DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant's response filed 6/11/2026 has been fully considered. The following rejections
and/or objections are either reiterated or newly applied.
Status of Claims
Claims 1-2, 4, 10, 12, and 14-18 pending and examined on the merits.
Claims 3, 5-9, 11, 13, and 19-23 canceled.
Priority
The instant application filed on 12/4/2024 claims the benefit of priority to U.S. Provisional Patent Application No. 63/605,884 filed on 12/4/2023 and U.S. Provisional Patent Application No. 63/700,029 filed on 9/27/2024. Thus, the effective filing date of the claims is 12/4/2023.
Claim Objections
The objection to claim 1 withdrawn in view of Applicant's claim amendments and remarks filed on 6/11/2026.
However, a newly applied objection to claim 1 because of informalities is necessitated by amendment. The last two limitations, "based on the determined one or more attributes of the plurality of cells, updating, by the first computing device, the one or more of the parameters; and retraining the first machine learning model using the updated one or more of the parameters" should read "based on the determined one or more attributes of the plurality of cells, updating, by the first computing device, the one or more of the parameters associated with blood cells; and retraining the first machine learning model using the updated one or more of the parameter associated with blood cells", in order to use consistent language and distinguish from the "blood sample parameters". Appropriate correction is required.
Examiner's Note
Applicant's comments regarding dependent claim 9 (Remarks 6/11/2026 pages 15-16) rejection under 35 USC 101 and 103 are moot in light of the claim having been canceled prior to the Office Action filed 3/11/2026.
Withdrawn Rejections
35 USC § 112(b)
The rejection of claims 1-2, 4, 10, 12, and 14-18 under 35 USC 112(b) withdrawn in view of Applicant's claim amendments and remarks filed on 6/11/2026.
35 USC § 103
The rejection of claims 1-2, 4, 10, 12, and 14-18 under 35 USC 103 withdrawn in view of Applicant's claim amendments and remarks filed on 6/11/2026.
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, 10, 12, 14-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1: “determining, [], diagnostic data associated with a first portion of the blood sample” provides an evaluation or comparison (determining diagnostic data requires evaluating the received cell data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“determining, [], one or more attributes of the plurality of cells” provides an evaluation or comparison (determining cell attributes requires evaluating the cell image data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“based on the determined one or more attributes of the plurality of cells, updating, by the first computing device, the one or more of the parameters” provides for organizing information (updating data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 2: “the one or more parameters associated with blood cells comprises one or more identifiable parameters associated with red blood cells; and the one or more parameters associated with red blood cells comprises one or more of the following: (i) total red blood count (RBC), (ii) mean corpuscular volume (MCV), (iii) hemoglobin (HGB), (iv) hematocrit (HCT), (v) mean corpuscular hemoglobin (MCH), (vi) mean corpuscular hemoglobin concentration (MCHC), (vii) red distribution width (RDW), (viii) reticulocyte count (Retic), (ix) percentage of reticulocyte (% Retic), (x) platelet count (PLT), (xi) mean platelet volume (MPV), (xii) plateletcrit (PCT), and (xiii) platelet distribution width (PDW)” provides an evaluation (determining blood cell parameters is part of the judicial exception of claim 1) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 4: “the one or more parameters associated with blood cells comprises one or more identifiable parameters associated with white blood cells; and the one or more parameters associated with white blood cells comprises one or more of the following: (i) white blood count (WBC), (ii) absolute neutrophil count (NEU), (iii) absolute lymphocyte count (LYM), (iv) absolute monocyte count (MONO), (v) absolute eosinophil count (EOS), (vi) absolute basophil count (BASO), (vii) percentage neutrophils (% NEU), (viii) percentage lymphocytes (% LYM), (ix) percentage monocytes (% MONO), (x) percentage absolute eosinophils (% EOS), and (xi) percentage basophils (% BASO)” provides an evaluation (determining blood cell parameters is part of the judicial exception of claim 1) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 10: “determining the diagnostic data comprises evaluating detected cell size and detected cell complexity or detected cell size and detected fluorescence” provides an evaluation (evaluating detected cell data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 14: “identifying a subset of the plurality of cells having a cell size or a cell morphology associated with left shift” provides an evaluation (identifying a subset of cells based on image data requires evaluating image data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 16: “identifying a subset of the plurality of cells having a cell size or a cell morphology associated with small pathologic red blood cells” provides an evaluation (identifying a subset of cells based on image data requires evaluating image data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
Claim 18: “identifying individual platelet clumps, and in response to identifying the individual platelet clumps, counting a number of individual platelets in the individual platelet clumps” provides an evaluation (identifying and counting individual platelet clumps requires evaluating image data) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
These recitations are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or are mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Additionally, while claim 1 recites performing some aspects of the analysis on “A computer-implemented method for detecting one or more conditions in a blood sample [] by a first computing device [] by a second computing device”, there are no additional limitations that indicate that this requires anything other than carrying out the recited mental processes or mathematical concepts in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-2, 4, 10, 12, 14-18 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exceptions listed above are not integrated into a practical application because the claims do not recite an additional element or elements that reflects an improvement to technology. Specifically, the claims recite the following additional elements:
Claim 1: “A computer-implemented method for detecting one or more conditions in a blood sample [] by a first computing device [] by a second computing device” provides insignificant extra-solution activities (running a system on generic computer components) that do not serve to integrate the judicial exceptions into a practical application.
