CTFR 18/581,720 CTFR 98460 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. Response to Arguments 07-37 AIA Applicant's arguments filed 6 April 2026 have been fully considered but they are not persuasive. Claims 1-20 are pending in this application and have been considered below. Argument: The applicant argues that Toporek does not teach or suggest the claims because Toporek only generates a feature map based blending parameters for spectral images and never creates the separate anatomy and contrast latent spaces required by the claims. Response: Applicant appears to be arguing that saving anatomy information or data structures in one location (anatomy latent space) and brightness information or data structures (contrast) in a second location (contrast latent space), is not taught by Toporek. This is not persuasive when Toporek et al. teaches using “anatomy or tissue information,” and separately using spectral data and using “different types of spectral data” which is brightness or contrast data. In addition, while Toporek et al. is sufficient to teach the current limitations, applicants attention is directed to US Patent Publication 2018 0025515 A1 to Shechter as noted in the additional references as clearly showing methods of running different types of optimizations on CT data and then combining the data for an improved image. Thus, International Patent Publication 2022 268618 A1 , (Toporek et al.) shows the limitation, train a latent space encoder to generate separate latent space information ("The encoder EC takes in training input data .Xj and compresses it to a low-dimensional representation in latent space f," Pg. 19, Lines 2-3 and "The training is run separately for each data type j to obtain the different models Mj . Training can be done one-by-one over the training data set, or can be done in batches preferably." Pg. 18, lines 25-27) by: encoding the multi-spectral images to generate anatomy latent space information (" anatomy awareness may be "fed" into the f eature extractor FE . The contextual data may include the said segmentation map ( e.g. denoting which spatial part belong to which anatomy or tissue type), or may include more generic information about a body region . Anatomy awareness may be exploited when creating the feature map ," Pg. 13, lines 14-18) and contrast latent space information (" Different types of spectral data may give rise to different contrast for different materials/tissues. Thus, some spectral data type may be more suitable in terms of improved contrast for imaging of certain materials/tissue ( or quantities thereof) than it is for imaging other materials/tissue (or quantities thereof)," Pg.4, lines 28-31) , the anatomy latent space information representing compressed anatomy information in the multi-spectral images ("The contextual data may include the said segmentation map ( e.g. denoting which spatial part belong to which anatomy or tissue type ), or may include more generic information about a body region. Anatomy awareness may be exploited when creating the feature map," Pg. 13, Lines 15-18) , the contrast latent space information representing compressed contrast information in the multi-spectral images ("spectral imaging allows resolving image contrast into plural energy windows . Resolving into two such energy windows , high E2 and low El, is sufficient for present purposes," Pg. 8, Lines 4-6) . Information Disclosure Statement The IDS dated 2 October 2024 has been considered and placed in the application file. Specification - Abstract Applicant has submitted a replacement abstract. The objection to the abstract is withdrawn. 07-30-03-h AIA Claim Interpretation Under MPEP 2143.03, "All words in a claim must be considered in judging the patentability of that claim against the prior art." In re Wilson, 424 F.2d 1382, 1385, 165 USPQ 494, 496 (CCPA 1970). As a general matter, the grammar and ordinary meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives , the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009). Claims 9 and 19 recite “one or more of.” Since “one or more of” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim . While citations have been provided for completeness and rapid prosecution, only one element is required . Because, on balance, it appears the disjunctive interpretation enjoys the most specification support and for that reason the disjunctive interpretation (one of A, B OR C) is being adopted for the purposes of this Office Action. Applicant’s comments and/or amendments relating to this issue are invited to clarify the claim language and the prosecution history. 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. