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 . The amendments provided 03/17/2026 have been entered and considered. Claims 1, 3, 9, 10, 14, and 19 have been amended. No new matter has been introduced.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/15/2026 has been entered.
Response to Amendment
Claim Rejections - 35 USC § 112
In view of the amendments provided 04/15/2026, the 112(a) and 112(b) rejections of the final rejection (12/17/2025) are hereby withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 19 recites the limitations "second medical image data", “third medical image data”, and “fourth medical image data” in lines 2-5. There is insufficient antecedent basis for this limitation in the claim. While there is mention of second, third, and fourth image data, they are not explicitly stated to be medical image data. Rather, they are only referred to as X-ray data. As such, it is unclear if these are referring to the respective image data mentioned in independent claim 1.
As such, the term “medical” will be read as “X-ray” for claim 19 (putting it in line with the claim language of preceding claims, like that of claim 3).
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.
Claim 1, 5-7, 10, 14, 15, 16 and 18-19 as best understood are rejected under 35 U.S.C. 103 as being unpatentable over Tang et al. (Non-patent literature provided by applicant titled “CCN-CL: A content-noise complementary network with contrastive learning for low-dose computed tomography denoising”; hereinafter “Tang”) in further view of Liao (US Publication 20240233134 A1; hereinafter “Liao”).
In re to claim 1 , Tang teaches wherein: an X-ray image processing method, comprising: receiving first X-ray image data (LDCT image data connected as input into denoising architecture; Fig. 1, shows the use of an image that is further denoised in order to be used in a contrastive loss model);
inputting the first X-ray image data to a trained model (section 3.3 para. 1 discloses the training of the system, with further discussion of its implementation in experimentation shown in section 3.4 para. 1. Thus, it is understood that the first X-ray image data (correspondent to the claims) is input into a trained model (understood as the model of Fig. 1 following at least one batch of training)); and
outputting, from the trained model, an X-ray image having an image quality higher than an image quality of the first X-ray image data, wherein the higher image quality corresponds to at least one of less blurriness and less noise (section 3.4 para. 4 lines 19-25, discloses output of a noise corrected output from the trained model (correspondent to the claims)), wherein the trained model was trained using contrastive learning (Fig. 1 and section 2.3 para. 1, denotes the use of a contrastive learning method was utilized for the model) using second X-ray image data (denoised image; Fig. 1, shows the use of a denoised image used in comparison to two other images (positive and negative examples according to section 2.3 para. 1) in order to determine loss), third X-ray image data (LDCT image during training; Fig. 1 shows the use of the LDCT image, with the use of negative information being stated to be used in section 2.3 para. 1 line 7-9 (see also abstract lines 7-8 which denotes use of the LDCT image as a negative training example)) and fourth X-ray image data (NDCT image during training; Fig. 1 shows the use of the NDCT image, with the use of positive information samples being stated to be used in section 2.3 para. 1 lines 7-9 (see also abstract lines 7-8 which denotes use of the NDCT image as a positive training example)), the second X-ray image data being data output from the trained model that has received an input of input image data (Fig. 1 shows that the denoised image is the result of the system having an output of the trained model (being the LDCT image following a denoising operation output, which due to being a portion of the model, is understood to be one of its outputs ). See also section 3.3 para. 1, which discloses the training of the system, with further discussion of its implementation in experimentation shown in section 3.4 para. 1. Thus, it is understood that the system’s outputs are those of the trained model, correspondent to the claims), the third X-ray image data being negative information (LDCT image data connected as negative training input; Fig. 1 shows the use of the LDCT image, with its use as negative information being stated in section 2.3 para. 1 line 7-9 (see also abstract lines 7-8 which denotes use of the LDCT image as a negative training example)) having lower image quality than the second X-ray image data, wherein the lower image quality corresponds to at least one of more noise and more blurriness (Fig. 1, shows the use of the LDCT image data as a direct input into the contrastive learning model as a negative example. Further, as this is the LDCT image, it has more noise relative to the second X-ray image data (correspondent to the claims) due to not undergoing a denoising operation), and the fourth image data being positive information having higher image quality than the second X-ray image data (section 3.4 para. 1, indicates the reduction of noise being the goal with respect to LDCT denoising. Further, given that the fourth X-ray image is used to guide the model positively to reduce noise (see section 2.3), it is understood to have less noise than the image its guiding the correction of (the second X-ray image data, correspondent to the claims). Additionally, the fourth X-ray image data is clear image data used to perform loss calculations, and is thus understood to have less noise to that of the second X-ray image data (each correspondent to the claims, respectively)).
