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
Status of the Application
Claims 10-16 are currently pending in this case and have been examined and addressed below. This communication is a Final Rejection in response to the Amendment to the Claims and Remarks filed on 05/29/2026.
Claims 1-9 are canceled and not considered at this time.
Claims 10-16 are newly added.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 10-16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 10 recites “quantizing and normalizing the collected clinical data” in line 6. However, there is not sufficient support in the specification for quantizing collected clinical data. There is no description of quantizing clinical data in the specification. The specification does disclose data quantification (Page 5, Para. 3 and Page 11, Para. 3) and data quantification on the clinical data (Page 12, Para. 3). However, data quantification and data quantizing do not have the same scope. Quantifying data, under BRI, means to measure the quantity of. Quantizing data, under BRI, means to subdivide into small increments. These do not result in the same functionality to be performed on the collected clinical data and therefore, there is not sufficient support for the concept of quantizing the collected clinical data.
As per Claims 11-16, the claims depend on Claim 10 and do not remedy the written description requirement issues of Claim 10. As dependent claims inherit the deficiencies of the claims they depend on, they are also rejected.
Claim Rejections - 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.
Claim 16 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 16 recites the limitation "the histogram of the estimated survival rates" in line 1-2. There is insufficient antecedent basis for this limitation in the claim. Claim 1 recites a histogram of the evaluation results, but this is not the same as the histogram of estimated survival rates.
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 10-16 are rejected because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 10-16 fall into the statutory category of a process.
Step 2A, Prong One
As per Claim 10, the limitations of estimating survival rates of an individual critically ill patient comprising: quantizing and normalization, including imputing missing values, calculating maximums, minimums, means, medians, and standard and quartile deviation measures, and reducing data dimensionality; inputting the quantized and normalized data and generating evaluation results predicting the 30-day, 60-day, and 90-day survival rates of the individual patient; dividing the clinical data into a training dataset and a testing dataset in a predetermined ratio; performing model training of the XGBoost-based machine learning engine with the training dataset; and performing testing with the testing dataset such that the XGBoost-based machine learning engine is verified and refined with standard performance evaluation indicators including area under a receiver operating characteristic curve, F1 Score, precision, recall, and accuracy, under its broadest reasonable interpretation, covers mathematical concepts. The steps of quantizing and normalizing 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 steps of dividing the clinical data into a training dataset and a testing dataset in a predetermined ratio, performing model training of the XGBoost-based machine learning engine with the training dataset, performing testing with the testing dataset, such that the XGBoost-based machine learning engine is verified and refined with standard performance evaluation indicators including area under a receiver operating characteristic curve, F1 score, precision, recall, and accuracy. These steps describe the training of the model which is not described beyond that which is data manipulations of an XGBoost machine learning engine which includes mathematical relationships. These steps also describe performing testing which involves calculation of evaluation indicators. The calculation of AUC, F1 score, precision, recall, and accuracy are mathematical calculations, equations, or algorithms. 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 – an XGBoost-based machine learning engine. 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, and displaying a histogram of the evaluation results as 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 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 and displaying a histogram of the evaluation results as 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 11-16 add further limitations which are also directed to an abstract idea. For example, Claims 11-14 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 15 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. Claim 16 includes displaying the histogram of the estimated survival rates of an ICU patient with an ICU survival rate and a hospital survival rate, which is an additional element that amounts to insignificant extra-solution activity that is well-understood, routine, and conventional because it amounts to mere data outputting, for similar reasons to Claim 1. 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 10, 12, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Ong et al. (US 2011/0224565 A1), hereinafter Ong, in view of Hong et al. (US 2021/0282649 A1), hereinafter Hong, 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 10, Ong discloses a method of estimating survival rates of an individual critically ill patient, comprising:
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);
quantizing and normalizing the collected clinical data ([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 calculating maximums, minimums, means, medians, and standard and quartile deviation measures ([0181] determining a maximum peak value, minimum value; [0329] performing min max normalization on the data; [0067] calculate median value and standard deviation value; [0069] calculating mean value, standard deviation measure of the data ), and reducing data dimensionality ([0221] reducing feature dimensionality);
inputting the quantized and normalized clinical data into a machine learning engine 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
displaying the evaluation results as 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, , [0224] feature selection is performed to combine feature vectors for the discriminatory information, i.e. select the features which contribute to the prediction see Fig. 20 which displays the overall prediction and the details of variables/features);
dividing 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);
performing model training with the training dataset (see Fig. 9 where the training data is used for model learning/training the model, [0222] classifier trained with training samples);
performing testing with 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), such that the machine learning engine is verified and refined ([0331] validate the algorithm, [0340] analyzing the parameters and determining the final prediction model), 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).
