DETAILED ACTION
Status of Claims
This action is in reply to the amendment filed on 03/05/2026.
Claims 1 and 11 have been amended.
Claim 10 has been cancelled.
Claim 21 has been newly added.
Claims 1-9 and 11-21 are currently 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 .
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 1-9 and 11-21 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-9 and 21 are directed to a method (i.e., a process) and claims 11-20 are directed to a system (i.e., a machine). Accordingly, claims 1-9 and 11-21 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 11 includes limitations that recite an abstract idea. Note that independent claim 11 is the system claim, while claim 1 covers a method claim.
Specifically, independent claim 11 recites:
An apparatus comprising:
a processor and a non-transitory memory having stored therein instructions executable by the processor to cause the processor to:
receive an image of a plurality of cells of a biological sample;
identify, by executing a first algorithm using image recognition machine-learning logic on the image, one or more cells of the plurality of cells as comprising one or more attributes associated with a first condition;
extract individual images of the one or more identified cells;
determine, by executing a second algorithm using second machine-learning logic, diagnostic data comprising one or more identifiable parameters;
determine whether the one or more identifiable parameters are associated with the first condition; and
display the individual images and the diagnostic data;
wherein the image recognition machine-learning logic and the second machine-learning logic are trained based on one or more sample disease states associated with sample biological cells of a plurality of patients from multiple-point sources.
The Examiner submits that the foregoing underlined limitations constitute: (a) “certain methods of organizing human activity” because identifying cells or attributes associated with a condition, determining diagnostic data comprising identifiable parameters and whether the identifiable parameters are associated with the condition are forms of diagnosing and providing healthcare services, which relate to managing human behavior/interactions between people. Furthermore, these limitations constitute (b) “a mental process” because identifying cells or attributes associated with a condition, determining diagnostic data comprising identifiable parameters, whether the identifiable parameters are associated with the condition and identifying sample disease states associated with sample biological cells of a plurality of patients are observations/evaluations/analysis that can be performed in the human mind or with a pen and paper. The foregoing underlined limitations also relate to claim 11 (similarly to claim 1).
Accordingly, the claim describes at least one abstract idea.
In relation to claims 6-8, 18-19 and 21, these claims merely recite specific kinds of data, such as: claims 6 & 17 - the first condition is large cell lymphoma, and the diagnostic data is a size distribution of lymphocytes in the biological sample, claims 7 & 18 - the first condition is acute inflammation in peripheral blood, claims 8 & 19 - the first condition is adipocytes, and the diagnostic data is depth distribution of cells in the biological sample and claim 21 - attributes comprise abnormal cell, morphological cell, cell type, subcellular component, cell maturation level, parasite, disease state, or a combination.
In relation to claims 2-5, 9, 12-14 and 20, these claims merely recite determining steps such as: claims 2 & 12 - displaying one or more reference images of cells not having the first condition, claims 3 & 13 - displaying reference diagnostic data associated with cells not having the first condition, claims 4 & 14 - displaying a mosaic image of the one or more identified cells, claims 5 & 16 - displaying one or more cutoff ranges of the diagnostic data, and claim s 9 & 20 – determining a first confidence level associated with the one or more attributes, determining a second confidence level associated with the one or more identifiable parameters, determining whether the first confidence level is greater than the second confidence level, in response to determining that the first confidence level is greater than the second confidence level, updating the second machine-learning logic and in response to determining that the second confidence level is greater than the first confidence level, updating the image recognition machine-learning logic.
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 11, as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the human mind but for the recitation of generic computer components. That is, other than reciting an apparatus, a processor executing a first algorithm and a non-transitory memory having stored therein instructions executable by the processor to perform the limitations, nothing in the claim elements precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation within a health care environment in the human mind but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” and “Mental Process” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the apparatus, processor executing the first algorithm and the non-transitory memory having stored therein instructions executable by the processor 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 “executing a first algorithm using image recognition machine-learning logic…” and “executing a second algorithm using second machine-learning logic” 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)). Regarding the additional limitation “receive an image of a plurality of cells of a biological sample” the Examiner submits that this additional limitation merely adds insignificant pre-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea (see MPEP § 2106.05(g)).
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 11, regarding the additional limitations of the apparatus, processor executing the first algorithm and the non-transitory memory having stored therein instructions executable by the processor, 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 11 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 1-9 and 11-21 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 1-3, 6, 11-13 and 16-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sanchez-Martin (US 2020/0152326 A1).