Tang does not explicitly teach wherein: the positive and negative information is label data.
However, in a similar field of endeavor, Liao teaches wherein: the positive and negative information is label data ([0059], discloses the usage of positive and negative labels to train the model as a part of the contrastive learning step. It is understood that the image data in relation to its labels is the label data).
Liao, like Tang, performs image processing using positive and negative examples in relation to a neural network model to produce an altered image output.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Tang to use positive and negative labeling data, as taught by Liao, to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been to train the noise correction to better address noise related to identifying elements of data, thus correcting noisy image data on a higher conceptual level (Tang Fig. 1 does not show consideration of labels, however, it is shown in Tang Fig. 3 and section 3.4 para 2 lines 1-4 that identification of high level structures is of particular interest).
In re to claim 5 [dependent on claim 1], Tang teaches wherein: the contrastive learning uses a negative loss function term to learn from negative images used for the third X-ray image data (“X” of equation 8; section 2.3 para. 2, denotes that the loss function of equation 8 is attempting to maximize the gap to that of the input image, denoted by the term “X,” which represents the input image (which is the same image as the LDCT image, per Fig. 1 and section 2.2 para. 1 lines 1). By virtue of the training being done a plurality of times, according to the use of multiple data sets per section 3.2, it is understood that the loss function term is used on multiple negative images (third X-ray images, correspondent to the claims, from multiple datasets)).
In re to claim 6 [dependent on claim 1], Tang teaches wherein: the contrastive learning simultaneously uses a negative loss term and a positive loss term (Section 2.3 para. 2, shows that the loss function (equation 8) uses a term for both a negative term (understood to be X) and a positive term (understood to be Y). Further, the terms are used in the same loss function (indicating their simultaneous use)).
In re to claim 7 [dependent on claim 5], Tang teaches wherein: the contrastive learning includes encoding (Fig. 1, shows the encoder and decoder structure of the network architecture. Thus, as the proposed network takes the images as input, it is understood that the images used are encoded) positive images, images predicted by the trained model, and the negative images (section 4 para. 3 lines 7-9, “Second, the pre-trained network to extract features and calculate the regularization loss is a VGG-19.” It is understood that, as VGG-19 is a neural network, the images processed by it are encoded. See also equation 8 and section 2.3 para. 2, it is disclosed that the regularization loss calculated by VGG-19 processes the NDCT and LDCT images, as well as the denoised image)so as to increase a weight for specific features (section 2.3 para. 2 lines 1-4, discloses the adjustment of the denoised image data according to the NDCT image (denoted by the term “clear image”). Thus, it is understood that for the model, which is a neural network based system, weights are increased to specific features (being the features that a determined to correspond to the NDCT image)).
As to claim 10, by virtue of using processing circuitry in the form of a computer (see section Tang 3.3 para. 1), claim 10 is the apparatus used to execute the method of claim 1, and as such, recites similar limitations. As a result, claim 10 is rejected for the same reasons as provided above.
In re to claim 14, Tang teaches wherein: a method of generating a trained model, comprising: receiving first X-ray image data (denoised image; Fig. 1, shows the use of a denoised image used in comparison to two other images (positive and negative examples according to section 2.3 para. 1) in order to determine loss);
receiving second X-ray image (LDCT image during training; Fig. 1 shows the use of the LDCT image, with the use of negative information being stated to be used in section 2.3 para. 1 line 7-9 (see also abstract lines 7-8 which denotes use of the LDCT image as a negative training example)), the second X-ray image data having lower image quality than the first X-ray image data, wherein the lower image quality corresponds to at least one of more noise and more blurriness (section 3.4 para. 1, indicates the reduction of noise being the goal with respect to LDCT denoising. Further, given that the first image (correspondent to the claims) is denoised, it is understood to have less noise than the first X-ray image data (correspondent to the claims));
receiving third X-ray image (NDCT image during training; Fig. 1 shows the use of the NDCT image, with the use of positive information samples being stated to be used in section 2.3 para. 1 lines 7-9 (see also abstract lines 7-8 which denotes use of the NDCT image as a positive training example)), the third X-ray image data being positive image data having higher image quality than the first X-ray image data, wherein the higher image quality corresponds to at least one of less blurriness and less noise (section 3.4 para. 1, indicates the reduction of noise being the goal with respect to LDCT denoising. Further, given that the NDCT image is used to guide the model positively to reduce noise (see section 2.3), it is understood to have less noise than the image its guiding the correction of (the first X-ray image data, correspondent to the claims). Additionally, the third X-ray image data is clear image data used to perform loss calculations, and is thus understood to have less noise to that of the first X-ray image data (each correspondent to the claims, respectively)); and
training the neural network model using contrastive learning (section 2.3, discusses the use of the loss function in relation to the pre-trained network in order to guide the model toward producing image outputs relating to the NDCT image) using the first X-ray image data as input data and the second X-ray image data and the third X-ray data as contrastive learning guiding information, wherein the contrastive learning includes a negative loss term for the neural network model to learn from the negative image data and a positive loss term for the neural network model to learn from the positive image data (Section 2.3 para. 2, shows that the loss function (equation 8) uses a term for both a negative term (understood to be X) and a positive term (understood to be Y). See also that in section 2.3 para. 2, the loss function is used to maximize the gap to that of the LDCT image, indicating learning from the wanted positive image data).
