DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed 1/9/2026 has been entered.
Status of Claims
This action is a non-final rejection
Claims 1-3, 6-11, 17-19, 21-23, 27-29 are pending
Claims 4-5, 12-16, 20, 24-26, 30 were cancelled
Claim 1 was amended
Claims 1-3, 6-11, 17-19, 21-23, 27-29 are rejected under 35 USC § 101
Claims 1-3, 6-11, 17-19, 21-23, 27-29are rejected under 35 USC § 103
Priority
Acknowledgement is made of Applicant’s claim for a domestic priority date of 7-26-2022
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 1-24-2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-3, 6-11, 17-19, 21-23, 27-29 are not patent eligible because the claimed invention is directed to an abstract idea without significantly more.
Analysis
First, claims are directed to one or more of the following statutory categories: a process, a machine, a manufacture, and a composition of matter. Regarding claims 1-3, 6-11, 17-19, 21-23, 27-29 the claims recite an abstract idea of “identifying the risk of post-cardiotomy cardiogenic shock in patients”.
Independent Claim 1 is rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claim 1 recites a method for predicting whether a patient is likely to develop post-cardiotomy cardiogenic shock (PCCS).
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES):
receiving medical information for a patient;
extracting one or more features from the received medical information including left ventricle ejection fraction and/or total bilirubin level;
providing the one or more features as input to a classification model configured to output a risk assessment that the patient is likely to develop PCCS; and
data including first patient medical information for a PCCS patient group and second patient medical information for a no PCCS patient group, wherein each of the first patient medical information and the second patient medical information includes left ventricle ejection fraction and/or total bilirubin level information
outputting an indication of the risk assessment , wherein outputting the indication of the risk assessment comprises displaying a visual indicator of the risk assessment in an health record of the patient.
belong to the grouping of mental processes under concepts performed in the human mind as it recites “identifying the risk of post-cardiotomy cardiogenic shock in patients”. Alternatively, the selected abstract idea belongs to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “identifying the risk of post-cardiotomy cardiogenic shock in patients”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claim 1 recites:
trained classification model;
the trained classification model having been trained using training data.
electronic health record
Amounting to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs [0009-0010]. [0039-0043]. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two,
Claim 1 recites:
trained classification model;
the trained classification model having been trained using training data.
electronic health record.
Amount to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs [0009-0010]. [0039-0043]. (refer to MPEP 2106.05(f)) Accordingly, even in combination the additional elements of the claim do not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Independent Claim 19 is rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claim 19 recites a method of training a risk model to predict whether a patient is likely to develop post-cardiotomy cardiogenic shock (PCCS).
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES):
receiving a dataset of patient medical information;
selecting, from the dataset of patient medical information, training data based on PCCS criteria and defined data fields, wherein the training data includes first patient medical information for a PCCS patient group and second patient medical information for a no PCCS patient group, wherein each of the first patient medical information and the second patient medical information includes left ventricle ejection fraction and/or total bilirubin information;
using the selected training data; and
outputting the risk model.
belong to the grouping of mental processes under concepts performed in the human mind as it recites “identifying the risk of post-cardiotomy cardiogenic shock in patients”. Alternatively, the selected abstract idea belongs to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “identifying the risk of post-cardiotomy cardiogenic shock in patients”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claim 19 recites:
training the risk model;
trained risk model.
Amounting to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs [0009-0010]. [0039-0043]. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two,
Claim 19 recites:
training the risk model;
trained risk model.
Amount to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs [0009-0010]. [0039-0043]. (refer to MPEP 2106.05(f)) Accordingly, even in combination the additional elements of the claim do not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Independent Claim 29 is rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claim 29 recites computer-implemented system for simulating a likelihood that a patient will develop post-cardiotomy cardiogenic shock (PCCS).
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES):
extracting one or more features including left ventricle ejection fraction and/or total bilirubin level from medical information for the patient;
providing the one or more features as input to a classification model configured to output a risk assessment that the patient is likely to develop PCCS;
display values for at least some of the one or more features;
receiving user input ... to change one or more of the values displayed
simulating, using the ...classification model, a risk assessment that the patient will develop PCCS based, at least in part, on the changed one or more values, to generate a simulated risk assessment;
displaying, on the user interface, an indication of the simulated risk assessment;
belong to the grouping of mental processes under concepts performed in the human mind as it recites “identifying the risk of post-cardiotomy cardiogenic shock in patients”. Alternatively, the selected abstract idea belongs to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “identifying the risk of post-cardiotomy cardiogenic shock in patients”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claim 29 recites:
a display
at least one hardware computer processor; and
at least one non-transitory computer readable medium encoded with a plurality of instructions that, when processed by the at least one hardware computer processor perform a method
providing, on the display, a user interface configured to display values;
user interface
trained classification model.
