CTNF 18/735,369 CTNF 78795 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 1. 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 08-33 AIA 2. 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 filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. 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Application 18/735,369 U.S. patent 11,378,915 B2 Claim 1: A head-mounted display (HMD) system comprising: a holographic plane; and a system to present a holographic image at the holographic plane, the system comprising circuitry configured to: apply a machine learning model to a target holographic image to generate a feedback strength value; apply an iterative propagation feedback model based on the target holographic image and the feedback strength value to generate a phase diffraction pattern; and output the phase diffraction pattern for presentation at the holographic plane for generation of a corresponding holographic image. Claim 1: A system for generating holographic images comprising: a memory to store a target holographic image to be presented to a user at an image plane; and one or more processors coupled to the memory, the one or more processors to: apply a deep neural network to the target holographic image to generate a feedback strength value for error feedback in determination of a phase only diffraction pattern image using an iterative propagation feedback model, the phase only diffraction pattern image for presentation at a holographic plane to generate a corresponding holographic image at the image plane; apply the iterative propagation feedback model based on the target holographic image and using the feedback strength value to generate a final phase only diffraction pattern image for presentation at the holographic plane; and output the final diffraction pattern image for presentment at the holographic plane for generation of a corresponding final holographic image at the image plane. Claim 2: The HMD system of claim 1, wherein to apply the iterative propagation feedback model based on the target holographic image and the feedback strength value, the circuitry is configured to: receive a current phase and amplitude image plane estimation; and replace an amplitude plane of the current phase and amplitude image plane estimation with a next target amplitude image comprising: a sum of the target holographic image and a product of the feedback strength value and a correction term comprising a difference between the target holographic image and the current phase and amplitude image plane to generate a current amplitude adjusted image. Claim 2: The system of claim 1, wherein the one or more processors to apply the iterative propagation feedback model based on the target holographic image and using the feedback strength value comprises the one or more processors to: receive a current phase and amplitude image plane estimation; and replace an amplitude plane of the current phase and amplitude image plane estimation with a next target amplitude image comprising a sum of the target holographic image and a product of the feedback strength value and a correction term comprising a difference between the target holographic image and the current phase and amplitude image plane to generate a current amplitude adjusted image. Claim 3: The HMD system of claim 2, wherein to apply the iterative propagation feedback model based on the target holographic image and the feedback strength value, the circuitry is configured to: apply an inverse propagation model to a previous amplitude adjusted image to generate a phase and amplitude holographic plane estimation; normalize an amplitude plane of the phase and amplitude holographic plane estimation to generate a phase only diffraction pattern image; and apply a forward propagation model to the phase only diffraction pattern image to generate the current phase and amplitude image plane estimation. Claim 3: The system of claim 2, wherein the one or more processors to apply the iterative propagation feedback model based on the target holographic image and using the feedback strength value further comprises the one or more processors to: apply an inverse propagation model to a previous amplitude adjusted image to generate a phase and amplitude holographic plane estimation; normalize an amplitude plane of the phase and amplitude holographic plane estimation to generate a phase only diffraction pattern image; and apply a forward propagation model to the phase only diffraction pattern image to generate the current phase and amplitude image plane estimation. Claim 4: The HMD system of claim 1, wherein the machine learning model corresponds to a trained neural network and the machine learning model comprises at least one of fewer convolutional kernels with respect to the trained neural network or a reduced bit depth of weights of the machine learning model with respect to the trained neural network. Claim 5: The HMD system of claim 4, wherein the target holographic image is downsampled prior to application of the machine learning model. Claim 4: The system of claim 1, wherein the deep neural network corresponds to a pretrained deep neural network, the deep neural network comprising at least one of fewer convolutional kernels with respect to the pretrained deep neural network or a reduced bit depth of weights of the deep neural network with respect to the pretrained deep neural network, and wherein the target holographic image is downsampled prior to application of the deep neural network. Claim 6: The HMD system of claim 1, wherein the target holographic image comprises first and second color planes, and the circuitry is configured to apply the machine learning model to the target holographic image to generate the feedback strength value for the first color plane and a second feedback strength value for the second color plane. Claim 7: The HMD system of claim 6, wherein the circuitry is further to: apply the iterative propagation feedback model based on the second color plane of the target holographic image; and use the second feedback strength value to generate a second phase diffraction pattern for presentation at the holographic plane or a second holographic plane. Claim 5: The system of claim 1, wherein the target holographic image comprises first and second color planes and the one or more processors to apply the deep neural network to the target holographic image generates the feedback strength value for the first color plane and a second feedback strength value for the second color plane, the one or more processors further to: apply the iterative propagation feedback model based on the second color plane of the target holographic image and using the second feedback strength value to generate a second final phase only diffraction pattern image for presentation at the holographic plane or a second holographic plane. Claim 9: A method for presenting holographic images at a holographic plane of a head-mounted display, the method comprising: applying, via one or more processors, a machine learning model to a target holographic image to generate a feedback strength value; applying an iterative propagation feedback model based on the target holographic image and the feedback strength value to generate a phase diffraction pattern; and outputting the phase diffraction pattern for presentation at the holographic plane for generation of a corresponding holographic image. Claim 12: A method for generating holographic images comprising: receiving a target holographic