“receiving, by a first computing device, cell data from one or more sensors communicatively coupled to the first computing device” and “receiving, by a second computing device, from one or more imaging sensors communicatively coupled to the second computing device, an image of a plurality of cells of a second portion of the blood sample” provides insignificant extra-solution activities (receiving cell and image data is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“the first machine learning model was trained using hematology training set data”, “the second machine learning model was trained using image training set data”, and “retraining the first machine learning model using the updated one or more of the parameters”
Claim 15: “retraining the first machine learning model ” Claim 17: “retraining the first machine learning model
Claim 12: “the second computing device comprises a morphology analyzer; and the morphology analyzer comprises one or more morphology processors communicatively coupled to the one or more imaging sensors, one or more energy sources optically coupled to the one or more imaging sensors, and an objective lens optically coupled to the one or more imaging sensors” provides insignificant extra-solution activities (a morphology analyzer) that do not serve to integrate the judicial exceptions into a practical application.
The steps for receiving data from a hematology and morphology analyzer are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application because they are pre- and post-solution activities involving data gathering, data manipulation, and sample manipulation steps (see MPEP 2106.04(d)(2)). Additionally, the machine learning training steps of claims 1, 15, and 17 is used to generally apply the abstract idea without placing any limitation on how the machine learning model operates. The claim omits any details as to how the machine learning solves a technical problem and instead recites only the idea of a solution or outcome. See MPEP 2106.05(f). Therefore, the limitation represents no more than mere instructions to implement the abstract idea of determining the parameters or attributes, which is equivalent to adding the words “apply it” to the recited judicial exception. In addition, the claim confines the use of the recited judicial exception recited in claim 1 to the technological environment of machine learning by generally linking the use of the judicial exception to the recited machine learning model. Therefore, this general training recitation does not integrate the judicial exception into a practical application. See MPEP 2106.05(h). Therefore, it can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to a particular field of use or a technological environment. Furthermore, the limitations regarding implementing program instructions do not indicate that they require anything other than mere instructions to implement the abstract idea in a generic way or in a generic computing environment. As such, this limitation equates to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Therefore, claims 1-2, 4, 10, 12, 14-18 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). As discussed above, there are no additional elements to indicate that the claimed “A computer-implemented method for detecting one or more conditions in a blood sample [] by a first computing device [] by a second computing device” requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. Additionally, the limitations for receiving data from a hematology and morphology analyzer are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by these limitations as they are well-understood, routine, and conventional, as evidenced by the instant specification relying on what is known in the art by not providing detail (para.0040 gives examples of a hematology analyzer without going into detail, and states that the morphology analyzer can be "any suitable morphology analyzer").
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-2, 4, 10, 12, 14-18 are not patent eligible.
Response to Arguments under 35 USC § 101
Applicant’s arguments filed 6/11/2026 are fully considered but they are not persuasive.
Applicant asserts they have an improved method of hematology analysis, which is an improvement in the abstract idea (an analysis of data), rather than any particular technology (Remarks 6/11/2026 pages 12-13). Applicant also asserts they are improving the accuracy and learning capabilities of the computing device (Remarks 6/11/2026 page 14). Examiner notes that for there to be an improvement to computer technology, "the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool." The instant invention is simply using a computer as a tool to carry out an improved abstract idea, but the functionality of the computer itself is not being improved (see MPEP 2106.05(a) I. for examples).
