DETAILED ACTION
Acknowledgements
This office action is in response to the claims filed June 12, 2026.
Claims 1, 2, 4, 6, 8, 10, 12-15, 17, 19-20, 22-24, 26, 28-33, 35, 37, and 55 are pending.
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 .
Request for Continued Examination
Claims 1, 2, 4, 6, 8, 10, 12-15, 17, 19-20, 22-24, 26, 28-33, 35, 37, and 55 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 2, 4, 6, 8, 10, 12-15, 17, 19-20, 22-24, 26, 28-33, 35, 37, and 55 are rejected to under 35 U.S.C 101 as not being directed to eligible subject matter based on the grounds set out in detail below:
Independent Claims 1, 19, 37, and 55:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
Independent claim 1 falls within the statutory category of machine
Independent claim 19 and 55 fall within the statutory category of method.
Independent claim 37 falls within the statutory category of article of manufacture.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Independent claims 1, 19, 37, and 55 claimed invention is directed to an abstract idea without significantly more.
The claim elements which set forth the abstract idea in the independent claims 1, 19, 37, and 55 (claim 1 being representative) are:
predicting cardiovascular disease,
data of a plurality of cardiovascular risk factors to output a cardiovascular risk score, the plurality of cardiovascular risk factors subdivided into a first training data subset and a second training data subset,
wherein the first training data subset comprises a preselected subset of cardiovascular risk factors, and wherein the second training data subset comprises a remaining subset of cardiovascular risk factors
and wherein the preselected subset of cardiovascular risk factors are used as training inputs,
the remaining subset of cardiovascular risk factors to generate a single computed risk score value that consolidates the remaining subset of cardiovascular risk factors, wherein the single computed risk score value is used as a training input
input user-specific cardiovascular data of a user wherein the user is a member of a geographic region, wherein the user-specific cardiovascular data of the user is data of the user corresponding to the preselected subset of cardiovascular risk factors and the remaining subset of cardiovascular risk factors,
and wherein the outputs a user-specific cardiovascular prediction of the user, the user-specific cardiovascular prediction comprising a cardiovascular risk score of the user;
and display, the user-specific cardiovascular prediction.
The abstract idea is “certain methods of organizing human activity” by following rules and instructions to determine a cardiovascular disease prediction of risk(see MPEP § 2106.04(a)(2))
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For Independent claims 1, 19, 37, and 55 judicial exception is not integrated into a practical application.
Independent claim 1 recites the additional elements below:
A machine learning (ML)-based system
Trained ML model stored on a computer memory
a set of computing instructions stored on the computer memory
a processor communicatively coupled to the computer memory
a graphical user interface (GUI) with a display
a trained ensemble learning algorithm
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, (c) and (d), are recited as executing the abstract idea as “apply-it” or an equivalent to analyze data
The additional element, (a) and (b), is recited as “apply-it” or an equivalent to analyze data
The additional element, (e), is recited as “apply-it” or an equivalent to output data
The additional element, (f), is recited as “apply-it” or an equivalent (e.g. applies) to analyze data
Independent claims 19 and 55 do not recite any additional elements not already recited in the independent claim 1
Independent claim 37 recites the additional elements not already recited in the independent claim 1 below:
A tangible, non-transitory computer-readable medium
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, (a), is recited as executing the abstract idea as “apply-it” or an equivalent to analyze data
Accordingly, independent claims 1, 19, 37, and 55 as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as analyzed above in step 2A prong 2, are merely applying the abstract idea and therefore, do not amount to significantly more. The claims are patent ineligible.
Dependent Claims 2, 4, 6, 8, 10, 12-15, 17, 20, 22-24, 26, 28-33, and 35
Eligibility Step 1 (does the subject matter fall within a statutory category?):
Dependent claims 2, 4, 6, 8, 10, 12-15, 17 fall within a statutory category as a machine
Dependent claims 20, 22-24, 26, 28-33, and 35 fall within the statutory category of method.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Dependent claims 2, 4, 6, 8, 10, 12-15, 17, 20, 22-24, 26, 28-33, and 35 claimed invention is directed to an abstract idea without significantly more. The claims continue to limit the independent claims 1 and 19 abstract idea by (1) further limiting the types of mathematical analysis, (2) further limiting the cardiovascular risk factor data, and (3) further limiting the output of cardiovascular risk and planning. Therefore, the dependent claims inherit the same abstract idea of “certain methods of organizing human activity” by following rules and instructions to determine a cardiovascular disease prediction of risk (see MPEP § 2106.04(a)(2))
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For claims 2, 4, 6, 8, 10, 12-15, 17, 20, 22-24, 26, 28-33, and 35 this judicial exception is not integrated into a practical application.
The dependent claims recite no additional elements not already recited in the independent claims thus purely considered as further limiting the abstract idea.
Accordingly, dependent claims 2, 4, 6, 8, 10, 12-15, 17, 20, 22-24, 26, 28-33, and 35 as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The dependent claims do not include additional elements that amount to significantly more for the same reasons given in Prong 2. The claims are patent ineligible.
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.