Tang does not explicitly teach wherein: the contrastive learning guiding information is label data.
However, in a similar field of endeavor, Liao teaches wherein: the contrastive learning guiding information is label data ([0059], discloses the usage of positive and negative labels to train the model as a part of the contrastive learning step. It is understood that the image data in relation to its labels is the label data).
Liao, like Tang, performs image processing using positive and negative examples in relation to a neural network model to produce an altered image output.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Tang to use positive and negative labeling data, as taught by Liao, to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been to train the noise correction to better address noise related to identifying elements of data, thus correcting noisy image data on a higher conceptual level (Tang Fig. 1 does not show consideration of labels, however, it is shown in Tang Fig. 3 and section 3.4 para 2 lines 1-4 that identification of high level structures is of particular interest).
In re to claim 15 [dependent on claim 14], Tang teaches wherein: the contrastive learning simultaneously uses the negative loss term in combination with the positive loss term (Section 2.3 para. 2, shows that the loss function (equation 8) uses a term for both a negative term (understood to be X) and a positive term (understood to be Y). Further, the terms are used in the same loss function (indicating their simultaneous use)).
In re to claim 16 [dependent on claim 14], Tang teaches wherein: the contrastive learning includes encoding (Fig. 1, shows the encoder and decoder structure of the network architecture. Thus, as the proposed network takes the images as input, it is understood that the images used are encoded) positive images, images predicted by the trained model, and the negative image data (section 4 para. 3 lines 7-9, “Second, the pre-trained network to extract features and calculate the regularization loss is a VGG-19.” It is understood that, as VGG-19 is a neural network, the images processed by it are encoded. See also equation 8 and section 2.3 para. 2, it is disclosed that the regularization loss calculated by VGG-19 processes the NDCT and LDCT images, as well as the denoised image) so as to increase a weight for specific features (section 2.3 para. 2 lines 1-4, discloses the adjustment of the denoised image data according to the NDCT image (denoted by the term “clear image”). Thus, it is understood that for the model, which is a neural network based system, weights are increased to specific features (being the features that a determined to correspond to the NDCT image)).
In re to claim 18 [dependent on claim 14], Tang teaches wherein: the contrastive learning includes training a discriminator (section 2.3, discloses the use of a pre-trained network that minimizes the gap between the positive image data and the denoised image. It is understood that due to the pre-trained network performing this adjustment, it is the discriminator) on an inverse of the contrastive loss using, as input to the discriminator, positive image data, an image predicted by the trained model, and the negative image data (Section 2.3, discloses the use of a pre-trained network that minimizes the gap between the positive image data and the denoised image, while further increasing the gap to the negative image data (indicating training to reduce loss based on equation 8 of section 2.3). Additionally (as it is pre-trained) it is disclosed that training occurs that enables it to take positive, predicted, and negative image data as input).
In re to claim 19 [dependent on claim 1], Tang teaches wherein: the trained model is trained based on a total loss obtained by adding a positive loss, which is based on the second medical image data and the fourth medical image data to a contrastive loss, which is based on (1) a loss based on the second medical image data and the third medical image data (L1 loss; section 2.3 para. 1 discloses determination based on a loss calculated based on the NDCT image, which per Fig. 1, shows a basis using the second medical image (correspondent to the claims)), and (2) a loss based on the second medical image data and the fourth medical image data (section 2.3 para. 2 describes a loss function for contrastive regularization that utilizes the LDCT image data, which further uses the second X-ray image data (correspondent to the claims) per the incorporation of the Xdenoised term. Further, the contrastive regularization loss is added to the L1 loss function in the overall loss function of equation 9 (shown in section 2.3 as the loss function that trains the system model)).