Applicant further argues that "claim 1 is directed to an unconventional and technically improved computer-implemented method of improving the functionality [of a diagnostic computing device] by leveraging capture and analysis of a second set of data (imaging data) to update and improve the [diagnostic computing device]" (Remarks 6/11/2026 pages 14-15). Examiner notes that the argument for conventionality is part of the abstract idea, which is not considered under Step 2B. Presently, the claims are “receiving, by a first/second computing device” [data], and then generally linking the abstract idea to machine learning models. There is no limitation of claim 1 which demonstrates that the type of data being received is conventional (data is abstract).
Therefore, without the steps for collecting the cell data from the one or more sensors and collecting an image from image sensors, the rejection of claims 1-2, 4, 10, 12, and 14-18 under USC 101 is maintained.
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 rejection of claims 1-2, 4, 10, 12, and 14-18 under 35 USC 103 contains new grounds of rejection necessitated by Applicant remarks.
Claims 1-2, 4, 10, 12, and 16-18 rejected under 35 U.S.C. 103 as being unpatentable over Pushkin et al. (US-20240420842; with priority to DE-102021004988 having effective publishing date 4/6/2023) in view of Sanchez-Martin et al. (US-20200152326), Ye et al. (US-20220326140), and Haase (US-20220406430).
Regarding claim 1, Pushkin teaches a computer-implemented method for detecting one or more conditions in a blood sample, and receiving cell data from one or more sensors communicatively coupled to the first computing device (Para.0015 "The measurements are copied by the analyzer e.g. as FCS files or in another format and transferred to an accessible PC or mobile computing device or a cloud for machine processing. These measurements contain blood parameters as properties of leukocytes, wherein leukocytes comprise neutrophils, eosinophils, basophils, lymphocytes and monocytes, wherein the properties comprise the size, granularity, lobularity and complexity (FIG. 2)").
Pushkin also teaches determining, by the first computing device, via a first machine learning model, and based at least in part on data from on the received cell data, diagnostic data associated with a first portion of the blood sample, wherein the diagnostic data comprises one or more parameters associated with blood cells, wherein the first machine learning model was trained using hematology training set data, wherein the first machine learning model is trained to identify one or more blood sample parameters (Para.0036 "In 110 patients, the diagnosis was classified as acute coronary syndrome (55 cases with STEMI and 55 cases with NSTEMI) by a cardiologist. In 111 patients, the disease was ruled out; they thus represent the healthy control cases. In all patients, the first blood sample was taken in the emergency department and measured in a hematology analyzer of the CELL-DYN Sapphire type (Abbott Laboratories, USA). The database was divided at random into a training dataset X.sub.train with 154 cases and a test dataset X.sub.test with 67 cases. The training dataset X.sub.train was used to train the machine and deep learning models").
Pushkin does not explicitly teach: receiving from one or more imaging sensors communicatively coupled to the second computing device, an image of a plurality of cells of a second portion of the blood sample; determining, by the second computing device, via a second machine learning model and based at least in part on the image of the plurality of cells of the second portion of the blood sample, one or more attributes of the plurality of cells, wherein the second machine learning model was trained using image training set data, wherein the second machine learning model is trained to identify one or more attributes associated with a plurality of blood sample cells; based on the determined one or more attributes of the plurality of cells, updating, by the first computing device, the one or more of the parameter; nor retraining the first machine learning model using the updated one or more of the parameters.
However, Sanchez-Martin teaches receiving from one or more imaging sensors communicatively coupled to the second computing device, an image of a plurality of cells of a portion of the blood sample; and determining via a machine learning model and based at least in part on the image of the plurality of cells of the portion of the blood sample, one or more attributes of the plurality of cells, wherein the machine learning model was trained using image training set data, wherein the machine learning model is trained to identify one or more attributes associated with a plurality of blood sample cells (Abstract "One or more images of a blood sample from a microscope is obtained, each image comprising a plurality of different types of cells. The one or more images are processed by a machine learning system to classify individual cells into one of a plurality of cell categories. The cells in each cell category are analyzed to determine characteristics of the respective cell category. A diagnosis or list of possible diagnosis are determined based on the classification and characteristics of the cells for the patient in an automated manner").