Claims 1, 2, 4, 6, 8, 10, 12-15, 17, 19-20, 22-24, 26, 28-33, 35, 37, and 55 are rejected under 35 U.S.C. 103 as being unpatentable over Weng et. al (hereinafter Weng) (WO2024226519A2) in view of LAI et. al (hereinafter LAI) (WO2024238728A2) and in further view of MATHUR et. al (hereinafter MATHUR) (US20250316391Al)
As per claim 1, Weng teaches:
A machine learning (ML)-based system for predicting cardiovascular disease, the ML-based system comprising: an ML model stored on a computer memory, the ML model trained with data of a plurality of cardiovascular risk factors to output a cardiovascular risk score, ([0003] discloses, “In a first aspect, a method for training a composite model to predict cardiovascular disease risk is provided that includes: (i) training a machine learning model of the composite model to output, based on an input photoplethysmographic waveform, a first set of features that are representative of the photoplethysmographic waveform; (ii) determining, based on the photoplethysmographic waveform, a heart rate; (iii) determining, based on the first set of features, a mapping of the composite model to project the first set of features to a second set of features, wherein the second set of features includes fewer features than the first set of features; (iv) determining a set of model coefficients of the composite model to predict, based on the second set of features, the heart rate, and a set of demographic information, a cardiovascular disease risk score; and (v) outputting an indication of the machine learning model, mapping, and set of model coefficients of the composite model.” And see [0006] discloses, “In another aspect, a non-transitory computer readable medium is provided having stored thereon program instructions executable by at least one processor to cause the at least one processor to perform any of the above methods.” And see [0007] discloses, “In another aspect a system is provided that includes: (i) at least one processor; and (ii) a non-transitory computer-readable medium, having stored therein instructions executable by the at least one processor to cause the system to perform any of the above methods” and see [0025] Thus, in one form of the disclosure, a “composite model” is proposed comprising a machine learning model for processing an input photoplethysmographic waveform (e.g. captured from a certain human subject) to generate a first set of features representative of the waveform, optionally a dimensionality-reduction process (e.g. as explained above based on a plurality of eigenvectors associated with the composite model, such as determined based on a PCA analysis) to generate a second set of features from the first set, and a further model defined by a set of model coefficients designed to process the second set of features (or, in the absence of the dimensionality reduction model, the first set of features), and typically further data such as (i) data derived from the input photoplethysmographic waveform (i.e. derived other than via a machine learning model) such as a heart rate for the subject and/or (ii) data (e.g. demographic data) characterizing the subject, to output a cardiovascular disease risk score.)”)
the plurality of cardiovascular risk factors subdivided into a first training data subset and a second training data subset prior to training the ML model, wherein the first training data subset comprises a preselected subset of cardiovascular risk factors, and wherein the second training data subset comprises a remaining subset of cardiovascular risk factors ([0025] discloses, “Thus, in one form of the disclosure, a “composite model” is proposed comprising a machine learning model for processing an input photoplethysmographic waveform (e.g. captured from a certain human subject) to generate a first set of features representative of the waveform, optionally a dimensionality-reduction process (e.g. as explained above based on a plurality of eigenvectors associated with the composite model, such as determined based on a PCA analysis) to generate a second set of features from the first set, and a further model defined by a set of model coefficients designed to process the second set of features (or, in the absence of the dimensionality reduction model, the first set of features), and typically further data such as (i) data derived from the input photoplethysmographic waveform (i.e. derived other than via a machine learning model) such as a heart rate for the subject and/or (ii) data (e.g. demographic data) characterizing the subject, to output a cardiovascular disease risk score.” And see [0056] discloses, “Figure 3 is a flowchart of an example computer-implemented method 300. The method 300 includes training a machine learning model of the composite model to output, based on an input photoplethysmographic waveform, a first set of features that are representative of the photoplethysmographic waveform (310). The method 300 additionally includes determining, based on the photoplethysmographic waveform, a heart rate (320). The method 300 additionally includes determining, based on the first set of features, a mapping of the composite model to project the first set of features to a second set of features, wherein the second set of features includes fewer features than the first set of features (330). The method 300 additionally includes determining a set of model coefficients of the composite model to predict, based on the second set of features, the heart rate, and a set of demographic information, a cardiovascular disease risk score (340). And see Fig. 2A and 2B)
a set of computing instructions stored on the computer memory and configured to access the ML model; a processor communicatively coupled to the computer memory, and the processor configured to access the set of computing instructions and the ML model, wherein the computing instructions, when executed by the processor, cause the processor to: (see [0006] discloses, “In another aspect, a non-transitory computer readable medium is provided having stored thereon program instructions executable by at least one processor to cause the at least one processor to perform any of the above methods.” And see [0007] discloses, “In another aspect a system is provided that includes: (i) at least one processor; and (ii) a non-transitory computer-readable medium, having stored therein instructions executable by the at least one processor to cause the system to perform any of the above methods”)
…[…]…input user-specific cardiovascular data of a user into the ML model, wherein the user is a member of a geographic region, wherein the user-specific cardiovascular data of the user as input into the ML model is data of the user corresponding to the preselected subset of cardiovascular risk factors and the remaining subset of cardiovascular risk factors, ([0065] discloses, “The DLS was developed and evaluated using data from the UKB dataset, filtered to focus on participants aged 40-74. UKB participants who had PPG waveforms recorded were stratified into three subsets: train (n=105,319), tune (n=46,868), and test (n=57,702) subsets based on geographic information on the site of data collection, i.e., latitude and longitude. This strategy comports with TRIPOD guidelines on external validation (specifically validation on a different geographic region) by allowing for non-random variation between data splits such as differences in data acquisition or environment.” And see [0066] discloses, “PPG waveforms from all visits for the participants were used in the training subset to train the PPG feature extractor in DLS. The low-dimensional numeric outputs (embeddings) computed by this model were used as additional input features to the Cox model. To develop the Cox model that generates DLS to predict MACE risk, additional clinical and demographic variables and inclusion/exclusion criteria were added. Participants with non-fatal myocardial infarction or stroke before their first visit, or that were missing any of the variables for the model (age, sex, and smoking status), were excluded. Those without body mass index (BMI) or systolic BP (SBP) were also excluded for a fair comparison against the other office- and lab-based risk prediction models. For each participant, only the measurements related to their first visit were included. All numerically measured variables were standard-scaled. Cox models were regularized using a ridge penalty. In the final cohort, 97,970, 43,539, and 54,856 participants were included to train, tune, and test the survival model, respectively (Figure 6). The descriptive statistics of this cohort are listed in Table 1.”)
