DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 05/15/2026 has been entered. Applicant’s amendments to the claims have overcome each and every objection previously set forth in the Non-Final Office Action mailed 02/23/2026. Claims 1, 3-14, 17, 18, and 20 remain pending in the application, with claims 2, 16, and 19 having been previously cancelled. Claims 14, 17, 18, and 20 are allowed.
Response to Arguments
Regarding the rejection of claim 1, Applicant’s arguments, pg. 7-10 of the Remarks filed 05/15/2026, have been considered but are moot because the new ground of rejection does not rely on any combination of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding the rejection of claim 5, Applicant’s arguments, pg. 10-11 of the Remarks filed 05/15/2026, have been fully considered but are not persuasive. In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, one of ordinary skill in the art can look to Lyu’s disclosure to incorporate recurrent layers in a machine learning model in order to generate sequential cardiac MRI data. Lyu’s disclosure utilizes its model for image generation (Lyu, abstract: “Indeed, our extensive experiments show that our method achieves better image quality for cine cardiac MRI images than existing state-of-the-art methods. In addition, our method can generate reliable missing intermediate frames based on their adjacent frames”). In view of the foregoing, the rejection of claim 5 is maintained.
Regarding the rejection of claim 6, Applicant’s arguments, pg. 11 of the Remarks filed 05/15/2026, have been fully considered but are not persuasive. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “architecture arranges these specific components as parallel 3-layer stacks per timepoint (up-sampling [Wingdings font/0xE0] styling [Wingdings font/0xE0] LSTM), where the LSTMs communicate across timepoints for temporal consistency”) are not recited in the rejected claim. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). The specific, three-tier architecture disclosed in the specification is not described or required by the current claim language. As described above, Lyu discloses that their method can generate image frames. In view of the foregoing, the rejection of claim 6 is maintained.
Regarding the rejection of claim 10, Applicant’s arguments, pg. 11 of the Remarks filed 05/15/2026, have been considered but are moot because the new ground of rejection does not rely on any combination of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
Claim 1 is objected to because of the following informalities: all instances of “the second machine-learning model” should read “the second machine- learned model”. Appropriate correction is required.
Claim Rejections - 35 USC § 103
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.
Claims 1, 3-4, 7, 9-10, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Abbasi-Sureshjani et al. (Abbasi-Sureshjani, S., Amirrajab, S., Lorenz, C., Weese, J., Pluim, J., & Breeuwer, M., 4D semantic cardiac magnetic resonance image synthesis on XCAT anatomical model (2020) in Medical Imaging with Deep Learning, Proceedings of Machine Learning Research, pp. 6-18), hereinafter Sureshjani, in view of Yin et al. (CN Patent No. 114820469 A), hereinafter Yin, and Amirrajab et al. (Amirrajab, S., Al Khalil, Y., Lorenz, C., Weese, J., Pluim, J., & Breeuwer, M., Label-informed cardiac magnetic resonance image synthesis through conditional generative adversarial networks (2022) in Computerized Medical Imaging and Graphics, 101, 102123), hereinafter Amirrajab.
Regarding claim 1, Sureshjani teaches a method for machine learning for a cardiac magnetic resonance imaging task, the method comprising:
generating a synthetic sample of cardiac magnetic resonance imaging (Sureshjani, 1st sentence on pg. 4: “synthesize realistic-looking cardiac MR images”), the synthetic sample output by a first machine-learned model (Sureshjani, pg. 4, 2nd sentence under Methodology section: “conditional image synthesis network is trained on real image data”) in response to input of a latent representation to the first machine-learned model (Sureshjani, pg. 8, 2nd sentence: “Our style encoder encodes the local semantic information of the input style image, in addition to global style information, to a latent vector”; pg. 4, 3rd to last sentence in paragraph ‘Conditional image synthesis’: “Then the generator’s task is to combine the encoded (global and local) style and the content”).
