DETAILED ACTION
This action is in response to the remarks and amendments filed on March 18th, 2026. Claims 1-11 and 13-21 are pending and have been examined.
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 Arguments
Applicant's arguments filed March 18th, 2026 have been fully considered but they are not persuasive.
Applicant alleges that "Veidman does not expressly disclose or suggest 'determining, using a trained machine learning model, whether each foreground tile of the plurality of foreground tiles contains a known or an unknown morphology,' as recited in claim 1… The cited passage of Veidman does not discuss or define the term 'unknown mass,' nor is this term discussed in any other passage of Veidman." Examiner respectfully disagrees. Under broadest reasonable interpretation, within the field of medical imaging, morphology is best understood to mean "the science of the form and structure of organisms" (as stated by the cancer.gov dictionary definition). Therefore, an unknown morphology of a whole slide image is best understood to mean an unknown structure or form of a tissue which is present within the whole slide image. Veidman discloses that an input tissue "may be obtained intra-operatively, during for example, a biopsy procedure, a FNA procedure, a core biopsy procedure, colonoscopy for removal of colon polyps, surgery for removal of an unknown mass, surgery for removal of a benign cancer, and/or surgery for removal of a malignant cancer" which implies that the input tissue is obtained from an unknown mass. Veidman then performs processing to extract the structure/form (or morphology) of the unknown tissue to further identify the unknown mass and generate instructions for treatment. Therefore, the rejection is maintained.
Applicant alleges that "Veidman does not disclose or suggest 'correlating each unknown morphology cluster with patient outcomes to identify a new known morphology cluster; and based on the new known morphology cluster, predicting at least one outcome for the patient,' as recited in claim 1… Veidman does not teach correlating either its unknown tissue type or its abnormal patches in a tissue image with patient outcomes to identify a new known morphology cluster. At most, Veidman teaches generating instructions to obtain a new tissue sample, or comparing the abnormal patches to a manual diagnosis of the slide to detect mismatch." Examiner respectfully disagrees. Veidman describes that patches are clustered, and that patches with the furthest distance, or those above a distance threshold, are identified as abnormal. These abnormal patches are sent for manual diagnosis, then those manual diagnoses are compared against the automatic determination of the slide level tissue type. When this manual diagnosis is made, and it is determined that there is a mismatch between the automatic identification and the manual diagnosis, the manual diagnosis outcome is stored and associated with that patch cluster (Veidman Column 40 Lines 46-52), which is analogous to correlating patient outcomes with a new known morphology cluster. Then, this new cluster can be used in a further iteration to identify an outcome for a patient at step 120 in figure 1. Therefore, the rejection is maintained.
The remaining arguments with respect to the 35 U.S.C. 103 rejections are moot due to their reliance on claims 1, 13, or 17.
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 1-2, 4, 6-9, 11, 13-14, 16-18, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Veidman (US11195274) in view of Amma (US20230062289).
In regards to claim 1, Veidman teaches a method for identifying morphologies present in digital whole slide images, the method comprising: receiving one or more digital whole slide images associated with a patient (Veidman Figure 1 Step 106/108; Column 7, Line 34 “In a further implementation form of the first, second, and third aspects, the tissue image is of a whole slide including a plurality of tissue regions and non-tissue background.”); determining a plurality of foreground tiles within the one or more digital whole slide images associated with a patient(Veidman Figure 1 Step 110/112; Column 23, Line 61 “The segmentation of the tissue is performed for segmenting tissue objects from the tissue image, for example, cells such as normal cells, cancer cells, pre-cancer cells, cells of the organ, immune system cells, and bacteria. The tissue objects represent portions of the tissue image for further analysis. The segmentation may be performed for exclusion of the background and/or noise.” Examiner note: This reference segments out the background in step 110, then determines tiles in 112. Therefore, those determined tiles are only of the foreground.); determining, using a trained machine learning model, whether each foreground tile of the plurality of foreground tiles Veidman Figure 1 Step 102; Column 22, Line 12 “At 102, tissue is obtained. The tissue may be obtained intra-operatively, during for example, a biopsy procedure, a FNA procedure, a core biopsy procedure, colonoscopy for removal of colon polyps, surgery for removal of an unknown mass, surgery for removal of a benign cancer, and/or surgery for removal of a malignant cancer.” Examiner note: This reference discloses that the tissue provided could be from an unknown mass, and would therefore need to be identified and is considered unknown.); upon determining that one or more foreground tiles contains an unknown morphology, determining a vector of features