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 .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/04/2026 has been entered.
Status of the Application
Claims 1 and 5-9 are currently pending in this case and have been examined and addressed below. This communication is a Non-Final Rejection in response to the Amendment to the Claims and Remarks filed on 03/04/2026.
Claims 1 and 5-7 are currently amended.
Claims 2-4 are canceled and not considered at this time.
Claim 9 is newly added.
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 and 5-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claims 1 is directed to a modeling device to estimate survival rates of an individual (apparatus claim). However the claim does not define any structural elements of the apparatus. The specification does not provide a description of the modeling device beyond the monitoring module, data processing module, AI evaluation module and display module (see specification page 12). The specification does not limit the interpretation of claim limitations "monitoring module", “data processing module”, “XGBoost-based machine learning engine” and "a display" and hence can be interpreted as computer programs. Since a computer program is merely a set of instructions capable of being executed by a computer, the computer program itself is not a process or physical structural elements of the apparatus and the examiner therefore will treat a claim for a computer program, without the non-transitory computer-readable medium needed to realize the computer program’s functionality, as non-statutory functional descriptive material. Claims 5-9 are dependent on Claim 1. As dependent claims inherit the insufficiencies of the claims they depend on and do not remedy the deficiencies of the claims they depend on, they are also rejected.
Claims 1 and 5-9 are rejected because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1 and 5-9 do not fall into one of the statutory categories because they do not include structural elements of an apparatus or system, as described above. However, for purposes of compact prosecution, the claims will be interpreted as a system.
Step 2A, Prong One
As per Claim 1, the limitations of estimating survival rates of a critically ill or ICU patient comprising: performing data quantification and normalization, including but not limited to missing value imputation, maximum calculation, minimum calculation, mean calculation, standard deviation calculation, median calculation, quartile deviation calculation, and data dimensionality reduction, on daily clinical data of the individual patient and creating related applicable clinical data; and inputting the related applicable data and generating evaluation results predicting the 30-day, 60-day, and 90-day survival rates of the individual patient, under its broadest reasonable interpretation, covers mathematical concepts. The steps of data quantification and normalization are described as including mathematical calculations as algorithms or equations such as missing value imputation, maximum calculation, minimum calculation, mean calculation, standard deviation calculation, median calculation, quartile deviation calculation, and data dimensionality reduction, which are mathematical concepts. Additionally, generating evaluation results predicting survival rates of the patient is a mathematical concept because it uses a specific mathematical equation/algorithm (XGBoost-based machine learning) for carrying out a calculation. The claim also includes use of the XGBoost-based machine learning engine to carry out steps such as dividing the clinical data into a training dataset for machine learning and a testing dataset in a predetermined ratio, performing model training on the training dataset, performing testing on the testing dataset, undergoing verification to refine the engine, and self-evaluating the performance of the model with standard performance evaluation indicators, which further specify the mathematical calculations, equations, or algorithms used in the model. Therefore, the claim is directed to mathematical concepts. If a claim limitation, under its broadest reasonable interpretation, includes mathematical equations, algorithms, or calculations, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application because the additional elements and combination of additional elements do not impose meaningful limits on the judicial exception. In particular, the claims recite the additional element – a monitoring module, data processing module, XGBoost-based machine learning engine, and display module. The modules are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The XGBoost-based machine learning engine is a mathematical algorithm which is used to carry out the abstract idea. The use of a mathematical algorithm to apply the abstract idea amounts to mere instructions to apply the exception, as per MPEP 2106.05(f)(2). The claims also recite the additional elements of collecting clinical data about the individual patient, the clinical data comprising personal features, test report data, and physiology measurements of the individual patient, receiving the evaluation result, and displaying the estimated 30-day, 60-day, and 90-day survival rates of the individual patient and a description of contributions of features in all categories, which amounts to insignificant extra-solution activity, as in MPEP 2106.05(g), because the steps of collecting data, receiving data, and displaying data are mere data gathering in conjunction with the abstract idea where the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). Because the additional elements do not impose meaningful limitations on the judicial exception, the claim is directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with the respect to integration of the abstract idea into a practical application, the additional element of a monitoring module, data processing module, and display module to perform the method of the invention amounts to no more than mere instructions to apply the exception using a generic computing component. The modules are recited at a high level of generality and are recited as generic computer components with no specific structure provided for the modules, which do not add meaningful limitations to the abstract idea beyond mere instructions to apply an exception. The XGBoost-based machine learning engine is a mathematical algorithm which applies the abstract idea, which is found to be mere instructions to apply the exception, as per MPEP 2106.05(f)(2). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims also include the additional elements of collecting clinical data about the ICU patient, receiving the evaluation result, and displaying the estimated 30-day, 60-day, and 90-day survival rates of the patient and a description of contributions of features in all categories, which are elements that are well-understood, routine and conventional computer functions in the field of data management because they are claimed at a high level of generality and include receiving or transmitting data, storing and retrieving information from memory, and presenting data, which have been found to be well-understood, routine and conventional computer functions by the Court (MPEP 2106.05(d)(II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); 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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added), (iv) Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, and (iv). presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves another technology. The claims do not amount to significantly more than the underlying abstract idea.