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. PNG media_image1.png 307 465 media_image1.png Greyscale Claims 1-20 are rejected under 35 U.S.C. 103 as obvious over International Patent Publication 2022 268618 A1 , (Toporek et al.) . Claim 1 [AltContent: textbox (Toporek et al. Fig. 2, showing data being separated and analyzed in several methods.)] Regarding Claim 1 , Toporek et al. teach a system for generating target images ("a system for image processing, comprising: an input interface for receiving plural spectral input data generated by a spectral X-ray imaging system; at least one feature extractor configured to process one type of the plural spectral input data and generate a feature map corresponding to the processed type of spectral input data; a combiner configured to combine, in a combination operation, the plural spectral input data into combined data, the combination operation controlled by a combiner parameter that is based on feature maps previously generated by the one or more feature extractor," Pg. 2, Lines 29-35) , comprising: a database of multi-spectral images ("Spectral imaging allows acquiring multi-energy projection data," Pg. 6, Lines 33-34, where data teaches a database and multi-energy projection data is multi-spectral images) ; and a processor ("a processor (e.g., a central processing unit (CPU), a microprocessor, etc.) and computer readable storage medium (which excludes transitory medium) such as physical memory," Pg. 7, Lines 23-24) configured to: train a latent space encoder to generate separate latent space information ("The encoder EC takes in training input data .Xj and compresses it to a low-dimensional representation in latent space f," Pg. 19, Lines 2-3 and "The training is run separately for each data type j to obtain the different models Mj . Training can be done one-by-one over the training data set, or can be done in batches preferably." Pg. 18, lines 25-27) by: encoding the multi-spectral images to generate anatomy latent space information (" anatomy awareness may be "fed" into the f eature extractor FE . The contextual data may include the said segmentation map ( e.g. denoting which spatial part belong to which anatomy or tissue type), or may include more generic information about a body region . Anatomy awareness may be exploited when creating the feature map ," Pg. 13, lines 14-18) and contrast latent space information (" Different types of spectral data may give rise to different contrast for different materials/tissues. Thus, some spectral data type may be more suitable in terms of improved contrast for imaging of certain materials/tissue ( or quantities thereof) than it is for imaging other materials/tissue (or quantities thereof)," Pg.4, lines 28-31) , the anatomy latent space information representing compressed anatomy information in the multi-spectral images ("The contextual data may include the said segmentation map ( e.g. denoting which spatial part belong to which anatomy or tissue type ), or may include more generic information about a body region. Anatomy awareness may be exploited when creating the feature map," Pg. 13, Lines 15-18) , the contrast latent space information representing compressed contrast information in the multi-spectral images ("spectral imaging allows resolving image contrast into plural energy windows . Resolving into two such energy windows , high E2 and low El, is sufficient for present purposes," Pg. 8, Lines 4-6) , combining selected features of the anatomy latent space information and the contrast latent space information to reproduce the multi-spectral images ("the combiner CMB, combines the Compton scatter and photo electric projection imagery Ac, AP and/or the set of projection imagery at different levels AEl, E2 as detected at the detector into cross-sectional imagery Ic.p in image domain ID," Pg. 10, Lines 10-13) , comparing the reproduced multi-spectral images to the multi-spectral images ("Training may be formulated as an optimization procedure based on an objective function F. The 20 objective function F may be configured as a utility or loss function," Pg. 18, Lines 19-20) , and adjusting the encoding of the multi-spectral images and repeating the training until the reproduced multi-spectral images match the multi-spectral images ("the machine learning parameters 0 of the machine learning model are adapted, preferably in iterations, until an iteration stopping condition is met at which point the model 25 is considered sufficiently trained," Pg. 18, Lines 23-25) , and after training the latent space encoder, train an output decoder ("the decoder part DC of the network is made to learn reconstructing the input image, using the low-dimensional representation in latent space. Parameters 5 of both networks parts, the encoder EC and decoder DC, are adjusted by minimizing the reconstruction error," Pg. 19, Lines 3-6) to generate target images by: inputting the anatomy latent space information to a prediction model ("anatomy awareness 15 may be "fed" into the feature extractor FE. The contextual data may include the said segmentation map ( e.g. denoting which spatial part belong to which anatomy or tissue type), or may include more generic information about a body region. Anatomy awareness may be exploited when creating the feature map," Pg. 13, Lines 14-18 and " prediction can be any task, such as a classification of the input image into high- or low-quality. Other tasks may include