Examiner notes that it would be obvious to a person of ordinary skill in the art at the time the invention was filed to provide the display of the evaluation results in the form of a histogram because the manner of presenting the data is a matter of design choice or aesthetic design change. The choice in how to present the evaluation results does not have an impact on the function of the claim. See MPEP 2144.04, In re Seid, 161 F.2d 229, 73 USPQ 431 (CCPA 1947) (Claim was directed to an advertising display device comprising a bottle and a hollow member in the shape of a human figure from the waist up which was adapted to fit over and cover the neck of the bottle, wherein the hollow member and the bottle together give the impression of a human body. Appellant argued that certain limitations in the upper part of the body, including the arrangement of the arms, were not taught by the prior art. The court found that matters relating to ornamentation only which have no mechanical function cannot be relied upon to patentably distinguish the claimed invention from the prior art.). But see Ex parte Hilton, 148 USPQ 356 (Bd. App. 1965) (Claims were directed to fried potato chips with a specified moisture and fat content, whereas the prior art was directed to french fries having a higher moisture content. While recognizing that in some cases the particular shape of a product is of no patentable significance, the Board held in this case the shape (chips) is important because it results in a product which is distinct from the reference product (french fries).); and In re Dailey, 357 F.2d 669, 149 USPQ 47 (CCPA 1966) (The court held that the configuration of the claimed disposable plastic nursing container was a matter of choice which a person of ordinary skill in the art would have found obvious absent persuasive evidence that the particular configuration of the claimed container was significant.).
However, Ong may not explicitly disclose the following which is taught by Hong: quantizing the collected clinical data including imputing missing values ([0061], [0066-0068], [0117]data imputation performed to fill in missing values, see also Claim 14 imputing a value for missing data).
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 imputing missing values from Hong with the generating of survival rates for a patient using artificial intelligence from Ong in order to remedy a situation in which data is missing based on clinical tests not being properly requested or results consistently collected in order to create a more reliable dataset for predictions (Hong [0065]).
However, Ong and 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 12, Ong, Hong, Fusaro, and Sweeney discloses the limitations of Claim 10. 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 15, Ong, Hong, Fusaro, and Sweeney discloses the limitations of Claim 10. However, Ong and Hong 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 and Hong in order to determine valuable predictions for a particular patient at a particular hospital to improve patient care (Fusaro [0003]).
However, Ong, Hong, 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, Hong, and Fusaro in order to improve the performance of the score in determining risk of mortality/survivability (Sweeney [0040]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Ong (US 2011/0224565 A1), in view of Hong (US 2021/0282649 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 11, Ong, Hong, Fusaro, and Sweeney discloses the limitations of Claim 10. 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, Hong, Fusaro, and Sweeney in order to determine variables of patient history that impact ICU mortality rates (O’Callaghan Page 1 Methods).
Claim 13 are rejected under 35 U.S.C. 103 as being unpatentable over Ong (US 2011/0224565 A1), in view of Hong (US 2021/0282649 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 13, Ong, Hong, Fusaro, and Sweeney discloses the limitations of Claim 10. 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, Hong, 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, Hong, Fusaro, and Sweeney in order to using variables representing all organ systems to predict mortality of a patient accurately (Bihorac [0004]).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Ong (US 2011/0224565 A1), in view of Hong (US 2021/0282649 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 14, Ong, Hong, Fusaro, and Sweeney discloses the limitations of Claim 10. Ong, Hong, 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, Hong, Fusaro, and Sweeney in order to determine what variables are good indicators to predict prognosis for the patient (Kim Page 1, Abstract).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Ong (US 2011/0224565 A1), in view of Hong (US 2021/0282649 A1), in view of Fusaro (US 2021/0391085 A1), in view of Sweeney (US 2023/0374589 A1), in view of Hart et al. (Hart, WH, Wolf H, Schneider CP, Küchenhoff H, Jauch KW. Acute and long-term survival in chronically critically ill surgical patients: a retrospective observational study. Crit Care. 2007;11(3):R55.), hereinafter Hart.