However, Ye teaches running multiple tests using the same blood sample (testing on portions of the same blood sample) to be used for generating data (para.0062 "The sample to be tested may be any user-specified sample, including a blood sample and a body fluid sample. The sample to be tested may alternatively be a sample that is screened and then re-extracted for retesting after being detected by a blood cell analysis device in various cell count tests (such as blood routine examination) [. . .] For example, when the test result of the sample to be tested, which is obtained by the blood cell analysis device, indicates that there may be a first abnormality in the sample to be tested, it is needed to use the cell image analysis device to image and analyze a sample smear prepared from the sample to be tested in the assigned analysis mode corresponding to the first abnormality").
However, Haase teaches retraining of a machine learning model using an updated parameter derived from the output of another machine learning model (Para.0044 "Referring still to FIG. 1, computing device 104 may be configured to train a second machine-learning process using a reference biomarker training data to generate first condition descriptor 116. Reference biomarker training data may correlate reference values for each biomarker to first conditions 116. First condition descriptor 116 may then be determined as a function of the value for each biomarker of the plurality of biomarkers and the second machine learning process. The reference biomarker training data may then correlate a reference value for each biomarker to a first condition. Computing device 104 may then determine first condition descriptor 116 where the second machine-learning model receives the value for biomarker as an input and outputs a first condition. The machine-learning process is described below in this disclosure with reference to FIG. 3. In an embodiment, computing device 104 may iteratively regenerate the reference biomarker training data as a function of first condition. The second machine-learning model is retrained using the regenerated biomarker training data. For example, new conditions that may involve new biomarkers may be incorporated into the training data and correct for model drift or predictive performance degradation").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Pushkin to further receive image sensor data and determine cell attributes via machine learning, as taught by Sanchez-Martin, in order to automatically determine a diagnosis for a patient, which is more standardized, leading to faster and less error-prone results (para.0005 "A diagnosis is determined based on the quantity, the classification, and characteristics of the cells to determine a diagnosis for the patient. Unlike traditional manners of analyzing blood samples, which rely on one or more pathologists to manually interpret the slides, present techniques provide for analyzing cells and generating a diagnosis in a standardized, uniform manner, as the same classification and analysis techniques are applied to each slide. The results can then be reviewed by a pathologist in a manner that is faster and less prone to error"). One skilled in the art would have a reasonable expectation of success because both methods are concerned with using blood samples to extract and analyze cell data for patient diagnosis.
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Pushkin to run multiple tests using the same blood sample for generating data, as taught by Ye, in order to identify a sample for retesting (para.0063 "The preset retest condition is used to indicate at least one of the followings: the test result includes an abnormal result indicating that there is an abnormality in the sample, [etc.]"). One skilled in the art would have a reasonable expectation of success because both methods are concerned with using blood samples to extract and analyze cell data for patient diagnosis.
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the machine learning model of Pushkin by retraining the machine learning model using the output of the Sanchez-Martin machine learning model, as taught by Haase, in order to correct for model drift or predictive performance degradation (para.0045 "For example, new conditions that may involve new biomarkers may be incorporated into the training data and correct for model drift or predictive performance degradation"). One skilled in the art would have a reasonable expectation of success because both methods are concerned with using blood samples to extract and analyze cell data for patient diagnosis.
Regarding claim 2 and 4, Pushkin in view of Sanchez-Martin, Ye, and Haase teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Sanchez-Martin also teaches: the one or more parameters associated with blood cells comprises one or more parameters associated with red blood cells, the one or more parameters associated with red blood cells comprises one or more of the following: (i) total red blood count (RBC), (ii) mean corpuscular volume (MCV), (iii) hemoglobin (HGB), (iv) hematocrit (HCT), (v) mean corpuscular hemoglobin (MCH), (vi) mean corpuscular hemoglobin concentration (MCHC), (vii) red distribution width (RDW), (viii) reticulocyte count (Retic), (ix) percentage of reticulocyte (% Retic), (x) platelet count (PLT), (xi) mean platelet volume (MPV), (xii) plateletcrit (PCT), and (xiii) platelet distribution width (PDW); and the one or more parameters associated with blood cells comprises one or more parameters associated with white blood cells, the one or more parameters associated with white blood cells comprises one or more of the following: (i) white blood count (WBC), (ii) absolute neutrophil count (NEU), (iii) absolute lymphocyte count (LYM), (iv) absolute monocyte count (MONO), (v) absolute eosinophil count (EOS), (vi) absolute basophil count (BASO), (vii) percentage neutrophils (% NEU), (viii) percentage lymphocytes (% LYM), (ix) percentage monocytes (% MONO), (x) percentage absolute eosinophils (% EOS), and (xi) percentage basophils (% BASO) (Para.0049 "Once the images are analyzed by the machine learning module 70, the machine learning module may provide various types of outputs. The machine learning output 490 may include but is not limited to cell count (e.g., total number of cells and relative number of cells per cell type). For example, cell counts may include the number of normal red blood cells, abnormal red blood cells, and the number of different types of white blood cells as well as other types of cells identified in the sample. In some aspects, cell counts may vary based on other factors including ethnicity, geographical location, and diet. These parameters may be tracked by the system as well").