image to be presented to a user at an image plane; applying a deep neural network to the target holographic image to generate a feedback strength value for error feedback in determination of a phase only diffraction pattern image using an iterative propagation feedback model, the phase only diffraction pattern image for presentation at a holographic plane to generate a corresponding holographic image at the image plane; applying the iterative propagation feedback model based on the target holographic image and using the feedback strength value to generate a final phase only diffraction pattern image for presentation at the holographic plane; and presenting the final diffraction pattern image at the holographic plane to generate a corresponding final holographic image at the image plane. Claim 10: The method of claim 9, wherein applying the iterative propagation feedback model based on the target holographic image and the feedback strength value comprises: receiving a current phase and amplitude image plane estimation; replacing an amplitude plane of the current phase and amplitude image plane estimation with a next target amplitude image comprising a sum of the target holographic image and a product of the feedback strength value and a correction term comprising a difference between the target holographic image and the current phase and amplitude image plane to generate a current amplitude adjusted image; applying an inverse propagation model to a previous amplitude adjusted image to generate a phase and amplitude holographic plane estimation; normalizing an amplitude plane of the phase and amplitude holographic plane estimation to generate a phase only diffraction pattern image; and applying a forward propagation model to the phase only diffraction pattern image to generate the current phase and amplitude image plane estimation. Claim 13: The method of claim 12, wherein applying the iterative propagation feedback model based on the target holographic image and using the feedback strength value comprises: receiving a current phase and amplitude image plane estimation; and replacing an amplitude plane of the current phase and amplitude image plane estimation with a next target amplitude image comprising a sum of the target holographic image and a product of the feedback strength value and a correction term comprising a difference between the target holographic image and the current phase and amplitude image plane to generate a current amplitude adjusted image. Claim 11: The method of claim 9, wherein the machine learning model corresponds to a trained neural network and the machine learning model comprises at least one of fewer convolutional kernels with respect to the trained neural network or a reduced bit depth of weights of the machine learning model with respect to the trained neural network. Claim 12: The method of claim 11, further comprising downsampling the target holographic image prior to applying the machine learning model. Claim 14: The method of claim 12, wherein the deep neural network corresponds to a pretrained deep neural network, the deep neural network comprising at least one of fewer convolutional kernels with respect to the pretrained deep neural network or a reduced bit depth of weights of the deep neural network with respect to the pretrained deep neural network, and wherein the target holographic image is downsampled prior to application of the deep neural network. Claim 16: A non-transitory machine readable medium comprising a plurality of instructions that, in response to being executed by a processor of a holographic head-mounted display system, causes the processor to perform operations comprising: applying a machine learning model to a target holographic image to generate a feedback strength value; applying an iterative propagation feedback model based on the target holographic image and the feedback strength value to generate a phase diffraction pattern for presentation at a holographic plane of the holographic head-mounted display system; and outputting the phase diffraction pattern for presentation at the holographic plane for generation of a corresponding holographic image. Claim 17: At least one non-transitory machine readable medium comprising a plurality of instructions that, in response to being executed on a computing device, cause the computing device to generate holographic images by: receiving a target holographic image to be presented to a user at an image plane; applying a deep neural network to the target holographic image to generate a feedback strength value for error feedback in determination of a phase only diffraction pattern image using an iterative propagation feedback model, the phase only diffraction pattern image for presentation at a holographic plane to generate a corresponding holographic image at the image plane; applying the iterative propagation feedback model based on the target holographic image and using the feedback strength value to generate a final phase only diffraction pattern image for presentation at the holographic plane; and presenting the final diffraction pattern image at the holographic plane to generate a corresponding final holographic image at the image plane. Claim 17: The non-transitory machine readable medium as in claim 16, wherein applying the iterative propagation feedback model based on the target holographic image and the feedback strength value includes operations comprising: receiving a current phase and amplitude image plane estimation; replacing an amplitude plane of the current phase and amplitude image plane estimation with a next target amplitude image comprising a sum of the target holographic image and a product of the feedback strength value and a correction term comprising a difference between the target holographic image and the current phase and amplitude image plane to generate a current amplitude adjusted image; applying an inverse propagation model to a previous amplitude adjusted image to generate a phase and amplitude holographic plane estimation; normalizing an amplitude plane of the phase and amplitude holographic plane estimation to generate a phase only diffraction pattern image; and applying a forward propagation model to the phase only diffraction pattern image to generate the current phase and amplitude image plane estimation. Claim 18: The machine readable medium of claim 17, wherein applying the iterative propagation feedback model based on the target holographic image and using the feedback strength value comprises: receiving a current phase and amplitude image plane estimation; and replacing an amplitude plane of the current phase and amplitude image plane estimation with a next target amplitude image comprising a sum of the target holographic image and a product of the feedback strength value and a correction term comprising a difference between the target holographic image and the current phase and amplitude image plane to generate a current amplitude adjusted image. Claim 18: The non-transitory machine readable medium as in claim 16, wherein the machine learning model corresponds to a trained neural network, the machine learning model comprises at least one of fewer convolutional kernels with respect to the trained neural network or a reduced bit depth of weights of the machine learning model with respect to the trained neural network, and the operations further comprise downsampling the target holographic image prior to applying the machine learning model. Claim 19: The machine readable medium of claim 17, wherein the deep neural network corresponds to a pretrained deep neural network, the deep neural network comprising at least one of fewer convolutional kernels with respect to the pretrained deep neural network or a reduced bit depth of weights of the deep neural network with respect to the pretrained deep neural network, and wherein the target holographic image is downsampled prior to application of the deep neural network. Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 11,378,915 B2. 08-36 Claim 2 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 2 of U.S. Patent No. 11,378,915 B2. 08-36 Claim 3 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 3 of U.S. Patent No. 11,378,915 B2. Claims 4 and 5 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 4 of U.S. Patent No. 11,378,915 B2. Claims 6 and 7 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 5 of U.S. Patent No. 11,378,915 B2. 08-36 Claim 9 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 12 of U.S. Patent No. 11,378,915 B2. 08-36 Claim 10 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 13 of U.S. Patent No. 11,378,915 B2. Claims 11 and 12 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 14 of U.S. Patent No. 11,378,915 B2. 08-36 Claim 16 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 17 of U.S. Patent No. 11,378,915 B2. 08-36 Claim 17 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 18 of U.S. Patent No. 11,378,915 B2. 08-36 Claim 18 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 19 of U.S. Patent No. 11,378,915 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because The claims of this instant invention are encompassed by the patented claims of U.S. patent 11,378,915 B2. Application 18/735,369 U.S. patent 11,650,542 B2 Claim 1: A head-mounted display (HMD) system comprising: a holographic plane; and a system to present a holographic image at the holographic plane, the system comprising circuitry configured to: apply a machine learning model to a target holographic image to generate a feedback strength value; apply an iterative propagation feedback model based on the target holographic image and the feedback strength value to generate a phase diffraction pattern; and output the phase diffraction pattern for presentation at the holographic plane for generation of a corresponding holographic image. Claim 1: A system for generating holographic images comprising: a memory to store instructions and a target holographic image; and one or more processors coupled to the memory, the one or more processors to execute the instructions to: apply a machine learning model to the target holographic image to generate a feedback strength value, the feedback strength value to enable determination of a phase diffraction pattern for presentation at a holographic plane; apply an iterative propagation feedback model based on the target holographic image and the feedback strength value to generate the phase diffraction pattern for presentation at the holographic plane; and output the phase diffraction pattern for presentation at the holographic plane for generation of a corresponding holographic image. Claim 2: The HMD system of claim 1, wherein to apply the iterative propagation feedback model based on the target holographic image and the feedback strength value, the circuitry is configured to: receive a current phase and amplitude image plane estimation; and replace an amplitude plane of the current phase and amplitude image plane estimation with a next target amplitude image comprising: a sum of the target holographic image and a product of the feedback strength value and a correction term comprising a difference between the target holographic image and the current phase and amplitude image plane to generate a current amplitude adjusted image. Claim 2: The system of claim 1, wherein to apply the iterative propagation feedback model based on the target holographic image and the feedback strength value, the one or more processors are configured to: receive a current phase and amplitude image plane estimation; and replace an amplitude plane of the current phase and amplitude image plane estimation with a next target amplitude image comprising: a sum of the target holographic image and a product of the feedback strength value and a correction term comprising a difference between the target holographic image and the current phase and amplitude image plane to generate a current amplitude adjusted image. Claim 3: The HMD system of claim 2, wherein to apply the iterative propagation feedback model based on the target holographic image and the feedback strength value, the circuitry is configured to: apply an inverse propagation model to a previous amplitude adjusted image to generate a phase and amplitude holographic plane estimation; normalize an amplitude plane of the phase and amplitude holographic plane estimation to generate a phase only diffraction pattern image; and apply a forward propagation model to the phase only diffraction pattern image to generate the current phase and amplitude image plane estimation. Claim 3: The system of claim 2, wherein to apply the iterative propagation feedback model based on the target holographic image and the feedback strength value, the one or more processors are configured to: apply an inverse propagation model to a previous amplitude adjusted image to generate a phase and amplitude holographic plane estimation; normalize an amplitude plane of the phase and amplitude holographic plane estimation to generate a phase only diffraction pattern image; and apply a forward propagation model to the phase only diffraction pattern image to generate the current phase and amplitude image plane estimation. Claim 4: The HMD system of claim 1, wherein the machine learning model corresponds to a trained neural network and the machine learning model comprises at least one of fewer convolutional kernels with respect to the trained neural network or a reduced bit depth of weights of the machine learning model with respect to the trained neural network. Claim 4: The system of claim 1, wherein the machine learning model corresponds to a trained neural network and the machine learning model comprises at least one of fewer convolutional kernels with respect to the trained neural network or a reduced bit depth of weights of the machine learning model with respect to the trained neural network. Claim 5: The HMD system of claim 4, wherein the target holographic image is downsampled prior to application of the machine learning model. Claim 5: The system of claim 4, wherein the target holographic image is downsampled prior to application of the machine learning model. Claim 6: The HMD system of claim 1, wherein the target holographic image comprises first and second color planes, and the circuitry is configured to apply the machine learning model to the target holographic image to generate the feedback strength value for the first color plane and a second feedback strength value for the second color plane. Claim 6: The system of claim 1, wherein the target holographic image comprises first and second color planes and the one or more processors are configured to apply the machine learning model to the target holographic image to generate the feedback strength value for the first color plane and a second feedback strength value for the second color plane. Claim 7: The HMD system of claim 6, wherein the circuitry is further to: apply the iterative propagation feedback model