and wherein the ML model outputs a user-specific cardiovascular prediction of the user, the user-specific cardiovascular prediction comprising a cardiovascular risk score of the user; ([0019] discloses, “The systems and methods described herein apply such PPG waveform data to a machine learning model (e.g., a ResNet18 model or other variety of deep learning model) to generate a set of output features that are representative of the PPG waveform. These features are then applied, in combination with a heart rate determined from the PPG waveform and demographic information (e.g., at least one of sex, age, and smoking status (whether the patient has ever been a smoker, and optionally all of these), and optionally BMI or other additional information), to a statistical model (e.g., a Cox proportional hazards model) to predict a score that is indicative of the likelihood that the patient will develop cardiovascular disease within a specified time period (e.g., ten years).” And see [0026] discloses, “Optionally, based on the cardiovascular disease risk score, corresponding information may be output (e.g. if the method is carried out by a computer which is a piece of user equipment, the information may be output to the user using a screen of that user equipment). For example, the information may be a warning if the risk score is above a threshold. The warning may be in the form of a message to consult a health specialist. Alternatively or additionally, based on the cardiovascular disease risk score, it may be determined whether to administer a drug and/or apply a treatment to the subject, and the method may include administering that drug and/or that treatment.”)
and display, by a graphical user interface (GUI), the user-specific cardiovascular prediction. ([0026] discloses, “Optionally, based on the cardiovascular disease risk score, corresponding information may be output (e.g. if the method is carried out by a computer which is a piece of user equipment, the information may be output to the user using a screen of that user equipment). For example, the information may be a warning if the risk score is above a threshold. The warning may be in the form of a message to consult a health specialist. Alternatively or additionally, based on the cardiovascular disease risk score, it may be determined whether to administer a drug and/or apply a treatment to the subject, and the method may include administering that drug and/or that treatment.” And see [0029] discloses, “Note that the systems and methods herein for the use of PPG waveforms and related demographic information to predict risk of cardiovascular disease can also be used to predict a risk score related to a variety of other progressive chronic diseases or disorders and/or medical events related thereto. For example, the systems and method escribed herein could be modified to predict a risk score relating to the likelihood of developing diabetes or hypertension within a specified time period. Additionally or alternatively, such systems and methods could be used to determine whether a patient is likely to be hospitalized, experience a cardiovascular event (e.g., heart attack, stroke), be prescribed a drug (e.g., a blood pressure drug, a heart disease drug, a diabetes drug), receive a treatment (e.g., an angioplasty, installation of a stent), or experience some other medical event or activity. Additionally or alternatively, the systems and methods herein may be used, in the case of the other progressive chronic diseases or disorders, to cause the display or a warning if a risk score is above a threshold, and/or determine a drug and/or a treatment to apply to a subject, and the method may include administering that drug and/or treatment.”)
However, Weng does not explicitly teach:
and wherein the preselected subset of cardiovascular risk factors are used as training inputs to the ML model,
an ensemble learning algorithm is applied to the remaining subset of cardiovascular risk factors to generate a single computed risk score value that consolidates the remaining subset of cardiovascular risk factors, wherein the single computed risk score value is used as a training input to the ML model;
However, LAI does teach:
wherein the preselected subset of cardiovascular risk factors have a linear relationship with the ML model, ([0039] discloses, “In the image (CMR) branch, the LGE-MRI image vector embeddings z are obtained by firstly creating n non-overlapping 3D image patches xi E R16x 16x4 from the original 3D image X E R96x96xzo, obtaining linear projections of the flattened image patches, appending a classification token zc1s (CLS-token), and adding positional embeddings:” and see [0040] discloses, “In Equation (1 ), E is a linear projection mapping each flattened image patch to Rd, with dimension d being a hyperparameter of the model, and p E RCn+i)xd is a learned positional embedding added to the embeddings to retain positional information. Learnable positional embeddings are employed in the ViT model here.”
and wherein the remaining subset of cardiovascular risk factors have a non- linear relationship with the ML model, ([0044] discloses, “In the EHR and CIR branches, processed EHR and CIR data are converted to vectors zEHR, zcrn fed into specific fully connected FNNs. The FNNs each include multiple linear transformation layers, with each layer also succeeded by a normalization layer, a non-linear activation function, and a drop-out layer. The latent vectors zEHR and icrn representing the EHR and CIR knowledge are extracted hereby.” And see [0050] discloses, “The CMR, EHR and CIR sub-networks were trained individually in a first step, and then the tri-channel reduction to practice was trained end-to-end with all the sub-networks and the multi-modality fusion module.”)
and wherein the preselected subset of cardiovascular risk factors are used as training inputs to the ML model, and wherein the remaining subset of cardiovascular risk factors are used to generate a non-linear covariate that is used as a training input to the ML model;([0021] discloses, “Various embodiments include a scalable multi-modality deep learning (DL) method for ventricular arrhythmia risk stratification, e.g., in HCM patients. The DL method can automatically analyze multiple modalities of input data, e.g., images, including raw late gadolinium enhancement cardiac magnetic resonance (LGE-MRI) images, and clinical covariates from electronic health records (EHR). According to some embodiments, the clinical covariates from EHR data may include cardiac imaging report (CIR) data. According to some embodiments, EHR data including CIRdata is passed to a single channel in a trained machine learning system; according to other embodiments, the CIR data is passed to a channel different from a channel that receives other EHR data. Various embodiments may use dedicated neural networks to extract features from raw LGE-MRI images (e.g., based on pixel intensity of the raw images) and EHR data, the latter of which may be split and passed to two or more channels, e.g., CIR data and other EHR data, fuse the multi-modality knowledge, and output a personalized risk score for VA.” And see
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Weng’s teachings of predicting cardiovascular disease risk as cited above with LAI’s teachings of linear and non-linear covariate math as previously cited, the motivation being Weng is concerned with the costly nature of determining cardiovascular risk with current methods (see e.g. [0002]) therefore it would be obvious to decrease cost and simply substitute the method of machine learning in LAI as it requires less computational cost (e.g. [0045]) while still coming to a risk of cardiovascular disease and would not render Weng inoperable.