Claim 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Tang, in view of Liao, in further view of Russel et al. (US Publication 20220148242 A1; hereinafter “Russel”).
In re to claim 8 [dependent on claim 7], Tang teaches wherein: encoding predicted images (section 4 para. 3 lines 7-9, “Second, the pre-trained network to extract features and calculate the regularization loss is a VGG-19.” It is understood that, as VGG-19 is a neural network, the images processed by it are encoded. See also equation 8 and section 2.3 para. 2, it is disclosed that the regularization loss calculated by VGG-19 processes the NDCT and LDCT images, as well as the denoised image (understood as the predicted image)).
Tang, in view of Liao, does not explicitly teach wherein: the encoding includes passing the images through a projection layer.
However, in a similar field of endeavor, Russel teaches wherein: the encoding includes passing the images through a projection layer ([0013] lines 7-11, “…the encoder neural network includes a feature extraction layer and a projection layer to determine feature representations of the digital content items and then project the feature representations to a lower dimension space…” which discloses the use of a projection layer in relation to encoding of image data (see also Fig. 2 which shows the use of image data as the processed data of the system)).
Russel, like Tang, teaches a contrastive learning model that processes image data to generate a desired output.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Tang, in view of Liao, to encode using a projection layer, as taught by Russel, to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been to enable the system to reduce the dimensionality of encoded data, reducing the complexity of the data as it is processed by later portions of the system.
In re to claim 17 [dependent on claim 16], Tang teaches wherein: encoding predicted images (section 4 para. 3 lines 7-9, “Second, the pre-trained network to extract features and calculate the regularization loss is a VGG-19.” It is understood that, as VGG-19 is a neural network, the images processed by it are encoded. See also equation 8 and section 2.3 para. 2, it is disclosed that the regularization loss calculated by VGG-19 processes the NDCT and LDCT images, as well as the denoised image (understood as the predicted image)).
Tang does not explicitly teach wherein: the encoding includes passing the predicted images through a projection layer.
However, in a similar field of endeavor, Russel teaches wherein: the encoding includes passing the predicted images through a projection layer ([0013] lines 7-11, “…the encoder neural network includes a feature extraction layer and a projection layer to determine feature representations of the digital content items and then project the feature representations to a lower dimension space…” which discloses the use of a projection layer in relation to encoding of image data (see also Fig. 2 which shows the use of image data as the processed data of the system)).
Russel, like Tang, teaches a contrastive learning model that processes image data to generate a desired output.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Tang, in view of Liao, to encode using a projection layer, as taught by Russel, to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been to enable the system to reduce the dimensionality of encoded data, reducing the complexity of the data as it is processed by later portions of the system.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Tang, in view of Liao, in further view of Yuan et al. (US publication 20230260164 A1; hereinafter “Yuan”) and Sloan et al. (US publication 20180336677 A1; hereinafter “Sloan”).
In re to claim 9 [dependent on claim 1], Tang, in view of Liao, teaches wherein: the contrastive learning includes training a discriminator (section 2.3, discloses the use of a pre-trained network that minimizes the gap between the positive image data and the denoised image. It is understood that due to the pre-trained network performing this adjustment, it is the discriminator) on an inverse of the contrastive loss using, as input to the discriminator, positive images, images predicted by the trained model, and the negative label (Liao [0059], discloses the usage of positive and negative labels to train the model as a part of the contrastive learning step. It is understood that the image data in relation to its labels is the label data) data (Tang Section 2.3, discloses the use of a pre-trained network that minimizes the gap between the positive image data and the denoised image, while further increasing the gap to the negative image data (indicating training to reduce loss based on equation 8 of section 2.3). Additionally (as it is pre-trained) it is disclosed that training occurs that enables it to take positive, predicted, and negative image data as input).
Tang, in view of Liao, does not explicitly teach wherein: that the system trains model generation and discriminator results separately.
However, in a similar field of endeavor, Yuan teaches wherein: that the system trains model generation and discriminator results separately ([0130]-[0132] discloses the training of the image generator portion of the system and the discriminator portion of the system, with the two basing their training off penalization via separate loss functions).