Regarding claim 10, Pushkin in view of Sanchez-Martin, Ye, and Haase teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Pushkin also teaches determining the diagnostic data comprises evaluating detected cell size and detected cell complexity or detected cell size and detected fluorescence (Para.0003 "In the process, in the hematological analyzer, various morphological characteristics such as size, complexity, lobularity and granularity are measured", para.0018 "the lymphocyte vector comprises at least two properties (size and complexity), the monocyte vector comprises at least two properties (size and complexity) and the neutrophil vector comprises at least two properties (size and complexity)", and para.0020 describes the evaluation of these data).
Regarding claim 12, Pushkin in view of Sanchez-Martin, Ye, and Haase teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Sanchez-Martin also teaches the second computing device comprises a morphology analyzer, the morphology analyzer comprises one or more morphology processors communicatively coupled to the one or more imaging sensors, one or more energy sources optically coupled to the one or more imaging sensors, and an objective lens optically coupled to the one or more imaging sensors (Para.0037 "Image acquisition system 50 may comprise a user profile 61, a microscope 65, a camera 62, camera control functions component 63, and acquired images 64 as well as a processor 22, a network interface 24, memory 23, a user interface 25, and a display 26. The user profile 61 may identify the operator of the image acquisition system 50. The camera 62 may acquire microscopic images (e.g., bright field, DIC, epifluorescent, etc.) of the cells, using the microscope, wherein the cells are typically fixed on slides. Camera control functions component 63 may be used to determine the settings for the camera 62 for image acquisition. Acquired images 64 may be provided to the blood pathology analysis system 15 for analysis").
Regarding claim 16, Pushkin in view of Sanchez-Martin, Ye, and Haase teach the method of Claim 1 on which this claim depends/these claims depend. Ye also teaches identifying a subset of the plurality of cells having a cell size or a cell morphology associated with small pathologic red blood cells, i.e. spherocytes (Para.0173 and Table 2 "Erythrocyte abnormalities identified during identification of cell images of erythrocytes [] Spherocytes").
Regarding claim 17, Pushkin in view of Sanchez-Martin, Ye, and Haase teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Kannan also teaches retraining models based on new results, and coupling this with Ye renders obvious retraining the first machine learning model in response to identifying the subset of the plurality of cells having the cell size of the cell morphology associated with small pathologic red blood cells (Para.158 "retraining the current best model with some data from organoid line C or employing domain adaptation techniques can facilitate better generalizability").
Regarding claim 18, Pushkin in view of Sanchez-Martin, Ye, and Haase teach the methods of Claim 1 on which this claim depends/these claims depend, respectively. Ye also teaches identifying individual platelet clumps, and in response to identifying the individual platelet clumps, counting a number of individual platelets in the individual platelet clumps (Para.0072 "In some embodiments, the second abnormality indicates that there are cells that meet the preset condition, such as aggregated platelet cells" and para.0124 "The platelet abnormality includes at least one of platelet aggregation and low platelet count (for example, a platelet count value is below a normal range). The platelet aggregation is used for indicating that platelets in the sample to be tested are aggregated (for example, aggregated in a specific region)").
Claims 14-15 rejected under 35 U.S.C. 103 as being unpatentable over Pushkin et al. (US-20240420842) in view of Sanchez-Martin et al. (US-20200152326), Ye et al. (US-20220326140), and Haase (US-20220406430) as applied to claims 1-2, 4, 10, 12, and 16-18 above, and further in view of Hauser et al. (American journal of clinical pathology 156.6 (2021): 1142-1148).
Pushkin et al. in view of Sanchez-Martin et al. and Kannan et al. are applied to claims 1-2, 4, 10, 12, and 16-18.
Regarding claim 14, Pushkin in view of Sanchez-Martin, Ye, and Haase teach the method of Claim 1 on which this claim depends/these claims depend.
