DETAILED ACTION
Status of Claims
This action is in reply to the amendment filed on 11/12/2025.
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 .
Claims 1, 5 and 24 have been amended.
Claims 3, 6-10, 13-14, 16, 18-23, 26-29, 31-39, 42 and 44-46 have been cancelled.
Claims 51-52 have been newly added.
Claims 1-2, 4-7, 11-12, 15, 17, 24-25, 30, 40-41, 43 and 47-52 are currently pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/12/2025 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 4-7, 11-12, 15, 17, 24-25, 30, 40-41, 43 and 47-52 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-2, 4-7, 11-12 and 14-17 are directed to a method (i.e., a process) and claims 24-25, 30, 40-41, 43 and 49-52 are directed to a system (i.e., a machine). Accordingly, claims 1-2, 4-7, 11-12, 15, 17, 24-25, 30, 40-41, 43 and 47-52 are all within at least one of the four statutory categories.
Step 2A - Prong One:
An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Representative independent claim 24 includes limitations that recite an abstract idea. Note that independent claim 24 is the system claim and claim 1 covers a method claim.
Specifically, independent claim 24 recites:
A system for generating a medical concordance score from a test subject medical data set using an electronic neural network, the system comprising:
a processor; and
a memory communicatively coupled to the processor, the memory storing instructions which, when executed on the processor, perform operations comprising:
passing a test subject medical data set through an electronic neural network, wherein the electronic neural network has been trained on a set of training data that comprises a plurality of reference subject medical data sets that are each labeled with a medical determination and are each assigned a ground truth concordance score generated by a plurality of experts, wherein a value of a given ground truth concordance score comprises a fraction of the plurality of experts, if any, that are in accord with the medical determination label of a given reference subject medical data set in the plurality of reference subject medical data sets, wherein the medical determination comprises an atypical status, a benign status, or a malignant status, wherein the test and reference subject medical data sets comprise images of histopathology slides, and wherein a final layer of the electronic neural network performs linear regression instead of a classification;
outputting from the electronic neural network a concordance score of the medical determination being indicated by the test subject medical data set; and
ordering one or more medical tests for, recommending administering one or more therapies to, and/or recommending discontinuing administering one or more therapies to, the test subject when the concordance score of the medical determination being indicated by the test subject medical data set varies from a predetermined threshold value.
The Examiner submits that the foregoing underlined limitations constitute: (a) “certain methods of organizing human activity” because determining a medical condition of a test subject by a group of experts, when the experts are in an concordance, determining an atypical status, benign status, or malignant status, recommending administering therapies and having agreement or consensus are providing healthcare services and diagnosing, which is managing human behavior/interactions between people. Furthermore, these limitations constitute (b) “a mathematical concept” because generating a medical concordance score, assigning and outputting a concordance score of the medical determination based on a predetermined threshold value and the test subject medical data set are done metathetically. The foregoing underlined limitations also relate to claim 1 (similarly to claim 24).
Accordingly, the claim describes at least one abstract idea.
In relation to claims 2, 5-7, 11-12, 15, 25, 30, 41, 43 and 47-50, these claims merely recite specific kinds of data, such as: claims 2 and 25 - the plurality of experts comprises human experts, machine experts, or a combination of human and machine experts, claim 5 - the medical determination comprises a diagnosis of a disease, condition, or disorder, claim 6 - the medical determination comprises a prognosis of a disease, condition, or disorder, claim 7 - the medical determination comprises a recommended treatment plan for a diagnosed disease, condition, or disorder, claim 11 – the medical determination comprises a survival quantification, claim 12 - the medical determination comprises a therapy response, claim 15 - the one or more medical tests comprise at least one histological stain of a sample obtained from the test subject, claim 30 - the medical determination comprises a recommended treatment plan for a diagnosed disease, condition, or disorder, claim 41 - the test and reference subject medical data sets comprise images of histopathology slides, claim 43 - the test and reference subject medical data sets comprise images selected from the group consisting of: a magnetic resonance (MR) image, a computed tomography (CT) image, a single photon emission computed tomography (SPECT) image, a positron emission tomography (PET) image, and a microscopy image, claim 47 and 49 -the medical determination comprises an in situ melanoma status or an invasive melanoma status and claims 48 and 50 - the images comprise whole slide images (WSJ).