based on the second color plane of the target holographic image; and use the second feedback strength value to generate a second phase diffraction pattern for presentation at the holographic plane or a second holographic plane. Claim 7: The system of claim 6, wherein the one or more processors are further to: apply the iterative propagation feedback model based on the second color plane of the target holographic image; and use the second feedback strength value to generate a second phase diffraction pattern for presentation at the holographic plane or a second holographic plane. Claim 8: The HMD system of claim 1, wherein the holographic image is a three dimensional holographic image. Claim 8: The system of claim 1, wherein the holographic image is a three dimensional holographic image. Claim 9: A method for presenting holographic images at a holographic plane of a head-mounted display, the method comprising: applying, via one or more processors, a machine learning model to a target holographic image to generate a feedback strength value; applying an iterative propagation feedback model based on the target holographic image and the feedback strength value to generate a phase diffraction pattern; and outputting the phase diffraction pattern for presentation at the holographic plane for generation of a corresponding holographic image. Claim 9: A method for generating holographic images comprising: accessing a target holographic image; applying, via one or more processors, a machine learning model to the target holographic image to generate a feedback strength value, the feedback strength value to enable determination of a phase diffraction pattern for presentation at a holographic plane; applying an iterative propagation feedback model based on the target holographic image and the feedback strength value to generate the phase diffraction pattern for presentation at the holographic plane; and outputting the phase diffraction pattern for presentation at the holographic plane for generation of a corresponding holographic image. Claim 10: The method of claim 9, wherein applying the iterative propagation feedback model based on the target holographic image and the feedback strength value comprises: receiving a current phase and amplitude image plane estimation; replacing an amplitude plane of the current phase and amplitude image plane estimation with a next target amplitude image comprising a sum of the target holographic image and a product of the feedback strength value and a correction term comprising a difference between the target holographic image and the current phase and amplitude image plane to generate a current amplitude adjusted image; applying an inverse propagation model to a previous amplitude adjusted image to generate a phase and amplitude holographic plane estimation; normalizing an amplitude plane of the phase and amplitude holographic plane estimation to generate a phase only diffraction pattern image; and applying a forward propagation model to the phase only diffraction pattern image to generate the current phase and amplitude image plane estimation. Claim 10: The method of claim 9, wherein applying the iterative propagation feedback model based on the target holographic image and the feedback strength value comprises: receiving a current phase and amplitude image plane estimation; replacing an amplitude plane of the current phase and amplitude image plane estimation with a next target amplitude image comprising a sum of the target holographic image and a product of the feedback strength value and a correction term comprising a difference between the target holographic image and the current phase and amplitude image plane to generate a current amplitude adjusted image; applying an inverse propagation model to a previous amplitude adjusted image to generate a phase and amplitude holographic plane estimation; normalizing an amplitude plane of the phase and amplitude holographic plane estimation to generate a phase only diffraction pattern image; and applying a forward propagation model to the phase only diffraction pattern image to generate the current phase and amplitude image plane estimation. Claim 11: The method of claim 9, wherein the machine learning model corresponds to a trained neural network and the machine learning model comprises at least one of fewer convolutional kernels with respect to the trained neural network or a reduced bit depth of weights of the machine learning model with respect to the trained neural network. Claim 11: The method of claim 9, wherein the machine learning model corresponds to a trained neural network and the machine learning model comprises at least one of fewer convolutional kernels with respect to the trained neural network or a reduced bit depth of weights of the machine learning model with respect to the trained neural network. Claim 12: The method of claim 11, further comprising downsampling the target holographic image prior to applying the machine learning model. Claim 12: The method of claim 11, further comprising downsampling the target holographic image prior to applying the machine learning model. Claim 13: The method of claim 9, wherein the target holographic image comprises first and second color planes and the method further comprises applying the machine learning model to the target holographic image to generate the feedback strength value for the first color plane and a second feedback strength value for the second color plane. Claim 13: The method of claim 9, wherein the target holographic image comprises first and second color planes and the method further comprises applying the machine learning model to the target holographic image to generate the feedback strength value for the first color plane and a second feedback strength value for the second color plane. Claim 14: The method of claim 13, further comprising: applying the iterative propagation feedback model based on the second color plane of the target holographic image; and using the second feedback strength value to generate a second phase diffraction pattern for presentation at the holographic plane or a second holographic plane. Claim 14: The method of claim 13, further comprising: applying the iterative propagation feedback model based on the second color plane of the target holographic image; and using the second feedback strength value to generate a second phase diffraction pattern for presentation at the holographic plane or a second holographic plane. Claim 15: The method of claim 9, wherein the holographic image is a three dimensional holographic image. Claim 15: The method of claim 9, wherein the holographic image is a three dimensional holographic image. Claim 16: A non-transitory machine readable medium comprising a plurality of instructions that, in response to being executed by a processor of a holographic head-mounted display system, causes the processor to perform operations comprising: applying a machine learning model to a target holographic image to generate a feedback strength value; applying an iterative propagation feedback model based on the target holographic image and the feedback strength value to generate a phase diffraction pattern for presentation at a holographic plane of the holographic head-mounted display system; and outputting the phase diffraction pattern for presentation at the holographic plane for generation of a corresponding holographic image. Claim 16: A non-transitory machine readable medium comprising a plurality of instructions