However LAI does not explicitly teach:
an ensemble learning algorithm is applied to the remaining subset of cardiovascular risk factors to generate a single computed risk score value that consolidates the remaining subset of cardiovascular risk factors, wherein the single computed risk score value is used as a training input to the ML model;
However, MATHUR does teach:
an ensemble learning algorithm is applied to the remaining subset of cardiovascular risk factors to generate a single computed risk score value that consolidates the remaining subset of cardiovascular risk factors, wherein the single computed risk score value is used as a training input to the ML model; ([0018] discloses, “In some aspects, the techniques described herein relate to a clinical decision support system, wherein one of the one or more pulmonary arterial hypertension risk algorithm includes an ensemble of one or more Bayesian (neural) networks,” and see [0048] discloses, “In one aspect, the enhanced risk prediction algorithm is a Bayesian algorithm. In some embodiments, the Bayesian algorithm is an ensemble of Tree-augmented Naive (TAN) Bayes algorithms. In some implementations, the algorithm integrated traditional clinical variables with new biomarkers as well as imaging and genomic data. Each class of variables ( e.g. clinical, biomarkers, imaging, and genomic), is represented by a separate TAN model. Each TAN model is trained on a discrete set of variables; in some aspects, the variables are selected based on physician surveys, independent statistical analysis ( e.g. Cox analysis), or other means for variable selection that are known in the field. The selected variables are related to measurable or discretized factors related to Pulmonary arterial hypertension. The ensemble of TAN models is further trained on the selected variables and provides a value of risk for survivability based on patient input variables.” And see [0038] discloses, “FIG. 7 illustrates a) Example of a PHORA model when some variables (highlighted in blue) are observed at baseline assessment. The values of these variables are noted in the dotted line box adjacent to each node. Variables in orange are yet to be reported as patients are undergoing work-up. b) Updated PH ORA model when additional parameters (previously in orange) are now available. Note change in the predicted outcome (survival at 12 months, green box) as additional data is input.” / examiner notes survival outcomes as input as variables for training data is considered a risk scoring)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Weng’s teachings of predicting cardiovascular disease risk as cited above and LAI’s teachings of covariate math as previously cited with MATHURs teaching of ensemble learning, the motivation being Weng may not explicitly state the model and data Weng is concerned with the costly nature of determining cardiovascular risk with current methods (see e.g. [0002]) therefore it would be obvious to decrease cost and simply substitute the method of machine learning in MATHUR as it requires less computational cost (e.g. [0045]) while still coming to a risk of cardiovascular disease and would not render Weng inoperable.
As per claim 2, Weng further teaches:
The ML-based system of claim 1, wherein the ML model is a Cox proportional hazards model, wherein the computing instructions are further configured, when executed by the processor, to implement or apply a gradient boosting algorithm to the second training data subset of the remaining subset of cardiovascular risk factors to enhance the Cox proportional hazards model.
However, MATHUR does teach:
The ML-based system of claim 1, wherein the ML model is a Cox proportional hazards model, ([0066] discloses, “Discussion: Risk stratification using the PHORA model, a Bayesian network model, provides improved discrimination to the existing Cox regression multivariate model and effectively depicted risk in two large external registry cohorts, COMPERA and PHSANZ.”) wherein the computing instructions are further configured, when executed by the processor, to implement or apply a gradient boosting algorithm to the second training data subset of the remaining subset of cardiovascular risk factors to enhance the Cox proportional hazards model. ([0173] discloses, “Machine learning techniques include, but are not limited to, logistic regression, support vector machines (SVMs), decision trees, Naive Bayes classifiers, and artificial neural networks.” And also see [0018]/ examiner notes the machine learning models can be decision trees therefore are gradient boosting models as as they are ensemble under BRI)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Weng’s teachings of predicting cardiovascular disease risk as cited above and LAI’s teachings of linear and non-linear covariate math as previously cited with MATHURs teaching for the same reasons given for claim 1.
As per claim 4, Weng further teaches:
The ML-based system of claim 1, wherein each of the plurality of cardiovascular risk factors is specific to a population of the geographic region and wherein the geographic region defining the plurality of cardiovascular risk factors on which the ML model is trained comprises a plurality subregions or cohorts comprising individuals located within each respective subregion or cohort. ([0065] The DLS was developed and evaluated using data from the UKB dataset, filtered to focus on participants aged 40-74. UKB participants who had PPG waveforms recorded were stratified into three subsets: train (n=105,319), tune (n=46,868), and test (n=57,702) subsets based on geographic information on the site of data collection, i.e., latitude and longitude. This strategy comports with TRIPOD guidelines on external validation (specifically validation on a different geographic region) by allowing for non-random variation between data splits such as differences in data acquisition or environment.” / examiner interprets the TRIPOD guidelines on external validation based on different geographic region for datasets as a plurality of cohorts with individuals located within each cohort)
As per claim 6, Weng further teaches:
The ML-based system of claim 1, wherein the preselected subset of cardiovascular risk factors comprises risk factors selected from one or more risk categories defining indications of cardiovascular health, and wherein the one or more risk categories comprise demographic factors, family history of disease, healthcare utilization, clinical laboratory testing, medication history, disease history, and drug use. ([0024] discloses, Training data used to train the machine learning model (and/or to determine coefficients of the statistical model that receives the output of the machine learning model) could include stored sets of PPG waveforms and associated demographic data (e.g., at least one of age, sex, smoking status, BMI) along with data about whether the corresponding individuals were diagnosed with cardiovascular disease (e.g., a date of diagnosis and/or experienced a myocardial infarction, stroke, cardiovascular-related death, or other event relative to a date of measurement of the PPG waveform and other demographic data, a severity of the cardiovascular disease suffered by the patient) and/or experienced some other cardiovascular event (e.g., myocardial infarction, stroke, cardiovascular-related death) such that they are in a higher-risk group. The amount of such data needed to train the machine learning model could be reduced by augmenting the available data. This could include using a process to generate additional PPG waveforms from the existing PPG waveforms by simulating random (e.g., pseudorandom) variations in the ‘rate of playback’ of the existing PPG waveforms, or otherwise imposing random time-varying time-shifts to the existing PPG waveforms. This could include generating a random time-varying playback speed (e.g., pseudo-randomly generated normally- distributed playback speed) and then using that playback speed to generate a corresponding time-varying time-shift (e.g., via a running sum) to be applied to the PPG waveform in order to simulate “playing back” the PPG according to the time-varying playback speed. Such a data augmentation method (which may be referred to as “Brownian tape speed augmentation”) could be applied in a variety of applications to a variety of training data, e.g., to training data that represents band-limited time-series data or other data whose essential features will not be abolished by the application of random (e.g., pseudo-random) time-shifts thereto.” And see [0061] discloses, “Multiple risk scores, such as the WHO/ISH risk chart and Globorisk scores, have been developed to triage CVD risk based on demographics, past medical history, vital signs, and laboratory data.” And see [0026] discloses, “Optionally, based on the cardiovascular disease risk score, corresponding information may be output (e.g. if the method is carried out by a computer which is a piece of user equipment, the information may be output to the user using a screen of that user equipment). For example, the information may be a warning if the risk score is above a threshold. The warning may be in the form of a message to consult a health specialist. Alternatively or additionally, based on the cardiovascular disease risk score, it may be determined whether to administer a drug and/or apply a treatment to the subject, and the method may include administering that drug and/or that treatment. For example, if the risk score is above a certain threshold, the system may issue an instruction to administer to the subject a dose of a drug (e.g. a drug previously prescribed for the patient) and/or a treatment.”)
