DETAILED ACTION
Applicant’s response, filed 12/09/2025, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Request for continued examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/09/2025 has been entered.
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 .
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 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.
Claim Status
Claims 1, 4-7, 9, and 12-15 are pending.
Claims 2, 3, 8, 10, 11, and 16 are canceled.
Claims 1, 4-7, 9, and 12-15 are rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) to App. No. TAIWAN 110123616, filed 06/28/2021. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Drawings
The Drawings submitted 09/13/2021 are accepted.
Claim Rejections- 35 USC § 112
The outstanding rejections to the claims are withdrawn in view of the amendments submitted herein.
35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1, 4-7, 9, and 12-15 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention. The instant rejection is newly stated and is necessitated by claim amendment.
Claims 1 and 9 recite “wherein a mean area under curve of a Receiver Operating Characteristic Curve (ROC Curve) of the sepsis diagnosis model is 0.84, and a cut-off value of the sepsis diagnosis model is 0.5” The ROC curve only gives a performance diagnostic of the model but does not give any information about the model or evaluation set. It is further unclear what the cutoff is referring to which makes the claims indefinite. Claim(s) 4-7, and 12-15 is/are rejected for the same reason because they depend from claims 1 and 9, and do not resolve the indefiniteness issue in those claims.
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.
For the following rejections, underlined text indicates newly recited portions necessitated by claim amendment.
Claims 1, 4-7, 9, and 12-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more. Any newly recited portions are necessitated by claim amendment.
MPEP 2106 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Framework with which to Evaluate Subject Matter Eligibility:
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1
With respect to Step 1: yes, the claims are directed to method and system, i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03].
Step 2A, Prong One
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas are as follows:
Independent claims 1 and 9:
read a sepsis database and at least one database to be tested of a storing unit, wherein the sepsis database comprises a plurality of sepsis data, and the at least one database to be tested comprises a plurality of data to be tested;
to create a sepsis data table according to the sepsis data and create a data table to be tested according to the data to be tested;
to train a sepsis diagnosis model with the sepsis data table according to a K-fold cross-validation and a machine learning algorithm; wherein the machine learning algorithm is an eXtreme Gradient Boosting (XGBoost), and a variable K of the K-fold cross-validation is 5;wherein the sepsis data are collected from a plurality of sepsis patients after a feature window, the feature window refers to a 12-hour period before a clinical recognition, and an inspection frequency of each of the sepsis patients within the feature window is not the same; wherein a mean area under curve of a Receiver Operating Characteristic Curve (ROC Curve) of the sepsis diagnosis model is 0.84, and acut-off value of the sepsis diagnosis model is 0.5;wherein in response to determine that the sepsis prediction result is greater than or equal to the cut-off value, a subject is judged to be the sepsis patient.
to calculate a sepsis prediction result; wherein the sepsis data are a patient basic data, a patient vital sign data and a patient blood test data;
Dependent claims 7 and 15:
to cut the sepsis data table into K data sets according to the K-fold cross-validation, wherein the K data sets comprise K-1 training sets and a validation set,
trains the K-1 training sets according to a plurality of initial hyperparameters and the machine learning algorithm to generate a plurality of initial models corresponding to each of the initial hyperparameters;
to calculate the initial models through the validation set to generate a plurality of mean area under curves corresponding to the initial models,
compare the mean area under curves to select a target hyperparameter from the initial hyperparameters;
to retrain the sepsis data table according to the target hyperparameter and the machine learning algorithm to generate the sepsis diagnosis model.
Dependent claims 4-6, 12-14 and 16 recite further steps that limit the judicial exceptions in independent claim 1 and, as such, also are directed to those abstract ideas. For example, claims 4 and 12 further limits the patient basic data of claim 2, claims 5 and 13 further limits the patient vital sign data of claims 2 and 9, claims 6 and 14 further limits the patient blood test data of claims 2 and 9,
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind and/or by mathematical operation because the method only requires a user to manually read, create, train, extract, integrate, cut, calculate, compare, and retrain. Without further detail as to the methodology involved in “read a sepsis database”, “to create a sepsis data table”, “ to calculate a sepsis prediction “, “to extract a maximum value, a minimum value “, “to integrate the maximum value, the minimum value, the latest value, the patient basic data and the patient blood test data“, “to cut the sepsis data table“, “to calculate the initial models “, “compare the mean area under curves “, and “to retrain the sepsis data table “under the BRI, one may simply, for example, use pen and paper to diagnosis sepsis.
Therefore, claims 1 and 9 and those claims dependent therefrom recite an abstract idea [Step 2A, Prong 1: YES; See MPEP § 2106.04].
Step 2A, Prong Two
Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two, provides that the claims must be examined further to determine whether they integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d).III).