that, in response to being executed by one or more processors of a computing device, cause the computing device to perform operations comprising: accessing a target holographic image; applying, via one or more processors, a machine learning model to the target holographic image to generate a feedback strength value, the feedback strength value to enable determination of a phase diffraction pattern for presentation at a holographic plane; applying an iterative propagation feedback model based on the target holographic image and the feedback strength value to generate the phase diffraction pattern for presentation at the holographic plane; and outputting the phase diffraction pattern for presentation at the holographic plane for generation of a corresponding holographic image. Claim 17: The non-transitory machine readable medium as in claim 16, wherein applying the iterative propagation feedback model based on the target holographic image and the feedback strength value includes operations comprising: receiving a current phase and amplitude image plane estimation; replacing an amplitude plane of the current phase and amplitude image plane estimation with a next target amplitude image comprising a sum of the target holographic image and a product of the feedback strength value and a correction term comprising a difference between the target holographic image and the current phase and amplitude image plane to generate a current amplitude adjusted image; applying an inverse propagation model to a previous amplitude adjusted image to generate a phase and amplitude holographic plane estimation; normalizing an amplitude plane of the phase and amplitude holographic plane estimation to generate a phase only diffraction pattern image; and applying a forward propagation model to the phase only diffraction pattern image to generate the current phase and amplitude image plane estimation. Claim 17: The non-transitory machine readable medium as in claim 16, wherein applying the iterative propagation feedback model based on the target holographic image and the feedback strength value includes operations comprising: receiving a current phase and amplitude image plane estimation; replacing an amplitude plane of the current phase and amplitude image plane estimation with a next target amplitude image comprising a sum of the target holographic image and a product of the feedback strength value and a correction term comprising a difference between the target holographic image and the current phase and amplitude image plane to generate a current amplitude adjusted image; applying an inverse propagation model to a previous amplitude adjusted image to generate a phase and amplitude holographic plane estimation; normalizing an amplitude plane of the phase and amplitude holographic plane estimation to generate a phase only diffraction pattern image; and applying a forward propagation model to the phase only diffraction pattern image to generate the current phase and amplitude image plane estimation. Claim 18: The non-transitory machine readable medium as in claim 16, wherein the machine learning model corresponds to a trained neural network, the machine learning model comprises at least one of fewer convolutional kernels with respect to the trained neural network or a reduced bit depth of weights of the machine learning model with respect to the trained neural network, and the operations further comprise downsampling the target holographic image prior to applying the machine learning model. Claim 18: The non-transitory machine readable medium as in claim 16, wherein the machine learning model corresponds to a trained neural network, the machine learning model comprises at least one of fewer convolutional kernels with respect to the trained neural network or a reduced bit depth of weights of the machine learning model with respect to the trained neural network, and the operations further comprise downsampling the target holographic image prior to applying the machine learning model. Claim 19: The non-transitory machine readable medium as in claim 16, wherein the target holographic image comprises first and second color planes and the operations further comprise applying the machine learning model to the target holographic image to generate the feedback strength value for the first color plane and a second feedback strength value for the second color plane. Claim 19: The non-transitory machine readable medium as in claim 16, wherein the target holographic image comprises first and second color planes and the operations further comprise applying the machine learning model to the target holographic image to generate the feedback strength value for the first color plane and a second feedback strength value for the second color plane. Claim 20: The non-transitory machine readable medium as in claim 19, wherein the holographic image is a three dimensional holographic image and the operations further comprise: applying the iterative propagation feedback model based on the second color plane of the target holographic image; and using the second feedback strength value to generate a second phase diffraction pattern for presentation at the holographic plane or a second holographic plane. Claim 20: The non-transitory machine readable medium as in claim 19, wherein the holographic image is a three dimensional holographic image and the operations further comprise: applying the iterative propagation feedback model based on the second color plane of the target holographic image; and using the second feedback strength value to generate a second phase diffraction pattern for presentation at the holographic plane or a second holographic plane. 08-36 Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 11,650,542 B2. 08-36 Claim 2 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 2 of U.S. Patent No. 11,650,542 B2. 08-36 Claim 3 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 3 of U.S. Patent No. 11,650,542 B2. 08-36 Claim 4 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 4 of U.S. Patent No. 11,650,542 B2. 08-36 Claim 5 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 5 of U.S. Patent No. 11,650,542 B2. 08-36 Claim 6 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 6 of U.S. Patent No. 11,650,542 B2. 08-36 Claim 7 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 7 of U.S. Patent No. 11,650,542 B2. 08-36 Claim 8 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 8 of U.S. Patent No. 11,650,542 B2. 08-36 Claim 9 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 9 of U.S. Patent No. 11,650,542 B2. 08-36 Claim 10 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 10 of U.S. Patent No. 11,650,542 B2. 08-36 Claim 11 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 11 of U.S. Patent No. 11,650,542 B2. 08-36 Claim 12 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 12 of U.S. Patent No. 11,650,542 B2. 08-36 Claim 13 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 13 of U.S. Patent No. 11,650,542 B2. 08-36 Claim 14 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 14 of U.S. Patent No. 11,650,542 B2. 08-36 Claim 15 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 15 of U.S. Patent No. 11,650,542 B2. 08-36 Claim 16 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 16 of U.S. Patent No. 11,650,542 B2. 08-36 Claim 17 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 17 of U.S. Patent No. 11,650,542 B2. 08-36 Claim 18 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 18 of U.S. Patent No. 11,650,542 B2. 