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 .
This Office Action is responsive to communications filed on 01/17/2025. Claims 1-20 are pending in the instant application. Claims 1, 11 and 18 are independent. An Office Action on the merits follows here below.
Priority
This application discloses and claims only subject matter disclosed in prior application number 18/346,391, filed 07/03/2023, and names the inventor or at least one joint inventor named in the prior application. Accordingly, this application has been examined as a continuation.
Specification
The abstract of the disclosure is objected to because the patent abstract should be concise statement that does not recite legal phraseology and should not merely recite an instant claim. Correction is required. See MPEP § 608.01[b].
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/17/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-6, 9, 11, 16, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Moradi (US 20190073770 A1) in combination with Jain (US 20240161276 A1).
Regarding Claim 1: Moradi discloses a computer-implemented method for screening for mutations (Refer to para [002]; “According to embodiments of the present disclosure, methods of and computer program products for disease detection from medical images are provided.”) comprising: receiving one or more digital images of a cytology specimen at a digital storage device (Refer to para [018]; “In various exemplary embodiments, the ratio of positive to negative samples is 1:17. However, it will be appreciated that various ratios of positive to negative samples are enabled by the present disclosure. For example, in some embodiments, the network is trained on a set comprising no more than 10% positive (abnormal) images.”) isolating one or more cell images using the one or more digital images by isolating a set of individual images of cells within the one or more digital images using a segmentation system and/or a detection system (Refer to para [019-021]; “In various embodiments, a fully convolutional segmentation network is used to characterize normal (negative) anatomy of the heart without seeing any abnormal (positive) samples. Segmentation networks output a label per pixel and thus provide the shapes and locations of the structures of interest. Thus, a trained segmentation network produces useful features for better classification accuracy on unbalanced data. Deviations from normal anatomy can be learned from the feature maps produced by this segmentation network using a binary convolutional network, trained on an extremely unbalanced training dataset with as little as one positive for 17 negative samples. The approaches provided herein outperform alternative generative models. In particular, simple generative models may not be able to characterize delicate variations that separate normal from disease in medical imaging. Highly complex generative models require very large training sets. Various advantages outlined herein are attributable to the ability of the segmentation network to transfer image information to semantic pixel level labels provided by experts such as radiologists. Cross-modal knowledge transfer from image to semantic text space can be used to detect unseen objects in new samples. The segmentation network used in various embodiments is optimized for segmentation of normal heart anatomy from four chamber cardiac CT slices. By concatenating the low and high level features from the segmentation network, a classification network can be trained on unbalanced data.”).
Moradi does not disclose “identifying a cell patch as a malignant cell or a non-malignant cell.”
Jain teaches “predicting response of triple-negative breast cancer to neoadjuvant chemotherapy using a deep convolutional neural network-based artificial intelligence tool a method of predicting patient response to therapy is disclosed.”
More specifically, Jain teaches “dividing a first set of patient tissue image slides into a plurality of tiles of a predetermined pixel size; training a convolution neural network using randomly sampled tiles of 64 pixels by 64 pixels at 20 times resolution of a sample, 256 pixels by 256 pixels at 20 times the resolution of the sample, and 1024 pixels by 1024 pixels at 5 times the resolution of the sample; transforming the plurality of tiles into a high-dimensional vector representation such that vectors of morphologically similar patterns cluster together; using the trained neural network, performing artifact detection and cancer classification to identify patterns and features that capture tumor cell heterogeneity along with stromal and tumor micro environment (TME) components of a second set of patient tissue image tiles; and performing feature ranking on the second set of patient tissue image tiles.”