In relation to claims 4, 17, 40 and 51-52, these claims merely recite determining steps such as: claim 4 - labeling the test subject medical data set with the medical determination prior to or when passing the test subject medical data set through the electronic neural network, claims 17 and 40 - generating or updating at least a portion of a medical report for the test subject when the concordance score of the medical determination being indicated by the test subject medical data set varies from a predetermined threshold value, claim 51 -the electronic neural network has been trained using a multiple instance learning (MIL) model and claim 52 - size of the images of histopathology slides exceeds a storage capacity of memory operably connected to a system that comprises the electronic neural network.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claims 1 and 24, as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitations by humans using math but for the recitation of generic computer components. That is, other than reciting a system, a processor, and a memory communicatively coupled to the processor, the memory storing instructions to perform the limitations, nothing in the claim elements precludes the steps from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation within a health care environment done by humans using math but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” and a “Mathematical Concept” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the system, processor, and memory communicatively coupled to the processor, the memory storing instructions are recited at high levels of generality (i.e., as generic computer components performing generic computer functions of receiving data/inputs, determining and providing data) such that it amounts no more than mere instructions to apply the exception using the generic computer components.
Regarding the additional limitations “using an electronic neural network”, “the electronic neural network has been trained on a set of training data”, and “machine experts” the Examiner submits that this additional limitation amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)).
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvements in the functioning of a computer or an improvement to another technology or technical field, apply or us the above-noted implement/use to above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see MPEP §2106.05). Their collective functions merely provide conventional computer implementation.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, in representative independent claim 24, regarding the additional limitations of the system, processor, and memory communicatively coupled to the processor, the memory storing instructions, the Examiner submits that these limitations amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)).
Thus, representative independent claim 24 and analogous independent claim 1 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
The dependent claims no not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reason discussed above with respect to determining that the dependent claims do not integrate the at least abstract idea into a practical application.
Therefore, claims 1-2, 4-7, 11-12, 15, 17, 24-25, 30, 40-41, 43 and 47-52 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 4-5, 12, 24-25 and 30 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cohen (US 2021/0256323 A1).
Claim 1:
Cohen discloses a method of ordering medical tests for, administering therapy to, and/or discontinuing administering therapy to, a test subject (See [P0043] The machine learning system yields a numerical risk score for each patient tested, which can be used by physicians to make treatment decisions concerning the therapy of cancer patients or, importantly, to further inform screening procedures to better predict and diagnose early stage cancer in patients. Also, see P0174, P0256-P0257 and P0391.), the method comprising:
passing a test subject medical data set through an electronic neural network, wherein the electronic neural network has been trained on a set of training data that comprises a plurality of reference subject medical data sets that are each labeled with a medical determination (See Fig. 1A-2B, neural networks mentioned in P0274-P0276, exemplary biomarker (P0173-P0174, P0176) and other detectable labels (P0179-P0180) serve as labelling with a medical determination. Also, see training and validation set mentioned in P0150-P0151 and P0241-P0242.) and are each assigned a ground truth concordance score generated by a plurality of experts, wherein a value of a given ground truth concordance score comprises a fraction of the plurality of experts (See composite score and risk score mentioned in P0014, P0032 shown in Fig. 10.), if any, that are in accord with the medical determination label of a given reference subject medical data set in the plurality of reference subject medical data sets, wherein the medical determination comprises an atypical status, a benign status, or a malignant status, wherein the test (See [P0014] classify the risk score into risk categories for advising the clinician the likelihood that the nodule is or is not malignant, wherein the risk categories are derived from a same cohort population as the patient and wherein each risk category is associated with a benign or malignant grouping, to determine a likelihood of the patient having benign nodules or malignant nodules.) and reference subject medical data sets comprise images of histopathology slides, and wherein a final layer of the electronic neural network performs linear regression instead of a classification (See raw images to imaging studies as unstructured data (P0069-P0070, P0072), AI image processing data (P0227, P0235) when training data sets in [P0241-P0242] the neural net can be trained to learn to identify specific medical data (e.g., images, unstructured, structured, etc.). Neural net NN3 may classify the data into different data types, e.g., raw images, numeric/structured data, BM velocity, unstructured data, etc. Also, see using a multivariate logistic regression (MLR) model (P0078, P0141) and normalization data include linear transformation (P0114, P0208).);
outputting from the electronic neural network a concordance score of the medical determination being indicated by the test subject medical data (See P0289-P0290 where exemplary neural net NN1 is necessary to compute a risk score shown in Fig. 3.); and
ordering one or more medical tests for, administering one or more therapies to, and/or discontinuing administering one or more therapies to, the test subject when the concordance score of the medical determination being indicated by the test subject medical data set varies from a predetermined threshold value (See P0043, P0226-P0228 where machine learning and the AI perform reasoning and analysis in order to make decisions, automatically advising the patient and the provider to be tested more frequently or to take other actions, predicted with risk score in P0230-P0231.).