Dependent Claims
Dependent Claims 5-9 add further limitations which are also directed to an abstract idea. For example, Claims 5-8 further describe the personal features and evaluation data which is collected in Claim 1, which merely further limits or specifies the limitations of the independent claim and is therefore directed to the same abstract idea. Claim 9 includes comparing the determined AU-ROC with a corresponding AU-ROC of APACHE II and SOFA, which can be performed using human mental evaluation, observation, judgment, and opinion and therefore falls into the abstract grouping of a Mental Process. The comparing is performed using the XGBoost-based machine learning engine which amounts to applying the machine learning engine to the abstract idea, which is mere instructions to apply the exception for the same reasons as the independent claim. Because the additional elements do not impose meaningful limitations on the judicial exception and the additional elements are well-understood, routine and conventional functionalities in the art, the claims are directed to an abstract idea and are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 6, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Ong et al. (US 2011/0224565 A1), hereinafter Ong, in view of Fusaro (US 2021/0391085 A1), hereinafter Fusaro, in view of Sweeney et al. (US 2023/0374589 A1), hereinafter Sweeney.
As per Claim 1, Ong discloses an artificial intelligence (AI) modeling device estimating survival rates of an individual critically ill or ICU (intensive care unit) patient, comprising:
a monitoring module collecting clinical data about the individual patient, the clinical data comprising personal features, test report data, and physiology measurements of the individual patient ([0007] measuring heart rate variability data, vital sign data, and obtaining patient characteristics of a patient, where a patient indicates an individual patient);
a data processing module performing data quantification and normalization ([0007] providing sets of normalized data values, [0096] processing the parameters to produce processed data, [0098] processed data is parameters represented as normalized data, also see [0106]), including but not limited to missing value imputation, maximum calculation, minimum calculation, mean calculation, standard deviation calculation, median calculation, quartile deviation calculation, and data dimensionality reduction ([0221] reducing feature dimensionality), on daily clinical data of the individual patient and creating related, applicable clinical data (Ong [0010] collection of patient information within time limits of 24 hours, i.e. daily);
a machine learning engine inputting the related, applicable clinical data and generating evaluation results predicting survival rates of the individual patient ([0096] input the processed data into the artificial neural network to generate an output which is a prediction about the survivability of the patient); and
a display receiving the evaluation result and displaying the estimated survival rates of the individual patient and a description of contributions of features in all categories, ([0293] output a prediction and display the prediction of survivability of the patient, see Fig. 20 which displays the overall prediction and the variables/features).
and wherein the machine learning engine further divides the clinical data into a training dataset and a testing dataset in a predetermined ratio (see Fig. 9/Fig. 22 where data is separated into testing data and training data; [0263] partition the data set by selecting N patients into training set and N patients into testing data set, [0348] separate patients into training (75 patients) and testing (25 patients) where a predetermined 75% training data samples), performs model training on the training dataset (see Fig. 9 where the training data is used for model learning/training the model, [0222] classifier trained with training samples), performs testing on the testing dataset (see Fig. 9 feature selection using testing data, [0224-0225] using testing data to label data; also see [0257], [0263] testing set used to predict labels for the data), undergoes verification to refine the XGBoost-based machine learning engine ([0331] validate the algorithm, [0340] analyzing the parameters and determining the final prediction model), and self-evaluates the performance of the XGBoost-based machine learning engine with standard performance evaluation indicators([0263] calculate the accuracy sensitivity and specificity of the model based on the labels, see [0337]) including area under a receiver operating characteristic curve (AU-ROC), FI score, precision, recall, and accuracy (see Fig. 29 Accuracy, sensitivity (recall), specificity (precision); [0327] area under curve is determined, [0332-0337] performance measures, sensitivity, specificity, accuracy calculated, Examiner notes that based on the calculation of True positives and true negatives, it would be obvious to determine an F1 score).