Claim 1:
Sanchez-Martin discloses A method comprising:
receiving an image of a plurality of cells of a biological sample (See Fig. 6, item 610 in P0059 obtaining images of blood sample cells.);
identifying, by a processor executing a first algorithm using image recognition machine- learning logic on the image, one or more cells of the plurality of cells as comprising one or more attributes associated with a first condition (See Fig. 1, P0025 processor, P0033 Biological sample data analytics module 76 may obtain results from the machine learning module 70, Fig. 6, items 620 & 630 in P0059 processing and analyzing the obtained images of blood sample cells. Also, see Fig. 7A-7D images of white and red blood cells, [P0060] FIG. 7B shows a representative image of a neutrophil and a monocyte in a field of red blood cells. The monocyte nucleus is more diffuse that the neutrophil nucleus, and has a characteristic stain that is more diffuse than the neutrophil nucleus. FIG. 7C and FIG. 7D show additional examples of representative training images of neutrophils and lymphocytes for the blood pathology system. In FIG. 7D, a representative image of a basophil is provided.);
extracting individual images of the one or more identified cells (See [P0032-P0033] Extracted information may be stored as extracted data 48 and provided to diagnosis module 78 to provide a diagnosis of a disease. Also, see Fig. 2, P0039-P0042.);
determining, by the processor executing a second algorithm using second machine-learning logic, diagnostic data comprising one or more identifiable parameters associated with the plurality of cells (See P0032-P0033, P0054, P0059 and Fig. 6, item 620 machine learning system to classify cells into categories & item 640 determining a diagnosis based on the classification and characteristics of the cells to determine a diagnosis for the patient.);
determining whether the one or more identifiable parameters are associated with the first condition (See P0059 where the classification (e.g., cell types) and characteristics (e.g., cell counts, expression level of markers, etc.) serv as parameters.); and
displaying the individual images and the diagnostic data (See Fig. 7A-7D the images of white and red blood cells mentioned in P0059-P0062 where the presence and amount of various cell types may be associated with a disease and/or categories of diseases and generating a diagnosis for a patient.);
wherein the image recognition machine-learning logic and the second machine-learning logic are trained based on one or more sample disease states associated with sample biological cells of a plurality of patients from multiple-point sources (See disease profiles, a list of candidate diagnoses, a list of potential misdiagnoses, as well as recommended follow-up testing serve as sample biological cells of a plurality of patients from multiple-point sources mentioned in [P0056-P0058] information from analysis of the biological sample may be combined with other parameters (e.g., such as age, ethnicity, etc.) to provide candidate diagnoses and/or suggestions of additional diagnostic tests that differentiate between the candidate diagnoses, and the diagnosis module 78 may evaluate different types of information, such as cell counts, cell types, presence of markers, morphological properties, etc. when determining a potential diagnosis. If the information provided by the ML module 490 and biological sample data analytics output 495 is definitively associated with a diagnosis, then the system will generate a diagnosis for the patient. See [P0073] The system may employ any number of any conventional or other databases, data stores or storage structures (e.g., files, databases, data structures, data or other repositories, etc.) to store information (e.g., acquired images 64, image training data 42, scientific/clinical literature 46, extracted data 48, image classification from the machine learning module 70, cell counts and other properties from the biological sample data analytics module 76, reports from the diagnosis module, etc.). Also, see P0028, 45, 47 and P0053 using optical recognition.).
Regarding claim 2, Sanchez-Martin discloses the method of claim 1, further comprising displaying one or more reference images of cells not having the first condition (See Fig. 7A-7D, P0060 where the contrasting neutrophil with a lobed, condensed nucleus that takes up less than about half of the cell image serves as cells not having a condition.).
Regarding claim 3, Sanchez-Martin discloses the method of claim 1, further comprising displaying reference diagnostic data associated with cells not having the first condition (See Fig. 7A-7D, P0060 where the contrasting neutrophil with a lobed, condensed nucleus that takes up less than about half of the cell image serves as cells not having a condition.).
Regarding 6, Sanchez-Martin discloses the method of claim 1, wherein the first condition is large cell lymphoma, and the diagnostic data is a size distribution of lymphocytes in the biological sample (See Fig. 7A-7D P0048, P0060 size and morphological appearance of lymphocytes.).