Claim 24:
Cohen discloses a system for ordering medical tests for, recommending administering therapy to, and/or recommending discontinuing administering therapy to, a test subject using an electronic neural network (Fig. 1A-2B, neural networks, [P0043] The machine learning system yields a numerical risk score for each patient tested, which can be used by physicians to make treatment decisions concerning the therapy of cancer patients or, importantly, to further inform screening procedures to better predict and diagnose early stage cancer in patients. Also, see P0174, P0256-P0257 and P0391.), the system comprising:
a processor (See processor in P0194, P0220-P0221.); and
a memory communicatively coupled to the processor, the memory storing instructions which, when executed on the processor (See coupled memory in P0155, P0221-P0222.) perform operations comprising:
passing a test subject medical data set through an electronic neural network, wherein the electronic neural network has been trained on a set of training data that comprises a plurality of reference subject medical data sets that are each labeled with a medical determination (See Fig. 1A-2B, neural networks mentioned in P0274-P0276, exemplary biomarker (P0173-P0174, P0176) and other detectable labels (P0179-P0180) serve as labelling with a medical determination. Also, see training and validation set mentioned in P0150-P0151 and P0241-P0242.) and are each assigned a ground truth concordance score generated by a plurality of experts, wherein a value of a given ground truth concordance score comprises a fraction of the plurality of experts (See composite score and risk score mentioned in P0014, P0032 shown in Fig. 10.), if any, that are in accord with the medical determination label of a given reference subject medical data set in the plurality of reference subject medical data sets, wherein the medical determination comprises an atypical status, a benign status, or a malignant status, wherein the test (See [P0014] classify the risk score into risk categories for advising the clinician the likelihood that the nodule is or is not malignant, wherein the risk categories are derived from a same cohort population as the patient and wherein each risk category is associated with a benign or malignant grouping, to determine a likelihood of the patient having benign nodules or malignant nodules.) and reference subject medical data sets comprise images of histopathology slides, and wherein a final layer of the electronic neural network performs linear regression instead of a classification (See raw images to imaging studies as unstructured data (P0069-P0070, P0072), AI image processing data (P0227, P0235) when training data sets in [P0241-P0242] the neural net can be trained to learn to identify specific medical data (e.g., images, unstructured, structured, etc.). Neural net NN3 may classify the data into different data types, e.g., raw images, numeric/structured data, BM velocity, unstructured data, etc. Also, see using a multivariate logistic regression (MLR) model (P0078, P0141) and normalization data include linear transformation (P0114, P0208).);
outputting from the electronic neural network a concordance score of the medical determination being indicated by the test subject medical data set (See P0289-P0290 where exemplary neural net NN1 is necessary to compute a risk score shown in Fig. 3.); and
ordering one or more medical tests for, recommending administering one or more therapies to, and/or recommending discontinuing administering one or more therapies to, the test subject when the concordance score of the medical determination being indicated by the test subject medical data set varies from a predetermined threshold value (See P0043, P0226-P0228 where machine learning and the AI perform reasoning and analysis in order to make decisions, automatically advising the patient and the provider to be tested more frequently or to take other actions, predicted with risk score in P0230-P0231.).