Sureshjani fails to explicitly teach 1) the latent representation generated by a second machine-learned model separate from the first machine-learned model, the second machine-learning model comprising an encoder, wherein the second machine-learning model is trained independently of the first machine-learned model and independently of a task model for the cardiac magnetic resonance imaging task, wherein the encoder is not part of an encoder-decoder network trained jointly with the first machine-learned model and 2) machine training the task model for the cardiac magnetic resonance imaging task using the synthetic sample as training data; and storing the task model as machine trained (Sureshjani, mentioned in future works, see last sentence on pg. 9: “The goal is to investigate the utility of the synthetic data in training deep learning algorithm for segmentation”).
However, Yin teaches a similar system wherein an encoder generates a latent representation that is input to an image generator (Yin, pg. 42, para n0076: “Encoder 453 can embed image encoding into the latent variable space and input it into generator 451 in the form of latent variables”). Yin teaches a generative model wherein the latent representation is generated by a second machine-learned model separate from the first machine-learned model, the second machine-learning model comprising an encoder, wherein the second machine-learning model is trained independently of the first machine-learned model and independently of a task model, (Yin, encoder is trained independently of the trained generator, pg. 52-53, para n0095: “The second generative adversarial network model 450 can first train the generator 451 and discriminator 452 separately, then freeze the network weight parameters of the generator 451 and discriminator 452, train the encoder 453 independently, and update the parameters of the encoder 453”; independent training makes the models separate), wherein the encoder is not part of an encoder-decoder network trained jointly with the first machine-learned model (Yin, The generator is pre-trained, then frozen, and is therefore not trained jointly. The parameters of both models are updated during separate training processes.).
Sureshjani discloses an encoder and generator, but does not disclose wherein the encoder is trained independently; however, Yin teaches an independently-trained encoder in a similar system (Yin, in a system for image generation, pg. 2, para n0001: “device for generating defective image samples based on generative adversarial networks”). A person of ordinary skill in the art, before the effective filing date of the claimed invention, would have recognized that the independently-trained encoder, taught by Yin, could have been substituted for the encoder of Sureshjani because both serve the purpose of generating latent representations for input to an image generator model. Furthermore, a person of ordinary skill in the art would have been able to carry out the substitution. Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to substitute the pre-trained encoder in the above teachings of Yin for the encoder of Sureshjani in order to generate realistic images having similar attributes as the original images (Yin, pg. 45, para n0084: “Under the condition of preserving the defect features of the input image, the fusion edge of the reconstructed defect is close to the background area, generating a more realistic defect sample image”).
Further, Amirrajab teaches machine training a task model (Amirrajab, “DL model” in citation below) for the cardiac magnetic resonance imaging task (Amirrajab, “segmentation” in citation below) using the synthetic sample as training data; and storing the task model as machine trained (Amirrajab, pg. 7, 1st sentence in left column: “We employ synthesized images for training a DL model to segment left and right ventricular cavities and myocardium of the heart”; machine trained task model is the trained DL model). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to combine the trained task model of Amirrajab with the method of Sureshjani in view of Yin in order to evaluate the generated synthetic images using a segmentation task model (Sureshjani, last para of pg. 8: “quantitative application-based evaluation of the synthetic images by deploying them in a heart segmentation task for multi-site, multi-vendor scenarios…utilize the synthetic images in different data augmentation strategies for the cardiac cavity segmentation task. The goal is to investigate the utility of the synthetic data in training deep learning algorithm for segmentation and evaluate that the data generated by this approach is clinically meaningful to replace the need for real data.”). Sureshjani suggests the use of a task model, while Amirrajab teaches a machine trained segmentation task model trained using synthetic samples (Amirrajab, pg. 11, 4th and 7th sentences under section ‘6.3’: “the addition of the synthetic data can substantially improve the model performance…aiding the training with multi-tissue synthetic images yields a better segmentation performance around the apex and base of the heart and more accurate segmentation of the right ventricular cavity and myocardium”).