Claim 11:
Sanchez-Martin discloses An apparatus comprising:
a processor and a non-transitory memory having stored therein instructions executable by the processor to cause the processor (See P0080-P0081 a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.) to:
receive an image of a plurality of cells of a biological sample (See Fig. 6, item 610 in P0059 obtaining images of blood sample cells.);
identify, by executing a first algorithm using image recognition machine-learning logic on the image, one or more cells of the plurality of cells as comprising one or more attributes associated with a first condition (See Fig. 1, P0025 processor, P0033 Biological sample data analytics module 76 may obtain results from the machine learning module 70, Fig. 6, items 620 & 630 in P0059 processing and analyzing the obtained images of blood sample cells. Also, see Fig. 7A-7D images of white and red blood cells, [P0060] FIG. 7B shows a representative image of a neutrophil and a monocyte in a field of red blood cells. The monocyte nucleus is more diffuse that the neutrophil nucleus, and has a characteristic stain that is more diffuse than the neutrophil nucleus. FIG. 7C and FIG. 7D show additional examples of representative training images of neutrophils and lymphocytes for the blood pathology system. In FIG. 7D, a representative image of a basophil is provided.);
extract individual images of the one or more identified cells (See [P0032-P0033] Extracted information may be stored as extracted data 48 and provided to diagnosis module 78 to provide a diagnosis of a disease.);
determine, by executing a second algorithm using second machine-learning logic, diagnostic data comprising one or more identifiable parameters (See P0032-P0033, P0054, P0059 and Fig. 6, item 620 machine learning system to classify cells into categories & item 640 determining a diagnosis based on the classification and characteristics of the cells to determine a diagnosis for the patient.);
determine whether the one or more identifiable parameters are associated with the first condition (See P0059 where the classification (e.g., cell types) and characteristics (e.g., cell counts, expression level of markers, etc.) serv as parameters.); and
display the individual images and the diagnostic data (See Fig. 7A-7D the images of white and red blood cells mentioned in P0059-P0062 where the presence and amount of various cell types may be associated with a disease and/or categories of diseases and generating a diagnosis for a patient.);
wherein the image recognition machine-learning logic and the second machine-learning logic are trained based on one or more sample disease states associated with sample biological cells of a plurality of patients from multiple-point sources (See disease profiles, a list of candidate diagnoses, a list of potential misdiagnoses, as well as recommended follow-up testing serve as sample biological cells of a plurality of patients from multiple-point sources mentioned in [P0056-P0058] information from analysis of the biological sample may be combined with other parameters (e.g., such as age, ethnicity, etc.) to provide candidate diagnoses and/or suggestions of additional diagnostic tests that differentiate between the candidate diagnoses, and the diagnosis module 78 may evaluate different types of information, such as cell counts, cell types, presence of markers, morphological properties, etc. when determining a potential diagnosis. If the information provided by the ML module 490 and biological sample data analytics output 495 is definitively associated with a diagnosis, then the system will generate a diagnosis for the patient. See [P0073] The system may employ any number of any conventional or other databases, data stores or storage structures (e.g., files, databases, data structures, data or other repositories, etc.) to store information (e.g., acquired images 64, image training data 42, scientific/clinical literature 46, extracted data 48, image classification from the machine learning module 70, cell counts and other properties from the biological sample data analytics module 76, reports from the diagnosis module, etc.). Also, see P0028, 45, 47 and P0053 using optical recognition.).
Regarding claim 12, Sanchez-Martin discloses the apparatus of claim 12, wherein the instructions, when executed, further cause the processor to display one or more reference images of cells not having the first condition (See Fig. 7A-7D, P0060 where the contrasting neutrophil with a lobed, condensed nucleus that takes up less than about half of the cell image serves as cells not having a condition.).
Regarding claim 13, Sanchez-Martin discloses the apparatus of claim 12, wherein the instructions, when executed, further cause the processor to display reference diagnostic data associated with cells not having the first condition (See Fig. 7A-7D, P0060 where the contrasting neutrophil with a lobed, condensed nucleus that takes up less than about half of the cell image serves as cells not having a condition.).
Regarding claim 16, Sanchez-Martin discloses the apparatus of claim 11, wherein the instructions, when executed, further cause the processor to display one or more cutoff ranges of the diagnostic data (See P0038 cell morphology with normal range estimates and P0053 normal range of cellular markers.).
Regarding 17, Sanchez-Martin discloses the apparatus of claim 11, wherein the first condition is large cell lymphoma, and the diagnostic data is a size distribution of lymphocytes in the biological sample (See Fig. 7A-7D P0048, P0060 size and morphological appearance of lymphocytes.).