Yuan, like Tang, teaches a system that generates image data based on contrastive learning that leverages positive and negative examples.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Tang, in view of Liao, to train the generator and discriminator portions of the systems using separate loss calculations, as taught by Yuan, to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been to enable the optimization of the system to avoid misclassified image data by penalizing the discriminator and the generator respective to one another in order to output a properly generated image, as avoiding the misclassification of image data by this method is a noted benefit of Yuan [0139].
Tang, in view of Liao and Yuan, does not explicitly teach wherein: the discriminator and the trained model are trained alternatively.
However, in a related field of endeavor, Sloan teaches wherein: the discriminator and the trained model are trained alternatively ([0059] discloses the alternation of the discriminator training and the synthesizer (understood to be the trained model, correspondent to the claims)).
Sloan, like Tang, teaches a system that processes medical image data in relation to a discriminative loss function for batch training.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Tang, in view of Liao and Yuan, to train the generator and discriminator portions of the systems using separate loss calculations in batch trainings, as taught by Yuan, to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been to enable the generator portion of the system to better produce simulated image data, as is a noted benefit of Sloan [0057].
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Tang, in view of Liao, in further view of Matviychuk et al. (Non-patent literature titled “LEARNING A MULTISCALE PATCH-BASED REPRESENTATION FOR IMAGE DENOISING IN X-RAY FLUOROSCOPY”; hereinafter “Matviychuk”).
In re to claim 11 [dependent on claim 10], Tang, in view of Liao, does not explicitly teach wherein: the processing circuitry is further configured to receive, as the first X-ray image data, X-ray fluoroscopy image data from a sequence of fluoroscopy images obtained by an image collector.
However, in a related field of endeavor, Matviychuk teaches wherein: the processing circuitry is further configured to receive, as the first X-ray image data, X-ray fluoroscopy image data from a sequence of fluoroscopy images obtained by an image collector (Fig. 3 and abstract, show the use of a fluoroscopy X-ray image as an input to a denoising system. Further, see introduction lines 4-5, which indicates the sequenced nature of fluoroscopic images obtained in real-time. Additionally, it is understood that the image collector is the portion of the system that receives the image as input).
Matviychuk, like Tang, teaches the processing of X-ray image data in order to reduce noise.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Tang, in view of Liao, to process fluoroscopy images, as taught by Matviychuk, to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been to enable the application of the method on fluoroscopy image data, allowing it to address the demand for the denoising of fluoroscopy images (as is done in Matviychuk).
Claim 12 as best understood is rejected under 35 U.S.C. 103 as being unpatentable over Tang, in view of Liao, in further view of Han et al. (Non-patent literature titled “Learning both Weights and Connections for Efficient Neural Networks”; hereinafter “Han”).
In re to claim 12 [dependent on claim 10], Tang, in view of Liao, does not explicitly teach wherein: the processing circuitry is further configured to: remove, from the trained neural network, weighted connections that are below a predetermined value), and reduce a precision of the weighted connections of the trained neural network.
However, in a related field of endeavor, Han teaches wherein: the processing circuitry is further configured to: remove, from the trained neural network, weighted connections that are below a predetermined value (section 3 para. 2, disclose the removal of low-weight connections in a neural network (which then requires retraining, thus indicating it is trained). Further, this removal of connections is in relation to said connections being below a threshold, understood to be indicative of a predetermined value), and reduce a precision of the weighted connections of the trained neural network (Fig. 5 and section 5 para. 1, shows that as a result of pruning network parameters (in the form of the weighted connections), accuracy is decreases. Thus, disclosing a reduction in precision due to accuracy decreasing in relation to the number of parameters (which is indicative of a lowered precision within the system due to its number of parameters correlating to its accuracy, denoting that said parameters increase the system’s ability to make a correct determination)).
Han, like Tang, teaches the use of a neural network to process data according to extracted features.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Tang, in view of Liao, to prune connections, as taught by Han, to arrive at the claimed invention discussed above. The motivation for the proposed modification would have been to reduce the required processing power to run the neural network by focusing on connections of higher weighted relevance (as is the benefit outlined by Han’s abstract by the mention of reducing the computational intensiveness of a network).
Allowable Subject Matter
Claim 13 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 13, the reasons for allowance are the same as provided in the non-final rejection of 06/10/2025.
Conclusion
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/KEVIN M COOMBER/Examiner, Art Unit 2663
/GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698
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