Furthermore, the use of synthetic images to train a machine learned task model is obvious to try (see MPEP 2143(I)(E)). As described by both references, there is a recognized need in the art to increase the amount of diverse training images to train computer vision machine learning models (Sureshjani, abstract: “this approach can result in a realistic virtual population to address different challenges the medical image analysis research community is facing such as expensive data collection…synthesize 4D controllable CMR images with annotations and adaptable styles to be used in various supervised multi-site, multi-vendor applications in medical image analysis”; Amirrajab, abstract: “Synthesis of a large set of high-quality medical images with variability in anatomical representation and image appearance has the potential to provide solutions for tackling the scarcity of properly annotated data in medical image analysis research”). Since an increase in medical images is required, a predictable potential solution to this problem is to utilize synthetically generated images, as opposed to real medical images captured by a medical imaging device. This is a known potential solution with a reasonable expectation of success, further suggested by Sureshjani and Amirrajab (see citations in the last paragraph – Sureshjani suggests the utility of synthetic data in training deep learning algorithms to replace the need for real data, while Amirrajab demonstrates this utilization to improve a segmentation model). Therefore, a person having ordinary skill in the art would have found combining the trained task model of Amirrajab with the method of Sureshjani in view of Yin as obvious to try, from a finite number of identified, predictable solutions, in order to successfully increase the accessibility to a diverse set of medical images for training a task model.
Regarding claim 3 (dependent on claim 1), Sureshjani in view of Yin and Amirrajab teaches wherein generating the synthetic sample comprises generating where the latent representation is generated as a representation of style of a cardiac magnetic resonance image (Sureshjani teaches encoding the style of cardiac MRI, pg. 8, 2nd sentence: “Our style encoder encodes the local semantic information of the input style image, in addition to global style information, to a latent vector”; pg. 7, 4th sentence: “The input images of the encoder (representing the style) are depicted in the first column”, see CMRI images in Figure 4 on pg. 8; In the combination with Yin, Yin similarly teaches wherein the encoder preserves features of the input image – see claim 1 rejection).
Regarding claim 4 (dependent on claim 1), Sureshjani in view of Yin and Amirrajab teaches wherein generating the synthetic sample comprises generating by the first machine-learned model comprising a generator having been trained as a generative adversarial network (Sureshjani, pg. 1, 2nd sentence of Abstract: “synthesizes CMR images via a data-driven Generative Adversarial Network”; see training of “SPADE GAN” in Figure 1 on pg. 5).
Regarding claim 7 (dependent on claim 1), Sureshjani in view of Yin and Amirrajab teaches wherein generating the synthetic sample comprises generating the synthetic sample and additional synthetic samples by variation of the latent representation input to the first machine-learned model (Sureshjani, 5th sentence on pg. 7: “Two different synthetic images for each style are shown in the second and third columns, and the label maps (the inputs of the SPADE layers) are shown on the top left corner of the resulting synthetic images”, see outputs for the two different inputted styles in Figure 4 on pg. 8; see further, last sentence on pg. 8: “We use our proposed approach to generate a large virtual population with various anatomical and style variations”).
Regarding claim 9 (dependent on claim 1), Sureshjani in view of Yin and Amirrajab teaches wherein generating the synthetic sample comprises generating cardiac magnetic resonance images as a plurality of slices at different times (Sureshjani, Figure 3 on pg. 7: “4D synthetic images on XCAT labels for 12 time frames from end-diastolic to end-systolic phase”; each image is a slice; Figure 7 on pg. 13 also shows a plurality of slices, apex, mid, and base location slices, each over 12 time frames).
Regarding claim 12 (dependent on claim 1), Sureshjani in view of Yin and Amirrajab teaches wherein machine training comprises machine training the task model for segmentation, ejection fraction computation, disease classification, plane classification, view classification, or landmark detection (Amirrajab, segmentation, pg. 7, 1st sentence in left column: “We employ synthesized images for training a DL model to segment left and right ventricular cavities and myocardium of the heart”).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Sureshjani in view of Yin, Amirrajab, Diller et al. (Diller, G. P., Vahle, J., Radke, R., Vidal, M. L. B., Fischer, A. J. et al., Utility of deep learning networks for the generation of artificial cardiac magnetic resonance images in congenital heart disease (2020) in BMC medical imaging, 20, 1-8), hereinafter Diller, and Lyu et al. (Lyu, Q., Shan, H., Xie, Y., Kwan, A. C., Otaki, Y., Kuronuma, K. et al., Cine cardiac MRI motion artifact reduction using a recurrent neural network (2021) in IEEE transactions on medical imaging, 40(8), 2170-2181), hereinafter Lyu.
