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
Claims 1-20 were previously pending and subject to non-final action filed on 04/03/2026. In the response filed 06/12/2026, claims 1-3, 6-7, 11-13 and 16 were amended. Therefore, claims 1-20 are currently pending and subject to the non-final action below.
Response to Arguments
Applicant’s arguments, see pages 9-12, filed on 06/12/2026 with respect to claim(s) 1-20 under 35 U.S.C. 103 have been considered but are moot because the arguments do not apply to the new combination of references being used in the current rejection.
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 1-2, 4-7, and 9-13 are rejected under 35 U.S.C. 103 as being unpatentable over Chaganti (US PGPUB: 20210304408 A1, Filed Date: Apr. 1, 2020) in view of Washko, JR. (US PGPUB: 20210233241 A1, Filed Date: Jan. 15, 2021, hereinafter “Washko”) in further view of Singh (Transfer learning–based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data, Pub Date: Mar. 18, 2021, hereinafter “Singh”) in view of Mahbubunnabi Tamai (An Integrated Framework with Machine Leaming and Radiomics for Accurate and Rapid Early Diagnosis of COVID-19 from Chest X-ray, Pub Date: Oct. 02, 2020).
Regarding independent claim 1, Chaganti teaches: A method for diagnosis of COVID-19 from at least one chest X-Ray image, comprising: (Chaganti – [abstract] The disease may be COVID-19 (coronavirus disease 2019) or diseases, such as, e.g., SARS (severe acute respiratory syndrome), MERS (Middle East respiratory syndrome), or other types of viral and non-viral pneumonia. [0024] The medical imaging data may be chest CT image 102 of FIG. 1 or input chest CT image 224 of FIG. 2. However, the medical imaging data may be of any suitable modality, such as, e.g., MRI (magnetic resonance imaging), ultrasound, x-ray, or any other modality or combination of modalities.)
imaging, by a chest x-ray machine, a person's chest area to obtain the at least one chest x-ray image; (Chaganti − [0024] an image acquisition device, such as, e.g., a CT scanner,)
performing, by processing circuitry, image segmentation of a human lung in the at least one chest x-ray image; (Chaganti − [0022] A lung segmentation 104 is generated by segmenting the lungs from chest CT 102 and an abnormality segmentation 106 is generated by segmenting abnormality regions associated with COVID-19 from chest CT 102. [0026-0029] At step 304, the lungs are segmented from the medical imaging data. In one example, the lungs are segmented at preprocessing step 202 of FIG. 2 and the segmented lungs may be lung segmentation 104 of FIG. 1.)
extracting, by the processing circuitry, a set of [71] radiomics features from the segmented lung; (Chaganti − [0033] feature extractor 206 and abnormality segmentation 208 to generate a classification of the disease as output 220. In one embodiment, the classification of the disease is determined by global classifier 212 based on the volume abnormality regions relative to the volume of the lungs, HU density histogram, texture, and other radiomic features of the abnormalities present in the lungs.)
extracting from within the [set of 71] radiomics features, by the processing circuitry, a subset of [the 71] radiomics features for classification ability between two classes of COVID-19 and non-COVID-19 lung diseases,
(Chaganti − [0033] During the online or testing stage, global classifier 212 receives features of abnormality regions from feature extractor 206 and abnormality segmentation 208 to generate a classification of the disease as output 220. In one embodiment, the classification of the disease is determined by global classifier 212 based on the volume abnormality regions relative to the volume of the lungs, HU density histogram, texture, and other radiomic features of the abnormalities present in the lungs.)
and outputting, by the processing circuitry, an indication of whether the patient is infected with COVID-19; (Chaganti − [0034] In one embodiment, the assessment of the disease is a diagnosis of the disease for screening. In one example, the diagnosis may be output 222 in FIG. 2 for COVID-19 screening. Global classifier 214 in FIG. 2 is trained with imaging data as well as other patient data, such as, e.g., clinical data, genetic data, lab testing, demographics, DNA data, symptoms, epidemiological factors, etc. During the online or testing stage, global classifier 214 receives patient data 204 and features of abnormality regions from feature extractor 206 to generate a diagnosis as output 222. In one embodiment, global classifier 214 estimates the detection of the disease based on the segmented lung, segmented abnormality regions, and features of the abnormality.)