08-36 Claim 19 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 19 of U.S. Patent No. 11,650,542 B2. 08-36 Claim 20 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 20 of U.S. Patent No. 11,650,542 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because The claims of this instant invention are encompassed by the patented claims of U.S. patent 11,650,542 B2. Application 18/735,369 U.S. patent 12,019,396 B2 Claim 1: A head-mounted display (HMD) system comprising: a holographic plane; and a system to present a holographic image at the holographic plane, the system comprising circuitry configured to: apply a machine learning model to a target holographic image to generate a feedback strength value; apply an iterative propagation feedback model based on the target holographic image and the feedback strength value to generate a phase diffraction pattern; and output the phase diffraction pattern for presentation at the holographic plane for generation of a corresponding holographic image. Claim 1: A heads up display (HUD) system comprising: a holographic plane; and a system for generating holographic images for presentation at the holographic plane, the system comprising circuitry configured to: apply a machine learning model to a target holographic image to generate a feedback strength value; apply an iterative propagation feedback model based on the target holographic image and the feedback strength value to generate a phase diffraction pattern; and output the phase diffraction pattern for presentation at the holographic plane for generation of a corresponding holographic image. Claim 2: The HMD system of claim 1, wherein to apply the iterative propagation feedback model based on the target holographic image and the feedback strength value, the circuitry is configured to: receive a current phase and amplitude image plane estimation; and replace an amplitude plane of the current phase and amplitude image plane estimation with a next target amplitude image comprising: a sum of the target holographic image and a product of the feedback strength value and a correction term comprising a difference between the target holographic image and the current phase and amplitude image plane to generate a current amplitude adjusted image. Claim 2: The HUD system of claim 1, wherein to apply the iterative propagation feedback model based on the target holographic image and the feedback strength value, the circuitry is configured to: receive a current phase and amplitude image plane estimation; and replace an amplitude plane of the current phase and amplitude image plane estimation with a next target amplitude image comprising: a sum of the target holographic image and a product of the feedback strength value and a correction term comprising a difference between the target holographic image and the current phase and amplitude image plane to generate a current amplitude adjusted image. Claim 3: The HMD system of claim 2, wherein to apply the iterative propagation feedback model based on the target holographic image and the feedback strength value, the circuitry is configured to: apply an inverse propagation model to a previous amplitude adjusted image to generate a phase and amplitude holographic plane estimation; normalize an amplitude plane of the phase and amplitude holographic plane estimation to generate a phase only diffraction pattern image; and apply a forward propagation model to the phase only diffraction pattern image to generate the current phase and amplitude image plane estimation. Claim 3: The HUD system of claim 2, wherein to apply the iterative propagation feedback model based on the target holographic image and the feedback strength value, the circuitry is configured to: apply an inverse propagation model to a previous amplitude adjusted image to generate a phase and amplitude holographic plane estimation; normalize an amplitude plane of the phase and amplitude holographic plane estimation to generate a phase only diffraction pattern image; and apply a forward propagation model to the phase only diffraction pattern image to generate the current phase and amplitude image plane estimation. Claim 4: The HMD system of claim 1, wherein the machine learning model corresponds to a trained neural network and the machine learning model comprises at least one of fewer convolutional kernels with respect to the trained neural network or a reduced bit depth of weights of the machine learning model with respect to the trained neural network. Claim 4: The HUD system of claim 1, wherein the machine learning model corresponds to a trained neural network and the machine learning model comprises at least one of fewer convolutional kernels with respect to the trained neural network or a reduced bit depth of weights of the machine learning model with respect to the trained neural network. Claim 5: The HMD system of claim 4, wherein the target holographic image is downsampled prior to application of the machine learning model. Claim 5: The HUD system of claim 4, wherein the target holographic image is downsampled prior to application of the machine learning model. Claim 6: The HMD system of claim 1, wherein the target holographic image comprises first and second color planes, and the circuitry is configured to apply the machine learning model to the target holographic image to generate the feedback strength value for the first color plane and a second feedback strength value for the second color plane. Claim 6: The HUD system of claim 1, wherein the target holographic image comprises first and second color planes, and the circuitry is configured to apply the machine learning model to the target holographic image to generate the feedback strength value for the first color plane and a second feedback strength value for the second color plane. Claim 7: The HMD system of claim 6, wherein the circuitry is further to: apply the iterative propagation feedback model based on the second color plane of the target holographic image; and use the second feedback strength value to generate a second phase diffraction pattern for presentation at the holographic plane or a second holographic plane. Claim 7: The HUD system of claim 6, wherein the circuitry is further to: apply the iterative propagation feedback model based on the second color plane of the target holographic image; and use the second feedback strength value to generate a second phase diffraction pattern for presentation at the holographic plane or a second holographic plane. Claim 8: The HMD system of claim 1, wherein the holographic image is a three dimensional holographic image. Claim 8: The HUD system of claim 1, wherein the holographic image is a three dimensional holographic image. Claim 9: A method for presenting holographic images at a holographic plane of a head-mounted display, the method comprising: applying, via one or more processors, a machine learning model to a target holographic image to generate a feedback strength value; applying an iterative propagation feedback model based on the target holographic image and the feedback strength value to generate a phase diffraction pattern; and outputting the phase diffraction pattern for presentation at the holographic plane for generation of a corresponding holographic image. Claim 9: A method for generating holographic images for presentation at a holographic plane of a heads up display, the method comprising: applying, via one or more processors, a machine learning model to a target