As per claim 8, Weng further teaches:
The ML-based system of claim 1, and wherein the preselected subset of cardiovascular risk factors comprises one or more of values related to: age, sex, family history of diabetes, accident and emergency visits per year, aspartate transaminase, alanine aminotransferase, low-density lipoprotein cholesterol, neutrophil, statins, myocardial infarction, angina, revascularization, atrial fibrillation, hypertension, and/or user history of diabetes. ([0019] discloses, “The systems and methods described herein apply such PPG waveform data to a machine learning model (e.g., a ResNet18 model or other variety of deep learning model) to generate a set of output features that are representative of the PPG waveform. These features are then applied, in combination with a heart rate determined from the PPG waveform and demographic information (e.g., at least one of sex, age, and smoking status (whether the patient has ever been a smoker, and optionally all of these), and optionally BMI or other additional information), to a statistical model (e.g., a 69proportional hazards model) to predict a score that is indicative of the likelihood that the patient will develop cardiovascular disease within a specified time period (e.g., ten years). The model-predicted output features are related to specific features of the PPG waveform morphology and are independent of heart rate.” And see [0020] discloses, “The features that are representative of the PPG waveform and that are applied to the statistical model could be directly output from the machine learning model, or could be the result of projecting the model output into a lower-dimensional set of features. For example, a set of principal component analysis (PCA) eigenvectors or some other dimensionality- reduction method (e.g., independent component analysis, PCA using a nonlinear kernel, a support vector machine, an isometric mapping, multidimensional scaling, linear discriminant analysis, factor analysis, singular value decomposition, non-negative matrix factorization) could be used to reduce the number p of “first” features output from the model (e.g., 512 or more features, e.g. independently varying features) to a smaller number l of “second” features (e.g., five features), where l is less than p. [0021] The eigenvectors may be selected based on a n x p matrix derived from the first features for PPG waveforms for a plurality of subjects, and denoted X, where the number of rows n is an integer and the number of columns p is an integer which is the number of first features. For example, n may be a number of human subjects (for whom a PPG waveform is available), each row may correspond to a different subject, and each value of each element of the matrix may be the output of the machine learning model for the corresponding feature when a PPG waveform captured by a measurement of the corresponding subject is input to the machine learning model.” And see [0022] discloses, “The calculation of the eigenvectors may be done by a standard method. For example, a number l of p-component orthogonal unit-length eigenvectors ^^^ ^ for may be successively selected such that each corresponding n-component vector ^^ ^ ^^^ has maximum variance. Once this is done, a dimensionality-reduction process may be applied to set of first features output by the machine learning model based on a PPG waveform for a specific process, by a linear projection based on the eigenvectors of the composite model, e.g. by obtaining the corresponding dot products of a p-component vector comprising the p first features respectively with each of the l eigenvectors, to give the l respective second features.” And see [0023] discloses, “The trained machine learning model could be trained in a variety of ways using a variety of training data. For example, the machine learning model could be trained to predict, from input PPG waveforms, human-interpretable features determined from the PPG waveforms (e.g., pulse wave reflection index, peak to peak time, pulse wave peak position, pulse wave notch position, pulse wave shoulder position, whether a dicrotic notch is present, and/or an arterial stiffness index; here the peaks may be in pressure or in any other parameter indicated by the PPG waveform which varies during the cardiac cycle, e.g. with a single peak in each cycle) or some other set of available information that is relevant to the condition(s) or event(s) to be predicted for the individuals from whom the PPG waveforms had been detected (e.g., sex, age, BMI, hypertension status, hba1c, total cholesterol, systolic blood pressure, previous experience of at least one previous major adverse cardiovascular event, and PPG dicrotic notch presence). This could include training the machine learning model in combination with an output model (which could itself be a machine learning model) such that the output of the machine learning model is applied as an input to the output model, which then generates predictions of the target features (e.g., human-interpretable features of the input PPG waveform, a set of physiological data like age, sex, BMI, etc.). Additionally or alternatively, the machine learning model could be trained in a self-supervised manner (e.g., as part of an encoder-decoder pair), semi-supervised manner, or fully supervised manner to generate feature vectors that represent useful latent features in the set of PPG waveforms used as training data.” And see [0024] discloses, “Training data used to train the machine learning model (and/or to determine coefficients of the statistical model that receives the output of the machine learning model) could include stored sets of PPG waveforms and associated demographic data (e.g., at least one of age, sex, smoking status, BMI) along with data about whether the corresponding individuals were diagnosed with cardiovascular disease (e.g., a date of diagnosis and/or experienced a myocardial infarction, stroke, cardiovascular-related death, or other event relative to a date of measurement of the PPG waveform and other demographic data, a severity of the cardiovascular disease suffered by the patient) and/or experienced some other cardiovascular event (e.g., myocardial infarction, stroke, cardiovascular-related death) such that they are in a higher-risk group. The amount of such data needed to train the machine learning model could be reduced by augmenting the available data.” Also see Figs. 6 and 7)
As per claim 12, Weng further teaches:
The ML-based system of claim 1, wherein a C-statistic for the ML model has a value of at least 0.69. ([0080] discloses, “DLS demonstrated non-inferiority to the office-based refit-WHO score. The ten-year MACE risk prediction performance of all methods was evaluated using the UKB test subset, which was held-out during the training process. The DLS yielded a C-statistic of 71.1% (95% CI [69.9, 72.4]).”)