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.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Sanchez-Martin (US 2020/0152326 A1) in view of Yang (US 2023/0087210 A1).
Regarding claim 4, although Sanchez-Martin discloses the method of claim 1 mentioned above, Sanchez-Martin does not explicitly teach displaying a mosaic image of the identified cells. Yang teaches
further comprising displaying a mosaic image of the one or more identified cells (See Fig. 16-18, P0082 where the selection section 1625 serves as a mosaic image of identified cells.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical image atlas management before the effective filing date of the claimed invention to modify the method and system of Sanchez-Martin to include displaying a mosaic image of the identified cells as taught by Yang to allow a user to perform clinical analysis without distraction based on irrelevant discrepancies between the images mentioned in Yang’s P0002, P00014.
Regarding claim 14, although Sanchez-Martin discloses the apparatus of claim 11mentioned above, Sanchez-Martin does not explicitly teach displaying a mosaic image of the identified cells. Yang teaches wherein the instructions, when executed, further cause the processor to display a mosaic image of the one or more identified cells (See Fig. 16-18, P0082 where the selection section 1625 serves as a mosaic image of identified cells.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical image atlas management before the effective filing date of the claimed invention to modify the method and system of Sanchez-Martin to include displaying a mosaic image of the identified cells as taught by Yang to allow a user to perform clinical analysis without distraction based on irrelevant discrepancies between the images mentioned in Yang’s P0002, P00014.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Sanchez-Martin (US 2020/0152326 A1) in view of Ki (US 12,163,194 B2).
Regarding claim 5, although Sanchez-Martin discloses the method of claim 1 mentioned above, Sanchez-Martin does not explicitly teach to include displaying cutoff ranges of the diagnostic data. Ki teaches further comprising displaying one or more cutoff ranges of the diagnostic data (See [column 5, lines 4-18] generating vectorized data with the chromosomal bin on the X-axis and the distance between nucleic acid fragments or amount thereof on the Y-axis, training a deep-learning model for it to calculate a DPI, comparing the DPI with a cut-off value to determine as to whether or not cancer develops, and determining a type of cancer showing the highest DPI among the calculated DPIs.).
Therefore, it would have been obvious to one of ordinary skill in the art of using AI to predict cancer before the effective filing date of the claimed invention to modify the method and system of Sanchez-Martin to include displaying cutoff ranges of the diagnostic data as taught by Ki to predict types of cancer as mentioned in Ki’s column 4, lines 15-22.
Regarding claim 15, although Sanchez-Martin discloses the apparatus of claim 11 mentioned above, Sanchez-Martin does not explicitly teach line plotting diagnostic data as identifiable parameters. Ki teaches wherein the diagnostic data comprises a line plot of the one or more identifiable parameters (See line of plotting accuracy for predicted cancer in Fig. 5, column 25, lines 2-47.).
Therefore, it would have been obvious to one of ordinary skill in the art of using AI to predict cancer before the effective filing date of the claimed invention to modify the method and system of Sanchez-Martin to include line plotting diagnostic data as identifiable parameters as taught by Ki to predict types of cancer as mentioned in Ki’s column 4, lines 15-22.
Claims 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Sanchez-Martin (US 2020/0152326 A1) in view of Berliner (US 2002/0001402 A1).
Regarding claim 7, although Sanchez-Martin discloses the method of claim 1 mentioned above, Sanchez-Martin does not explicitly teach when the condition is acute inflammation in peripheral blood, and the diagnostic data is left shift concentration in the biological sample. Berliner teaches
wherein the first condition is acute inflammation in peripheral blood, and the diagnostic data is left shift concentration in the biological sample (Established in P0007 as diagnose inflammatory response in P0057, exemplary increased fibrinogen concentration in P0050 and counted degrees of concentration in P0105-P0106.).
Therefore, it would have been obvious to one of ordinary skill in the art of profiling a body fluid sample before the effective filing date of the claimed invention to modify the method and system of Sanchez-Martin when the condition is acute inflammation in peripheral blood, and the diagnostic data is left shift concentration in the biological sample as taught by Berliner to detect and diagnose an inflammatory condition in an individual mentioned in Berliner’s P0002.