Amounting to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs [0009-0010]. [0039-0043]. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two,
Claim 29 recites:
a display
at least one hardware computer processor; and
at least one non-transitory computer readable medium encoded with a plurality of instructions that, when processed by the at least one hardware computer processor perform a method
providing, on the display, a user interface configured to display values
user interface
trained classification model.
Amount to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs [0009-0010]. [0039-0043]. (refer to MPEP 2106.05(f)) Accordingly, even in combination the additional elements of the claim do not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Dependent Claims:
Step 2A Prong One: The following dependent claims recite additional limitations that further define the abstract idea of “identifying the risk of post-cardiotomy cardiogenic shock in patients”. These claim limitations include:
Claim 2: wherein the medical information for the patient includes one or more of an health record, a laboratory report, a medical procedure report, physician notes, and a medical imaging report;
Claim 3: wherein the medical information includes structured data and unstructured data;
Claim 6: receiving data indicating whether the patient developed PCCS;
Claim 7: wherein the risk assessment includes a numerical value, and outputting an indication of the risk assessment comprises displaying the numerical value and/or information based on the numerical value;
Claims 8: performing based on the numerical value, categorization of the patient into a risk group of a plurality of risk groups, wherein outputting the indication of the risk assessment comprises outputting an indication of the risk group for the patient;
Claims 9;
wherein performing categorization of the patient into a risk group comprises determining whether the numerical value is above a threshold value; and
classifying a
Claims 10: wherein outputting an indication of the risk group for the patient comprises displaying a color coded indication of the risk group;
Claims 11: wherein the risk assessment is a categorization of the patient into a risk group of a plurality of risk groups, and outputting the indication of the risk assessment comprises outputting an indication of the risk group for the patient
Claims 17:
display values for the one or more features;
receiving user input to change one or more of the values for the one or more features;
simulating a risk assessment that the patient is likely to develop PCCS based, at least in part, on the changed one or more values, to generate a simulated risk assessment; and
displaying, the simulated risk assessment.
Claims 18: wherein outputting an indication of the risk assessment comprises outputting a cumulative score associated with the risk assessment;
Claims 20:
defining a plurality of PCCS criteria;
generating the at least two risk groups of patients based on the PCCS criteria;
Claims 21:
receiving input regarding the data fields to define; and
defining the data fields based, at least in part, on the received input.
Claims 22: validating the model using at least some patient medical information not used the model, wherein outputting the risk model comprises outputting the validated model.
Claims 23:
receiving an indication to update the risk model; and
receiving the indication to update the risk model
Claims 27: receiving additional medical information.
Claims 28: wherein the additional medical information includes medical information for a plurality of patients at a medical facility on which cardiac surgery was performed
Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO). The following dependent claims recite additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claims as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims include:
Claim 2:
electronic health record;
Claim 6:
retraining the trained classification model based, at least in part, on the received data;
Claim 7:
user interface;
Claim 10:
user interface
Claim 17:
user interface;
Claim 21:
user interface;
Claim 22:
trained risk model;
train the model.
Claim 23:
trained risk model;
retraining the risk model.
Claim 27:
retraining the trained risk model.
Step 2B (Does the additional elements of the claim provide an inventive concept?: NO). As discussed previously with respect to Step 2A Prong Two, the following dependent claims recite additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, even in combination the additional elements of the claim do not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible. The claims include:
Claim 2:
electronic health record;
Claim 6:
retraining the trained classification model based, at least in part, on the received data;
Claim 7:
user interface;
Claim 10:
user interface
Claim 17:
user interface;
Claim 21:
user interface;
Claim 22:
trained risk model;
train the model.
Claim 23:
trained risk model;
retraining the risk model.
Claim 27:
retraining the trained risk model.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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
non-obviousness.
Claims 1-3, 7-9, 11, 17-22, 29 are rejected by 35 U.S.C. 103 as being un-patentable by Heldt et.al (US 20170042483 A1) hereinafter “Heldt ”, in view of Parlikar et.al (US 20080287753 A1) hereinafter “Parlikar”, in further view of Chen et.al (US 20200162412 A1) hereinafter “Chen”.