any one of i) binary or multi-class classification (malignant vs. benign tumor), ii) object detection , localization or segmentation (tumor localization ), iii) image quality or similarity assessment (good vs. low-quality acquisition), iv) image denoising or artefact removal using generative networks such as variational autoencoder (VAE), v) style transfer 15 using generative adversarial networks (GAN)," page 20, lines 10-15) , predicting target images by the prediction model based on the anatomy latent space information ( prediction can be any task, such as a classification of the input image into high- or low-quality. Other tasks may include any one of i) binary or multi-class classification (malignant vs. benign tumor), ii) object detection , localization or segmentation (tumor localization ), iii) image quality or similarity assessment (good vs. low-quality acquisition), iv) image denoising or artefact removal using generative networks such as variational autoencoder (VAE), v) style transfer 15 using generative adversarial networks (GAN)," page 20, lines 10-15) , comparing the predicted target images to reference target images related to the multi-spectral images ("Training may be formulated as an optimization procedure based on an objective function F. The 20 objective function F may be configured as a utility or loss function," Pg. 18, Lines 19-20) , and adjusting weights of the prediction model based on the comparison and repeating the training until the images predicted by the prediction model match the reference target images related to the multi-spectral images ("the machine learning parameters 0 of the machine learning model are adapted, preferably in iterations, until an iteration stopping condition is met at which point the model 25 is considered sufficiently trained," Pg. 18, Lines 23-25) . The rejection of system claim 1 above applies mutatis mutandis to the corresponding limitations of method claim 11 while noting that the rejection above cites to both device and method disclosures. Claim 11 is mapped below for clarity of the record and to specify any new limitations not included in claim 1. It is recognized that the citations and evidence provided above are derived from potentially different embodiments of a single reference. Nevertheless, it 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 to employ combinations and sub-combinations of these complementary embodiments, because Toporek et al. explicitly motivates doing so at least in lines 11-20 on page 2, page 8, lines 22-24, page 23, lines 15-19 including “While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive. The invention is not limited to the disclosed embodiments. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing a claimed invention, from a study of the drawings, the disclosure, and the dependent claims.” and otherwise motivating experimentation and optimization. Claim 2 Regarding claim 2, Toporek et al. teach the system of claim 1, wherein the processor is configured to generate the latent space information by applying a convolutional neural network to the multi-spectral images to extract and separate anatomy and contrast information from the multi-spectral images ("Y-j 's are the targets associated with each training input specimen XJ. As in Figure 5A, after training, output from the intermediate convolutional layers Li results in an intermediate feature map," Pg. 20, Lines 6-8, and "classification of the input image into high- or low-quality. Other tasks may include any one of i) binary or multi-class classification (malignant vs. benign tumor ), ii) object detection, localization or segmentation (tumor localization), iii) image quality or similarity assessment (good vs. low-quality acquisition), iv) image denoising or artefact removal using generative networks such as variational autoencoder (VAE), v) style transfer using generative adversarial networks (GAN)," Pg. 20, lines 10-15,where contrast information is taught as part of image denoising and is separate from classification, which is anatomy information) . Claim 3 Regarding claim 3, Toporek et al. teach the system of claim 1, wherein the processor is configured to select anatomy features as common anatomy features between the anatomy latent space information ("The contextual data may include the said segmentation map ( e.g. denoting which spatial part belong to which anatomy or tissue type ), or may include more generic information about a body region. Anatomy awareness may be exploited when creating the feature map," Pg. 13, Lines 15-18) . Claim 4 Regarding claim 4, Toporek et al. teach the system of claim 1, wherein the processor is configured to combine the anatomy latent space information and the contrast latent space information by concatenating the anatomy latent space information and the contrast latent space information ("the output of the selected models Mj is a set of different combiner parameter aj, one for each data type j. The models can be so applied in parallel or in sequence ," Pg. 11-12, Lines 37-2, where in sequence