associated with each of the one or more foreground tiles (Veidman Column 38, Line 6 “An encoding (which may sometimes be substituted with the term embedding) is computed for the selected patch by a trained autoencoder code, optionally an autoencoder CNN. The encoding may be stored as a feature vector.”) with an unknown morphology by mapping each of the one or more foreground tiles to one or more patterns (Veidman Column 38, Line 6 “An encoding (which may sometimes be substituted with the term embedding) is computed for the selected patch by a trained autoencoder code, optionally an autoencoder CNN. The encoding may be stored as a feature vector. One or more similar patches are identified from a dataset of patch encodings, according to a requirement of a similarity distance between the encoding of the selected patch and encoding of the patches in the dataset.”; Column 38 Line 30 “The matching similar patch(es) may be presented in the GUI. Optionally, the identified patch(es) are presented in association with previously analyzed results, for example, tissue types seen in the patch(es), overall diagnosis for the tissue image from which the slide was taken, procedure used to obtain the patch, and data of the patient from which the tissue for the patch was extracted. The similar patch(s) may be presented alongside the current patch.”), and providing each vector of features to a clustering algorithm, the clustering algorithm associating each of the one or more foreground tiles with an unknown morphology cluster (Veidman Column 39, Line 63 “A second exemplary process for detecting abnormal patches and/or abnormal visual object(s) in patches is now described. The process is based on determining that the patch and/or tissue object is abnormal according to a comparison to other patches and/or tissue objects of the tissue image. The patches of the tissue image are clustered. Optionally, each patch is passed through the autoencoder CNN and the encodings are clustered, for example, by clusterization code” Examiner note: This excerpt, along with the previous excerpt describing the “encodings”, shows that the tiles are encoded into a feature vector, and clustered.); correlating each unknown morphology cluster with patient outcomes to identify a new known morphology cluster (Veidman Column 40 Line 10 “One or more patches are identified as abnormal when the statistical distance between the encoding of the respective patch and the cluster center is above an abnormality threshold. Alternatively of additionally, patches located a certain distance away from the center(s) of the cluster(s) are determined to be abnormal. For example, the furthest predefined percentage of patches (e.g., 1-5%, or other values) are determined to be abnormal. Alternatively or additionally, one or more clusters located furthest away (e.g., and/or above an abnormality threshold) from the other clusters (e.g., according to cluster centers) are identified as being abnormal. The patches of the abnormal cluster(s) are identified as being abnormal. Alternatively or additionally, patches located furthest away (e.g., and/or above an abnormality threshold) from other patches are identified as being abnormal.“ Examiner note: This reference teaches that once a patch has been identified to not belong to one of the pre-identified “normal” clusters, it can be described as abnormal, and diagnosed by a physician to identify the outcomes associated with this cluster. Once this is done, the cluster can be forever associated with the physician’s diagnosis, thus making it “known”.); and based on the new known morphology cluster, predicting at least one outcome for the patient. (Veidman Figure 1 Step 118; Column 27, Line 52 “At 118, one or more slide-level tissue types are computed for the tissue image as a whole according to the analysis of the patches. The slide-level tissue type(s) may be computed according to the patch-level tissue type(s), optionally according to the relative location of the respective patches within the tissue image. The slide-level tissue type may be, for example, an overall diagnosis made for the tissue slide as a whole. For example, malignancy, a benign tumor, a certain disease, or normal tissue.”)
Veidman does not teach determining, using a trained machine learning model, whether each foreground tile of the plurality of foreground tiles contains a known morphology or an unknown morphology.
However, Amma teaches determining, using a trained machine learning model, whether each foreground tile of the plurality of foreground tiles contains a known morphology or an unknown morphology (Amma Paragraph [0008] “A first disclosure is directed to a method performed by one or more computers for learning model classifying an input image into known classes and an unknown class by extracting a feature of the input image, each of the known classes showing that the input image falls into any one of specific categories and the unknown class showing that the input image does not fall into any of the specific categories.” Examiner note: While this reference does not specifically disclose using morphologies, it can be appreciated that a morphology is a type of class of object, and this reference teaches classifying input images based on the class being known or unknown. Therefore, this reference suggests that known morphologies would be classified based on their known class data, and the remaining unknown classes would be provided to Veidman for further processing.).