“FIG. 3 of Jain illustrates an exemplary feature extraction module according to an embodiment…” wherein a processing application is capable for extracting at least one cell patch from the one or more digital images (Refer to para [050]; “In a next step, the system performs feature extraction by a cancer and TME clustering module. In a next step, the system performs Region of Interest (ROI) identification via a ROI identification module. In the last step, the system determines an output of a therapy response prediction.”) identifying at least one cell patch as a malignant cell or a non-malignant cell (Refer to para [088]; “We demonstrate the performance of a novel AI prediction model based on CNN using a training and a testing cohort of TNBC patients with known response to NAC. Although the sample size of the testing cohort was limited in this first report, the results are promising and show potential for guiding clinical management of TNBC patients and warrant further validation and the simplicity of scanned slides as the starting point makes this effort highly scalable. Improvements to the model, including validation in a larger cohort of TNBC patients and incorporation of additional biomarkers to the model, are underway. Our study exemplifies the utility of WSIs for building CNN-based AI prediction models for TNBC patients that have the potential to function as robust ancillary digital pathology tools in conjunction with standard-of-care pathologic evaluation of malignant tumors.”) extracting one or more cellular features using the one or more cell images (Refer to para [057]; “In step 930, the system transforms the plurality of tiles into a high-dimensional vector representation such that vectors of morphologically similar patterns cluster together. In step 940, using the trained neural network, the system performs artifact detection and cancer classification to identify patterns and features that capture tumor cell heterogeneity along with stromal and tumor micro environment (TME) components of a second set of patient tissue image tiles. In step 950, the system performs feature ranking on the second set of patient tissue image tiles.”) and determining presence of a specific gene mutation or a biomarker from one or more cellular features using a trained output aggregation model (Refer to para [068]; “The diagnosis of TNBC was based on conventional histopathological examination of H & E-stained tissue sections of formalin-fixed and paraffin-embedded tissue blocks from ultrasound-guided core needle biopsies, including the results of ancillary immunohistochemical staining for ER, PR, and HER2 with or without fluorescence in situ hybridization testing for evaluation of HER2 gene amplification status in selected patients. TNBC diagnosis was established based on ASCO/CAP guidelines with documentation of the presence of invasive tumor in H & E sections together with lack of expression for ER, PR, and HER2 proteins on immunohistochemical staining and negative findings for HER2 gene amplification. All patients included in the retrospective study received NAC that included anthracycline and taxanes at our institution and underwent surgical resection after completion of NAC, consisting of partial or complete mastectomy along with sentinel node biopsy or either targeted or complete axillary lymph node dissection. The surgical resection specimen slices, were radiographed, and the tumor bed was thoroughly sampled to establish the presence or absence of residual tumor. The patients' demographics, initial clinical stage, and final pathologic tumor stage based on the American Joint Committee on Cancer staging system, eighth edition along with extensive clinico-pathological, treatment and outcomes data were recorded. A pCR to NAC was defined as no residual invasive tumor in the breast and no evidence of metastatic tumor in the lymph nodes and thus included pathologic T categories TO or Tis and N categories N0 or N(i+).”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Moradi by enhancing image processors in order to “identify morphometric features on patient tissues that correlate with a particular molecular change, such as protein loss or gene mutations without the need for a molecular test such as immunohistochemistry (IHC) or gene panel testing…” as taught by Jain.
The suggestion/motivation for combining the teachings of Moradi and Jain would have been in order to enhance identification of biomarkers by providing “…unsupervised artificial intelligence architectures to scan tissue sample image data at a much faster speed and also identify biomarkers predictive of patient outcome that has never been previously identified.” (at para [039], Jain).
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Moradi and Jain in order to obtain the specified claimed elements of Claim 1. It is for at least the aforementioned reasons that the Examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding Claim 4: Jain discloses training at least one of the trained output aggregation model to indicate presence of specific gene mutations or biomarkers (Refer to para [065 and 066]; “Test: The trained model is used to predicted on a blinded set of previously unseen cases to determine the model accuracy, resulting in 70-90% accuracy across subsets of the data. In some embodiments, digital whole-slide images (WSI) of hematoxylin and eosin—stained core biopsies of 165 (pathologic complete response (pCR) in 60 and non-pCR in 105) and 78 (pCR in 31 and non-pCR in 47) TNBC patients were used for training and validation of the model. The CNN based AI model was trained to extract morphometric features at visual and sub-visual levels from the WSIs in an unsupervised way.”).
Regarding Claim 5: Moradi discloses the set of individual images of cells (Refer to para [045]; “The segmentation network is trained on normal images only, but produces features for both normal and diseased cases. Significant gains are shown in positive detection rate using these feature maps on a classification network compared to using the original images with the same network.”) are isolated within the at least one cell patch (Refer to para [045]; “Examples provided herein include cases with two cardiac diseases that are each detected on a different binary classifier. It will be appreciated that the present disclosure is applicable to additional disease types by combining multiple binary classifiers, or by training a multiclass disease detector.”).