is concatenation) . Claim 5 Regarding claim 5, Toporek et al. teach the system of claim 1, wherein the processor is configured to compare the reproduced multi-spectral images to the multi-spectral images by computing a loss function of the reproduced multi-spectral images as compared to the multi-spectral images and repeating the training until the loss function is less than a loss function threshold ("the machine learning parameters 0 of the machine learning model are adapted, preferably in iterations, until an iteration stopping condition is met at which point the model 25 is considered sufficiently trained," Pg. 18, Lines 23-25) . Claim 6 Regarding claim 6, Toporek et al. teach the system of claim 1, wherein the multi-spectral images are images of anatomy captured by medical imaging devices from a common frame of reference relative to the anatomy and operating at different spectral frequencies ("the imaging apparatus IA, such as the CT scanner in Figure 1, is configured for spectral imaging. The X-ray imaging apparatus SIA thus produces sets of spectral projection raw data Aj,j2:2 for two or more energy windows," Pg. 8, Lines 1-3) . Claim 7 Regarding claim 7, Toporek et al. teach the system of claim 1, wherein the processor is configured to average the anatomy latent space information prior to inputting the anatomy latent space information to the prediction model ("the Grad-CAM method is based on calculating a gradient of a class with respect to the last multi-dimensional convolutional layer. Weights of some or each filter output at a given layer Li is then obtained by averaging, such as global average pooling," Pg. 16, Lines 33-35) . Claim 8 Regarding claim 8, Toporek et al. teach the system of claim 1, wherein the prediction model is a neural network that predicts the target images and in supervised manner compares the predicted target images to reference target images related to the multi-spectral images to determine corrective measures to adjust the weights of the prediction model which are neural network weights ("Referring now to a supervised learning embodiment , reference is now made to Figure 5B. The neural network Mj may be trained on any arbitrary task. The network may be chosen as a CNN as mentioned earlier in Figure 3B," Pg. 19, Lines 34-36) . Claim 9 Regarding claim 9, Toporek et al. teach the system of claim 1, wherein the target images are medical images related to states of human anatomy and comprise one or more of virtual monoenergetic images, virtual non-contrast images, material maps and downstream segmentations ("Other spectral data type includes virtual monoenergetic images generated by combining basis images at different energy levels (typically 40 to 200 keV), Other spectral data types include material maps MM, such as for Iodine density Irct (or other contrast agent), virtual non-contrast imagery VNC, uric acid pair imagery, effective atomic number imagery, etc, generated from any material decomposition algorithm known in the art," Pg. 9, Lines 22-26) . Claim 10 Regarding claim 10, Toporek et al. teach the system of claim 1, wherein the processor is configured to utilize the trained latent space encoder to generate new latent space information for new multi-spectral images and input the new latent space information to the trained output decoder to generate new target images ("This may be implemented for IO instance by combining feature extractor FE with a segmentation mask extracted from a segmentation model, or by training feature extractor FE' s model on a segmentation task rather than classification task. The segmentation information or other anatomical context information may be used as contextual data, in addition to the spectral image data. Both, the contextual data and the input spectral data may be co-processed by the ML model Mj during training," Pg. 13, Lines 9-14) . Claim 11 Regarding claim 11, Toporek et al. teach a method for generating target images ("a system for image processing, comprising: an input interface for receiving plural spectral input data generated by a spectral X-ray imaging system; at least one feature extractor configured to process one type of the plural spectral input data and generate a feature map corresponding to the processed type of spectral input data; a combiner configured to combine, in a combination operation, the plural spectral input data into combined data, the combination operation controlled by a combiner parameter that is based on feature maps previously generated by the one or more feature extractor," Pg. 2, Lines 29-35) , comprising: train a latent space encoder to generate separate latent space information ("The encoder EC takes in training input data .Xj and compresses it to a low-dimensional representation in latent space f," Pg. 19, Lines 2-3 and "The training is run separately for each data type j to obtain the different models Mj . Training can be done one-by-one over the training data set, or can be done in batches preferably." Pg. 18, lines 25-27) by: encoding the multi-spectral images to generate