Amma is considered analogous to the claimed invention because they are both solving the same problem of unknown image classification. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Veidman to include the teachings of Amma, to provide the advantage of improved classification accuracy between known and unknown classes (Amma Paragraph [0120] “As can be seen in the table shown in FIG. 7, by applying the learning method according to the present embodiment, it is possible to improve the classification accuracy.”)
In regards to claim 2, Veidman in view of Amma teaches the method of claim 1, and further teaches wherein the one or more foreground tiles are determined by thresholding based on pixel variance, thresholding based on minimizing intra-class intensity variance, thresholding based on maximizing inter- class intensity variance, and/or comparing foreground tile pixel values to a reference foreground distribution (Veidman Column 25, Line 60 “Optionally, an indication of an amount of the out-of-focus is computed. Different measures of the extent of the patches being out-of-focus may be used. When the indication of the amount of being out-of-focus is above an out-of-focus threshold, the process may be stopped such that the out-of-focus patches are not classified by the patch-level classifier and/or none of the patches of the tissue image are classified.” Examiner note: This reference teaches that during step 110, out of focus values for each patch (tile) is computed, and based on that, foreground tiles are identified.).
In regards to claim 4, Veidman in view of Amma teaches the method of claim 1, and further teaches wherein whether each foreground tile of the plurality of foreground tiles contains a known morphology or an unknown morphology is determined using an open-set classifier (Veidman Column 38, Line 64 “At 404, abnormal patches are detected. Alternatively or additionally, abnormal tissue objects within patches are detected. Such abnormal patches and/or tissue objects are indicative of tissue regions where the tissue type(s) cannot be computed, and/or where the probability indicative of the computed tissue type(s) is below a threshold. For example, a diagnosis cannot be made due to the abnormal patches.” Examiner note: Open-set classifier is defined as a machine learning model that can identify known classes and unknown classes. This reference shows that abnormal (unknown) tissue can be identified, and by necessity, normal (known) tissue is simultaneously identified.).
In regards to claim 6, Veidman in view of Amma teaches the method of claim 1, wherein providing the one or more foreground tiles to the clustering algorithm comprises: clustering a plurality of vectors associated with foreground tiles of unknown tissue morphology. (Veidman Column 38, Line 6 “An encoding (which may sometimes be substituted with the term embedding) is computed for the selected patch by a trained autoencoder code, optionally an autoencoder CNN. The encoding may be stored as a feature vector. One or more similar patches are identified from a dataset of patch encodings, according to a requirement of a similarity distance between the encoding of the selected patch and encoding of the patches in the dataset.” Examiner note: This reference shows that patches (tiles) have feature vectors extracted. Then those feature vectors are grouped based on similarity, which is analogous to clustering.)
In regards to claim 7, Veidman in view of Amma teaches the method of claim 6, and further teaches wherein the clustering algorithm may cluster the plurality of vectors using hand-engineered features, pre-trained convolutional neural network (CNN) embeddings using supervised learning, pre-trained CNN embeddings using self-supervised learning techniques, or pre-trained transformer neural network features (Veidman Column 38, Line 6 “An encoding (which may sometimes be substituted with the term embedding) is computed for the selected patch by a trained autoencoder code, optionally an autoencoder CNN. The encoding may be stored as a feature vector. One or more similar patches are identified from a dataset of patch encodings, according to a requirement of a similarity distance between the encoding of the selected patch and encoding of the patches in the dataset. The similarity distance may be computed, for example, by considering each encoding as a point in a multidimensional space and computing the distance between the points in the multidimensional space, a correlation function that computes a correlation value, and/or other methods.”).
In regards to claim 8, Veidman in view of Amma teaches the method of claim 1, and further teaches wherein predicting at least one outcome for the patient further comprises: receiving patient data associated with the unknown morphology cluster; and determining outcome data using the received patient data (Veidman Column 28, Line 35 “Exemplary methods for computing the slide-level tissue type(s) are now described. One or a combination of multiple of the following methods may be implemented. The slide-level tissue type(s) may be computed according to a received indication. The received indication may be obtained, for example, automatically detected by code, manually entered by a user (e.g., selected from a list of indications presented in the GUI), and/or extracted from the electronic medical record of the user. The received indication may include, for example, the organ from which tissue is extracted, fluid sample that includes the extracted tissue, clinical procedure used to extract the tissue, clinical indication performed to obtain the extracted tissue.” Examiner note: In this reference, the tissue type is identified based on what was operation was performed on the patient. This “received indication” can be interpreted as patient data, and the outcome data is determined based on that indication.).