Regarding claim 5 (dependent on claim 4), Sureshjani in view of Yin and Amirrajab teaches wherein generating the synthetic sample comprises generating where the generative adversarial network was a conditional generative adversarial network (Sureshjani, SPADEGAN, pg. 4, Methodology section under heading ‘Conditional image synthesis’: “conditional image synthesis network”, see SPADEGAN details in subsequent paragraph), but fails to teach where the generative adversarial network was a recurrent progressive conditional generative adversarial network.
However, Diller teaches a progressive generative adversarial network (Diller, pg. 3, left column, “Progressive GAN”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to combine the progressive GAN components of Diller (progressively increasing image size, see further “Progressive GAN” section on pg. 3 of Diller) with the method of Sureshjani in view of Yin and Amirrajab in order to generate high resolution synthetic images (Diller, pg. 7, 1st paragraph in left column: “start with a low-resolution GAN and increasing image size step by step during training…thereby supporting the generator and stabilizing the model”).
Further, Lyu teaches a recurrent generative adversarial network (Lyu, pg. 4, 1st sentence: “We propose a recurrent generative adversarial network model for cardiac MRI motion artifact reduction”; see Figure 2 on pg. 18). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to combine the recurrent GAN architecture of Lyu (incorporating recurrent generator components, see Figure 2 of Lyu) with the method of Sureshjani in view of Yin and Amirrajab in order to capture the relationship between dynamic cardiovascular images (Lyu, 3rd to last sentence of ‘Abstract’ on pg. 2: “We demonstrate a decent generalizability of the proposed method thanks to the novel architecture of our deep network that captures the essential relationship of cardiovascular dynamics”). Additionally, Sureshjani teaches the generation of time series data (See Figure 6 below from pg. 12 of Sureshjani). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to utilize the recurrent GAN architecture of Lyu to produce time series CMRI images (Lyu, abstract on pg. 2: “This model utilizes bi-directional convolutional long short-term memory (ConvLSTM) and multi-scale convolutions to improve the performance of the proposed network, in which bi-directional ConvLSTMs handle long-range temporal features while multiscale convolutions gather both local and global features”).
PNG
media_image1.png
458
787
media_image1.png
Greyscale
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Sureshjani in view of Yin, Amirrajab and Lyu.
Regarding claim 6 (dependent on claim 1), Sureshjani in view of Yin and Amirrajab teaches wherein generating the synthetic sample comprises generating by the first machine-learned model comprising an up sampling deep neural network (Sureshjani, pg. 4, 2nd sentence in paragraph ‘Conditional image synthesis’: “a series of the residual blocks with SPADE normalization, followed by nearest neighbor up-sampling layers”) and a styling deep neural network (Sureshjani, performed by the generator synthesizing images using style information, pg. 4, 3rd to last sentence in paragraph ‘Conditional image synthesis’: “Then the generator’s task is to combine the encoded (global and local) style and the content coming from the semantic segmentation mask to synthesize an image. This setup is useful in controlling the style of synthetic images and the reconstruction of the surrounding organs of the heart”), but fails to teach a plurality of up sampling and styling deep neural networks, and a plurality of long-short term memories.
Lyu teaches a plurality of neural networks (Lyu, encoder and decoder blocks in a model that each generate an image in a time series, Fig. 3 on pg. 19), and a plurality of long-shot term memories (Lyu, abstract on pg. 2: “This model utilizes bi-directional convolutional long short-term memory (ConvLSTM)”; see plurality of LSTM blocks in Fig. 3 on pg. 19). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to combine the plurality of neural network blocks and long-short term memories of Lyu with the up sampling and styling deep neural networks and model architecture of Sureshjani (create a recurrent model architecture similar to Lyu), respectively, in the method of Sureshjani in view of Yin and Amirrajab in order to improve image quality (Lyu, 3rd sentence on pg. 9: “Each frame in the sequence was fed into the encoder, and the resultant features were concatenated with those generated from the ConvLSTM branches in each frame state”; temporal information feeds into each encoder, improving image quality of each slice generated, pg. 11, 3rd sentence under ‘Discussions’: “incorporating ConvLSTM branches and involving temporal information in the networks leads to the image quality better than that only based on spatial information”). Additionally, Sureshjani teaches the generation of time series data (See Figure 6 of Sureshjani in the claim 5 rejection). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to utilize the recurrent GAN architecture of Lyu to produce time series CMRI images (Lyu, abstract on pg. 2: “This model utilizes bi-directional convolutional long short-term memory (ConvLSTM) and multi-scale convolutions to improve the performance of the proposed network, in which bi-directional ConvLSTMs handle long-range temporal features while multiscale convolutions gather both local and global features”).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Sureshjani in view of Yin, Amirrajab, and Peña et al. (Peña, H., Gómez, S., Romo-Bucheli, D., & Martinez, F., Cardiac disease representation conditioned by spatio-temporal priors in cine-MRI sequences using generative embedding vectors (2021) in 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, pp. 5570-73), hereinafter Peña.