As per claim 13, Weng further teaches:
The ML-based system of claim 1, wherein the user-specific cardiovascular prediction is a cardiovascular disease (CVD) risk prediction for the user in a 10-year timeframe. ([0087] discloses, “A deep learning PPG-based CVD risk score as described herein, DLS, was developed to predict ten-year MACE risk using age, sex, smoking status, heart rate and deep learning-derived PPG features.”)
As per claim 14, Weng further teaches:
The ML-based system of claim 1, wherein the ML model is further trained with data of one or more drug classes identified for reducing cardiovascular disease (CVD), and wherein the user-specific cardiovascular data of the user as input into the ML model further comprises a selection of one or more of the drug classes, and wherein the user-specific cardiovascular prediction of the user comprises a CVD risk prediction that predicts the user's cardiovascular after using the one or more of the drug classes as selected. ([0029] discloses, For example, the systems and method escribed herein could be modified to predict a risk score relating to the likelihood of developing diabetes or hypertension within a specified time period. Additionally or alternatively, such systems and methods could be used to determine whether a patient is likely to be hospitalized, experience a cardiovascular event (e.g., heart attack, stroke), be prescribed a drug (e.g., a blood pressure drug, a heart disease drug, a diabetes drug), receive a treatment (e.g., an angioplasty, installation of a stent), or experience some other medical event or activity. Additionally or alternatively, the systems and methods herein may be used, in the case of the other progressive chronic diseases or disorders, to cause the display or a warning if a risk score is above a threshold, and/or determine a drug and/or a treatment to apply to a subject, and the method may include administering that drug and/or treatment.”)
As per claim 15, Weng further teaches:
The ML-based system of claim 1, wherein the GUI is configured to receive the user-specific cardiovascular data of the user, and wherein the GUI is further configured to provide the user-specific cardiovascular data as input to the ML model, wherein the GUI provides graphical fields or selections for selecting one or more types of drug classes for selection or generation of a user-specific plan to address the user's cardiovascular health. ([0029] discloses, “Additionally or alternatively, the systems and methods herein may be used, in the case of the other progressive chronic diseases or disorders, to cause the display or a warning if a risk score is above a threshold, and/or determine a drug and/or a treatment to apply to a subject, and the method may include administering that drug and/or treatment.” And see [0026] discloses, “Optionally, based on the cardiovascular disease risk score, corresponding information may be output (e.g. if the method is carried out by a computer which is a piece of user equipment, the information may be output to the user using a screen of that user equipment). For example, the information may be a warning if the risk score is above a threshold. The warning may be in the form of a message to consult a health specialist. Alternatively or additionally, based on the cardiovascular disease risk score, it may be determined whether to administer a drug and/or apply a treatment to the subject, and the method may include administering that drug and/or that treatment. For example, if the risk score is above a certain threshold, the system may issue an instruction to administer to the subject a dose of a drug (e.g. a drug previously prescribed for the patient) and/or a treatment.”)
As per claim 17, Weng further teaches:
The ML-based system of claim 1, wherein the user- specific cardiovascular prediction comprises at least one of: a user-specific medical prescription predicted to reduce the user's cardiovascular disease (CVD) risk or causes generation of a user-specific activity predicted to reduce the user's cardiovascular disease (CVD) risk. ([0026] discloses, “Optionally, based on the cardiovascular disease risk score, corresponding information may be output (e.g. if the method is carried out by a computer which is a piece of user equipment, the information may be output to the user using a screen of that user equipment). For example, the information may be a warning if the risk score is above a threshold. The warning may be in the form of a message to consult a health specialist. Alternatively or additionally, based on the cardiovascular disease risk score, it may be determined whether to administer a drug and/or apply a treatment to the subject, and the method may include administering that drug and/or that treatment. For example, if the risk score is above a certain threshold, the system may issue an instruction to administer to the subject a dose of a drug (e.g. a drug previously prescribed for the patient) and/or a treatment.”)
As per claims 19, 20, 22, 23, 24, 26, 29, 30, 31, 32, 33, 35, and 55 they are method claims which repeats the same limitations of claim 1, 2, 4, 6, 8, 10, 12, 13, 14, 15, and 17 the corresponding system claims, as a series of process steps as opposed to a collection of elements. Since the collective teaching of Weng, LAI, and MATHUR as well as motivations to combine disclose the structural elements that constitute the system of claims 1, 2, 4, 6, 8, 10, 12, 13, 14, 15, and 17, it is respectfully submitted that they perform the underlying process steps, as well. As such, the limitations of claim 19, 20, 22, 23, 24, 26, 29, 30, 31, 32, 33, 35, and 55 are rejected for the same reasons given above for claim 1, 2, 4, 6, 8, 10, 12, 13, 14, 15, and 17.
As per claim 37, it is an article of manufacture claim which repeats the same limitations of claim 19, the corresponding method claim, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings of Weng, LAI, and MATHUR as well as motivations to combine disclose the underlying process steps that constitute the method of claim 19 it is respectfully submitted that they likewise disclose the executable instructions that perform the steps as well. As such, the limitations of claim 37 are rejected for the same reasons given above for claim 19.