In regards to claim 9, Veidman in view of Amma teaches the method of claim 1, and further teaches wherein the at least one outcome comprises at least one of a patient prognosis, a patient prognosis including years of survival, likelihood of response to medication, likelihood of recurrence, likelihood of metastasis, survival rate, effective medication type, effective treatment type, and a 5-year survival rate (Veidman Column 27, Line 52 “At 118, one or more slide-level tissue types are computed for the tissue image as a whole according to the analysis of the patches. The slide-level tissue type(s) may be computed according to the patch-level tissue type(s), optionally according to the relative location of the respective patches within the tissue image. The slide-level tissue type may be, for example, an overall diagnosis made for the tissue slide as a whole. For example, malignancy, a benign tumor, a certain disease, or normal tissue.”).
In regards to claim 11, Veidman in view of Amma teaches The method of claim 1, further comprising visualizing the one or more foreground tiles assigned to clusters to be analyzed by a medical professional (Veidman Column 33, Line 50 “At 122, the GUI is updated for presentation of the computed slide-level tissue type(s) and/or multi-slide level tissue type(s). The GUI is designed to present tissue images and/or patches of the tissue image and/or the registered slide(s) and/or the 3D tissue image. The GUI presents computed results described herein, for example, segmentation of tissue objects, classification results of tissue type(s) per patch and/or slide-level tissue type(s). The GUI may present a generated report, which may be based on a pathology template.” ).
In regards to claim 13, Veidman in view of Amma teaches a system for identifying morphologies present in digital medical images, the system comprising: at least one memory storing instructions; and at least one processor configured to execute the instructions to perform operations (Veidman Column 1, Line 46 “According to a second aspect, a system for computing at least one slide-level tissue type for a tissue image of tissue extracted from a patient, comprises: a non-transitory memory having stored thereon a code for execution by at least one hardware processor”), Veidman in view of Amma renders obvious the remaining claim limitations as in the consideration of claim 1 above.
In regards to claim 14, Veidman in view of Amma renders obvious the claim limitations as in the consideration of claims 2 and 13 above.
In regards to claim 16, Veidman in view of Amma renders obvious the claim limitations as in the consideration of claims 6 and 13 above.
In regards to claim 17, Veidman in view of Amma teaches a non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations for identifying morphologies present in digital medical images (Veidman Column 1, Line 66 “According to a third aspect, a computer program product for computing at least one slide-level tissue type for a tissue image of tissue extracted from a patient, comprises: a non-transitory memory having stored thereon a code for execution by at least one hardware processor”), and renders obvious the remaining claim limitations as in the consideration of claim 1 above.
In regards to claim 18, Veidman in view of Amma renders obvious the claim limitations as in the consideration of claims 2 and 17 above.
In regards to claim 20, Veidman in view of Amma renders obvious the claim limitations as in the consideration of claims 6 and 17 above.
In regards to claim 21, Veidman in view of Amma teaches the method of claim 1, wherein determining whether each foreground tile of the plurality of foreground tiles contains a known morphology or an unknown morphology comprises: determining an abnormality score for each foreground tile (Amma Paragraph [0091] “The inventors of the present disclosure have obtained the idea of employing a method of metric learning for constructing the feature space with the first mechanism. The typical loss functions for metric learning (e.g., triplet loss and contrastive loss) encourages samples with the same class to move closer to each other, and samples with different classes to be further away in the feature space. Thus, it can be expected to construct the feature space that is easily separable by class.” Examiner note: Amma teaches that when clustering samples in the feature space, the samples which share a class are closer to each other than samples which do not share a class. Therefore, the distance from a cluster center to a sample could be considered an abnormality score, as it describes how well a sample is represented by a certain cluster center); and determining whether the abnormality score exceeds a threshold corresponding to a rare morphology (Amma Figures 5-6; Veidman Column 40 Lines 18-23 “Alternatively or additionally, one or more clusters located furthest away (e.g., and/or above an abnormality threshold) from the other clusters (e.g., according to cluster centers) are identified as being abnormal. The patches of the abnormal cluster(s) are identified as being abnormal.” Examiner note: When a sample is within a threshold distance of a cluster center (represented by dashed lines in figures 5 and 6), it is considered part of that cluster class. Therefore, when the distance (analogous to abnormality score) to a cluster center is greater than the threshold distance, it is considered unknown or abnormal. Thus, when considering Veidman in view of Amma, the patches distance from the center of a cluster would be analogous to the abnormality score, and the threshold distance is analogous to the threshold corresponding to a rare patches morphology).