Additional elements, Step 2A, Prong Two
With respect to the instant recitations, the claims recite the following additional elements:
Independent claim 1:
A sepsis database
A storing unit
to input the data table to be tested into the sepsis diagnosis model to calculate a sepsis prediction result.
to extract a maximum value, a minimum value and a latest value of the patient vital sign data; and performing a data integrating step to drive the processing unit to collect the maximum value, the minimum value, the latest value, the patient basic data and the patient blood test data to generate the sepsis data table
Independent claim 9:
a storing unit
a sepsis database
input the data table to be tested into the sepsis diagnosis model to calculate a sepsis prediction result.
The claims also include non-abstract computing elements. For example, independent claim 1 includes “a processing unit” and claim 9 includes “an artificial intelligence assisted medical diagnosis system”.
Considerations under Step 2A, Prong Two
With respect to Step 2A, Prong Two, the additional elements of the claims do not integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to data gathering, such as “inputting”, “collect”, and “extract”, perform functions of collecting the data needed to carry out the judicial exceptions. Data gathering and outputting do not impose any meaningful limitation on the judicial exceptions, or on how the judicial exceptions are performed. Data gathering and outputting steps are not sufficient to integrate judicial exceptions into a practical application (MPEP 2106.05(g)).
Further steps directed to additional non-abstract elements of “a system and a processor” do not describe any specific computational steps by which the “computer parts” perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, and therefore the claim does not integrate that judicial exceptions into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (MPEP 2106.05(f)).
With respect, the additional elements of a database and storing unit, do not integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to using a storing unit and a database do not impose any meaningful limitations on the abstract idea, or on how the abstract idea is performed. These steps are insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)(2))
Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exceptions [Step 2A, Prong 2: NO; See MPEP § 2106.04(d)].
Step 2B (MPEP 2106.05.A i-vi)
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims, the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a merely generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(II)(i)).
As such, the claims simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP2106.05(d)). The data gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)).
With respect to claims 1 and 9 and those claims dependent therefrom, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (see MPEP 2106.06(A)). The specification also notes that computer processors and systems, as example, are commercially available or widely used at [0038]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
With respect to claims 1 and 9 and those claims dependent therefrom, the additional elements of a storing unit and database are well-understood, routine, and conventional in the art as the specification discloses a storing unit can be a Hospital Information System (HIS) or a cloud server [0037].
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [Step 2B: NO; See MPEP § 2106.05].
Therefore, the instant claims are not drawn to eligible subject matter as they are directed to one or more judicial exceptions without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
Response to Applicant Arguments
Applicant submits the features "wherein the machine learning algorithm is an extreme Gradient Boosting (XGBoost), and a variable K of the K-fold cross-validation is 5" are added for further defining the type of the machine learning algorithm and the variable K of the K-fold cross-validation, and this is not a conventional function or limitation widely known in the prior art. Applicant considers that the additional element not only integrates abstract ideas into practical applications but also reduces the bias of the sepsis diagnosis model and improves the accuracy of the diagnosis of sepsis [p. 12, par. 3-4].
It is respectfully found not persuasive. The limitation of a machine learning algorithm is an extreme Gradient Boosting (XGBoost), and a variable K of the K-fold cross-validation is 5 is a mathematical concept and is therefore a judicial exception. It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. Furthermore, it is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements or by the additional element(s) in combination with the recited judicial exception. See MPEP 2106.05(a).
Claim Rejections - 35 USC § 102
The outstanding rejections to the claims are withdrawn in view of the amendments submitted herein. The prior art did not disclose the exact ROC of .84, but a similar ROC of .89.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
For the following rejections, instantly claimed elements which are considered to be equivalent to the prior art teachings are described in bold for all claims, and underlined text indicates newly recited portions necessitated by claim amendment.
A. Claims 1, 4-5, 7, 9, 12-13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Yuan et al. (Yuan, Kuo-Ching, et al. "The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit." International journal of medical informatics 141 (2020), and supplemental material, previously cited).
The instant rejection is maintained from the previous Office Action and any newly recited portions are necessitated by claim amendment.