Chaganti does not explicitly teach: the set of radiomics features
However, Washko teaches: extracting from within the set of [71] radiomics features, by the processing circuitry, a subset of the [71] radiomics features for classification ability between two classes of COVID-19 and non-COVID-19 lung diseases, (Washko – [0008] [0030] [0056] In various embodiments, radiomic features comprise one or more of first order statistics, 3D shape based features, 2D shape based features, gray level cooccurrence matrix, gray level run length matrix, gray level size zone matrix, neighboring gray tone difference matrix, and gray level dependence matrix. In various embodiments, radiomic features are extracted from an image that has been transformed by applying a filter, such as a wavelet filter or a gaussian filter. [0122] such as PyRadiomics. Example radiomic features can include first order statistics, 3D shape based features, 2D shape based features, gray level cooccurrence matrix, gray level run length matrix, gray level size zone matrix, neighboring gray tone difference matrix, and gray level dependence matrix. In various embodiments, radiomic features are extracted from an image that has been transformed by applying a filter, such as a wavelet filter or a gaussian filter. Thus, any of first order statistics, 3D shape based features, 2D shape based features, gray level cooccurrence matrix, gray level run length matrix, gray level size zone matrix, neighboring gray tone difference matrix, and gray level dependence matrix can be extracted from a wavelet transformed image or a gaussian transformed image.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Chaganti and Washko as each invention teaches predicting lung disease from medical imagery data. Adding the teaching of Washko provide Chaganti with a plurality of radiomic features for extracting features from medical imagery. One of ordinary skill in the art would have been motivated to improve prediction of lung diseases from region of interest in medical imagery data [abstract].
Chaganti does not explicitly teach: an ensemble bagged model
However, Singh teaches: classifying, by the processing circuitry, between COVID-19 and non-COVID-19, using an ensemble bagged model having a plurality of classifiers; (Singh – [pdf pages 6-8] 2.7 different classification models are evaluated. (b) bagging ensemble with SVM. 2.7.4 Fig. 5 bagging SVM as the classifier.)
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Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Chaganti, Washko and Singh as each invention teaches predicting lung disease from medical imagery data. Adding the teaching of Singh provide Chaganti and Washko with an ensemble bagging model for classifying lung diseases. One of ordinary skill in the art would have been motivated to improve prediction of lung diseases from region of interest in medical imagery data [abstract].
Chaganti does not explicitly teach: wherein said set of 71 radiomics features consists of:
However, Tamal teaches: extracting from within the set of 71 radiomics features, by the processing circuitry, a subset of the 71 radiomics features for classification ability between two classes of COVID-19 and non-COVID-19 lung, wherein said set of 71 radiomics features consists of:
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(Tamal – [pdf pages 8, 9, 23-24] 100 radiomics features were then extracted using the segmented lung and PyRadiomics tool for each lung separately [23]. This yielded 18 first-order statistics, 9 2D shape-based, 22 Gray Level Co-occurrence Matrix (GLCM), 16 Gray Level Run Length Matrix (GLRLM), 16 Gray Level Size Zone Matrix (GLSZM), 5 Neighboring Gray Tone Difference Matrix (NGTDM) and 14 Gray Level Dependence Matrix (GLDM) features. Out of these 71 features 13 are first order, , 3 2D shape based, 20 GLCM, 8 GLDM, 10 GLRLM, 13 GLSZM and 4 NGTDM extracted features. [pdf page 9] extracting 71 radiomic features out of the 100; [pdf page [22-23] Supplementary Table 2: showing the 71 radiomics features extracted.)
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Accordingly, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Chaganti, Washko, Singh, and Tamal, as the references teach analysis and classification of lung disease from medical imaging data. Adding the teaching of Tamal would provide Chaganti, Washko and Singh with a set of 71 radiomic features shown to have significant classification ability between COVID-19 and non-COVID-19. One of ordinary skill in the art would have been motivated to improve classification of lung disease from medical imagery using 71 radiomic features shown to distinguish COVID-19 from non-COVID-19 cases.
Regarding dependent claim 2, depends on claim 1, Chaganti does not explicitly teach: producing a heatmap
However, Singh teaches: wherein the extracting by processing circuitry of the set of radiomics features includes producing a heatmap of Z-scores for the radiomics features to identify the subset of radiomics features that significantly classify the two classes of COVID- 19 and other lung diseases. (Singh – [pdf pages 6-8] 2.7.4 Fig. 5 bagging SVM as the classifier.)