anatomy latent space information (" anatomy awareness may be "fed" into the f eature extractor FE . The contextual data may include the said segmentation map ( e.g. denoting which spatial part belong to which anatomy or tissue type), or may include more generic information about a body region . Anatomy awareness may be exploited when creating the feature map ," Pg. 13, lines 14-18) and contrast latent space information (" Different types of spectral data may give rise to different contrast for different materials/tissues. Thus, some spectral data type may be more suitable in terms of improved contrast for imaging of certain materials/tissue ( or quantities thereof) than it is for imaging other materials/tissue (or quantities thereof)," Pg.4, lines 28-31) , the anatomy latent space information representing compressed anatomy information in the multi-spectral images ("The contextual data may include the said segmentation map ( e.g. denoting which spatial part belong to which anatomy or tissue type ), or may include more generic information about a body region. Anatomy awareness may be exploited when creating the feature map," Pg. 13, Lines 15-18) , the contrast latent space information representing compressed contrast information in the multi-spectral images ("spectral imaging allows resolving image contrast into plural energy windows . Resolving into two such energy windows , high E2 and low El, is sufficient for present purposes," Pg. 8, Lines 4-6) , comparing the reproduced multi-spectral images to the multi-spectral images ("Training may be formulated as an optimization procedure based on an objective function F. The 20 objective function F may be configured as a utility or loss function," Pg. 18, Lines 19-20) , and adjusting the encoding of the multi-spectral images and repeating the training until the reproduced multi-spectral images match the multi-spectral images ("the machine learning parameters 0 of the machine learning model are adapted, preferably in iterations, until an iteration stopping condition is met at which point the model 25 is considered sufficiently trained," Pg. 18, Lines 23-25) , and after training the latent space encoder, training, by the processor, an output decoder ("the decoder part DC of the network is made to learn reconstructing the input image, using the low-dimensional representation in latent space. Parameters 5 of both networks parts, the encoder EC and decoder DC, are adjusted by minimizing the reconstruction error," Pg. 19, Lines 3-6) to generate target images by: inputting the anatomy latent space information to a prediction model ("anatomy awareness 15 may be "fed" into the feature extractor FE. The contextual data may include the said segmentation map ( e.g. denoting which spatial part belong to which anatomy or tissue type), or may include more generic information about a body region. Anatomy awareness may be exploited when creating the feature map," Pg. 13, Lines 14-18 and " prediction can be any task, such as a classification of the input image into high- or low-quality. Other tasks may include any one of i) binary or multi-class classification (malignant vs. benign tumor), ii) object detection , localization or segmentation (tumor localization ), iii) image quality or similarity assessment (good vs. low-quality acquisition), iv) image denoising or artefact removal using generative networks such as variational autoencoder (VAE), v) style transfer 15 using generative adversarial networks (GAN)," page 20, lines 10-15) , predicting target images by the prediction model based on the anatomy latent space information ( prediction can be any task, such as a classification of the input image into high- or low-quality. Other tasks may include any one of i) binary or multi-class classification (malignant vs. benign tumor), ii) object detection , localization or segmentation (tumor localization ), iii) image quality or similarity assessment (good vs. low-quality acquisition), iv) image denoising or artefact removal using generative networks such as variational autoencoder (VAE), v) style transfer 15 using generative adversarial networks (GAN)," page 20, lines 10-15) , comparing the predicted target images to reference target images related to the multi-spectral images ("Training may be formulated as an optimization procedure based on an objective function F. The 20 objective function F may be configured as a utility or loss function," Pg. 18, Lines 19-20) , and adjusting weights of the prediction model based on the comparison and repeating the training until the images predicted by the prediction model match the reference target images related to the multi-spectral images ("the machine learning parameters 0 of the machine learning model are adapted, preferably in iterations, until an iteration stopping condition is met at which point the model 25 is considered sufficiently trained," Pg. 18, Lines 23-25) . Claim 12 Regarding claim 12, Toporek et al. teach the method of claim 11, generating, by the processor, the latent space information by performing