensor data (Fig. 1: Input image 108) generated to a trained generative adversarial model, (Fig.1: an image corrector 102 ) wherein the trained generative adversarial model is trained to accept input sensor data (Fig. 1: Input image 108) from the input environment sensor and output a modified sensor data (Fig.1: Corrected Image) from a target environmental sensor (Fig.1: Real image (without defective pixel) having a target quality and not having the one or more known defects, (Paragraph 31: “ The real images are original images that contain no defective pixels and are acquired directly from an imaging device 202 without further processing.”; Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels”; Paragraph 36: “Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels”) ; and generating, by the trained generative adversarial model, (Fig.1 image corrector 102) a first modified sensor data (Fig.1: Corrected Image) having the target quality and not having the one or more known defects. (Paragraph 19: “image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels.”; Paragraph 32: “At 304, correction module 206 performs adversarial training of corrector 102 and classifier 104 with first and second training image datasets respectively. In some implementations, the corrector 102 includes a deep convolutional network. FIG. 4 shows an exemplary corrector 102. The corrector 102 is trained to take as input an image 402 with defective pixels and generate a corrected image 412. The exemplary corrector 102 may include an encoder subnetwork 404, a defective pixel detector subnetwork 406, a raw correction subnetwork 408 and a fusion subnetwork 410.”) However, Schafer does not disclose wherein the target quality is a higher resolution than a first resolution of the input environmental sensor Ramos discloses receiving a first set of sensor data generated by an input environmental sensor; (Fig.4: camera 302; Digital image 316, Generated image 324; Col 4- lines 32-38: “the method includes receiving, at a control system 70, the digital image 316 (see FIG. 4) of the first portion of the user's face in real time, as in block 108. The camera is unable to view the second portion of the user's face, therefore a generated image 324 of the digital image is incomplete and/or lacking in resolution for the second portion of the user's face.”) providing the first set of sensor data generated to a trained generative adversarial model, (Fig.4: GAN 310) wherein the trained generative adversarial model is trained to accept input sensor data (Fig.4: Generated image 324) from the input environment sensor (Fig.4: Camera 302) and output a modified sensor data (Fig.4: Complete enhanced digital facial image 330) from a target environmental sensor (Fig.4: Real Image 318, Image Example 314) having a target quality, (Figs 3-4; Col 7- lines 25-29: “ using a GAN 310 (Generative Adversarial Network), imperfections due to low resolution or even missing data can be generated. The GAN can be trained with images 318 of the user. These images can be retrieved from the user or from user devises with the user's permission.”; Col 5- lines 51-56: “Paragraph 26: “ the method 100 includes generating, in real time, a complete enhanced digital facial image 330 of the user's face, using the GAN, which includes the digital image of the first portion of the user's face 324, the first additional user facial images 318, and the AI generated enhanced additional facial images 312, as in block 132.) wherein the target quality is a higher resolution than a first resolution of the input environmental sensor; (Col 5- lines 25-36: “the improving of the resolution includes receiving the digital image at the AI system 308 which includes a Generative Adversarial Network (GAN) 310 (see FIG. 4). The GAN uses first additional user facial images of the user to generate enhanced second additional facial images using a training method by the GAN. … The method can check for more first additional user facial images at block 120, depicted as real images 318 which are generated from image examples 314. If there are more images (e.g., image examples 314), the method uses the images (real images 318) to improve resolution in the enhanced second additional facial images , as in block 116.”) and generating, by the trained generative adversarial model, a first modified sensor data having the target quality (Figs 3-4 and Col 5- lines 51-61: “ the method 100 includes generating, in real time, a complete enhanced digital facial image 330 of the user's face, using the GAN, which includes the digital image of the first portion of the user's face 324, the first additional user facial images 318, and the AI generated enhanced additional facial images 312, as in block 132. The AI generated enhanced additional facial images and the first additional user facial images correspond to the second portions of the user's facial image, for the generation, in real time, of the complete enhanced digital facial image of the user's face 330 (see FIG. 4).”) . Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Schafer by including the AI system which includes a Generative Adversarial Network (GAN) that is taught by Ramos, to make the invention that using artificial intelligence (AI) to enhance a digital image; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving resolution in the generated images using Generative Adversarial Network. (Ramos: Paragraph 23) Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention. Regarding claim 9, Schafer, as modified by Ramos, discloses all the claims invention. Schafer further discloses comprising: obtaining, from a generator model of the generative adversarial model (Fig.1: The corrector 102) and using one or more data items in the first set of sensor data (Fig.1: Input image 108) , a set of modified sensor data having a quality different from a first quality of the first set of sensor data; (Fig.1: Corrected Image 106) (Figs.1-3 and Paragraph 32: “At 304, correction module 206 performs adversarial training of corrector 102 and classifier 104 with first and second training image datasets respectively. In some implementations, the corrector 102 includes a deep convolutional network. FIG. 4 shows an exemplary corrector 102. The corrector 102 is trained to take as input an image 402 with defective pixels and generate a corrected image 412. The exemplary corrector 102 may include an encoder subnetwork 404, a defective pixel detector subnetwork 406, a raw correction subnetwork 408 and a fusion subnetwork 410.”) inputting a set of data items comprising one or more data items in a second set of sensor data and the set of modified sensor data into a discriminator model of the generative adversarial model, wherein the second set of sensor data is of the target quality and does not have the one or more defects in the first set of sensor data; (Fig.1 and Paragraph 38-39: “Classifier 104 is trained to classify input images as being a real image without any defects or a synthetic or corrected image