Regarding Claim 6: Jain discloses the segmentation system and/or the detection system is a machine learning model (Refer to para [088]; “We demonstrate the performance of a novel AI prediction model based on CNN using a training and a testing cohort of TNBC patients with known response to NAC. Although the sample size of the testing cohort was limited in this first report, the results are promising and show potential for guiding clinical management of TNBC patients and warrant further validation and the simplicity of scanned slides as the starting point makes this effort highly scalable. Improvements to the model, including validation in a larger cohort of TNBC patients and incorporation of additional biomarkers to the model, are underway. Our study exemplifies the utility of WSIs for building CNN-based AI prediction models for TNBC patients that have the potential to function as robust ancillary digital pathology tools in conjunction with standard-of-care pathologic evaluation of malignant tumors.”).
Regarding Claim 9: Jain discloses outputting the specific gene mutation or biomarker (Refer to para [050]; “In the last step, the system determines an output of a therapy response prediction.”) to a digital storage device and/or display (Refer to para [091]; “The computer 1600 may include peripherals 1605. Peripherals 1605 may include input peripherals such as a keyboard, mouse, trackball, video camera, microphone, and other input devices. Peripherals 1605 may also include output devices such as a display.”).
Regarding Claim 11: Moradi discloses a system for identifying a specimen category (Refer to para [002]; “According to embodiments of the present disclosure, methods of and computer program products for disease detection from medical images are provided.”) comprising: at least one memory storing instructions (Refer to para [053]; “System memory 28 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and/or cache memory 32. Computer system/server 12 may further include other removable/non-removable, volatile/non-volatile computer system storage media.”) and at least one processor (Refer to para [050]; “The components of computer system/server 12 may include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including system memory 28 to processor 16.”) configured to execute instructions to perform operations comprising: receiving one or more digital images of a cytology specimen at a digital storage device (Refer to para [018]; “In various exemplary embodiments, the ratio of positive to negative samples is 1:17. However, it will be appreciated that various ratios of positive to negative samples are enabled by the present disclosure. For example, in some embodiments, the network is trained on a set comprising no more than 10% positive (abnormal) images.”) isolating one or more cell images using the one or more digital images by isolating a set of individual images of cells within the one or more digital images using a segmentation system and/or a detection system (Refer to para [019-021]; “In various embodiments, a fully convolutional segmentation network is used to characterize normal (negative) anatomy of the heart without seeing any abnormal (positive) samples. Segmentation networks output a label per pixel and thus provide the shapes and locations of the structures of interest. Thus, a trained segmentation network produces useful features for better classification accuracy on unbalanced data. Deviations from normal anatomy can be learned from the feature maps produced by this segmentation network using a binary convolutional network, trained on an extremely unbalanced training dataset with as little as one positive for 17 negative samples. The approaches provided herein outperform alternative generative models. In particular, simple generative models may not be able to characterize delicate variations that separate normal from disease in medical imaging. Highly complex generative models require very large training sets. Various advantages outlined herein are attributable to the ability of the segmentation network to transfer image information to semantic pixel level labels provided by experts such as radiologists. Cross-modal knowledge transfer from image to semantic text space can be used to detect unseen objects in new samples. The segmentation network used in various embodiments is optimized for segmentation of normal heart anatomy from four chamber cardiac CT slices. By concatenating the low and high level features from the segmentation network, a classification network can be trained on unbalanced data.”).
Moradi does not disclose “identifying a cell patch as a malignant cell or a non-malignant cell.”
Jain teaches “predicting response(s) of triple-negative breast cancer to neoadjuvant chemotherapy using a deep convolutional neural network-based artificial intelligence tool a method of predicting patient response to therapy is disclosed.”
More specifically, Jain teaches “dividing a first set of patient tissue image slides into a plurality of tiles of a predetermined pixel size; training a convolution neural network using randomly sampled tiles of 64 pixels by 64 pixels at 20 times resolution of a sample, 256 pixels by 256 pixels at 20 times the resolution of the sample, and 1024 pixels by 1024 pixels at 5 times the resolution of the sample; transforming the plurality of tiles into a high-dimensional vector representation such that vectors of morphologically similar patterns cluster together; using the trained neural network, performing artifact detection and cancer classification to identify patterns and features that capture tumor cell heterogeneity along with stromal and tumor micro environment (TME) components of a second set of patient tissue image tiles; and performing feature ranking on the second set of patient tissue image tiles.”