convolutions on the multi-spectral images to extract and separate anatomy and contrast information from the multi-spectral images ("Y-j 's are the targets associated with each training input specimen XJ. As in Figure 5A, after training, output from the intermediate convolutional layers Li results in an intermediate feature map," Pg. 20, Lines 6-8, and "classification of the input image into high- or low-quality. Other tasks may include any one of i) binary or multi-class classification (malignant vs. benign tumor ), ii) object detection, localization or segmentation (tumor localization), iii) image quality or similarity assessment (good vs. low-quality acquisition), iv) image denoising or artefact removal using generative networks such as variational autoencoder (VAE), v) style transfer using generative adversarial networks (GAN)," Pg. 20, lines 10-15,where contrast information is taught as part of image denoising and is separate from classification, which is anatomy information) . Claim 13 Regarding claim 13, Toporek et al. teach the method of claim 11, selecting, by the processor, anatomy features as common anatomy features between the anatomy latent space information ("The contextual data may include the said segmentation map ( e.g. denoting which spatial part belong to which anatomy or tissue type ), or may include more generic information about a body region. Anatomy awareness may be exploited when creating the feature map," Pg. 13, Lines 15-18) . Claim 14 Regarding claim 14, Toporek et al. teach the method of claim 11, combining, by the processor, the anatomy latent space information and the contrast latent space information by concatenating the anatomy latent space information and the contrast latent space information ("the output of the selected models Mj is a set of different combiner parameter aj, one for each data type j. The models can be so applied in parallel or in sequence ," Pg. 11-12, Lines 37-2, where in sequence is concatenation) . Claim 15 Regarding claim 15, Toporek et al. teach the method of claim 11, comparing, by the processor, the reproduced multi-spectral images to the multi-spectral images by computing a loss function of the reproduced multi-spectral images as compared to the multi-spectral images and repeating the training until the loss function is less than a loss function threshold ("the machine learning parameters 0 of the machine learning model are adapted, preferably in iterations, until an iteration stopping condition is met at which point the model 25 is considered sufficiently trained," Pg. 18, Lines 23-25) . Claim 16 Regarding claim 16, Toporek et al. teach the method of claim 11, wherein the multi-spectral images are images of anatomy captured by medical imaging devices from a common frame of reference relative to the anatomy and operating at different spectral frequencies ("the imaging apparatus IA, such as the CT scanner in Figure 1, is configured for spectral imaging. The X-ray imaging apparatus SIA thus produces sets of spectral projection raw data Aj,j2:2 for two or more energy windows," Pg. 8, Lines 1-3) . Claim 17 Regarding claim 17, Toporek et al. teach the method of claim 11, averaging, by the processor, the anatomy latent space information prior to inputting the anatomy latent space information to the prediction model ("the Grad-CAM method is based on calculating a gradient of a class with respect to the last multi-dimensional convolutional layer. Weights of some or each filter output at a given layer Li is then obtained by averaging, such as global average pooling," Pg. 16, Lines 33-35) . Claim 18 Regarding claim 18, Toporek et al. teach the method of claim 11, wherein the prediction model is a neural network that predicts the target images and in supervised manner compares the predicted target images to reference target images related to the multi-spectral images to determine corrective measures to adjust the weights of the prediction model which are neural network weights ("Referring now to a supervised learning embodiment , reference is now made to Figure 5B. The neural network Mj may be trained on any arbitrary task. The network may be chosen as a CNN as mentioned earlier in Figure 3B," Pg. 19, Lines 34-36) . Claim 19 Regarding claim 19, Toporek et al. teach the method of claim 11, wherein the target images are medical images related to states of human anatomy and comprise one or more of virtual monoenergetic images, virtual non-contrast images, material maps and downstream segmentations ("Other spectral data type includes virtual monoenergetic images generated by combining basis images at different energy levels (typically 40 to 200 keV), Other spectral data types include material maps MM, such as for Iodine density Irct (or other contrast agent), virtual non-contrast imagery VNC, uric acid pair imagery, effective atomic number imagery, etc, generated from any material decomposition algorithm known in the art," Pg. 9, Lines 22-26) . Claim 20 Regarding claim 20, Toporek et al. teach the method of claim 11, utilizing, by the processor, the