generated by corrector 102. … Corrector 102 and classifier 104 may be trained by using an adversarial training strategy. For example, both the corrector 102 and classifier 104 may be trained simultaneously, with the goal of the corrector 102 being to produce a corrected image that looks as real as possible, and the goal of the classifier 104 being to recognize whether an input image is real or generated by the classifier 102 (i.e., synthetic). The goal of the adversarial training is to minimize the overall loss functions for both the corrector 102 and the classifier 104”) determining, by the discriminator model and using the set of data items, a classification for each of the data items, the classification indicative of whether a data item originates from the set of modified sensor data or the second set of sensor data; (Fig.1 and Paragraph 38: “ Classifier 104 is trained to classify input images as being a real image without any defects or a synthetic or corrected image generated by corrector 102. FIG. 5 shows an exemplary classifier 104. Classifier 104 may include a deep convolutional network 504. … The input image 502 may be a corrected image generated by corrector 102 or a real image. Classifier may output a probability 506 of the image 502 being a real image.”) determining a classification error based on the classification for each data item; ( Paragraph 44: “the adversarial term L.sub.adversarial drives the optimization so that the image generated by the corrector 102 fools the classifier 104 and gets classified as “real”. The adversarial term L.sub.adversarial may be defined as follows: (Equation 5) wherein Class( ) is the classifier function.”) and adjusting, based on the classification error, the discriminator model and the generator model. (Paragraph 45: “pseudo-code 602 for training the corrector 102 and the classifier 104. For a given number of epochs (nbEpochs), the classifier 104 and the corrector 102 may be trained simultaneously. During one epoch, the classifier 104 (θ.sup.class) may be trained given a batch of real and corrected (or synthetic) images for a certain number of iterations (nbIters). Afterwards, the corrector 102 (θ.sup.corr) may be trained for a certain number of iterations by computing the predictions of the classifier 104 using the current state of the classifier 104. Classical optimizers, such as Stochastic Gradient Descent (SGD) or Adam, may be used to train the corrector 102 and the classifier 104. ”) Regarding claim 10, Schafer, as modified by Ramos, discloses all the claims invention. Schafer further discloses wherein each of the input environmental sensor and the target environmental sensor acquires one or more of (i) sounds, (ii) images, or (iii) video. (Paragraph 46: “ The current image may be a radiographic image (2D X-ray image) of a patient. The current image may be acquired by, for example, imaging device 202, using the same modality (e.g., flat panel detector) as that used to acquire the first and second training image datasets.”; Paragraph 26: “Imaging device 202 acquires medical image data 220 associated with at least one patient. Such medical image data 220 may be processed and stored in database 209. Imaging device 202 may be a radiology scanner (e.g., X-ray, MR or a CT scanner)”) Regarding claim 11, Schafer, as modified by Ramos, discloses all the claims invention. Schafer further discloses further comprising: inputting the first set of sensor data into the generative adversarial model, wherein the first set of sensor data has a first quality; (Fig.1 and Paragraph 46: “Returning to FIG. 3, at 306, correction module 206 applies the trained corrector 102 to a current image to correct defective pixels. The trained classifier 104 may be discarded once training is completed. The current image may be a radiographic image (2D X-ray image) of a patient. The current image may be acquired by, for example, imaging device 202, using the same modality (e.g., flat panel detector) as that used to acquire the first and second training image datasets”; Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels .”) and obtaining, using the generator model of the generative adversarial model, the modified sensor data based on the input first sensor data, the modified sensor data having a quality higher than the first quality. (Paragraph 47: “at 308, correction module 206 presents the corrected image. The corrected image may be displayed to the user at, for example, workstation 203. The probabilistic map may also be displayed; Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels.”) Regarding claim 15, Schafer discloses a non-transitory computer readable medium storing instructions that, when executed by one or more data processing apparatus, cause the one or more data processing apparatus to perform operations (Fig.2 and Paragraphs 22-23: “computer system 201 comprises a processor or central processing unit (CPU) 204 coupled to one or more non-transitory computer-readable media 205 (e.g., computer storage or memory), display device 210 (e.g., monitor) and various input devices 211 (e.g., mouse or keyboard) via an input-output interface 221. … The present technology may be implemented in various forms of hardware, software, firmware, special purpose processors, or a combination thereof, either as part of the microinstruction code or as part of an application program or software product, or a combination thereof, which is executed via the operating system”) comprising: receiving a first set of sensor data (Fig. 1: Input image 108) generated by an input environmental sensor; (Paragraph 26: “Imaging device 202 acquires medical image data 220 associated with at least one patient. Such medical image data 220 may be processed and stored in database 209”; Fig.3 and Paragraph 30: “The images in first and second training image datasets may be acquired, directly or indirectly, by using techniques such as high-resolution computed tomography (HRCT), magnetic resonance (MR) imaging, computed tomography (CT), helical CT, X-ray, angiography, positron emission tomography (PET), fluoroscopy, ultrasound, single photon emission computed tomography (SPECT), or a combination thereof.”) determining one or more known defects with the input environmental sensor; (Fig.3 and Paragraph 31: “The images in the first training dataset may be associated with their segmentation masks delineating defective pixels and corrected images.”) providing the first set of sensor data (Fig. 1: Input image 108) generated to a trained generative adversarial model, (Fig.1: an image corrector 102 ) wherein the trained generative adversarial model is trained to accept input sensor data (Fig. 1: Input image 108) from the input environment sensor and output a modified sensor data (Fig.1: Corrected Image) from a target environmental sensor (Fig.1: Real image (without defective pixel) having a target quality and not having the one or more known defects, (Paragraph 31: “ The real images are original images that contain no defective pixels and are