“FIG. 3 of Jain illustrates an exemplary feature extraction module according to an embodiment…” wherein a processing application is capable for extracting at least one cell patch from the one or more digital images (Refer to para [050]; “In a next step, the system performs feature extraction by a cancer and TME clustering module. In a next step, the system performs Region of Interest (ROI) identification via a ROI identification module. In the last step, the system determines an output of a therapy response prediction.”) identifying the at least one cell patch as a malignant cell or a non-malignant cell (Refer to para [088]; “We demonstrate the performance of a novel AI prediction model based on CNN using a training and a testing cohort of TNBC patients with known response to NAC. Although the sample size of the testing cohort was limited in this first report, the results are promising and show potential for guiding clinical management of TNBC patients and warrant further validation and the simplicity of scanned slides as the starting point makes this effort highly scalable. Improvements to the model, including validation in a larger cohort of TNBC patients and incorporation of additional biomarkers to the model, are underway. Our study exemplifies the utility of WSIs for building CNN-based AI prediction models for TNBC patients that have the potential to function as robust ancillary digital pathology tools in conjunction with standard-of-care pathologic evaluation of malignant tumors.”) extracting one or more cellular features using the one or more cell images isolated (Refer to para [057]; “In step 930, the system transforms the plurality of tiles into a high-dimensional vector representation such that vectors of morphologically similar patterns cluster together. In step 940, using the trained neural network, the system performs artifact detection and cancer classification to identify patterns and features that capture tumor cell heterogeneity along with stromal and tumor micro environment (TME) components of a second set of patient tissue image tiles. In step 950, the system performs feature ranking on the second set of patient tissue image tiles.”) and determining presence of a specific gene mutation or a biomarker from the one or more cellular features using a trained output aggregation model (Refer to para [068]; “The diagnosis of TNBC was based on conventional histopathological examination of H & E-stained tissue sections of formalin-fixed and paraffin-embedded tissue blocks from ultrasound-guided core needle biopsies, including the results of ancillary immunohistochemical staining for ER, PR, and HER2 with or without fluorescence in situ hybridization testing for evaluation of HER2 gene amplification status in selected patients. TNBC diagnosis was established based on ASCO/CAP guidelines with documentation of the presence of invasive tumor in H & E sections together with lack of expression for ER, PR, and HER2 proteins on immunohistochemical staining and negative findings for HER2 gene amplification. All patients included in the retrospective study received NAC that included anthracycline and taxanes at our institution and underwent surgical resection after completion of NAC, consisting of partial or complete mastectomy along with sentinel node biopsy or either targeted or complete axillary lymph node dissection. The surgical resection specimen slices, were radiographed, and the tumor bed was thoroughly sampled to establish the presence or absence of residual tumor. The patients' demographics, initial clinical stage, and final pathologic tumor stage based on the American Joint Committee on Cancer staging system, eighth edition along with extensive clinico-pathological, treatment and outcomes data were recorded. A pCR to NAC was defined as no residual invasive tumor in the breast and no evidence of metastatic tumor in the lymph nodes and thus included pathologic T categories TO or Tis and N categories N0 or N(i+).”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Moradi by enhancing image processors in order to “identify morphometric features on patient tissues that correlate with a particular molecular change, such as protein loss or gene mutations without the need for a molecular test such as immunohistochemistry (IHC) or gene panel testing…” as taught by Jain.
The suggestion/motivation for combining the teachings of Moradi and Jain would have been in order to enhance identification of biomarkers by providing “…unsupervised artificial intelligence architectures to scan tissue sample image data at a much faster speed and also identify biomarkers predictive of patient outcome that has never been previously identified.” (at para [039], Jain).
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Moradi and Jain in order to obtain the specified claimed elements of Claim 11. It is for at least the aforementioned reasons that the Examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding Claim 14: Moradi discloses the set of individual images of cells (Refer to para [045]; “The segmentation network is trained on normal images only, but produces features for both normal and diseased cases. Significant gains are shown in positive detection rate using these feature maps on a classification network compared to using the original images with the same network.”) are isolated within the at least one cell patch (Refer to para [045]; “Examples provided herein include cases with two cardiac diseases that are each detected on a different binary classifier. It will be appreciated that the present disclosure is applicable to additional disease types by combining multiple binary classifiers, or by training a multiclass disease detector.”).