Claims 1 and 9 are directed to performing a database reading step to drive a processing unit to read a sepsis database and at least one database to be tested of a storing unit, wherein the sepsis database comprises a plurality of sepsis data, and the at least one database to be tested comprises a plurality of data to be tested; performing a data table creating step to drive the processing unit to create a sepsis data table according to the sepsis data and create a data table to be tested according to the data to be tested; performing a model training step to drive the processing unit to train a sepsis diagnosis model with the sepsis data table according to a K-fold cross-validation and a machine learning algorithm; and performing a sepsis predicting step to drive the processing unit to input the data table to be tested into the sepsis diagnosis model to calculate a sepsis prediction result; wherein the sepsis data are a patient basic data, a patient vital sign data and a patient blood test data; wherein the data table creating step comprises: performing a value extracting step to drive the processing unit to extract a maximum value, a minimum value and a latest value of the patient vital sign data; and performing a data integrating step to drive the processing unit to collect the maximum value, the minimum value, the latest value, the patient basic data and the patient blood test data to generate the sepsis data table; wherein the machine learning algorithm is an eXtreme Gradient Boosting (XGBoost), and a variable K of the K-fold cross-validation is 5;wherein the sepsis data are collected from a plurality of sepsis patients after a feature window, the feature window refers to a 12-hour period before a clinical recognition, and an inspection frequency of each of the sepsis patients within the feature window is not the same; wherein a mean area under curve of a Receiver Operating Characteristic Curve (ROC Curve) of the sepsis diagnosis model is 0.84, and a cut-off value of the sepsis diagnosis model is 0.5;wherein in response to determine that the sepsis prediction result is greater than or equal to the cut-off value, a subject is judged to be the sepsis patient.
Yuan teaches model selection several machine learning methods, such as Logistic Regression, Support Vector Machine, XGBoost, and Neural Network, and use 5-fold cross-validation for assessment of model performance [p. 2, col. 2, par4]. Cross-validation is a resampling procedure used to evaluate machine learning models, and “5” refers to the number of groups that a given data sample is to be split into [p. 2, col. 2, par4]. Yuan teaches performing evaluation on 7 different AI models and providing AUROC scores for each [supp, sup tbl. 1] where XgBoost had the highest AUROC of 0.89. Yuan further teaches after adjusting the probability threshold for sepsis diagnosis so that the F1 score (the harmonic average of the precision and recall) of our model is maximized, they found that XGBoost, a decision-tree-based algorithm, had the best performance and therefore, adopted the XGBoost for the AI algorithm development [p. 2, col. 2, par. 4]. Yuan also teaches using XGBoost to fit the training dataset and the probability threshold, they computed the sepsis probability of testing dataset as well as the accuracy, recall (sensitivity), specificity, and precision given that specific threshold [p. 3, col. 1, par 1.]. Yuan further teaches computing a receiver operating characteristic (ROC) curve and precision-recall curve and adopting these two curves as the major factors for performance evaluation [p. 2, col. 2, par. 1]. Yuan also teaches performing a feature weight analysis to differentiate the contribution of each feature in the performance of the algorithm to evaluate the algorithm’s fitness to the clinical scenario. Feature weighting is closely associated with AUROC, sensitivity, and precision of an algorithm and can be obtained during cross-validation in model training. Yuan further teaches using a minimum and maximum value for white blood cell count compared to test subject for clinical presentation of sepsis [p. 4, tbl. 2 and supp. tbl. 2] which reads on a patient blood test data. Yuan further teaches for the laboratory test, we use the latest result [p. 2, col. 2, par. 1]. Yuan also teaches body temperature range of <36 C or >38C compared to test subject data [supp. Tbl. 2] which reads on a patient vital sign data. Yuan further teaches the five vitals-sign-based novel features were generated using the following methods: First, we adopted the total mean and variance over the 12-h window, prior to the time of labeling, as two novel features [p. 2, col. 2, par. 3]. Yuan also teaches the vital sign data over the 12-h window were further split in half, grouped into the “former” group (7−12 h before the time of diagnosis) and the “latter” group (1−6 h prior to time of diagnosis) [p. 2, col. 2, p[ar. 3] which reads on inspection frequency of each of the sepsis patients within the feature window is not the same.
A skilled artisan would readily appreciate that the ROC of .89 in the prior art and the ROC of .84 of the claims are an analysis of the data and it would be obvious based on the data being analyzed with respect to samples that the ROC can be higher or lower.
Claims 4 and 12 are directed to wherein the patient basic data comprises a patient biological age information and a patient sexuality information.
Yuan teaches age and gender distribution of sepsis and non-sepsis cohort [p. 3, tbl. 1].
Claims 5 and 13 are directed to wherein the patient vital sign data comprises a temperature, a respiration rate, a Systolic Blood Pressure (SBP), a Diastolic Blood Pressure (DBP), a heart rate, a Glasgow Coma Scale (GCS) and a peripheral oxygen saturation (Sp02).
Yuan teaches vital sign data, which had hourly sequential records, including heart rate, systolic pressure, diastolic pressure, mean arterial pressure, respiratory rate, body temperature, and peripheral capillary oxygen saturation [p. 2, col. 2, par. 2] and GCS [p. 4, tbl 2].