acquired directly from an imaging device 202 without further processing.”; Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels”; Paragraph 36: “Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels”) ; and generating, by the trained generative adversarial model, (Fig.1 image corrector 102) a first modified sensor data (Fig.1: Corrected Image) having the target quality and not having the one or more known defects. (Paragraph 19: “image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels.”; Paragraph 32: “At 304, correction module 206 performs adversarial training of corrector 102 and classifier 104 with first and second training image datasets respectively. In some implementations, the corrector 102 includes a deep convolutional network. FIG. 4 shows an exemplary corrector 102. The corrector 102 is trained to take as input an image 402 with defective pixels and generate a corrected image 412. The exemplary corrector 102 may include an encoder subnetwork 404, a defective pixel detector subnetwork 406, a raw correction subnetwork 408 and a fusion subnetwork 410.”) However, Schafer does not disclose wherein the target quality is a higher resolution than a first resolution of the input environmental sensor Ramos discloses receiving a first set of sensor data generated by an input environmental sensor; (Fig.4: camera 302; Digital image 316, Generated image 324; Col 4- lines 32-38: “the method includes receiving, at a control system 70, the digital image 316 (see FIG. 4) of the first portion of the user's face in real time, as in block 108. The camera is unable to view the second portion of the user's face, therefore a generated image 324 of the digital image is incomplete and/or lacking in resolution for the second portion of the user's face.”) providing the first set of sensor data generated to a trained generative adversarial model, (Fig.4: GAN 310) wherein the trained generative adversarial model is trained to accept input sensor data (Fig.4: Generated image 324) from the input environment sensor (Fig.4: Camera 302) and output a modified sensor data (Fig.4: Complete enhanced digital facial image 330) from a target environmental sensor (Fig.4: Real Image 318, Image Example 314) having a target quality, (Figs 3-4; Col 7- lines 25-29: “ using a GAN 310 (Generative Adversarial Network), imperfections due to low resolution or even missing data can be generated. The GAN can be trained with images 318 of the user. These images can be retrieved from the user or from user devises with the user's permission.”; Col 5- lines 51-56: “Paragraph 26: “ the method 100 includes generating, in real time, a complete enhanced digital facial image 330 of the user's face, using the GAN, which includes the digital image of the first portion of the user's face 324, the first additional user facial images 318, and the AI generated enhanced additional facial images 312, as in block 132.) wherein the target quality is a higher resolution than a first resolution of the input environmental sensor; (Col 5- lines 25-36: “the improving of the resolution includes receiving the digital image at the AI system 308 which includes a Generative Adversarial Network (GAN) 310 (see FIG. 4). The GAN uses first additional user facial images of the user to generate enhanced second additional facial images using a training method by the GAN. … The method can check for more first additional user facial images at block 120, depicted as real images 318 which are generated from image examples 314. If there are more images (e.g., image examples 314), the method uses the images (real images 318) to improve resolution in the enhanced second additional facial images , as in block 116.”) and generating, by the trained generative adversarial model, a first modified sensor data having the target quality (Figs 3-4 and Col 5- lines 51-61: “ the method 100 includes generating, in real time, a complete enhanced digital facial image 330 of the user's face, using the GAN, which includes the digital image of the first portion of the user's face 324, the first additional user facial images 318, and the AI generated enhanced additional facial images 312, as in block 132. The AI generated enhanced additional facial images and the first additional user facial images correspond to the second portions of the user's facial image, for the generation, in real time, of the complete enhanced digital facial image of the user's face 330 (see FIG. 4).”) Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Schafer by including the AI system which includes a Generative Adversarial Network (GAN) that is taught by Ramos, to make the invention that using artificial intelligence (AI) to enhance a digital image; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving resolution in the generated images using Generative Adversarial Network. (Ramos: Paragraph 23) Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention. Regarding claim 16, Schafer, as modified by Ramos, discloses all the claims invention. Schafer further discloses comprising: obtaining, from a generator model of the generative adversarial model (Fig.1: The corrector 102) and using one or more data items in the first set of sensor data (Fig.1: Input image 108) , a set of modified sensor data having a quality different from a first quality of the first set of sensor data; (Fig.1: Corrected Image 106) (Figs.1-3 and Paragraph 32: “At 304, correction module 206 performs adversarial training of corrector 102 and classifier 104 with first and second training image datasets respectively. In some implementations, the corrector 102 includes a deep convolutional network. FIG. 4 shows an exemplary corrector 102. The corrector 102 is trained to take as input an image 402 with defective pixels and generate a corrected image 412. The exemplary corrector 102 may include an encoder subnetwork 404, a defective pixel detector subnetwork 406, a raw correction subnetwork 408 and a fusion subnetwork 410.”) inputting a set of data items comprising one or more data items in a second set of sensor data and the set of modified sensor data into a discriminator model of the generative adversarial model, wherein the second set of sensor data is of the target quality and does not have the one or more defects in the first set of sensor data; (Fig.1 and Paragraph 38-39: “Classifier 104 is trained to classify input images as being a real image without any defects or a synthetic or corrected image generated by corrector 102. … Corrector 102 and classifier 104 may be trained by using an adversarial training strategy. For example, both the corrector 102 and classifier 104 may be trained simultaneously, with the goal of the corrector 102 being to produce a corrected image that looks as real as possible, and the goal of the classifier 104 being to recognize whether an input image is real or generated by the classifier 102 (i.e., synthetic). The goal of the adversarial training is to minimize the overall loss functions for both the corrector 102 and the classifier 