holographic image to generate a feedback strength value; applying an iterative propagation feedback model based on the target holographic image and the feedback strength value to generate a phase diffraction pattern; and outputting the phase diffraction pattern for presentation at the holographic plane for generation of a corresponding holographic image. Claim 10: The method of claim 9, wherein applying the iterative propagation feedback model based on the target holographic image and the feedback strength value comprises: receiving a current phase and amplitude image plane estimation; replacing an amplitude plane of the current phase and amplitude image plane estimation with a next target amplitude image comprising a sum of the target holographic image and a product of the feedback strength value and a correction term comprising a difference between the target holographic image and the current phase and amplitude image plane to generate a current amplitude adjusted image; applying an inverse propagation model to a previous amplitude adjusted image to generate a phase and amplitude holographic plane estimation; normalizing an amplitude plane of the phase and amplitude holographic plane estimation to generate a phase only diffraction pattern image; and applying a forward propagation model to the phase only diffraction pattern image to generate the current phase and amplitude image plane estimation. Claim 10: The method of claim 9, wherein applying the iterative propagation feedback model based on the target holographic image and the feedback strength value comprises: receiving a current phase and amplitude image plane estimation; replacing an amplitude plane of the current phase and amplitude image plane estimation with a next target amplitude image comprising a sum of the target holographic image and a product of the feedback strength value and a correction term comprising a difference between the target holographic image and the current phase and amplitude image plane to generate a current amplitude adjusted image; applying an inverse propagation model to a previous amplitude adjusted image to generate a phase and amplitude holographic plane estimation; normalizing an amplitude plane of the phase and amplitude holographic plane estimation to generate a phase only diffraction pattern image; and applying a forward propagation model to the phase only diffraction pattern image to generate the current phase and amplitude image plane estimation. Claim 11: The method of claim 9, wherein the machine learning model corresponds to a trained neural network and the machine learning model comprises at least one of fewer convolutional kernels with respect to the trained neural network or a reduced bit depth of weights of the machine learning model with respect to the trained neural network. Claim 11: The method of claim 9, wherein the machine learning model corresponds to a trained neural network and the machine learning model comprises at least one of fewer convolutional kernels with respect to the trained neural network or a reduced bit depth of weights of the machine learning model with respect to the trained neural network. Claim 12: The method of claim 11, further comprising downsampling the target holographic image prior to applying the machine learning model. Claim 12: The method of claim 11, further comprising downsampling the target holographic image prior to applying the machine learning model. Claim 13: The method of claim 9, wherein the target holographic image comprises first and second color planes and the method further comprises applying the machine learning model to the target holographic image to generate the feedback strength value for the first color plane and a second feedback strength value for the second color plane. Claim 13: The method of claim 9, wherein the target holographic image comprises first and second color planes and the method further comprises applying the machine learning model to the target holographic image to generate the feedback strength value for the first color plane and a second feedback strength value for the second color plane. Claim 14: The method of claim 13, further comprising: applying the iterative propagation feedback model based on the second color plane of the target holographic image; and using the second feedback strength value to generate a second phase diffraction pattern for presentation at the holographic plane or a second holographic plane. Claim 14: The method of claim 13, further comprising: applying the iterative propagation feedback model based on the second color plane of the target holographic image; and using the second feedback strength value to generate a second phase diffraction pattern for presentation at the holographic plane or a second holographic plane. Claim 15: The method of claim 9, wherein the holographic image is a three dimensional holographic image. Claim 15: The method of claim 9, wherein the holographic image is a three dimensional holographic image. Claim 16: A non-transitory machine readable medium comprising a plurality of instructions that, in response to being executed by a processor of a holographic head-mounted display system, causes the processor to perform operations comprising: applying a machine learning model to a target holographic image to generate a feedback strength value; applying an iterative propagation feedback model based on the target holographic image and the feedback strength value to generate a phase diffraction pattern for presentation at a holographic plane of the holographic head-mounted display system; and outputting the phase diffraction pattern for presentation at the holographic plane for generation of a corresponding holographic image. Claim 16: A non-transitory machine readable medium comprising a plurality of instructions that, in response to being executed by a processor of a holographic heads up display system, causes the processor to perform operations comprising: applying a machine learning model to a target holographic image to generate a feedback strength value; applying an iterative propagation feedback model based on the target holographic image and the feedback strength value to generate a phase diffraction pattern for presentation at a holographic plane of the holographic heads up display system; and outputting the phase diffraction pattern for presentation at the holographic plane for generation of a corresponding holographic image. Claim 17: The non-transitory machine readable medium as in claim 16, wherein applying the iterative propagation feedback model based on the target holographic image and the feedback strength value includes operations comprising: receiving a current phase and amplitude image plane estimation; replacing an amplitude plane of the current phase and amplitude image plane estimation with a next target amplitude image comprising a sum of the target holographic image and a product of the feedback strength value and a correction term comprising a difference between the target holographic image and the current phase and amplitude image plane to generate a current amplitude adjusted image; applying an inverse propagation model to a previous amplitude adjusted image to generate a phase and amplitude holographic plane estimation; normalizing an amplitude plane of the phase and amplitude holographic plane estimation to generate a phase only diffraction pattern image; and applying a forward propagation model to the phase only diffraction pattern image to generate the