Accordingly, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Chaganti, Washko, Singh, and Tamal, as the references teach analysis and classification of lung disease from medical imaging data. Adding the teaching of Tamal would provide Chaganti, Washko and Singh with a set of 71 radiomic features shown to have significant classification ability between COVID-19 and non-COVID-19. One of ordinary skill in the art would have been motivated to improve classification of lung disease from medical imagery using 71 radiomic features shown to distinguish COVID-19 from non-COVID-19 cases.
Regarding dependent claim 4, depends on claim 1, Chaganti does not explicitly teach: wherein the plurality of classifiers for the ensemble bagged model are decision trees
However, Singh teaches: wherein the plurality of classifiers for the ensemble bagged model are decision trees. (Singh – [pdf page 7, 2.7.4 Bagging ensemble with SVM] The individual classifiers are trained independently with the bootstrap technique. Bootstrap technique is decision trees.)
Accordingly, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Chaganti, Washko, Singh, and Tamal, as the references teach analysis and classification of lung disease from medical imaging data. Adding the teaching of Tamal would provide Chaganti, Washko and Singh with a set of 71 radiomic features shown to have significant classification ability between COVID-19 and non-COVID-19. One of ordinary skill in the art would have been motivated to improve classification of lung disease from medical imagery using 71 radiomic features shown to distinguish COVID-19 from non-COVID-19 cases.
Regarding dependent claim 5, depends on claim 1, Chaganti teaches: wherein the imaging, by a chest x-ray machine, is performed for a plurality of different persons to obtain a plurality of chest x-ray images for the different persons, (Chaganti − [0014] FIG. 5 shows a table of details of a dataset used for training and testing a network for the segmentation of lungs, in accordance with one or more embodiments; [0015] FIG. 6 shows a table of details of a dataset used for training and testing a network for the segmentation of abnormality regions, in accordance with one or more embodiments;)
and wherein the plurality of chest x-ray images are grouped by a level of severity based on the extent of involvement by ground glass opacities, (Chaganti − [0005] In one embodiment, the disease may be COVID-19 (coronavirus disease 2019) and the abnormality regions associated with COVID-19 comprise opacities of one or more of ground glass opacities (GGO), consolidation, and crazy-paving pattern.)
and the classifying is separately performed for each group (Chaganti − [0016] FIG. 7 shows a scatter plot comparing ground truth and predicted lung severity scores, in accordance with one or more embodiments;)
and wherein said method further comprises treating a diagnosed patient for COVID-19. (In one embodiment, the disease may be COVID-19 (Chaganti − [0005] coronavirus disease 2019) and the abnormality regions associated with COVID-19 comprise opacities of one or more of ground glass opacities (GGO), consolidation, and crazy-paving pattern. However, the disease may be any other disease, such as, e.g., SARS (severe acute respiratory syndrome), MERS (Middle East respiratory syndrome), other types of viral pneumonia, bacterial pneumonia, fungal pneumonia, mycoplasma pneumonia, and other types of pneumonia.)
Regarding independent claim 6, Chaganti, Washko, Singh and Tamal teach the limitations of claim 6 for substantially the same reasons discussed with respect to claim 1. Chaganti further teaches: computing device for diagnosis of COVID-19 from at least one Chest X-Ray image, comprising: (Chaganti – [abstract] The disease may be COVID-19 (coronavirus disease 2019) or diseases, such as, e.g., SARS (severe acute respiratory syndrome), MERS (Middle East respiratory syndrome), or other types of viral and non-viral pneumonia. [0024] The medical imaging data may be chest CT image 102 of FIG. 1 or input chest CT image 224 of FIG. 2. However, the medical imaging data may be of any suitable modality, such as, e.g., MRI (magnetic resonance imaging), ultrasound, x-ray, or any other modality or combination of modalities. [0051] Computer 902 may also include one or more input/output devices 908 that enable user interaction with computer 902 (e.g., display, keyboard, mouse, speakers, buttons, etc.)
a display device; (Chaganti – [0051] Computer 902 may also include one or more input/output devices 908 that enable user interaction with computer 902 (e.g., display, keyboard, mouse, speakers, buttons, etc.)