Claims 10 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Weng et. al (hereinafter Weng) (WO2024226519A2) in view of LAI et. al (hereinafter LAI) (WO2024238728A2) and in further view of MATHUR et. al (hereinafter MATHUR) (US20250316391Al)
As per claim 10 and similarly claim 28, Weng further teaches the underlined portions:
The ML-based system of claim 1, wherein at least a portion of the preselected subset of cardiovascular risk factors comprises imputed data generated to replace missing values and wherein the remaining subset of cardiovascular risk factors are not imputed, ([0075] discloses, “All models were trained on the same train subset and tuned on the tune subset except for laboratory-based refit-WHO score, metadata + PPG morphology, and the Full models that were trained, tuned, and compared based on a subset of the testing data without missing values of the input features.”)
and wherein the ML model is further trained with data defining one or more threshold risks, where each threshold risk defines a magnitude of a clinical health benefit to a user of the geographic region. ([0078] discloses, “Additional evaluation metrics included the category-free net reclassification improvement (cfNRI), and after defining a specific risk threshold (model operating point), sensitivity, specificity, NRI, and adjusted hazard ratio (HRs). For NRI and cfNRI, the respective event and non-event components were also reported. Risk thresholds were selected in three ways: (1) matching the sensitivity of SBP-140 (described next), (2) matching the specificity of SBP-140, and (3) the 10% predicted risk threshold suggested by the Globorisk study. Elevated SBP above 140 mmHg (“SBP-140”) was used for threshold selection because it was used as a simple single-visit indicator of BP control in the healthcare program of some countries such as India.”)
However Weng and LAI do not teach:
The ML-based system of claim 1, wherein at least a portion of the preselected subset of cardiovascular risk factors comprises imputed data generated to replace missing values…[…]…
However, MATHUR does teach:
The ML-based system of claim 1, wherein at least a portion of the preselected subset of cardiovascular risk factors comprises imputed data generated to replace missing values…[…]…( [0078] disclose, “There was an excellent separation between low, intermediate, & high-risk groups in REVEAL, COMPERA & PHSANZ (P<0.001). Two unique advantages of PHORA are the ability to illustrate the dynamic interdependencies among the variables (FIG. 4) & the ability to estimate outcome probability based on observations (i.e., allowing for missing data), without knowledge of the presence or absence of remaining risk factors.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Weng’s teachings of predicting cardiovascular disease risk as cited above and LAIs teachings as cited previously with MATHUR’s teachings for the same reasons given in claim 1.
Response to Arguments Regarding 35 U.S.C § 101 Rejections
Applicant’s arguments on pages 1-3 of remarks have been considered. The Examiner has alleged that claims 1, 2, 4, 6, 8, 10, 12-15, 17, 19-20, 22-24, 26, 28- 33, 35, 37, and 55 are directed to "certain methods of organizing human activity" by following rules and instructions to determine cardiovascular disease. Applicant respectfully traverses this rejection.
As an initial matter, Applicant submits that the claims do not recite "certain methods of organizing human activity." Independent claims 1, 19, 37, and 55, as amended, do not recite organizing human activity, managing personal behavior, or following rules for human interactions. Rather, the claims recite a specific technical architecture for training an ML model-subdividing cardiovascular risk factors into subsets, applying an ensemble learning algorithm to one subset to generate a single computed risk score value that consolidates that subset, and using that single value as a training input to the ML model, thereby reducing a number of training inputs derived from the remaining subset of cardiovascular risk factors to a single value. This is a specific technical implementation for training an ML model, not rules for human behavior.
Even assuming arguendo that the claims recite an abstract idea, Applicant submits that claims 1, 19, 37, and 55, as amended, integrate any alleged abstract idea into a practical application under Step 2A Prong Two because they recite specific technical features that provide concrete improvements in computer functionality.
Claims 1, 19, 37, and 55, as amended, now recite that "an ensemble learning algorithm is applied to the remaining subset of cardiovascular risk factors to generate a single computed risk score value that consolidates the remaining subset of cardiovascular risk factors, wherein the single computed risk score value is used as a training input to the ML model," thereby reducing a number of training inputs derived from the remaining subset of cardiovascular risk factors to a single value. This data reduction step is a concrete technical improvement: it reduces memory storage requirements and processing power needed to train the ML model because the system need not store or process complete datasets across all risk factors. As described in the specification, "the present disclosure describes improvements in the functioning of the computer itself or 'any other technology or technical field' because the underlying computing device can operate with reduced memory storage (e.g., in need not store complete datasets across all of the risk factors in order to the train or otherwise generate the disclosed ML model)." As-Filed Specification, paragraph [0027]. The specification further explains that "[u]se of raw data for remaining subset of cardiovascular risk factors (504r) allows the invention herein to operate with reduced memory data storage requirements, while still allowing the ML model to be highly predictive." As-Filed Specification, paragraph [0098].
This is analogous to claims found eligible in USPTO Subject Matter Eligibility Example 47 (Training a Neural Network to Predict Anomalies), where claims that recited specific technical steps beyond the abstract idea were found to integrate the exception into a practical application. Here, the amended claims recite a specific algorithmic step-applying an ensemble learning algorithm to the remaining subset of cardiovascular risk factors-that produces a concrete technical result: consolidating multiple risk factors into a single computed risk score value. This is not mere data management; it is a specific technical implementation that reduces the computational resources required to train the ML model.
The Examiner has alleged that "management of what data is preselected and managed" is part of the abstract idea. Office Action, page 8. However, the amended claims go beyond mere data management. The claims now recite a specific algorithmic step (applying an ensemble learning algorithm) that produces a concrete technical result (consolidating the remaining subset of cardiovascular risk factors into a single computed risk score value), which reduces the computational resources required to train the ML model. This specific technical architecture- where the ensemble learning algorithm consolidates potentially hundreds of incomplete risk factors into a single value used as a training input-is a concrete improvement to computer functionality, not an abstract concept.