Regarding Claim 16: Jain discloses at least one of a display or a digital storage device (Refer to para [091]; “The computer 1600 may include peripherals 1605. Peripherals 1605 may include input peripherals such as a keyboard, mouse, trackball, video camera, microphone, and other input devices. Peripherals 1605 may also include output devices such as a display.”) wherein the operations further comprise outputting the presence of a specific gene mutation or biomarker to the at least one of the display or the digital storage device (Refer to para [050]; “In the last step, the system determines an output of a therapy response prediction.”).
Regarding Claim 18: Moradi discloses a non-transitory computer readable medium storing instructions that (Refer to para [049]; “Computer system/server 12 may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system/server 12 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.”) when executed by a processor (Refer to para [050]; “The components of computer system/server 12 may include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including system memory 28 to processor 16.”) cause the processor to perform a method for identifying a specific gene mutation or a biomarker (Refer to para [001]; “Embodiments of the present disclosure relate to disease detection from medical images, and more specifically, to disease detection algorithms trainable with small number of positive samples.”) the method comprising: receiving one or more digital images of a cytology specimen at a digital storage device (Refer to para [018]; “In various exemplary embodiments, the ratio of positive to negative samples is 1:17. However, it will be appreciated that various ratios of positive to negative samples are enabled by the present disclosure. For example, in some embodiments, the network is trained on a set comprising no more than 10% positive (abnormal) images.”) isolating one or more cell images using the one or more digital images, wherein isolating the one or more cell images comprises isolating a set of individual images of cells within the at least one cell patch using a segmentation system and/or a detection system (Refer to para [019-021]; “In various embodiments, a fully convolutional segmentation network is used to characterize normal (negative) anatomy of the heart without seeing any abnormal (positive) samples. Segmentation networks output a label per pixel and thus provide the shapes and locations of the structures of interest. Thus, a trained segmentation network produces useful features for better classification accuracy on unbalanced data. Deviations from normal anatomy can be learned from the feature maps produced by this segmentation network using a binary convolutional network, trained on an extremely unbalanced training dataset with as little as one positive for 17 negative samples. The approaches provided herein outperform alternative generative models. In particular, simple generative models may not be able to characterize delicate variations that separate normal from disease in medical imaging. Highly complex generative models require very large training sets. Various advantages outlined herein are attributable to the ability of the segmentation network to transfer image information to semantic pixel level labels provided by experts such as radiologists. Cross-modal knowledge transfer from image to semantic text space can be used to detect unseen objects in new samples. The segmentation network used in various embodiments is optimized for segmentation of normal heart anatomy from four chamber cardiac CT slices. By concatenating the low and high level features from the segmentation network, a classification network can be trained on unbalanced data.”).
Moradi does not disclose “identifying a cell patch as a malignant cell or a non-malignant cell.”
Jain teaches “predicting response of triple-negative breast cancer to neoadjuvant chemotherapy using a deep convolutional neural network-based artificial intelligence tool a method of predicting patient response to therapy is disclosed.”
More specifically, Jain teaches “dividing a first set of patient tissue image slides into a plurality of tiles of a predetermined pixel size; training a convolution neural network using randomly sampled tiles of 64 pixels by 64 pixels at 20 times resolution of a sample, 256 pixels by 256 pixels at 20 times the resolution of the sample, and 1024 pixels by 1024 pixels at 5 times the resolution of the sample; transforming the plurality of tiles into a high-dimensional vector representation such that vectors of morphologically similar patterns cluster together; using the trained neural network, performing artifact detection and cancer classification to identify patterns and features that capture tumor cell heterogeneity along with stromal and tumor micro environment (TME) components of a second set of patient tissue image tiles; and performing feature ranking on the second set of patient tissue image tiles.”