Pushkin, Sanchez-Martin, Ye, nor Haase explicitly teaches identifying a subset of the plurality of cells having a cell size or a cell morphology associated with left shift.
However, Hauser teaches identifying patients presenting with a left shift in granulocyte differentiation, and that this is a classic circumstance which would make its application to a machine learning algorithm obvious to one of ordinary skill in the art (Page 1 col 2 paragraph 2 "Classically, patients present with persistent unexplained leukocytosis and a left shift in the granulocyte differentiation").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Pushkin, Sanchez-Martin, Ye, and Haase as taught by Hauser in order to detect shifts in the differentiation (para.0017 "In addition to hierarchical cluster analysis, the following cluster algorithms can also be used: k-means, k-medians, affinity propagation, mean shift, spectral clustering, Ward's hierarchical clustering, DBSCAN, OPTICS, Gaussian mixtures and Birch" and Abstract "diagnosing at least one disease by means of an ensemble, the ensemble comprising at least one machine learning model and at least one deep learning model, the at least one machine learning model receiving at least one reduced global vector as an input variable and the at least one deep learning model receiving at least one scatter plot image as an input variable" suggest that the models may be used to detect these shifts in scatter plots for diagnosis). One skilled in the art would have a reasonable expectation of success because both methods are using machine learning for diagnosing blood disorders.
Regarding claim 15, Pushkin in view of Sanchez-Martin, Ye, Haase, and Hauser teach the methods of Claim 14 on which this claim depends/these claims depend, respectively. Haase also teaches retraining models based on new results (and specifically cites correcting for model drift), and coupling this with Hauser renders obvious retraining the first machine learning model in response to identifying the subset of the plurality of cells having the cell size of the cell morphology associated with left shift (Para.0044 “The second machine-learning model is retrained using the regenerated biomarker training data. For example, new conditions that may involve new biomarkers may be incorporated into the training data and correct for model drift or predictive performance degradation" and para.0045 “For example, new conditions that may involve new biomarkers may be incorporated into the training data and correct for model drift or predictive performance degradation").
Response to Arguments under 35 USC § 103
Applicant’s arguments filed 6/11/2026 are fully considered but they are not persuasive.
Applicant asserts that Examiner has made a "legal error" because "the Office does not address the claim 1 recited element or concept of performing the method step of 'based on the determined one or more attributes of the plurality of cells, updating, by the first computing device, the one or more of the parameters'" and instead merely cites that "Sanchez-Martin allegedly discloses 'updating the hematology training data set'" (Remarks 6/11/2026 page 3). First, Examiner notes that the recited element is an amended element of claim 1, and therefore would naturally have not been previously addressed. Second, Examiner notes that the Office Action filed 3/11/2026 explicitly interprets the cited unamended step as "updating the hematology training data set with the determined one or more blood sample parameters" (see sections 35 USC 112(b) (page 4) and 103 (page 13) of Office Action 3/11/2026), and was addressed under this interpretation in that Office Action. Third, Examiner has indicated above that Haase teaches this amended limitation and would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Pushkin, Sanchez-Martin, and Ye as taught by Haase (see section "Claim Rejections - 35 USC 103" for details).
Applicant further asserts that Kannan does not "teach or otherwise disclose the concept of analysing the same sample to update and retrain a machine learning model" (Remarks 6/11/2026 page 4). Examiner concedes that Kannan is not required for this limitation, as the clarified interpretation of the updating step has necessitated new grounds for rejection in view of Haase (above), which teaches or suggests updating/retraining of machine learning models. Examiner notes that this now renders other arguments against Kannan moot. Examiner further notes that Ye (which has previously been relied upon, with the other references, for the rejection of claims 16-18) teaches multiple testing using the same blood sample, which when combined with Haase renders the limitation obvious for the reasons provided above (see section "Claim Rejections - 35 USC 103" for details).
Therefore, the rejection of claims 1-2, 4, 10, 12, and 16-18 under USC 103 is maintained.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Srisuwananukorn et al. "Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary." Haematologica 108.8 (2023): 1993
Rodellar et al. "Image processing and machine learning in the morphological analysis of blood cells." International journal of laboratory hematology 40 (2018): 46-53
Conclusion
No claims are allowed.
This Office action is a non-final Office action that contains a new grounds of rejection under 35 U.S.C. 103 that was not necessitated by applicant’s amendment received 11 June 2026.
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/R.A.P./Examiner, Art Unit 1686
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
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