Regarding claim 18, although Sanchez-Martin discloses the apparatus of claim 11 mentioned above, Sanchez-Martin does not explicitly teach when the condition is acute inflammation in peripheral blood, and the diagnostic data is left shift concentration in the biological sample. Berliner teaches
wherein the first condition is acute inflammation in peripheral blood, and the diagnostic data is left shift concentration in the biological sample (Established in P0007 as diagnose inflammatory response in P0057, exemplary increased fibrinogen concentration in P0050 and counted degrees of concentration in P0105-P0106.).
Therefore, it would have been obvious to one of ordinary skill in the art of profiling a body fluid sample before the effective filing date of the claimed invention to modify the method and system of Sanchez-Martin when the condition is acute inflammation in peripheral blood, and the diagnostic data is left shift concentration in the biological sample as taught by Berliner to detect and diagnose an inflammatory condition in an individual mentioned in Berliner’s P0002.
Claims 8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sanchez-Martin (US 2020/0152326 A1) in view of Wang (US 2019/0309054 A1).
Regarding claim 8, although Sanchez-Martin discloses the method of claim 1 mentioned above, Sanchez-Martin does not explicitly teach when the condition is adipocytes and the diagnostic data is depth distribution of cells in the biological sample. Wang teaches wherein the first condition is adipocytes, and the diagnostic data is depth distribution of cells in the biological sample (See Fig. 2, Fig. 7 distribution patterns in tissue biopsy and cells in P0008 where A=Adipocytes, plasma adiponectin levels in P0013. Also, see P0029, P0038.).
Therefore, it would have been obvious to one of ordinary skill in the art of diagnosing and treating cardiometabolic diseases before the effective filing date of the claimed invention to modify the method and system of Sanchez-Martin when the condition is adipocytes and the diagnostic data is depth distribution of cells in the biological sample as taught by Wang to detect additional preexisting heart conditions.
Regarding claim 19, although Sanchez-Martin discloses the apparatus of claim 11 mentioned above, Sanchez-Martin does not explicitly teach when the condition is adipocytes and the diagnostic data is depth distribution of cells in the biological sample. Wang teaches wherein the first condition is adipocytes, and the diagnostic data is depth distribution of cells in the biological sample (See Fig. 2, Fig. 7 distribution patterns in tissue biopsy and cells in P0008 where A=Adipocytes, plasma adiponectin levels in P0013. Also, see P0029, P0038.).
Therefore, it would have been obvious to one of ordinary skill in the art of diagnosing and treating cardiometabolic diseases before the effective filing date of the claimed invention to modify the method and system of Sanchez-Martin when the condition is adipocytes and the diagnostic data is depth distribution of cells in the biological sample as taught by Wang to detect additional preexisting heart conditions.
Claims 9 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sanchez-Martin (US 2020/0152326 A1) in view of Heckenbach (WO 2024/220824 A1).
Regarding 9, although Sanchez-Martin discloses the method of claim 1mentioned above, Sanchez-Martin does not explicitly teach updating machine-learning logic and the image recognition machine-learning logic when an attribute confidence level is greater than an identifiable parameter confidence level. Heckenbach teaches further comprising:
determining a first confidence level associated with the one or more attributes (See Fig. 2, P0003-P0004, P0046 where numerical scores (such as softmax) serve as a confidence level.);
determining a second confidence level associated with the one or more identifiable parameters (See Fig. 2, items 210 and 222 in P0004, P0046 where scoring identified nuclei serve as a confidence level associated with identifiable parameters.);
determining whether the first confidence level is greater than the second confidence level; in response to determining that the first confidence level is greater than the second confidence level, updating the second machine-learning logic; and in response to determining that the second confidence level is greater than the first confidence level, updating the image recognition machine-learning logic (See prediction model generate labeled senescence state outputs in [P0046] the label may comprise a numeric value indicating likelihood of senescence or other numerical value that increases above a threshold indicating senescence state, such as the softmax function with one- hot output for senescent or non-senescent output states.).
Therefore, it would have been obvious to one of ordinary skill in the art of biopsy images using machine learning before the effective filing date of the claimed invention to modify the method and system of Sanchez-Martin when the condition is adipocytes and the diagnostic data is depth distribution of cells in the biological sample as taught by Heckenbach to more successfully treat cancer while also reducing the need for repeat biopsies and other invasive screening procedures mentioned in Heckenbach’s P0018.