Regarding dependent claim 7, depends on claim 6, Chaganti does not explicitly teach: producing a heatmap
However, Singh teaches: wherein the processing circuitry is further configured to produce a heatmap of Z-scores for the 71 radiomics features to identify the subset of radiomics features that significantly classify the two classes of COVID-19 and other lung diseases. (Singh – [pdf pages 6-8] 2.7.4 Fig. 5 bagging SVM as the classifier.)
Accordingly, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Chaganti, Washko, Singh, and Tamal, as the references teach analysis and classification of lung disease from medical imaging data. Adding the teaching of Tamal would provide Chaganti, Washko and Singh with a set of 71 radiomic features shown to have significant classification ability between COVID-19 and non-COVID-19. One of ordinary skill in the art would have been motivated to improve classification of lung disease from medical imagery using 71 radiomic features shown to distinguish COVID-19 from non-COVID-19 cases.
Regarding dependent claim 9, depends on claim 6, Chaganti does not explicitly teach: wherein the plurality of classifiers for the ensemble bagged model are decision trees.
However, Singh teaches: wherein the plurality of classifiers for the ensemble bagged model are decision trees. (Singh – [pdf page 7, 2.7.4 Bagging ensemble with SVM] The individual classifiers are trained independently with the bootstrap technique. Bootstrap technique is decision trees.)
Accordingly, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Chaganti, Washko, Singh, and Tamal, as the references teach analysis and classification of lung disease from medical imaging data. Adding the teaching of Tamal would provide Chaganti, Washko and Singh with a set of 71 radiomic features shown to have significant classification ability between COVID-19 and non-COVID-19. One of ordinary skill in the art would have been motivated to improve classification of lung disease from medical imagery using 71 radiomic features shown to distinguish COVID-19 from non-COVID-19 cases.
Regarding dependent claim 10, depends on claim 6, Chaganti teaches: further comprising: communication circuitry configured to wirelessly communicate with at least one chest x-ray machine to receive the at least one chest x-ray image. (Chaganti – [abstract] The disease may be COVID-19 (coronavirus disease 2019) or diseases, such as, e.g., SARS (severe acute respiratory syndrome), MERS (Middle East respiratory syndrome), or other types of viral and non-viral pneumonia. [0024] The medical imaging data may be chest CT image 102 of FIG. 1 or input chest CT image 224 of FIG. 2. However, the medical imaging data may be of any suitable modality, such as, e.g., MRI (magnetic resonance imaging), ultrasound, x-ray, or any other modality or combination of modalities.)
Regarding dependent claim 11, depends on claim 10, Chaganti, Washko, Singh, and Tamal teaches the limitations of claim 6 for the reasons discussed above.
Chaganti teaches: wherein the communication circuitry is configured to wirelessly communicate with a plurality of chest x-ray machines to receive a respective plurality of chest x-ray images for a plurality of patients, (Chaganti − [0055] An image acquisition device 914 can be connected to the computer 902 to input image data (e.g., medical images) to the computer 902. It is possible to implement the image acquisition device 914 and the computer 902 as one device. It is also possible that the image acquisition device 914 and the computer 902 communicate wirelessly through a network. Fig. 5 and Fig. 6 datasets.)
wherein the processing circuitry is further configured to perform image segmentation of a human lung in each of the plurality of chest X-ray images; (Chaganti − [0022] A lung segmentation 104 is generated by segmenting the lungs from chest CT 102 and an abnormality segmentation 106 is generated by segmenting abnormality regions associated with COVID-19 from chest CT 102. [0026-0029] At step 304, the lungs are segmented from the medical imaging data. In one example, the lungs are segmented at preprocessing step 202 of FIG. 2 and the segmented lungs may be lung segmentation 104 of FIG. 1.)
Regarding independent claim 12, is directed to a non-transitory computer readable storage medium (Chaganti − [0050]). Claim 12 have similar/same technical features/limitation as claim 1 and claim 12 is rejected under the same rationale.
Regarding dependent claim 13, depends on claim 12, Chaganti does not explicitly teach: producing a heatmap
However, Singh teaches: wherein the diagnostic method further includes producing a heatmap of Z-scores for the set of radiomics features to identify the subset of radiomics features that significantly classify the two classes of COVID- 19 and other lung diseases. (Singh – [pdf pages 6-8] 2.7.4 Fig. 5 bagging SVM as the classifier.)