Dependent claims 2, 4, 6, 8, 10, 12-15, and 17 depend from independent claim 1, and dependent claims 20, 22-24, 26, 28-33, and 35 depend from independent claim 19. These dependent claims are patent-eligible for at least the same reasons as their respective independent claims.
Accordingly, Applicant respectfully requests withdrawal of the rejection under 35 U.S.C. § 101.
Examiner appreciates applicant’s arguments but respectfully does not find them persuasive. The MPEP states The Alice/Mayo two-part test is the only test that should be used to evaluate the eligibility of claims under examination. While the machine-or-transformation test is an important clue to eligibility, it should not be used as a separate test for eligibility. Instead it should be considered as part of the "integration" determination or "significantly more" determination articulated in the Alice/Mayo test. Bilski v. Kappos, 561 U.S. 593, 605, 95 USPQ2d 1001, 1007 (2010). See MPEP § 2106.04(d) for more information about evaluating whether a claim reciting a judicial exception is integrated into a practical application and MPEP § 2106.05(b) and MPEP § 2106.05(c) for more information about how the machine-or-transformation test fits into the Alice/Mayo two-part framework.
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I); (Mathematical Calculations - A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.)
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Examiners should determine whether a claim recites an abstract idea by (1) identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and (2) determining whether the identified limitations(s) fall within at least one of the groupings of abstract ideas listed above. Furthermore, the MPEP state in 2106.04(d), “Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical applications.”
The positively recited claim 1 (as representative) is directed to a judicial exception (i.e. certain methods of organizing human activity) as merely following rules or instructions to determine a cardiovascular risk prediction of a patient this is abstract in substance as a physician today with or without the aid of a computer environment can determine based on abstract algorithm or reasoning whether data indicates risk of cardiovascular disease by following rules and instructions. Being implemented by a computer environment and applying machine learning does not make the recited claim dispositive of being certain methods of organizing human activity. Furthermore, the claim is taken at its broadest reasonable interpretation based on claim construction and the specification is not read into the claims and it’s not would a human do it but could a human do it and it is not dispositive of being an abstract idea just because a computer is used. Examiner maintains the claims are certain methods of organizing human activity.
Further responding to applicants arguments, the judicial exception (abstract idea) cannot integrate itself into a practical application but identification of any additional elements recited in the claim can be evaluated to determine if the additional elements integrate the exception into a practical application. The claims additional element of a trained ensemble learning model are not recited as being an improvement to a technology field or a technology confined to the computer processor environment in which the claims recite. A technical problem must first be identified in instant application specification and reflected in the claims. Applicant states the claims recite and reflect providing a concrete technical solution for reduced memory storage and reduction in the computational resources by consolidating ga single computed risk score value. The additional element trained ensemble learning model is apply it level as the claim construction even recited the broad recitation of the algorithm as “applies” and the claims do not recite technical improvements to the machine learning whether alone or in combination with the abstract idea which would reflect reduction in computational resources or data storage. The machine learning recited itself is not improved. Rather the storage requirements and processing power are a product of the processor computing environment in which the machine learning is applied on. Examiner notes the technology of the memory and the processor of a computer are not reflected or recited to be improved in the claim construction. Rather the abstract idea of managing data into sub groups at a high level is claimed and cannot bring forth the practical application or significantly more. No reflection or recitation of how the training process confined to the computer in regards to the additional elements achieves this alleged improvement to reduction of computational resources for training of a machine learning model as the claim recites simply this data is used to train the model. The process of a being trained is not the same as a reduction in computational resources for training without additional element steps or elements which recite or reflect the reduction to the computation itself as training is executed. This is further confirmed by USPTO Subject Matter Eligibility Example 47 (Training a Neural Network to Predict Anomalies). There is no direct nexus between this case and example 47 but examiner notes the reasoning for why the instant application is not integrated into a practical application is more similar to claim 2 of example 47 because as aforementioned there is no limitation reflecting or reciting how the computational resources are reduced in regards to the additional elements of training and machine learning or the processor itself to tie back to the technical problem in the specification as mere conclusory statements are not enough and mere application of a trained machine learning model to subsets of data is “apply-it” level and the abstract idea of managing data cannot be integration into a practical application. Claim 3 of example 47 on the other hand does give additional element detail which ties back directly to the technical problem confined to the computer environment the example claimed. Examiner maintains the claims are directed to an abstract idea and do not integrate into a practical application. Therefore, they also do not amount to significantly more.
Examiner maintains the 35 U.S.C § 101 rejection
Response to Arguments Regarding 35 U.S.C § 102/103 Rejections
Applicant’s arguments on pages 3-7 of remarks have been considered. Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Therefore, Examiner maintains the 35 USC 103 rejection.
Prior Art not cited but made of record
US20110105852- Morris et. al
Techniques for generating prediction of risks of medical outcomes and benefit scores for medical interventions, with imputation of missing patient data values, are disclosed. Apparatus or computer program products may be configured to receive a patient record for the patient from a database of a data storage unit, wherein one or more demographic data values or biometric data values in the patient record are missing or have null values; create and store a plurality of clone patient records in the database; impute a plurality of different Substitute demographic data values or biometric data values and substitute a different one of the plurality of substitute values into each one of the clone patient records; determine, create and store a first metric, based at least in part on the clone patient records, wherein the first metric comprises a current health related metric for the patient; determine, create and store one or more medical intervention metrics, each based at least in part on an associated medical intervention and the clone patient records, representing a predicted health related metric for the patient when the associated medical intervention is performed; transform the database by updating the patient record to include the first metric and the one or more medical intervention metrics.
Sharma et. al (hereinafter Sharma) (WO2024249688A2)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley Elizabeth Evans whose telephone number is (571) 270-0110. The examiner can normally be reached Monday – Friday 8:00 AM – 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached on (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned 571-273-8300.
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/ASHLEY ELIZABETH EVANS/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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