Regarding 20, although Sanchez-Martin discloses the apparatus of claim 11 mentioned above, Sanchez-Martin does not explicitly teach updating machine-learning logic and the image recognition machine-learning logic when an attribute confidence level is greater than an identifiable parameter confidence level. Heckenbach teaches:
wherein the instructions, when executed, further cause the processor (See Fig. 1, processor in P0004, P0006.) to:
determine a first confidence level associated with the one or more attributes (See Fig. 2, P0003-P0004, P0046 where numerical scores (such as softmax) serve as a confidence level.);
determine a second confidence level associated with the one or more identifiable parameters (See Fig. 2, items 210 and 222 in P0004, P0046 where scoring identified nuclei serve as a confidence level associated with identifiable parameters.);
determine whether the first confidence level is greater than the second confidence level;
in response to determining that the first confidence level is greater than the second confidence level, update the second machine-learning logic; and in response to determining that the second confidence level is greater than the first confidence level, update the image recognition machine-learning logic (See prediction model generate labeled senescence state outputs in [P0046] the label may comprise a numeric value indicating likelihood of senescence or other numerical value that increases above a threshold indicating senescence state, such as the softmax function with one- hot output for senescent or non-senescent output states.).
Therefore, it would have been obvious to one of ordinary skill in the art of biopsy images using machine learning before the effective filing date of the claimed invention to modify the method and system of Sanchez-Martin when the condition is adipocytes and the diagnostic data is depth distribution of cells in the biological sample as taught by Heckenbach to more successfully treat cancer while also reducing the need for repeat biopsies and other invasive screening procedures mentioned in Heckenbach’s P0018.
Regarding claim 21, Sanchez-Martin teaches the method of claim 1, wherein the one or more attributes comprise abnormal cell, morphological cell, cell type, subcellular component, cell maturation level, parasite, disease state, or a combination thereof (See [P0006] The machine learning module may classify the cells into a respective cell category based upon morphological patterns of the cell. In an optional embodiment, the morphologic patterns may include cell shape, cell size, size of the nucleus, shape of the nucleus, granularity of the cytoplasm, or a fluorescent molecule that specifically binds to a marker on the surface of the cell. Also, see P0028, P0038.).
Response to Arguments
Applicant argues that amended independent claims 1 and 11 do not recite certain methods of organizing human activity or an abstract idea that falls within the enumerated grouping of mental processes, liken to Example 39, see pgs. 8-10 of Remarks – Examiner disagrees.
Regarding this application, a Scientist or Biologist incorporating knowledge of biological and cellular properties, diagnosing patients with medical conditions or diseases by observing biological sample cells, comparing the biological sample cells within multiple-point sources and controlling a microscope apparatus are activities that the Scientist or Biologist would be expected to do. Unlike Example 39 that expands a training set of facial images, the instant case claims already identified attributes associated with a medical condition, determines unknown parameters associated with cells and the medical condition.
Applicant argues that amended independent claims 1 and 11, when considered as a whole, improve the functioning of image recognition and disease diagnosis using two machine-learning logics, see pgs. 11-14 of Remarks – Examiner disagrees.
Beside not explaining how the invention is applied in a meaningful way and merely indicating that the functioning of image recognition and disease diagnosing is done by using two machine-learning logics, the claimed invention is not explained and not solving a technological problem with a technological solution. In fact, see paragraph 16 of Applicant’s specification, “displaying both the images and the diagnostic data, the user may be able to quickly discern a disease state.” Here, the user is determining the disease state by viewing images and diagnostic data. In other words, how is data actually being trained with the image recognition machine-learning logic and second machine-learning logic? Using the image recognition and disease diagnosis with two machine-learning logics are ways of merely using the computer as a tool to implement the abstract idea (saying “apply it”) and is merely using the computer in the manner in which it was designed to be used, i.e., performing generic computer functions.
Applicant’s arguments that Sanchez-Martin does not teach “extracting individual images of the one or more identified cells," and "identifying ... one or more cells of the plurality of cells as comprising one or more attributes associated with a first condition”, with respect to amended claims 1 and 11. See Fig. 2, P0039-P0042 where biological sample data are obtained such as red/white blood cell types, cell morphology according to size, color, shape, nucleus shape, nucleus size, heterogeneous cells, cell cytoplasm, cellular stains types and more. Also, see [P0032-P0033] Extracted information may be stored as extracted data 48 and provided to diagnosis module 78 to provide a diagnosis of a disease.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Basiji (US 2014/0030729 A1), Remiszeski (US 10,043,054 B2) & Holmes (US 12,085,583 B2).
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.
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/T.S.W./Examiner, Art Unit 3687 05/29/2026
/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
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