Claims 3, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Veidman in view of Amma, and further in view of Mahoor (US20170053398).
In regards to claim 3, Veidman in view of Amma teaches determining a plurality of foreground tiles within the one or more digital whole slide images. Veidman in view of Amma does not teach normalizing the digital whole slide images for magnification levels.
However, Mahoor teaches normalizing the digital whole slide images for magnification levels (Mahoor Paragraph [0043] “Each image of the set may correspond to a particular type of tissue, such as prostate or breast tissue. At block 310, the images may also be normalized to account for hardware, magnification, and staining variations as detailed in relation to system 100.”).
Mahoor is considered to be analogous to the claimed invention because they are in the same field of identifying diseases based on tissue sample imaging. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Veidman in view of Amma to include the teachings of Mahoor, to provide the advantage of accounting for any differences between the input images (Mahoor Paragraph [0043] “At block 310, the images may also be normalized to account for hardware, magnification, and staining variations as detailed in relation to system 100.”).
In regards to claim 15, Veidman in view of Amma and Mahoor renders obvious the claim limitations as in the consideration of claim 3 and 13 above.
In regards to claim 19, Veidman in view of Amma and Mahoor renders obvious the claim limitations as in the consideration of claim 3 and 17 above.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Veidman in view of Amma, and further in view of Madabhushi (US20190251687)
In regards to claim 5, Veidman in view of Amma teaches the method of claim 1, including a clustering algorithm. Veidman in view of Amma does not teach wherein the clustering algorithm uses a Mixture Model, a K-Means Model, agglomerative clustering, and/or an Expectation- Maximization Algorithm approach.
However, Madabhushi teaches wherein the clustering algorithm uses a Mixture Model, a K-Means Model, agglomerative clustering, and/or an Expectation- Maximization Algorithm approach (Madabhushi Paragraph [0034] “The set of operations 200 also includes, at 220, clustering members of the plurality of pixels in red-green-blue (RGB) space. In one embodiment, members of the plurality of pixels are clustered in RGB space using k-means clustering, where k=4. In another embodiment, other values of k may be employed, or other color spaces may be employed.”).
Madabhushi is considered to be analogous to the claimed invention because they are in the same field of identifying diseases based on tissue sample imaging. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Veidman in view of Amma to include the teachings of Madabhushi, to provide the advantage of having a system that outputs predictable results for patients (Madabhushi Paragraph [0012] “The pathologist's diagnosis is based on a subjective examination, and has limited accuracy and reproducibility due to the limits of human perception, intra-observer variability, and intra-observer variability. Thus there is an unmet need for an objective, reproducible, and accurate approach for predicting BCR in PCa patients.”).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Veidman in view of Amma, and further in view of Rim (US20190221313)
In regards to claim 10, Veidman in view of Amma teaches the method of claim 1, including predicting at least one outcome. Veidman in view of Amma does not teach wherein the at least one outcome is predicted using a binary model based on presence of unknown tiles.
However, Rim teaches wherein the at least one outcome is predicted using a binary model based on presence of unknown tiles (Rim Paragraph [0167] “For example, a neural network model may be a binary classification model that classifies input data as a normal or abnormal class in relation to target diagnosis assistance information such as a specific disease or abnormal symptoms.”).
Rim is considered to be analogous to the claimed invention because they are in the same field of diagnosing diseases based on sample imaging. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Veidman in view of Amma to include the teachings of Rim, to provide the advantage of having an accurate and quick processing solution (Rim Paragraph [0005] “However, in a case in which a plurality of values are desired to be predicted from a single test data through a deep learning trained model, there has been a problem in that accuracy of prediction is reduced and processing speed is lowered. Accordingly, there is a need for a system for learning and diagnosis that enables accurate prediction of a plurality of diagnostic characteristics at a high data processing speed.”).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
“Prognostic stratification of glioblastoma patients by unsupervised clustering of morphology patterns on whole slide images furthering our disease understanding” teaches a method of performing clustering on patches of whole slide images, and outputting a survival prediction for the patient.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CALEB LOGAN ESQUINO whose telephone number is (703)756-1462. The examiner can normally be reached M-Fr 8:00AM-4:00PM EST.
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/CALEB L ESQUINO/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
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