Regarding claims 2 and 25, Cohen discloses wherein the plurality of experts comprises human experts, machine experts, or a combination of human and machine experts (See P0007 P0043 and P0226-P0228 assisting human experts, machine learning and the AI perform reasoning and analysis in order to make decisions.).
Regarding claim 4, Cohen discloses the method of claim 1, comprising labeling the test subject medical data set with the medical determination prior to or when passing the test subject medical data set through the electronic neural network (See P0130, P0179-P0181 label giving context when screening. Also, see [P0268-P0269] NN12 is trained to produce the following outputs, as shown at block 180, including patient risk scores (e.g., an individual patient's % risk in a given cohort, margin of error, size of cohort, labels of cohort, etc.).).
Regarding claims 5 and 30, Cohen discloses the method of claim 1 and the system of claim 24, wherein the medical determination comprises a diagnosis or a prognosis of, or a recommended treatment plan for, a disease, condition, or disorder (See cancer diagnosis, recommended diagnosis (DX), treatment success factor, success rates and outcomes mentioned in P0267, P0269-P0270.).
Regarding claim 12, Cohen discloses the method of claim 1, wherein the medical determination comprises a therapy response (Besides treatment success factors in P0269-P0270, see known monitoring response to therapy in P0011.).
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.
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.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Cohen (US 2021/0256323 A1) in view of McClelland (US 2011/0236903 A1).
Regarding claim 11, although Cohen discloses the method of claim 1 mentioned above, Cohen does not explicitly teach determining a survival quantification. McClelland teaches wherein the medical determination comprises a survival quantification (See post-diagnosis survival of the subject, length of survival, recognizing aggressiveness in P0086-P0087, prostate associated genes (P0206) and disease-free survival time (P0309) as quantifying data.).
Therefore, it would have been obvious to one of ordinary skill in the art of diagnosing and prognosis of prostate cancer before the effective filing date of the claimed invention to modify the method of Cohen to include determining a survival quantification as taught by McClelland to help identify patients having prostate cancer by analyzing gene cell expression levels mentioned on McClelland’s P0009.
Claims 15, 41 and 43 are rejected under 35 U.S.C. 103 as being unpatentable over Cohen (US 2021/0256323 A1) in view of Hartman (WO 2023/052367 A1).
Regarding claim 15, although Cohen discloses the method of claim 1 mentioned above, Cohen does not explicitly teach when a histological stain of a sample is obtained from the test subject. Hartman teaches wherein the one or more medical tests comprise at least one histological stain of a sample obtained from the test subject (See histological stain of a sample on page 7, line 25 to page 8, line 5.).
Therefore, it would have been obvious to one of ordinary skill in the art of neural network training to predict cancer before the effective filing date of the claimed invention to modify the method of Cohen for when the histological stain of a sample is obtained from the test subject as taught by Hartman for classifying patient that belongs to the intermediate cancer progress risk group mentioned on Hartman’s page 2, lines 1-3.
Regarding claim 41, although Cohen discloses the system of claim 24 mentioned above, Cohen does not explicitly teach images of histopathology slides. Hartman teaches wherein the test and reference subject medical data sets comprise images of histopathology slides (See histological stain of a sample on page 7, line 25 to page 8, line 5.).
Therefore, it would have been obvious to one of ordinary skill in the art of neural network training to predict cancer before the effective filing date of the claimed invention to modify the method of Cohen to include images of histopathology slides as taught by Hartman for classifying patient that belongs to the intermediate cancer progress risk group mentioned on Hartman’s page 2, lines 1-3.
Regarding claim 43, although Cohen discloses the system of claim 24 mentioned above, Cohen does not explicitly teach a microscopy image. Hartman teaches wherein the test and reference subject medical data sets comprise images selected from the group consisting of: a magnetic resonance (MR) image, a computed tomography (CT) image, a single photon emission computed tomography (SPECT) image, a positron emission tomography (PET) image, and a microscopy image (See glass slides in microscope on page 7, line 25 to page 8, line 5, page 10, lines 4-14.).