Regarding claim 8 (dependent on claim 1), Sureshjani in view of Yin and Amirrajab teaches wherein generating the synthetic sample comprises generating with the input to the first machine-learned model of the latent representation (Sureshjani, pg. 8, 2nd sentence: “Our style encoder encodes the local semantic information of the input style image, in addition to global style information, to a latent vector”; pg. 4, 3rd to last sentence in paragraph ‘Conditional image synthesis’: “Then the generator’s task is to combine the encoded (global and local) style and the content”), but fails to teach input of values for one or more parameters, the one or more parameters comprising a pathology, base and apex indices, an electrocardiogram, an ejection fraction, or a number of slices.
However, Peña teaches input to a machine-learned model of values for one or more parameters comprising a pathology (Peña, pg. 5571, 4th sentence under header ‘Deep conditioned and generative representation’: “In the MCIGAN, the generator is fed with a random vector in the latent space, the associated heart pathology and vector information related with prior structural or motion information”; see Fig. 2 on pg. 5571). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to combine the pathology input parameter of Peña with the method of Sureshjani in view of Yin and Amirrajab in order to produce a GAN model that can differentiate between cardiac pathologies (Peña, pg. 5570, 1st sentence in last paragraph: “This work presents a strategy based on a conditional GAN architecture, conditioned by cardiac prior information, to recover embedding patterns and to differentiate among five different cardiac pathologies”).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Sureshjani in view of Yin, Amirrajab, and Aliper et al. (U.S. Patent No. 2019/0392304 A1), hereinafter Aliper.
Regarding claim 10 (dependent on claim 1), Sureshjani in view of Yin and Amirrajab fails to explicitly teach wherein generating the synthetic sample comprises generating where the latent representation is generated by the second machine-learned model having been trained with a loss based on comparison of an output image with a ground truth image and based on comparison of latent representations of different samples to each other.
However, Aliper teaches an encoder architecture having been trained with a loss based on comparison of an output image with a ground truth image and based on comparison of latent representations of different samples to each other (Aliper, MSE and triplet loss coefficients, para 139: “The reconstruction coefficient is set to 10 and MSE and triplet coefficients are at 1”). Doing so improves the performance of the model by ensuring the encoder captures features necessary for reconstruction and accurately encodes different samples. While Sureshjani in view of Yin and Amirrajab teaches a reconstruction loss value (see para n0078 of Yin), Aliper teaches an encoder loss value based on both a ground truth image and different latent samples. Aliper teaches a known technique of utilizing a triplet loss value to train an encoder model. A person having ordinary skill in the art, before the effective filing date of the claimed invention, could have applied the known technique, as taught by Aliper, in the same way to the method of Sureshjani in view of Yin and Amirrajab and achieved predictable results of training an encoder model based on different latent representation samples.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Sureshjani in view of Yin, Amirrajab, and Beetz et al. (Beetz, M., Banerjee, A., & Grau, V., Multi-domain variational autoencoders for combined modeling of MRI-based biventricular anatomy and ECG-based cardiac electrophysiology (2022) in Frontiers in physiology, 13, 886723), hereinafter Beetz.