current phase and amplitude image plane estimation. Claim 17: The non-transitory machine readable medium as in claim 16, wherein applying the iterative propagation feedback model based on the target holographic image and the feedback strength value includes operations comprising: receiving a current phase and amplitude image plane estimation; replacing an amplitude plane of the current phase and amplitude image plane estimation with a next target amplitude image comprising a sum of the target holographic image and a product of the feedback strength value and a correction term comprising a difference between the target holographic image and the current phase and amplitude image plane to generate a current amplitude adjusted image; applying an inverse propagation model to a previous amplitude adjusted image to generate a phase and amplitude holographic plane estimation; normalizing an amplitude plane of the phase and amplitude holographic plane estimation to generate a phase only diffraction pattern image; and applying a forward propagation model to the phase only diffraction pattern image to generate the current phase and amplitude image plane estimation. Claim 18: The non-transitory machine readable medium as in claim 16, wherein the machine learning model corresponds to a trained neural network, the machine learning model comprises at least one of fewer convolutional kernels with respect to the trained neural network or a reduced bit depth of weights of the machine learning model with respect to the trained neural network, and the operations further comprise downsampling the target holographic image prior to applying the machine learning model. Claim 18: The non-transitory machine readable medium as in claim 16, wherein the machine learning model corresponds to a trained neural network, the machine learning model comprises at least one of fewer convolutional kernels with respect to the trained neural network or a reduced bit depth of weights of the machine learning model with respect to the trained neural network, and the operations further comprise downsampling the target holographic image prior to applying the machine learning model. Claim 19: The non-transitory machine readable medium as in claim 16, wherein the target holographic image comprises first and second color planes and the operations further comprise applying the machine learning model to the target holographic image to generate the feedback strength value for the first color plane and a second feedback strength value for the second color plane. Claim 19: The non-transitory machine readable medium as in claim 16, wherein the target holographic image comprises first and second color planes and the operations further comprise applying the machine learning model to the target holographic image to generate the feedback strength value for the first color plane and a second feedback strength value for the second color plane. Claim 20: The non-transitory machine readable medium as in claim 19, wherein the holographic image is a three dimensional holographic image and the operations further comprise: applying the iterative propagation feedback model based on the second color plane of the target holographic image; and using the second feedback strength value to generate a second phase diffraction pattern for presentation at the holographic plane or a second holographic plane. Claim 20: The non-transitory machine readable medium as in claim 19, wherein the holographic image is a three dimensional holographic image and the operations further comprise: applying the iterative propagation feedback model based on the second color plane of the target holographic image; and using the second feedback strength value to generate a second phase diffraction pattern for presentation at the holographic plane or a second holographic plane. 08-36 Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12,019,396 B2. 08-36 Claim 2 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 2 of U.S. Patent No. 12,019,396 B2. 08-36 Claim 3 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 3 of U.S. Patent No. 12,019,396 B2. 08-36 Claim 4 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 4 of U.S. Patent No. 12,019,396 B2. 08-36 Claim 5 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 5 of U.S. Patent No. 12,019,396 B2. 08-36 Claim 6 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 6 of U.S. Patent No. 12,019,396 B2. 08-36 Claim 7 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 7 of U.S. Patent No. 12,019,396 B2. 08-36 Claim 8 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 8 of U.S. Patent No. 12,019,396 B2. 08-36 Claim 9 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 9 of U.S. Patent No. 12,019,396 B2. 08-36 Claim 10 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 10 of U.S. Patent No. 12,019,396 B2. 08-36 Claim 11 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 11 of U.S. Patent No. 12,019,396 B2. 08-36 Claim 12 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 12 of U.S. Patent No. 12,019,396 B2. 08-36 Claim 13 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 13 of U.S. Patent No. 12,019,396 B2. 08-36 Claim 14 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 14 of U.S. Patent No. 12,019,396 B2. 08-36 Claim 15 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 15 of U.S. Patent No. 12,019,396 B2. 08-36 Claim 16 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 16 of U.S. Patent No. 12,019,396 B2. 08-36 Claim 17 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 17 of U.S. Patent No. 12,019,396 B2 08-36 Claim 18 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 18 of U.S. Patent No. 12,019,396 B2. 08-36 Claim 19 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 19 of U.S. Patent No. 12,019,396 B2. 08-36 Claim 20 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 20 of U.S. Patent No. 12,019,396 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because The claims of this instant invention are encompassed by the patented claims of U.S. patent 12,019,396 B2. Regarding claims 1-20: Prior art was not found for the claimed subject matter therefore, there is no art rejection made on the claims but the claims are still rejected under double patenting with three patents. Contact Information 3. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANAND BHATNAGAR whose telephone number is (571)272-7416. The examiner can normally be reached on M-F 7:30am-4:00pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice . If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached on 571-272-4650. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ANAND P BHATNAGAR/Primary Examiner, Art Unit 2668 March 28, 2026 Application/Control Number: 18/735,369 Page 2 Art Unit: 2668 Application/Control Number: 18/735,369 Page 3 Art Unit: 2668 Application/Control Number: 18/735,369 Page 4 Art Unit: 2668 Application/Control Number: 18/735,369 Page 5 Art Unit: 2668 Application/Control Number: 18/735,369 Page 6 Art Unit: 2668 Application/Control Number: 18/735,369 Page 7 Art Unit: 2668 Application/Control Number: 18/735,369 Page 8 Art Unit: 2668 Application/Control Number: 18/735,369 Page 9 Art Unit: 2668 Application/Control Number: 18/735,369 Page 10 Art Unit: 2668 Application/Control Number: 18/735,369 Page 11 Art Unit: 2668 Application/Control Number: 18/735,369 Page 12 Art Unit: 2668 Application/Control Number: 18/735,369 Page 13 Art Unit: 2668 Application/Control 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