However, Ong may not explicitly disclose the following which is taught by Fusaro:
the machine learning engine is an XGBoost-based machine learning engine inputting the related, applicable clinical data and generating evaluation results predicting survival rates of the individual patient ([0018] machine learning model for predicting mortality rate for a patient in the hospital/ICU, machine learning classifier is based on decision tree algorithm which is based on XGBoost algorithm, which is referred to as XGB tree classifier, [0021] determine features which are to be input to model to predict mortality rates, where the determined features are those that are related, applicable data, Examiner interprets the mortality rate to be equivalent to determining a survivability rate as one metric determines the other).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of inputting data to an XGBoost-based model to predict survival rates of a patient from Fusaro with the generating of survival rates for a patient using artificial intelligence from Ong in order to determine valuable predictions for a particular patient at a particular hospital to improve patient care (Fusaro [0003]).
However, Ong and Fusaro may not explicitly disclose the following which is taught by Sweeney: the survival rates of the patient are for the 30-day, 60-day, and 90-day survival rates of the ICU patient ([0039] calculating an outcome in a subject such as survival for an ICU admission for 30 days, 60 days, or 90 days).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of generating survival rates of varying timeframes for a patient in the ICU from Sweeney with the generating of survival rates for a patient using artificial intelligence from Ong and Fusaro in order to determine whether a patient needs close monitoring and to provide proper selection of treatment for a patient (Sweeney [0003]).
As per Claim 6, Ong, Fusaro, and Sweeney discloses the limitations of Claim 1. Ong also teaches the physiology measurements of the individual patient are made within 24 hours ([0010-0011] time limit for data collection/analysis is between 4 and 24 hours, [0007] measuring parameters of a patient, which indicates an individual patient), and the
test report data is the latest piece of blood test data collected within 48 hours ([0010-0011] time limit for data collection/analysis is between 4 and 72 hours where 48 hours is within this range).
As per Claim 9, Ong, and Fusaro, and Sweeney discloses the limitations of Claim 1. However, Ong may not explicitly disclose the following which is taught by Fusaro:
the machine learning engine is an XGBoost-based machine learning engine inputting the related, applicable clinical data and generating evaluation results predicting survival rates of the individual patient ([0018] machine learning model for predicting mortality rate for a patient in the hospital/ICU, machine learning classifier is based on decision tree algorithm which is based on XGBoost algorithm, which is referred to as XGB tree classifier, [0021] determine features which are to be input to model to predict mortality rates, where the determined features are those that are related, applicable data, Examiner interprets the mortality rate to be equivalent to determining a survivability rate as one metric determines the other).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of inputting data to an XGBoost-based model to predict survival rates of a patient from Fusaro with the generating of survival rates for a patient using artificial intelligence from Ong in order to determine valuable predictions for a particular patient at a particular hospital to improve patient care (Fusaro [0003]).
However, Ong and Fusaro may not explicitly disclose the following which is taught by Sweeney: the machine learning engine compares the determined AU-ROC with a corresponding AU-ROC of Acute Physiology and Chronic Health Evaluation (APACHE) II and with a corresponding Sequential Organ Failure Assessment (SOFA) score ([0216] compare AU-ROC for the signature classifier which represents the performance for the subset of patients used in the classifier to the AUROC for APACHE II and SOFA, see Table 9A).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of an comparing AU-ROC of the prediction model with the AU-ROC of APACHE II and SOFA scores from Sweeney with the XGBoost-based machine learning model for generating survival rates for a patient from Ong and Fusaro in order to improve the performance of the score in determining risk of mortality/survivability (Sweeney [0040]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Ong (US 2011/0224565 A1), in view of Fusaro (US 2021/0391085 A1), in view of Sweeney (US 2023/0374589 A1), in view of O’Callaghan (O'Callaghan DJ, Jayia P, Vaughan-Huxley E, Gribbon M, Templeton M, Skipworth JR, Gordon AC. An observational study to determine the effect of delayed admission to the intensive care unit on patient outcome. Crit Care. 2012 Oct 1;16(5):R173.), hereinafter O’Callaghan.