As per Claim 16, Ong, Hong, Fusaro, and Sweeney discloses the limitations of Claim 10. Ong, Hong, Fusaro, and Sweeney may not explicitly disclose the following which is taught by Hart: displaying the histogram of the estimated survival rates of an ICU patient with an ICU survival rate and a hospital survival rate (Table 2 shows the determined survival rates for an ICU patient and also the survival rate of the hospital, i.e. general population over different time periods of stay).
Examiner notes that it would be obvious to a person of ordinary skill in the art at the time the invention was filed to provide the display of the estimated survival rates of an ICU patient with ICU survival rate and hospital survival rate in the form of a histogram because the manner of presenting the data is a matter of design choice or aesthetic design change. The choice in how to present the evaluation results does not have an impact on the function of the claim. See MPEP 2144.04, In re Seid, 161 F.2d 229, 73 USPQ 431 (CCPA 1947) (Claim was directed to an advertising display device comprising a bottle and a hollow member in the shape of a human figure from the waist up which was adapted to fit over and cover the neck of the bottle, wherein the hollow member and the bottle together give the impression of a human body. Appellant argued that certain limitations in the upper part of the body, including the arrangement of the arms, were not taught by the prior art. The court found that matters relating to ornamentation only which have no mechanical function cannot be relied upon to patentably distinguish the claimed invention from the prior art.). But see Ex parte Hilton, 148 USPQ 356 (Bd. App. 1965) (Claims were directed to fried potato chips with a specified moisture and fat content, whereas the prior art was directed to french fries having a higher moisture content. While recognizing that in some cases the particular shape of a product is of no patentable significance, the Board held in this case the shape (chips) is important because it results in a product which is distinct from the reference product (french fries).); and In re Dailey, 357 F.2d 669, 149 USPQ 47 (CCPA 1966) (The court held that the configuration of the claimed disposable plastic nursing container was a matter of choice which a person of ordinary skill in the art would have found obvious absent persuasive evidence that the particular configuration of the claimed container was significant.).
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 displaying the ICU and overall hospital survival rates from Hart with the generating of survival rates for a patient using artificial intelligence from Ong, Hong, Fusaro, and Sweeney in order to compare whether ICU stays impact survival rates over time (Hart Page 6, Col. 1 Para One).
Response to Arguments
Applicant’s arguments, see Page 5, filed 05/29/2026 with respect to claims 10-16 with regard to U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues that new claims 10-16 are in full compliance with 35 U.S.C. §101. However, there are no specific arguments as to why the claims are not directed to an abstract idea. Examiner is not persuaded and the claims are rejected as per the rejection above.
Applicant’s arguments, see Pages 5-7, filed 05/29/2026 with respect to newly added claims 10-16 with regard to U.S.C. 103 have been fully considered. As the claims are newly added, they are newly rejected, as per the 103 rejections above.
Applicant argues that the previously cited references do not teach displaying a histogram of the evaluation results and contributions of features. Examiner notes that the displaying of information in the form of a histogram is a matter of design choice, as per MPEP 2144.04, as it does not add functionality to the claim. Ong teaches displaying the evaluation results and also determining the features which contribute to the prediction through feature selection and also discloses outputting the prediction as well as details about a feature which contributes to the prediction ([0293] output a prediction and display the prediction of survivability of the patient, [0224] feature selection is performed to combine feature vectors for the discriminatory information, i.e. select the features which contribute to the prediction see Fig. 20 which displays the overall prediction and the details of variables/features).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/EVANGELINE BARR/Primary Examiner, Art Unit 3682
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