Claim 7 and 15 are directed to wherein the model training step comprises: performing an initial model training step to drive the processing unit to cut the sepsis data table into K data sets according to the K-fold cross-validation, wherein the K data sets comprise K-1 training sets and a validation set, and then the processing unit trains the K-1 training sets according to a plurality of initial hyperparameters and the machine learning algorithm to generate a plurality of initial models corresponding to each of the initial hyperparameters; performing a target hyperparameter selecting step to drive the processing unit to calculate the initial models through the validation set to generate a plurality of mean area under curves corresponding to the initial models, and then compare the mean area under curves to select a target hyperparameter from the initial hyperparameters; and performing a sepsis diagnosis model training step to drive the processing unit to retrain the sepsis data table according to the target hyperparameter and the machine learning algorithm to generate the sepsis diagnosis model.
Yuan teaches model selection several machine learning methods, such as Logistic Regression, Support Vector Machine, XGBoost, and Neural Network, and use 5-fold cross-validation for assessment of model performance [p. 2, col. 2, par4]. Cross-validation is a resampling procedure used to evaluate machine learning models, and “5” refers to the number of groups that a given data sample is to be split into [p. 2, col. 2, par4]. Yuan teaches performing evaluation on 7 different AI models and providing AUROC scores for each [supp, sup tbl. 1] where XgBoost had the highest AUROC of 0.89. Yuan further teaches after adjusting the probability threshold for sepsis diagnosis so that the F1 score (the harmonic average of the precision and recall) of our model is maximized, they found that XGBoost, a decision-tree-based algorithm, had the best performance and therefore, adopted the XGBoost for the AI algorithm development [p. 2, col. 2, par. 4]. Yuan also teaches using XGBoost to fit the training dataset and the probability threshold, they computed the sepsis probability of testing dataset as well as the accuracy, recall (sensitivity), specificity, and precision given that specific threshold [p. 3, col. 1, par 1.]. Yuan further teaches computing a receiver operating characteristic (ROC) curve and precision-recall curve and adopting these two curves as the major factors for performance evaluation [p. 2, col. 2, par. 1]. Yuan also teaches performing a feature weight analysis to differentiate the contribution of each feature in the performance of the algorithm to evaluate the algorithm’s fitness to the clinical scenario. Feature weighting is closely associated with AUROC, sensitivity, and precision of an algorithm and can be obtained during cross-validation in model training.
B. Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Yuan et al. (Yuan, Kuo-Ching, et al. "The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit." International journal of medical informatics 141 (2020), and supplemental material, previously cited), in view of George-Gay et al. (George-Gay, Beverly, and Katherine Parker. "Understanding the complete blood count with differential." Journal of PeriAnesthesia Nursing 18.2 (2003), previously cited).
Claims 6 and 14 are directed to a white blood cell count, a red blood cell count, a hemoglobin concentration, a hematocrit, a mean corpuscular volume, a mean corpuscular hemoglobin, a mean corpuscular hemoglobin concentration, a platelet count, a red blood cell distribution width, a platelet distribution width, a mean platelet volume, a neutrophil, a lymphocyte, a monocyte, an eosinophil, a basophil and a C-reactive protein.
Yuan discloses patient blood test data that includes white blood cell count, hemoglobin, platelet, and C reactive protein as features adopted in the algorithm [supplement tbl. 2]. Yuan is silent on a red blood cell count, a hemoglobin concentration, a hematocrit, a mean corpuscular volume, a mean corpuscular hemoglobin, a mean corpuscular hemoglobin concentration, a red blood cell distribution width, a platelet distribution width, a mean platelet volume, a neutrophil, a lymphocyte, a monocyte, an eosinophil, and a basophil blood test result.
However, George-Gay discloses the complete blood count (CBC) with differential is one of the most common laboratory tests performed today [abstract]. George-Gay further discloses the neutrophil counts below 500/_L predispose the patient to serious bacterial infection and opportunistic infections of the skin, mouth, pharynx, and lungs, where counts that fall below 100, the chance of gram-negative and gram positive sepsis and fungal infections increases dramatically [p. 12, col. 2, par. 2].
In regards to claim(s) 6 and 14, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Yuan with George-Gay as they both are directed to diagnosing disease with patient blood test data. The motivation would have been to modify the methods of Yuan to include the CBC of George-Gay to as these tests are helpful in diagnosing anemia, certain cancers, infection, acute hemorrhagic states, allergies, and immunodeficiencies as well as monitoring for side effects of certain drugs that cause blood dyscrasias as disclosed by George-Gay [abstract].
Conclusion
No claims are allowed.
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/D.M.B./Examiner, Art Unit 1685 /Soren Harward/Primary Examiner, TC 1600