Accordingly, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Chaganti, Washko, Singh, and Tamal, as the references teach analysis and classification of lung disease from medical imaging data. Adding the teaching of Tamal would provide Chaganti, Washko and Singh with a set of 71 radiomic features shown to have significant classification ability between COVID-19 and non-COVID-19. One of ordinary skill in the art would have been motivated to improve classification of lung disease from medical imagery using 71 radiomic features shown to distinguish COVID-19 from non-COVID-19 cases.
Claims 3, 8, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Chaganti, Washko, Singh and Tamal as applied to claims 1, 6, 12 above, and further in view of Antoniadis (US PGPUB: 20210374951 A1, Filed Date: Sept. 18, 2019).
Regarding dependent claim 3, depends on claim 1, Chaganti does not explicitly teach: wherein the selecting of radiomics features includes determining a one-way analysis of variance test to find the subset of features that have a statistically significant difference between means of the two classes., with criteria p<0.05, where p-value is a probability.
However, Antoniadis teaches: wherein the selecting of the set of radiomics features includes determining a one-way analysis of variance test to find the subset of features that have a statistically significant difference between means of the two classes., with criteria p<0.05, where p-value is a probability. (Antoniadis − [0208] The 53 stable volume- and orientation-independent radiomic features that were associated with the clinical endpoint above the threshold value (p<0.05; where p is the probability value))
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teaching of Antoniadis in order to develop a system for improving prediction of lung diseases from region of interest in medical imagery data [abstract].
Regarding dependent claim 8, depends on claim 6, Chaganti does not explicitly teach: wherein the processing circuitry is further configured to determine a one-way analysis of variance test to find the subset of features that have a statistically significant difference between means of the two classes., with criteria p<0.05, where p-value is a probability.
However, Antoniadis teaches: wherein the processing circuitry is further configured to determine a one-way analysis of variance test to find the subset of features that have a statistically significant difference between means of the two classes., with criteria p<0.05, where p-value is a probability. (Antoniadis − [0208] The 53 stable volume- and orientation-independent radiomic features that were associated with the clinical endpoint above the threshold value (p<0.05; where p is the probability value))
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teaching of Antoniadis in order to develop a system for improving prediction of lung diseases from region of interest in medical imagery data [abstract].
Regarding dependent claim 14, depends on claim 12, Chaganti does not explicitly teach: wherein the diagnostic method further includes determining a one-way analysis of variance test to find the subset of features that have a statistically significant difference between means of the two classes., with criteria p<0.05, where p-value is a probability.
However, Antoniadis teaches: wherein the diagnostic method further includes determining a one-way analysis of variance test to find the subset of features that have a statistically significant difference between means of the two classes., with criteria p<0.05, where p-value is a probability. (Antoniadis − [0208] The 53 stable volume- and orientation-independent radiomic features that were associated with the clinical endpoint above the threshold value (p<0.05; where p is the probability value))
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teaching of Antoniadis in order to develop a system for improving prediction of lung diseases from region of interest in medical imagery data [abstract].
Claims 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chaganti, Washko, Singh and Tamal as applied to claims 1, 6, 12 above, and further in view of Sabbir Ahmed (ReCoNet: Multi-level Preprocessing of Chest X-rays for COVID-19 Detection Using Convolutional Neural Networks, Pub Date: July 11, 2020, hereinafter “Ahmed”).
Regarding dependent claim 15, depends on claim 1, Chaganti does not explicitly teach: wherein the person has an early stage of COVID-19 where signs of COVID-19 are not detectable by a chest x-ray.
However, Ahmed teaches: wherein the person has an early stage of COVID-19 where signs of COVID-19 are not detectable by a chest x-ray. (Ahmed – [abstract, pdf page 3] ReCoNet (residual image-based COVID-19 detection network) for early COVID-19 detection; assisting professional for CheXpert Dataset used negative dataset. A negative dataset is a chest x-ray that does not show COVID-19 )
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It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teaching of Ahmed machine learning algorithm (ReCoNet) for improving accuracy of early detection of lung disease as shown in RT-PCR lab testing for lung disease such as COVID-19 [abstract].
Regarding dependent claim 16, depends on claim 1, Chaganti teaches wherein the subset of radiomics features but does not explicitly teach: and machine learning algorithm are configured to detect COVID-19 directly from chest X-ray (CXR) with a sensitivity and specificity at least comparable to that of RT-PCR.