104”) determining, by the discriminator model and using the set of data items, a classification for each of the data items, the classification indicative of whether a data item originates from the set of modified sensor data or the second set of sensor data; (Fig.1 and Paragraph 38: “ Classifier 104 is trained to classify input images as being a real image without any defects or a synthetic or corrected image generated by corrector 102. FIG. 5 shows an exemplary classifier 104. Classifier 104 may include a deep convolutional network 504. … The input image 502 may be a corrected image generated by corrector 102 or a real image. Classifier may output a probability 506 of the image 502 being a real image.”) determining a classification error based on the classification for each data item; ( Paragraph 44: “the adversarial term L.sub.adversarial drives the optimization so that the image generated by the corrector 102 fools the classifier 104 and gets classified as “real”. The adversarial term L.sub.adversarial may be defined as follows: (Equation 5) wherein Class( ) is the classifier function.”) and adjusting, based on the classification error, the discriminator model and the generator model. (Paragraph 45: “pseudo-code 602 for training the corrector 102 and the classifier 104. For a given number of epochs (nbEpochs), the classifier 104 and the corrector 102 may be trained simultaneously. During one epoch, the classifier 104 (θ.sup.class) may be trained given a batch of real and corrected (or synthetic) images for a certain number of iterations (nbIters). Afterwards, the corrector 102 (θ.sup.corr) may be trained for a certain number of iterations by computing the predictions of the classifier 104 using the current state of the classifier 104. Classical optimizers, such as Stochastic Gradient Descent (SGD) or Adam, may be used to train the corrector 102 and the classifier 104. ”) Regarding claim 17, Schafer, as modified by Ramos, discloses all the claims invention. Schafer further discloses wherein each of the input environmental sensor and the target environmental sensor acquires one or more of (i) sounds, (ii) images, or (iii) video. (Paragraph 46: “ The current image may be a radiographic image (2D X-ray image) of a patient. The current image may be acquired by, for example, imaging device 202, using the same modality (e.g., flat panel detector) as that used to acquire the first and second training image datasets.”; Paragraph 26: “Imaging device 202 acquires medical image data 220 associated with at least one patient. Such medical image data 220 may be processed and stored in database 209. Imaging device 202 may be a radiology scanner (e.g., X-ray, MR or a CT scanner)”) Regarding claim 18, Schafer, as modified by Ramos, discloses all the claims invention. Schafer further discloses further comprising: inputting the first set of sensor data into the generative adversarial model, wherein the first set of sensor data has a first quality; (Fig.1 and Paragraph 46: “Returning to FIG. 3, at 306, correction module 206 applies the trained corrector 102 to a current image to correct defective pixels. The trained classifier 104 may be discarded once training is completed. The current image may be a radiographic image (2D X-ray image) of a patient. The current image may be acquired by, for example, imaging device 202, using the same modality (e.g., flat panel detector) as that used to acquire the first and second training image datasets”; Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels .”) and obtaining, using the generator model of the generative adversarial model, the modified sensor data based on the input first sensor data, the modified sensor data having a quality higher than the first quality. (Paragraph 47: “at 308, correction module 206 presents the corrected image. The corrected image may be displayed to the user at, for example, workstation 203. The probabilistic map may also be displayed; Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels.”) 07-21-aia AIA Claim (s) 5, 12 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schafer et al (U.S. 20200065945 A1; Schafer) , in view of Ramos et al (U.S. 10,628,931 B1; Ramos) , and in further view of Heller et al (U.S. 6,293,465 B1; Heller) . Regarding claim 5, Schafer, as modified by Ramos, discloses all the claims invention. Schafer further discloses determining the one or more known defects with the input environmental sensor further comprises: receiving a first set of sensor data generated by the input environmental sensor having a first quality; (Paragraph 46: “Returning to FIG. 3, at 306, correction module 206 applies the trained corrector 102 to a current image to correct defective pixels. The trained classifier 104 may be discarded once training is completed. The current image may be a radiographic image (2D X-ray image) of a patient. The current image may be acquired by, for example, imaging device 202, using the same modality (e.g., flat panel detector) as that used to acquire the first and second training image datasets) inputting the adjusted first set of sensor data into the generative adversarial model; ( Fig.1 and Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels .”) and obtaining, by the generator model of the generative adversarial model, a modified sensor data based on the adjusted first set of sensor data. (Paragraph 47: “at 308, correction module 206 presents the corrected image. The corrected image may be displayed to the user at, for example, workstation 203. The probabilistic map may also be displayed; Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels.”) However, Schafer, as modified by Ramos, does not disclose inputting information about the input environmental sensor to a known defects data structure that stores the known defects for different environmental sensors; obtaining, from the known defects data structure, known defects for the input environmental sensor; adjusting the first set of sensor data based on the known defects for the input environmental sensor. Heller teaches inputting information about the input environmental sensor to a known defects data structure that stores the known defects for different environmental sensors; (Col 7– lines 49-57: “programmable memory 14 includes a storage location for defective pixel location information 32. In a preferred embodiment, defective pixel location information 32 contains a table of the coordinates of the defective pixels located in sensor array 12. As described below, sensor array 12 is tested during manufacture to determine the defective pixels located therein, so as to determine the location of all defective pixels located in sensor array 12.”) obtaining, from the known defects data structure, known defects for the input environmental sensor; (Fig.6 and Col 8 – lines 44-63: “in step 80, system controller 122 accesses defective pixel location information 32 to read out the stored coordinates of the defective pixels in sensor array 12. As described above, the coordinates are store in a tabular format. … In step 84, compensation pixel values are generated to replace the missing data from the defective pixels in sensor array 12”) adjusting the first set of sensor data based on the known defects for the input environmental sensor. (Fig.6 and Col 8– line 61 to Col 9 – line 5: “In step 84, compensation pixel values are generated to replace the missing data from the defective pixels in sensor array 12. … In step 86, a new image is created with the appropriate compensation pixel values in the locations where the respective defective pixels are located.”) Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Schafer and Ramos by including an integrated circuit in capturing an image and providing defective pixel correction that is taught by Heller, to make the invention integrated circuit imaging devices requiring identification for manufacturing and security purposes; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving the image accuracy and quality as well as reducing the cost for in the manufacturing process. Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention. Regarding claim 12, Schafer, as modified by Ramos, discloses all the claims invention. Schafer further discloses determining the one or more known defects with the input environmental sensor further comprises: receiving a first set of sensor data generated by the input environmental sensor having a first quality; (Paragraph 46: “Returning to FIG. 3, at 306, correction module 206 applies the trained corrector 102 to a current image to correct defective pixels. The trained classifier 104 may be discarded once training is completed. The current image may be a radiographic image (2D X-ray image) of a patient. The current image may be acquired by, for example, imaging device 202, using the same modality (e.g., flat panel detector) as that used to acquire the first and second training image datasets) inputting the adjusted first set of sensor data into the generative adversarial model; ( Fig.1 and Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels .”) and obtaining, by the generator model of the generative adversarial model, a modified sensor data based on the adjusted first set of sensor data. (Paragraph 47: “at 308, correction module 206 presents the corrected image. The corrected image may be displayed to the user at, for example, workstation 203. The probabilistic map may also be displayed; Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels.”) However, Schafer, as modified by Ramos, does not disclose inputting information about the input environmental sensor to a known defects data structure that stores the known defects for different environmental sensors; obtaining, from the known defects data structure, known defects for the input environmental sensor; adjusting the first set of sensor data based on the known defects for the input environmental sensor. Heller teaches inputting information about the input environmental sensor to a known defects data structure that stores the known defects for different environmental sensors; (Col 7– lines 49-57: “programmable memory 14 includes a storage location for defective pixel location information 32. In a preferred embodiment, defective pixel location information 32 contains a table of the coordinates of the defective pixels located in sensor array 12. As described below, sensor array 12 is tested during manufacture to determine the defective pixels located therein, so as to determine the location of all defective pixels located in sensor array 12.”) obtaining, from the known defects data structure, known defects for the input environmental sensor; (Fig.6 and Col 8 – lines 44-63: “in step 80, system controller 122 accesses defective pixel location information 32 to read out the stored coordinates of the defective pixels in sensor array 12. As described above, the coordinates are store in a tabular format. … In step 84, compensation pixel values are generated to replace the missing data from the defective pixels in sensor array 12”) adjusting the first set of sensor data based on the known defects for the input environmental sensor. (Fig.6 and Col 8– line 61 to Col 9 – line 5: “In step 84, compensation pixel values are generated to replace the missing data from the defective pixels in sensor array 12. … In step 86, a new image is created with the appropriate compensation pixel values in the locations where the respective defective pixels are located.”) Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Schafer and Ramos by including an integrated circuit in capturing an image and providing defective pixel correction that is taught by Heller, to make the invention integrated circuit imaging devices requiring identification for manufacturing and security purposes; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving the image accuracy and quality as well as reducing the cost for in the manufacturing process. Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention. Regarding claim 19, Schafer, as modified by Ramos, discloses all the claims invention. Schafer further discloses determining the one or more known defects with the input environmental sensor further comprises: receiving a first set of sensor data generated by the input environmental sensor having a first quality; (Paragraph 46: “Returning to FIG. 3, at 306, correction module 206 applies the trained corrector 102 to a current image to correct defective pixels. The trained classifier 104 may be discarded once training is completed. The current image may be a radiographic image (2D X-ray image) of a patient. The current image may be acquired by, for example, imaging device 202, using the same modality (e.g., flat panel detector) as that used to acquire the first and second training image datasets) inputting the adjusted first set of sensor data into the generative adversarial model; ( Fig.1 and Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels .”) and obtaining, by the generator model of the generative adversarial model, a modified sensor data based on the adjusted first set of sensor data. (Paragraph 47: “at 308, correction module 206 presents the corrected image. The corrected image may be displayed to the user at, for example, workstation 203. The probabilistic map may also be displayed; Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels.”) However, Schafer, as modified by Ramos, does not disclose inputting information about the input environmental sensor to a known defects data structure that stores the known defects for different environmental sensors; obtaining, from the known defects data structure, known defects for the input environmental sensor; adjusting the first set of sensor data based on the known defects for the input environmental sensor. Heller teaches inputting information about the input environmental sensor to a known defects data structure that stores the known defects for different environmental sensors; (Col 7– lines 49-57: “programmable memory 14 includes a storage location for defective