Regarding claim 11 (dependent on claim 1), Sureshjani in view of Yin and Amirrajab teaches wherein generating the synthetic sample comprises generating with the input comprising the latent representation (Sureshjani, pg. 4, 3rd to last sentence in paragraph ‘Conditional image synthesis’: “Then the generator’s task is to combine the encoded (global and local) style and the content”) and a cardiac magnetic resonance image (Sureshjani, pg. 4, 2nd sentence in paragraph ‘The real dataset’: “This dataset consists of Cine MR images”; see ACDC cardiac data input in Figure 1 on pg. 5), but fails to teach wherein the input comprises an electrocardiogram.
However, Beetz teaches generating a cardiac model (Beetz, abstract) with an electrocardiogram (Beetz, pg. 2, 1st bullet point in the right column: “We propose a novel multi-domain variational autoencoder capable of modeling combined cardiac anatomy and ECG data”; pg. 4, 1st sentence in section ‘2.3’: “architecture with three branches that share a common latent space for inter-modal information sharing”; see ECG input into model in Figure 1c on pg. 3). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to combine the electrocardiogram input parameter of Beetz with the method of Sureshjani in view of Yin and Amirrajab in order to improve the identification of cardiovascular disease (Beetz, 2nd to last sentence in ‘Conclusion’ on pg. 13: “we have observed that combined anatomy and ECG representations improve the identification of cardiovascular disease compared to single-domain approaches”).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Sureshjani in view of Yin, Amirrajab and Shayani et al. (U.S. Patent No. 2023/0326158 A1), hereinafter Shayani.
Regarding claim 13 (dependent on claim 1), Sureshjani in view of Yin and Amirrajab teaches wherein machine training further comprises machine training the task model with input of the synthetic sample (Amirrajab, pg. 7, 1st sentence in left column: “We employ synthesized images for training a DL model”), but fails to teach input of the latent representation (Sureshjani teaches wherein the latent vector is inputted to the generator, but not input to a task model).
However, Shayani teaches machine training a task model with input of the latent representation (Shayani, para 80: “style-generation engine 126 can train machine learning model 228 to learn a more complex style code distribution 230, given the set of style codes”; style codes are latent space, para 82: “The embedding space associated with the style codes in the pairs could correspond to the latent space of the style codes”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to combine the method of training a model with input of a latent representation of Shayani with the method of Sureshjani in view of Yin and Amirrajab in order to learn a distribution of style information (Shayani, para 80: “could train one or more components of a…and/or another type of generative model to learn a latent manifold corresponding to style code distribution 230 within the latent space occupied by style codes generated by style network 214”) and/or train a model to disentangle style and content information (Shayani, para 117: “with the disclosed techniques, attributes pertaining to style in 3D shapes can be disentangled from attributes pertaining to content”).
Allowable Subject Matter
Claims 14, 17, 18, and 20 are allowed. The statement of reasons for the indication of allowable subject matter described in the Final Rejection of 12/05/2025 applies to claims 18 and 20.
Regarding claim 14, while Sureshjani in view of Amirrajab, Peña, and Zhang teach the input of latent variables for number of slices, slice position relative to anatomy, pathology, and functional measurement to the machine-learned model, the relied upon prior art fails to teach in reasonable combination wherein the latent representation generated by the encoder (for example, the encoder in the rejection of claim 1 taught by Sureshjani in view of Yin and Amirrajab) comprises all types of the listed values. Therefore, the prior art fails to teach as a whole generating synthetic samples of cardiac magnetic resonance imaging, the synthetic samples output by a machine-learned model in response to input of a latent representation comprising values for a number of slices, pathology, functional measurement, and slice position relative to anatomy, the latent representation generated by an encoder that is trained independently of the machine-learned model and independently of a task model for the cardiac magnetic resonance imaging task; machine training the task model for the cardiac magnetic resonance imaging task using the synthetic samples as training data; and storing the task model as machine trained.
In view of the foregoing, the prior art references alone or in reasonable combination are insufficient to teach the invention as a whole, as claimed in claim 14. Due to its dependence on claim 14, claim 17 is similarly allowed.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 EMMA E DRYDEN whose telephone number is (571)272-1179. The examiner can normally be reached M-F 9-5 EST.
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, ANDREW BEE can be reached on (571) 270-5183. 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.
/EMMA E DRYDEN/Examiner, Art Unit 2677 /ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
Prosecution Insights