Therefore, it would have been obvious to one of ordinary skill in the art of neural network training to predict cancer before the effective filing date of the claimed invention to modify the method of Cohen to include a microscopy image as taught by Hartman for classifying patient that belongs to the intermediate cancer progress risk group mentioned on Hartman’s page 2, lines 1-3.
Claims 17 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Cohen (US 2021/0256323 A1) in view of Giftakis (US 2020/0139129 A1).
Regarding claims 17 and 40, although Cohen discloses the method of claim 1 and system of claim 24 mentioned above, Cohen does not explicitly teach generating or updating at a medical report for the test subject when the concordance score of the medical determination being indicated by the test subject medical data set varies from a predetermined threshold value. Giftakis teaches further comprising generating or updating at least a portion of a medical report for the test subject when the concordance score of the medical determination being indicated by the test subject medical data set varies from a predetermined threshold value (See Fig. 17B where seizure severity score is reported as mentioned in P0219-P0220. Also, see Fig. 5 event summery page as reporting, Fig. 7, P0157-P0158 where the event score is greater than or equal to the threshold before adjusting therapy.).
Therefore, it would have been obvious to one of ordinary skill in the art of therapy adjustment before the effective filing date of the claimed invention to modify the method of Cohen to include generating or updating at a medical report for the test subject when the concordance score of the medical determination being indicated by the test subject medical data set varies from a predetermined threshold value as taught by Giftakis in order to find the most effective therapy parameters to treat a condition mentioned in Giftakis’ P0005.
Claims 47-50 are rejected under 35 U.S.C. 103 as being unpatentable over Cohen (US 2021/0256323 A1) in view of Aneja (US 2015/0346191 A1).
Regarding claims 47 and 49, although Cohen discloses the method of claim 1 and the system of claim 24 mentioned above, Cohen does not explicitly teach when the medical determination comprises an in situ melanoma status or an invasive melanoma status. Aneja teaches wherein the medical determination comprises an in situ melanoma status or an invasive melanoma status (See kinds of melanomas in P0043 and P0048.).
Therefore, it would have been obvious to one of ordinary skill in the art of prognosis and treatment of neoplasm before the effective filing date of the claimed invention to modify the system, software and method of Cohen when the medical determination comprises an in situ melanoma status or an invasive melanoma status as taught by Aneja for predicting clinical outcomes, selecting cancer therapies, and assessing a cancer patient's response to a cancer therapy mentioned in Aneja’s P0003.
Regarding claims 48 and 50, although Cohen discloses the method of claim 1 and the system of claim 24 mentioned above, Cohen does not explicitly teach whole slide images (WSJ). Aneja teaches wherein the images comprise whole slide images (WSJ) (See whole slide images P0062 and [P0107] Images could either be obtained from 10-15 microscopic fields of view for each sample or by whole-slide imaging as long as optical sections are acquired for 3D volume rendering. For slides stained immunofluorescently for centrosomes, imaging is carried out in areas determined to be “tumor areas” based on comparison with a serial section stained with hematoxylin eosin (wherein tumor areas are pre-marked).).
Therefore, it would have been obvious to one of ordinary skill in the art of prognosis and treatment of neoplasm before the effective filing date of the claimed invention to modify the system, software and method of Cohen the whole slide images (WSJ) as taught by Aneja for predicting clinical outcomes, selecting cancer therapies, and assessing a cancer patient's response to a cancer therapy mentioned in Aneja’s P0003.
Claims 51-52 are rejected under 35 U.S.C. 103 as being unpatentable over Cohen (US 2021/0256323 A1) in view of Park (US 2021/0334994 A1).
Regarding claim 51, although Cohen discloses the method of claim 1 mentioned above, Cohen does not explicitly teach training the electronic neural network using a multiple instance learning (MIL) model. Park teaches wherein the electronic neural network has been trained using a multiple instance learning (MIL) model (See Fig. 2 contrastive learning applied to a multiple instance learning setting mentioned in P0084-P0085. Also, see the multiple instance learning model shown in Fig. 9, mentioned in P0129-P0131.).