pixel location information 32. In a preferred embodiment, defective pixel location information 32 contains a table of the coordinates of the defective pixels located in sensor array 12. As described below, sensor array 12 is tested during manufacture to determine the defective pixels located therein, so as to determine the location of all defective pixels located in sensor array 12.”) obtaining, from the known defects data structure, known defects for the input environmental sensor; (Fig.6 and Col 8 – lines 44-63: “in step 80, system controller 122 accesses defective pixel location information 32 to read out the stored coordinates of the defective pixels in sensor array 12. As described above, the coordinates are store in a tabular format. … In step 84, compensation pixel values are generated to replace the missing data from the defective pixels in sensor array 12”) adjusting the first set of sensor data based on the known defects for the input environmental sensor. (Fig.6 and Col 8– line 61 to Col 9 – line 5: “In step 84, compensation pixel values are generated to replace the missing data from the defective pixels in sensor array 12. … In step 86, a new image is created with the appropriate compensation pixel values in the locations where the respective defective pixels are located.”) Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Schafer and Ramos by including an integrated circuit in capturing an image and providing defective pixel correction that is taught by Heller, to make the invention integrated circuit imaging devices requiring identification for manufacturing and security purposes; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving the image accuracy and quality as well as reducing the cost for in the manufacturing process. Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention . 07-21-aia AIA Claim (s) 6-7, 13-14 and 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schafer et al (U.S. 20200065945 A1; Schafer) , in view of Ramos et al (U.S. 10,628,931 B1; Ramos) , and in further view of Lai et al (U.S. 20190236759 B1; Lai) . Regarding claim 6, Schafer, as modified by Ramos, discloses all the claims invention. Schafer further discloses comprising: identifying, using the one or more known defects of the first set of sensor data, the generative adversarial model being trained to correct a respective defect from the one or more defects in a set of sensor data. (Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels.”; Paragraph 39: “Corrector 102 and classifier 104 may be trained by using an adversarial training strategy.”) However, Schafer, as modified by Ramos does not discloses identifying, using the one or more known defects of the first set of sensor data, the generative adversarial model from a plurality of generative adversarial models, each generative adversarial model from the plurality of generative adversarial models being trained to correct a respective defect from the one or more defects in a set of sensor data. Lai discloses identifying, using the one or more known defects of the first set of sensor data, the generative adversarial model from a plurality of generative adversarial models, (Fig.1 ; Paragraphs 30: Step 1: constructing the image repair model and constructing a plurality of conditional generative adversarial networks according to a plurality of object types . First, the generative adversarial network (GAN) may be applied for the deep learning method of image completion”) each generative adversarial model from the plurality of generative adversarial models being trained to correct a respective defect from the one or more defects in a set of sensor data. (Fig.1 and Paragraphs 38-41: Step S2: Inputting the training image corresponding to the plurality of object types and conducting the corruption feature training on the plurality of conditional generative adversarial networks respectively . … a mask processor may be applied to generate different corruption types for the differences of the corruption types in the training process. Through different types of defect mask models, the conditional generative adversarial networks may effectively repair different types of defects … Respectively conducting the image repair through the plurality of conditional generative adversarial networks to generate a plurality of repaired images .”) Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Schafer and Ramos by including the model of image repair according to the base of a plurality of conditional generative adversarial networks that is taught by Lai, to make the invention a method for repairing or editing images according to the lightweight conditional generative adversarial networks (cGAN) formed by different object types; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving the operation efficiency by simultaneously accessing each of the conditional generative adversarial networks. (Lai: Paragraph 37) Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention. Regarding claim 7, Schafer, as modified by Ramos and Lai, discloses comprising: inputting a first set of sensor data generated by the input environmental sensor having a first quality into the generative adversarial model, wherein the first set of sensor data comprises a particular defect corresponding to the identified generative adversarial model; (Schafer: Paragraph 19: “The pipeline 100 includes an image corrector 102 and a classifier 104. Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels.”; ) and obtaining, using a generator model of the identified generative adversarial model, a modified sensor data based on the input first set of sensor data, wherein the modified sensor data generated from the generator model comprises at least one correction of the particular defect from the input first set of sensor data. (Lai: Fig.1; Paragraphs 30: Step 1: constructing the image repair model and constructing a plurality of conditional generative adversarial networks according to a plurality of object types. First, the generative adversarial network (GAN) may be applied for the deep learning method of image completion”; Paragraphs 38-41: Step S2: Inputting the training image corresponding to the plurality of object types and conducting the corruption feature training on the plurality of conditional generative adversarial networks respectively . … a mask processor may be applied to generate different corruption types for the differences of the corruption types in the training process. Through different types of defect mask models, the conditional generative adversarial networks may effectively repair different types of defects … Respectively conducting the image repair through the plurality of conditional generative adversarial networks to generate a plurality of repaired images .”) Regarding claim 13, Schafer, as modified by Ramos, discloses all the claims invention. Schafer further discloses comprising: identifying, using the one or more known defects of the first set of sensor data, the generative adversarial model being