trained latent space encoder to generate new latent space information for new multi-spectral images and input the new latent space information to the trained output decoder to generate new target images ("This may be implemented for IO instance by combining feature extractor FE with a segmentation mask extracted from a segmentation model, or by training feature extractor FE' s model on a segmentation task rather than classification task. The segmentation information or other anatomical context information may be used as contextual data, in addition to the spectral image data. Both, the contextual data and the input spectral data may be co-processed by the ML model Mj during training," Pg. 13, Lines 9-14) . Reference Cited 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Non Patent Publication “Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement” to Pain et al. discloses methods which integrate deep learning into the image reconstruction framework as either deep learning-based regularization or as a fully data-driven mapping from measured signal to images. Deep learning-based post-processing methods for low-dose imaging, temporal resolution enhancement and spatial resolution enhancement are also disclosed. Finally, the challenges associated with applying deep learning to enhance PET images in the clinical setting are disclosed and future research directions to address these challenges are presented. US Patent Publication 2017 0243380 A1 to Proksa et al. discloses a reconstruction processor (114) configured to execute computer readable instructions, which cause the reconstruction processor to: receive, in electronic format, non-spectral projection data, reconstruct the non-spectral projection data to generate a nonspectral image, retrieve a non-spectral to spectral voxel value map for a basis material of interest from a set of non-spectral to spectral voxel value maps, generate a spectral iterative reconstruction start image based on the nonspectral image and the non-spectral to spectral voxel value map, and reconstruct a spectral image, in electronic format, for the material basis of interest from the non-spectral projection data with a spectral iterative reconstruction algorithm and the spectral iterative reconstruction start image. US Patent Publication 2018 0025515 A1 to Shechter discloses generating a material landmark images in a low and high energy image domain. The material landmark image estimates a change of a value of an image pixel caused by adding a small amount of a known material to the pixel. The method further includes generating an air values image in the low and high energy image domain. The air values image estimates a value for each image pixel where a value of a pixel is replaced by a value representing air. The method further includes extracting from de-noised low and high images generated from the low and high line integrals, a material composition of each image pixel based on the material landmark images and air values image . Conclusion 07-39 AIA THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to HEATH E WELLS whose telephone number is (703)756-4696. The examiner can normally be reached Monday-Friday 8:00-4:00. 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, Ms. Jennifer Mehmood can be reached on 571-272-2976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /H.E.W/Examiner, Art Unit 2664 Date: 26 May 2026 /JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664 Application/Control Number: 18/581,720 Page 2 Art Unit: 2664 Application/Control Number: 18/581,720 Page 3 Art Unit: 2664 Application/Control Number: 18/581,720 Page 4 Art Unit: 2664 Application/Control Number: 18/581,720 Page 5 Art Unit: 2664 Application/Control Number: 18/581,720 Page 6 Art Unit: 2664 Application/Control Number: 18/581,720 Page 7 Art Unit: 2664 Application/Control Number: 18/581,720 Page 8 Art Unit: 2664 Application/Control Number: 18/581,720 Page 9 Art Unit: 2664 Application/Control Number: 18/581,720 Page 10 Art Unit: 2664 Application/Control Number: 18/581,720 Page 11 Art Unit: 2664 Application/Control Number: 18/581,720 Page 12 Art Unit: 2664 Application/Control Number: 18/581,720 Page 13 Art Unit: 2664 Application/Control Number: 18/581,720 Page 14 Art Unit: 2664 Application/Control Number: 18/581,720 Page 15 Art Unit: 2664 Application/Control Number: 18/581,720 Page 16 Art Unit: 2664 Application/Control Number: 18/581,720 Page 17 Art Unit: 2664 Application/Control Number: 18/581,720 Page 18 Art Unit: 2664 Application/Control Number: 18/581,720 Page 19 Art Unit: 2664 Application/Control Number: 18/581,720 Page 20 Art Unit: 2664 Application/Control Number: 18/581,720 Page 21 Art Unit: 2664 Application/Control Number: 18/581,720 Page 22 Art Unit: 2664 Application/Control Number: 18/581,720 Page 23 Art Unit: 2664 Application/Control Number: 18/581,720 Page 24 Art Unit: 2664 Application/Control Number: 18/581,720 Page 25 Art Unit: 2664 Application/Control Number: 18/581,720 Page 26 Art Unit: 2664
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