As per Claim 5, Ong, Fusaro, and Sweeney discloses the limitations of Claim 1. Ong also teaches the personal features include categories as follows:
first category: pregnancy state, age, sex, body height, body weight, body mass index (BMI), and smoking history ([0059]/[0075] input data such as age, gender of patient; see Fig. 4 input of age, gender, medical history, where it would be obvious to a person of ordinary skill in the art that a patient’s pregnancy state/height/weight/BMI/smoking is all part of their routine medical history, [0008] collect information including demographics and patient medical history).
However, Ong may not explicitly disclose the following which is taught by O’Callaghan:
second category: Where was the patient before being admitted to the ICU? Did the patient receive cardiopulmonary resuscitation before being admitted to the ICU? Did a cardiac arrest event occur before being admitted to the ICU? (Page 3 data indicates where patient was admitted from including the ward, the emergency department or the theatre suite, Page 3 Table 1 where data is collected for the patient to indicate if patient has been resuscitated after cardiac arrest, Page 4 Table 2 demographics for patient include cause for ICU admission including cardiac arrest/failure) and
third category: Did the patient receive elective surgery before being admitted to the ICU? Was admission to the ICU planned? Did the patient receive intubation for mechanical ventilation? How was the partial pressure of inspired oxygen (FiO2) (Page 4 Table 2 demographics collected for patient include Intubated, Lowest FiO2, referred from surgical, and reason for admission is operative intervention, i.e. had surgery before being admitted, intubated in first 24 hours after referral).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of patient medical data collected from ICU patients from O’Callaghan with the generating of survival rates for a patient using artificial intelligence of Ong, Fusaro, and Sweeney in order to determine variables of patient history that impact ICU mortality rates (O’Callaghan Page 1 Methods).
Claim 7 are rejected under 35 U.S.C. 103 as being unpatentable over Ong (US 2011/0224565 A1), in view of Fusaro (US 2021/0391085 A1), in view of Sweeney (US 2023/0374589 A1), in view of Bihorac et al. (US 2022/0044809 A1), hereinafter Bihorac.
As per Claim 7, Ong, Fusaro, and Sweeney discloses the limitations of Claim 1. Ong also teaches the physiology measurements of the individual patient include categories as follows:
first category: body temperature, heart rate, respiratory rate (and its oxygen utilization or mechanical ventilation state), systolic blood pressure, systolic blood pressure, and Glasgow Coma Scale (GCS) (see Fig. 11 where characteristics include temperature, respiratory rate, SpO2, pulse, systolic and diastolic blood pressure, GCS, [0007] measuring parameters of a patient, which indicates an individual patient).
Ong, Fusaro, and Sweeney may not explicitly disclose the following which is taught by Bihorac: mean arterial pressure; and second category: volume of urine excreted in a 24-hour period ([0081] data captured for a patient as indicators of health include mean arterial pressure, and urine output).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of data collected for a patient includes mean arterial pressure and urine volume from Bihorac with the generating of survival rates for a patient using artificial intelligence from Ong, Fusaro, and Sweeney in order to using variables representing all organ systems to predict mortality of a patient accurately (Bihorac [0004]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Ong (US 2011/0224565 A1), in view of Fusaro (US 2021/0391085 A1), in view of Sweeney (US 2023/0374589 A1), in view of Kim et al. Kim Y, Kwon S, Kim SG, Lee J, Han CH, Yu S, Kim B, Paek JH, Park WY, Jin K, Han S, Kim DK, Lim CS, Kim YS, Lee JP. Impact of decreased levels of total CO2 on in-hospital mortality in patients with COVID-19. Sci Rep. 2023 Oct 4;13(1):16717), hereinafter Kim.