trained to correct a respective defect from the one or more defects in a set of sensor data. (Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels.”; Paragraph 39: “Corrector 102 and classifier 104 may be trained by using an adversarial training strategy.”) However, Schafer, as modified by Ramos does not discloses identifying, using the one or more known defects of the first set of sensor data, the generative adversarial model from a plurality of generative adversarial models, each generative adversarial model from the plurality of generative adversarial models being trained to correct a respective defect from the one or more defects in a set of sensor data. Lai discloses identifying, using the one or more known defects of the first set of sensor data, the generative adversarial model from a plurality of generative adversarial models, (Fig.1 ; Paragraphs 30: Step 1: constructing the image repair model and constructing a plurality of conditional generative adversarial networks according to a plurality of object types . First, the generative adversarial network (GAN) may be applied for the deep learning method of image completion”) each generative adversarial model from the plurality of generative adversarial models being trained to correct a respective defect from the one or more defects in a set of sensor data. (Fig.1 and Paragraphs 38-41: Step S2: Inputting the training image corresponding to the plurality of object types and conducting the corruption feature training on the plurality of conditional generative adversarial networks respectively . … a mask processor may be applied to generate different corruption types for the differences of the corruption types in the training process. Through different types of defect mask models, the conditional generative adversarial networks may effectively repair different types of defects … Respectively conducting the image repair through the plurality of conditional generative adversarial networks to generate a plurality of repaired images .”) Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Schafer and Ramos by including the model of image repair according to the base of a plurality of conditional generative adversarial networks that is taught by Lai, to make the invention a method for repairing or editing images according to the lightweight conditional generative adversarial networks (cGAN) formed by different object types; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving the operation efficiency by simultaneously accessing each of the conditional generative adversarial networks. (Lai: Paragraph 37) Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention. Regarding claim 14, Schafer, as modified by Ramos and Lai, discloses comprising: inputting a first set of sensor data generated by the input environmental sensor having a first quality into the generative adversarial model, wherein the first set of sensor data comprises a particular defect corresponding to the identified generative adversarial model; (Schafer: Paragraph 19: “The pipeline 100 includes an image corrector 102 and a classifier 104. Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels.”; ) and obtaining, using a generator model of the identified generative adversarial model, a modified sensor data based on the input first set of sensor data, wherein the modified sensor data generated from the generator model comprises at least one correction of the particular defect from the input first set of sensor data. (Lai: Fig.1; Paragraphs 30: Step 1: constructing the image repair model and constructing a plurality of conditional generative adversarial networks according to a plurality of object types. First, the generative adversarial network (GAN) may be applied for the deep learning method of image completion”; Paragraphs 38-41: Step S2: Inputting the training image corresponding to the plurality of object types and conducting the corruption feature training on the plurality of conditional generative adversarial networks respectively . … a mask processor may be applied to generate different corruption types for the differences of the corruption types in the training process. Through different types of defect mask models, the conditional generative adversarial networks may effectively repair different types of defects … Respectively conducting the image repair through the plurality of conditional generative adversarial networks to generate a plurality of repaired images .”) Regarding claim 20, Schafer, as modified by Ramos, discloses all the claims invention. Schafer further discloses comprising: identifying, using the one or more known defects of the first set of sensor data, the generative adversarial model being trained to correct a respective defect from the one or more defects in a set of sensor data. (Paragraph 19: “Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels.”; Paragraph 39: “Corrector 102 and classifier 104 may be trained by using an adversarial training strategy.”) However, Schafer, as modified by Ramos does not discloses identifying, using the one or more known defects of the first set of sensor data, the generative adversarial model from a plurality of generative adversarial models, each generative adversarial model from the plurality of generative adversarial models being trained to correct a respective defect from the one or more defects in a set of sensor data. Lai discloses identifying, using the one or more known defects of the first set of sensor data, the generative adversarial model from a plurality of generative adversarial models, (Fig.1 ; Paragraphs 30: Step 1: constructing the image repair model and constructing a plurality of conditional generative adversarial networks according to a plurality of object types . First, the generative adversarial network (GAN) may be applied for the deep learning method of image completion”) each generative adversarial model from the plurality of generative adversarial models being trained to correct a respective defect from the one or more defects in a set of sensor data. (Fig.1 and Paragraphs 38-41: Step S2: Inputting the training image corresponding to the plurality of object types and conducting the corruption feature training on the plurality of conditional generative adversarial networks respectively . … a mask processor may be applied to generate different corruption types for the differences of the corruption types in the training process. Through different types of defect mask models, the conditional generative adversarial networks may effectively repair different types of defects … Respectively conducting the image repair through the plurality of conditional generative adversarial networks to generate a plurality of repaired images .”) Therefore, it would been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Schafer and Ramos by including the model of image repair according to the base of a plurality of conditional generative adversarial networks that is taught by Lai, to make the invention a method for repairing or