Therefore, it would have been obvious to one of ordinary skill in the art of diagnosing based on multiple instance learning (MIL) before the effective filing date of the claimed invention to modify the system, software and method of Cohen when training the electronic neural network using a multiple instance learning (MIL) model as taught by Park for histopathology classification capable of accurately predicting instance labels mentioned in Park’s P0002.
Regarding claim 52, although Cohen discloses the method of claim 1 mentioned above, Cohen does not explicitly teach histopathology slides exceed a storage capacity of memory. Park teaches wherein a size of the images of histopathology slides exceeds a storage capacity of memory operably connected to a system that comprises the electronic neural network (See [P0009-P0012] digitizing glass slides into histopathological images using a whole-slide image (WSI) scanner and a storage means for storing the digitized WSIs mentioned in P0145-P0148.).
Therefore, it would have been obvious to one of ordinary skill in the art of diagnosing based on multiple instance learning (MIL) before the effective filing date of the claimed invention to modify the system, software and method of Cohen when histopathology slides exceed a storage capacity of memory as taught by Park for extracting rich features from clinical datasets, and including a wide range of application areas such as organ segmentation and disease diagnosis mentioned in Park’s P0007.
Response to Arguments
Applicant argues that the claims do not merely recite an abstract idea (e.g., "analyzing information and making decisions"). Instead, they recite a specific technological solution to a technical problem in computer-assisted medical diagnostics. See pgs. 8-9 of Remarks – Examiner disagrees.
The use of a neural network specifically trained on a dataset with expert- assigned concordance scores, final linear regression layer, which departs from conventional classification layers, enabling continuous concordance scoring, rather than discrete classification, processing medical images of histopathology slides and automatically linking neural network outputs to clinical actions that the Applicant is talking about is merely using mathematics to achieve, and are not using technology to solve. For example, in Applicant’s paragraph 30 of the specification, generating a medical concordance score from a test subject medical data set that uses generic reference subject medical data sets labeled with a medical determination does not need a neural network, linear regression layer to achieve. Scoring is a basic data processing task that any human expert and/or generic computer would be expected to do. Furthermore, in the instant case, the system comprising a processor is used to order medical tests and pass test subject medical data set through an electronic neural network, which is mere data gathering and is not claiming any technology used for automatically diagnosing cancer determined as an atypical, benign, or malignant status.
The claimed invention in Enfish creates a specific type of “self-referential table” that provided an improvement to the way computers operate, thus providing a “practical application.” Here there is no specific type of data structure described. The claim is merely assigning concordance scores and linking neural network outputs to clinical actions that could be achieved with the generic computer manipulating observed data. For example, paragraph 30 of Applicant’s specification talks about a plurality of reference subject medical data sets that are each labeled with a medical determination and are each assigned a ground truth concordance score generated by a plurality of experts (e.g., human and/or machine experts), which can be used to understand labelling significant data to score. This is not an improvement to the computer within the meaning of Enfish and unlike to Rapid Litigation’s repeating cryopreservation of hepatocytes.
Contrary to Applicant’s argument with respect to DDR, the claims are not necessarily rooted in advanced computer technology. Rather, Applicant’s invention does not identify any problem particular to computer networks and/or the internet that the claims allegedly overcome. For example, it doesn’t appear the computer’s operations are more efficient or improved upon based on the claimed functions of training dataset with expert- assigned concordance scores, final linear regression layer, departed from conventional classification layers, enabling continuous concordance scoring, processing medical images of histopathology slides and automatically linking neural network outputs to clinical actions, having steps of engaging a medical professional in a computer system.
Applicant’s arguments have been fully considered, but are now moot in view of the new grounds of rejection. The Examiner has entered a new rejection under 35 USC § 102 & USC § 103 by applying new art and art already of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. (See Lapointe (US 2003/0004906 A1) & (US 6,678,669 B2).).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TERESA S WILLIAMS whose telephone number is (571)270-5509. The examiner can normally be reached Mon-Fri, 8:30 am -6:30 pm.
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/T.S.W./Examiner, Art Unit 3687 03/18/2026
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687