As per Claim 8, Ong, Fusaro, and Sweeney discloses the limitations of Claim 6. Ong, Fusaro, and Sweeney may not explicitly disclose the following which is taught by Kim: the test report data includes white blood cell count, hemoglobin, platelet count, blood sodium level, blood potassium level, blood creatinine level (mg/dL), estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN) (mg/dL), serum albumin level (g/dL), blood bilirubin level (mg/dL), blood sugar level (mg/dL), blood lactic acid level, partial pressure of carbon dioxide in arterial blood (PaCO2), partial pressure of oxygen in arterial blood (PaO2), and arterial pH (Page 3 Table 1 shows the patient characteristics collected to be used as variables in the model including WBC (white blood cell count), hemoglobin, platelets, potassium, creatinine, eGFR, BUN, albumin, bilirubin, glucose, Page 5 final paragraph includes details of arterial blood gas analysis for the patient which includes PaCO2, PaO2, and pH, also Page 7 Clinical parameters and data acquisition/Sensitivity analysis gives the details of all the laboratory data collected from the patient to determine the outcome of mortality risk).
Therefore, it would have been obvious to a person of ordinary skill in the art before the filing of the present application to combine the known concept of using laboratory data collected for a patient from Kim with the generating of survival rates for a patient using artificial intelligence from Ong, Fusaro, and Sweeney in order to determine what variables are good indicators to predict prognosis for the patient (Kim Page 1, Abstract).
Response to Arguments
Applicant’s arguments, see Pages 5-7, filed 03/04/2026 with respect to claims 1 and 5-9 with regard to U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues that the amendments to the claims recite specific elements which provide significantly more than the abstract idea. Specifically, Applicant argues that reciting a monitoring module that collects physiology measurements of the individual patient provides significantly more than the abstract idea because it is not simple generic gathering or manipulation of data but rather a more specific requirement of obtaining physiological measurements of a specific, individual patient. Examiner respectfully disagrees. The monitoring module is recited at a high-level of generality and not specified in the specification beyond merely a part of the AI model device. Therefore, the monitoring module amounts to mere instructions to apply the exception. The module carries out the function of collecting clinical data about the individual patient. Collecting data, regardless of whether the data is from an individual or a group of patients, is necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). The data which is gathered is used for the data analysis of the abstract idea. Mere data gathering amounts to insignificant extra-solution activity, which does not integrate the abstract idea into a practical application. The collection of clinical data from a monitoring module is well-understood, routine, and conventional computer functions claimed in a merely generic manner such as receiving or transmitting data or storing and retrieving information in memory, as described in MPEP 2106.05(d)(II). Therefore, this does not amount to significantly more than the abstract idea and the rejection is maintained.
Applicant’s arguments, see Pages 7-10, filed 03/04/2026 with respect to claim 1 with regard to U.S.C. 103 have been fully considered but they are not persuasive.
Applicant argues that Ong, Fusaro, or Sweeney fail to teach the newly recited limitations of collecting clinical data about the individual patient, the clinical data comprising personal features, test report data, and physiology measurements of the individual patient. Examiner respectfully disagrees. Ong teaches in [0007], measuring heart rate variability data, vital sign data, and obtaining patient characteristics of a patient, where a patient indicates an individual patient. This teaches three separate types of clinical data collected from a patient which include measurement of heart rate variability (physiology measurement), vital sign data (test report data, under BRI of the term test report which can be any report of tested data), and patient characteristics (personal features). Therefore, the newly amended limitation is taught by the Ong reference.
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the references are all analogous art which are in the same field of endeavor to make medical predictions about a patient’s health, specifically risk of survival or mortality, using machine learning techniques. The rejections above include motivations to combine each reference from the references themselves. Applicant submits that the Examiner has failed to provide an “articulated reasoning with some rationale underpinning to support the legal conclusion of obviousness”. However, there is a rationale with motivation from the specified prior art reference for each combination of claim limitations provided in the 103 rejections above. Applicant does not provide any particular limitation or teaching from the cited prior art which would not be obvious based on the given citation in the prior art or the provided motivation to combine.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Evangeline Barr whose telephone number is (571)272-0369. The examiner can normally be reached Monday to Friday 8:00 am to 4:00 pm.
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/EVANGELINE BARR/Primary Examiner, Art Unit 3682