“FIG. 3 of Jain illustrates an exemplary feature extraction module according to an embodiment…” wherein a processing application is capable for extracting at least one cell patch from the one or more digital images (Refer to para [050]; “In a next step, the system performs feature extraction by a cancer and TME clustering module. In a next step, the system performs Region of Interest (ROI) identification via a ROI identification module. In the last step, the system determines an output of a therapy response prediction.”) identifying the at least one cell patch as a malignant cell or a non-malignant cell (Refer to para [088]; “We demonstrate the performance of a novel AI prediction model based on CNN using a training and a testing cohort of TNBC patients with known response to NAC. Although the sample size of the testing cohort was limited in this first report, the results are promising and show potential for guiding clinical management of TNBC patients and warrant further validation and the simplicity of scanned slides as the starting point makes this effort highly scalable. Improvements to the model, including validation in a larger cohort of TNBC patients and incorporation of additional biomarkers to the model, are underway. Our study exemplifies the utility of WSIs for building CNN-based AI prediction models for TNBC patients that have the potential to function as robust ancillary digital pathology tools in conjunction with standard-of-care pathologic evaluation of malignant tumors.”) extracting one or more cellular features using the one or more cell images; (Refer to para [057]; “In step 930, the system transforms the plurality of tiles into a high-dimensional vector representation such that vectors of morphologically similar patterns cluster together. In step 940, using the trained neural network, the system performs artifact detection and cancer classification to identify patterns and features that capture tumor cell heterogeneity along with stromal and tumor micro environment (TME) components of a second set of patient tissue image tiles. In step 950, the system performs feature ranking on the second set of patient tissue image tiles.”) and determining presence of a specific gene mutation or a biomarker from the one or more cellular features using a trained output aggregation model (Refer to para [068]; “The diagnosis of TNBC was based on conventional histopathological examination of H & E-stained tissue sections of formalin-fixed and paraffin-embedded tissue blocks from ultrasound-guided core needle biopsies, including the results of ancillary immunohistochemical staining for ER, PR, and HER2 with or without fluorescence in situ hybridization testing for evaluation of HER2 gene amplification status in selected patients. TNBC diagnosis was established based on ASCO/CAP guidelines with documentation of the presence of invasive tumor in H & E sections together with lack of expression for ER, PR, and HER2 proteins on immunohistochemical staining and negative findings for HER2 gene amplification. All patients included in the retrospective study received NAC that included anthracycline and taxanes at our institution and underwent surgical resection after completion of NAC, consisting of partial or complete mastectomy along with sentinel node biopsy or either targeted or complete axillary lymph node dissection. The surgical resection specimen slices, were radiographed, and the tumor bed was thoroughly sampled to establish the presence or absence of residual tumor. The patients' demographics, initial clinical stage, and final pathologic tumor stage based on the American Joint Committee on Cancer staging system, eighth edition along with extensive clinico-pathological, treatment and outcomes data were recorded. A pCR to NAC was defined as no residual invasive tumor in the breast and no evidence of metastatic tumor in the lymph nodes and thus included pathologic T categories TO or Tis and N categories N0 or N(i+).”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Moradi by enhancing image processors in order to “identify morphometric features on patient tissues that correlate with a particular molecular change, such as protein loss or gene mutations without the need for a molecular test such as immunohistochemistry (IHC) or gene panel testing…” as taught by Jain.
The suggestion/motivation for combining the teachings of Moradi and Jain would have been in order to enhance identification of biomarkers by providing “…unsupervised artificial intelligence architectures to scan tissue sample image data at a much faster speed and also identify biomarkers predictive of patient outcome that has never been previously identified.” (at para [039], Jain).
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Moradi and Jain in order to obtain the specified claimed elements of Claim 18. It is for at least the aforementioned reasons that the Examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding Claim 19: Moradi discloses the set of individual images of cells (Refer to para [045]; “The segmentation network is trained on normal images only, but produces features for both normal and diseased cases. Significant gains are shown in positive detection rate using these feature maps on a classification network compared to using the original images with the same network.”) are isolated within the at least one cell patch (Refer to para [045]; “Examples provided herein include cases with two cardiac diseases that are each detected on a different binary classifier. It will be appreciated that the present disclosure is applicable to additional disease types by combining multiple binary classifiers, or by training a multiclass disease detector.”).
Allowable Subject Matter
Claims 2, 3, 7, 8, 10, 12, 13, 15 and 17 are 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.
The prior art either singly or in combination does not teach, disclose or suggest at least the following claim limitation(s): “… receiving one or more training digital images of at least one cytology specimen; receiving at least one first training label for the cytology specimen indicating malignancy or non-malignancy; receiving at least one second training label for the cytology specimen indicating specific gene mutations or biomarkers; isolating one or more cell images using the one or more training digital images; and extracting one or more cellular features from the isolated one or more cell images.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MIA M THOMAS whose telephone number is (571)270-1583. The examiner can normally be reached M-Th 8:30am-4:30pm.
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MIA M. THOMAS
Primary Examiner
Art Unit 2665
/MIA M THOMAS/Primary Examiner
Art Unit 2665