However, Ahmed teaches: wherein the subset of radiomics features and machine learning algorithm are configured to detect COVID-19 directly from chest X-ray (CXR) with a sensitivity and specificity at least comparable to that of RT-PCR. (Ahmed – [pdf page 7-8] Our proposed method achieves an overall Sensitivity, Specificity, Accuracy and MCC of 97:39%, 97:53%, 97:48% and 92:49%,)
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It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teaching of Ahmed machine learning algorithm (ReCoNet) for improving accuracy of early detection of lung disease as shown in RT-PCR lab testing for lung disease such as COVID-19 [abstract].
Regarding dependent claim 17, depends on claim 1, Chaganti does not explicitly teach: wherein the support vector machine (SVM) and ensemble bagged model (EBM) based machine learning has an overall sensitivity of 99.6% and 87.8% respectively; and a specificity of 85% and 97%, respectively.
However, Ahmed teaches: wherein the support vector machine (SVM) and ensemble bagged model (EBM) based machine learning has an overall sensitivity of 99.6% and 87.8% respectively; and a specificity of 85% and 97%, respectively. (Ahmed – [pdf pages 7-8] Another unique characteristic of this study is that ReCoNet achieved a sensitivity result of 100% for COVID-19 detection. Our proposed method achieves an overall Sensitivity, Specificity, Accuracy and MCC of 97:39%, 97:53%, 97:48% and 92:49%,)
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It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teaching of Ahmed machine learning algorithm (ReCoNet) for improving accuracy of early detection of lung disease as shown in RT-PCR lab testing for lung disease such as COVID-19 [abstract].
Regarding dependent claim 18, depends on claim 6, Chaganti does not explicitly teach: detect COVID- 19 directly from CXR with a sensitivity and specificity at least comparable to that of RT-PCR.
However, Ahmed teaches: detect COVID- 19 directly from CXR with a sensitivity and specificity at least comparable to that of RT-PCR. (Ahmed – [abstract, pdf page 3] ReCoNet (residual image-based COVID-19 detection network) for early COVID-19 detection; assisting professional for CheXpert Dataset used negative dataset. A negative dataset is a chest x-ray that does not show COVID-19. Our proposed method achieves an overall Sensitivity, Specificity, Accuracy and MCC of 97:39%, 97:53%, 97:48% and 92:49%, )
Regarding dependent claim 19, depends on claim 12, Chaganti does not explicitly teach: wherein said method for diagnosis detects COVID-19 directly from CXR with a sensitivity and specificity at least comparable to that of RT-PCR.
However, Ahmed teaches: wherein said method for diagnosis detects COVID-19 directly from CXR with a sensitivity and specificity at least comparable to that of RT-PCR. (Ahmed – [abstract, pdf page 3] ReCoNet (residual image-based COVID-19 detection network) for early COVID-19 detection; assisting professional for CheXpert Dataset used negative dataset. A negative dataset is a chest x-ray that does not show COVID-19. Our proposed method achieves an overall Sensitivity, Specificity, Accuracy and MCC of 97:39%, 97:53%, 97:48% and 92:49%, )
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teaching of Ahmed machine learning algorithm (ReCoNet) for improving accuracy of early detection of lung disease as shown in RT-PCR lab testing for lung disease such as COVID-19 [abstract].
Regarding dependent claim 20, depends on claim 15, Chaganti teaches: wherein said method does not require sample collection or other manual intervention. (Chaganti − [0024] The medical imaging data may be of any suitable modality, such as, e.g., MRI (magnetic resonance imaging), ultrasound, x-ray, or any other modality or combination of modalities.)
Chaganti does not explicitly teach: detects COVID-19 directly from CXR with a sensitivity and specificity at least comparable to that of RT-PCR,
However, Ahmed teaches: detects COVID-19 directly from CXR with a sensitivity and specificity at least comparable to that of RT-PCR, (Ahmed – [abstract, pdf page 3] ReCoNet (residual image-based COVID-19 detection network) for early COVID-19 detection; assisting professional for CheXpert Dataset used negative dataset. A negative dataset is a chest x-ray that does not show COVID-19. Our proposed method achieves an overall Sensitivity, Specificity, Accuracy and MCC of 97:39%, 97:53%, 97:48% and 92:49%, )
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teaching of Ahmed machine learning algorithm (ReCoNet) for improving accuracy of early detection of lung disease as shown in RT-PCR lab testing for lung disease such as COVID-19 [abstract].
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.
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/CARL E BARNES JR/Examiner, Art Unit 2178
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178
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