editing images according to the lightweight conditional generative adversarial networks (cGAN) formed by different object types; thus, one of ordinary skilled in the art would have been motivated to combine the references since this will improving the operation efficiency by simultaneously accessing each of the conditional generative adversarial networks. (Lai: Paragraph 37) Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention. Regarding claim 21, Schafer, as modified by Ramos and Lai, discloses comprising: inputting a first set of sensor data generated by the input environmental sensor having a first quality into the generative adversarial model, wherein the first set of sensor data comprises a particular defect corresponding to the identified generative adversarial model; (Schafer: Paragraph 19: “The pipeline 100 includes an image corrector 102 and a classifier 104. Image corrector 102 may be implemented using, for example, a two-dimensional (2D) deep convolutional network for pixel correction. Image corrector 102 may serve to generate a 2D image 106 in which defective pixels have been corrected given an input image 108 with defective pixels.”; ) and obtaining, using a generator model of the identified generative adversarial model, a modified sensor data based on the input first set of sensor data, wherein the modified sensor data generated from the generator model comprises at least one correction of the particular defect from the input first set of sensor data. (Lai: Fig.1; Paragraphs 30: Step 1: constructing the image repair model and constructing a plurality of conditional generative adversarial networks according to a plurality of object types. First, the generative adversarial network (GAN) may be applied for the deep learning method of image completion”; Paragraphs 38-41: Step S2: Inputting the training image corresponding to the plurality of object types and conducting the corruption feature training on the plurality of conditional generative adversarial networks respectively . … a mask processor may be applied to generate different corruption types for the differences of the corruption types in the training process. Through different types of defect mask models, the conditional generative adversarial networks may effectively repair different types of defects … Respectively conducting the image repair through the plurality of conditional generative adversarial networks to generate a plurality of repaired images .”) Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Wang et al (U.S. 20170347061 A1) , “ Machine Learning For Visual Processing”, teaches about a method for developing an enhancement model for low-quality visual data, the method comprising the steps of receiving one or more sections of higher-quality visual data; and training a hierarchical algorithm. The hierarchical algorithm is operable to increase the quality of one or more sections of lower-quality visual data so as to substantially reproduce the one or more sections of higher-quality visual data. The hierarchical algorithm is then outputted. Schafer et al (U.S. 20180253624 A1), “Defective Pixel Identification Using Machine Learning”, teaches about the framework performs a machine learning technique to train a classifier using a training image dataset. The trained classifier is applied to a current image to identify one or more defective pixels, which may then be corrected. Hsieh et al (U.S. 20190026608 A1), “Deep Learning Medical System and Methods for Image Reconstruction and Quality Evaluation”, teaches about methods and apparatus to automatically generate an image quality metric for an image includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network; computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Duy A Tran whose telephone number is (571)272-4887. The examiner can normally be reached Monday-Friday 8:00 am - 5:00 pm. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DUY TRAN/Examiner, Art Unit 2674 /ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674 Application/Control Number: 18/770,481 Page 2 Art Unit: 2674 Application/Control Number: 18/770,481 Page 3 Art Unit: 2674 Application/Control Number: 18/770,481 Page 4 Art Unit: 2674 Application/Control Number: 18/770,481 Page 5 Art Unit: 2674 Application/Control Number: 18/770,481 Page 6 Art Unit: 2674 Application/Control Number: 18/770,481 Page 7 Art Unit: 2674 Application/Control Number: 18/770,481 Page 8 Art Unit: 2674 Application/Control Number: 18/770,481 Page 9 Art Unit: 2674 Application/Control Number: 18/770,481 Page 10 Art Unit: 2674 Application/Control Number: 18/770,481 Page 11 Art Unit: 2674 Application/Control Number: 18/770,481 Page 12 Art Unit: 2674 Application/Control Number: 18/770,481 Page 13 Art Unit: 2674 Application/Control Number: 18/770,481 Page 14 Art Unit: 2674 Application/Control Number: 18/770,481 Page 15 Art Unit: 2674 Application/Control Number: 18/770,481 Page 16 Art Unit: 2674 Application/Control Number: 18/770,481 Page 17 Art Unit: 2674 Application/Control Number: 18/770,481 Page 18 Art Unit: 2674 Application/Control Number: 18/770,481 Page 19 Art Unit: 2674 Application/Control Number: 18/770,481 Page 20 Art Unit: 2674 Application/Control Number: 18/770,481 Page 21 Art Unit: 2674 Application/Control Number: 18/770,481 Page 22 Art Unit: 2674 Application/Control Number: 18/770,481 Page 23 Art Unit: 2674 Application/Control Number: 18/770,481 Page 24 Art Unit: 2674 Application/Control Number: 18/770,481 Page 25 Art Unit: 2674 Application/Control Number: 18/770,481 Page 26 Art Unit: 2674 Application/Control Number: 18/770,481 Page 27 Art Unit: 2674 Application/Control Number: 18/770,481 Page 28 Art Unit: 2674 Application/Control Number: 18/770,481 Page 29 Art Unit: 2674 Application/Control Number: 18/770,481 Page 30 Art Unit: 2674 Application/Control Number: 18/770,481 Page 31 Art Unit: 2674 Application/Control Number: 18/770,481 Page 32 Art Unit: 2674 Application/Control Number: 18/770,481 Page 33 Art Unit: 2674 Application/Control Number: 18/770,481 Page 34 Art Unit: 2674 Application/Control Number: 18/770,481 Page 35 Art Unit: 2674 Application/Control Number: 18/770,481 Page 36 Art Unit: 2674 Application/Control Number: 18/770,481 Page 37 Art Unit: 2674 Application/Control Number: 18/770,481 Page 38 Art Unit: 2674 Application/Control Number: 18/770,481 Page 39 Art Unit: 2674 Application/Control Number: 18/770,481 Page 40 Art Unit: 2674 Application/Control Number: 18/770,481 Page 41 Art Unit: 2674 Application/Control Number: 18/770,481 Page 42 Art Unit: 2674 Application/Control Number: 18/770,481 Page 43 Art Unit: 2674 Application/Control Number: 18/770,481 Page 44 Art Unit: 2674 Application/Control Number: 18/770,481 Page 45 Art Unit: 2674 Application/Control Number: 18/770,481 Page 46 Art Unit: 2674 Application/Control Number: 18/770,481 Page 47 Art Unit: 2674 Application/Control Number: 18/770,481 Page 48 Art Unit: 2674 Application/Control Number: 18/770,481 Page 49 Art Unit: 2674
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