Regarding claim 1 Heldt teaches:
receiving medical information for a patient; (See at least [0064] via: “…The method 600 includes the steps of receiving a dataset of heart rate and arterial blood pressure (step 602),..”) In addition see Figure 6, step 602.
extracting one or more features from the received medical information; (See at least [0079] via: “…At step 802, a the receiver 552 receives a patient's data including heart rate and arterial blood pressure...”; in addition see at least [0081] via: “…At step 806, the feature extractor 555 extracts patient features from the incoming heart rate and arterial blood pressure data. This stage also includes feature extraction from patient record, for example patient history, demographic information and other static parameters associated with the patient…”), In addition see at least Figure 8, step 802 and see Figure 8, step 806. ..”)
providing the one or more features as input to a trained classification model configured to output a risk assessment that the patient is likely to develop PCCS; (See at least [0082] via: “…At step 808, the classifiers vote on the heart rate, arterial blood pressure and other extracted features of the patient. In particular, the record of the patient received at 802 may correspond to a patient with an unknown serum lactate level, and it is desirable to use the systems and methods described herein to determine the serum lactate level for the patient...”; in addition see at least [0083] via: “…At step 810, the serum lactate level determined is used to stratify the risk of sepsis of the patient. The serum lactate level may be used to predict hypoperfusion, lung disease and cardiac shock...”; in addition see Figure 8, steps 808 and step 810...”) The Examiner notes that it is a well known fact that serum lactate levels can be used as a predictor of post-cardiotomy cardiogenic shock (PCCS) as recited in [0083] via “…At step 810, the serum lactate level determined is used to stratify the risk of sepsis of the patient. The serum lactate level may be used to predict hypoperfusion, lung disease and cardiac shock...”
and outputting an indication of the risk assessment wherein outputting the indication of the risk assessment comprises displaying a visual indicator of the risk assessment in an electronic health record of the patient (See at least [0083] via: “…At step 810, the serum lactate level determined is used to stratify the risk of sepsis of the patient. The serum lactate level may be used to predict hypoperfusion, lung disease and cardiac shock. …”; in addition see at least [0082] via: “…The likelihoods and/or most likely serum lactate level for the patient may be displayed to a user such as a clinician over the display renderer 562 within the user interface 560…”) in addition see Figure 8, step 810...”)
Nevertheless Heldt is silent regarding extracting features including left ventricle ejection fraction and/or total bilirubin level from the received medical information as taught by Parlikar:
wherein the one or more features include left ventricle ejection fraction and/or total bilirubin level. (See at least [0022] via: “…the invention relates to a system for predicting or detecting circulatory shock comprising a blood pressure measuring device, a processor, a display, a user interface, and a memory storing computer executable instructions, which when executed by the processor cause the processor to receive measurements of arterial blood pressure from the blood pressure device, compute mean arterial blood pressure from the received arterial blood pressure measurements, receive or compute estimates of at least one of heart rate, total peripheral resistance, cardiac output, stroke volume, ejection fraction, ventricular end-diastolic volume, and cardiac contractility, and classify each of these estimates as one of low, normal, or high, and predict or detect a type of circulatory shock based in part on one or more values of the received measurements and estimates. ..”; in addition see at least [0023] via: “… the type of shock includes one of septic shock, hypovolemic shock, anaphylactic shock, hemorrhagic shock, and cardiogenic shock. In one embodiment, the type of circulatory shock is determined to be … (e) cardiogenic shock if the mean arterial blood pressure is low, total peripheral resistance is high, ejection fraction is low, end-diastolic volume is high, cardiac output is low…”; in addition see at least [0059] via: “…the data set is comprised of measurements of central arterial blood pressure (cABP) measured at the aorta, carotid arterial blood pressure (carABP), and femoral arterial blood pressure (fABP), all sampled at 250 Hz with 16 bit resolution. In addition, there are intermittent echocardiography measurements of heart rate, left ventricular end-systolic volume and left ventricular end-diastolic volume, from which one can compute left ventricular ejection fraction using the EF and LVEDV methods..”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Heldt with Parlikar. Heldt teaches method to quantitatively predict a patient's serum lactate level, comprising measuring arterial blood pressure and heart rate from the patient, computing estimates of one or more cardiovascular parameters from the measured arterial blood pressure and heart rate, providing one or more classifiers that have been trained on a training data set including a reference set of arterial blood pressure, heart rate, and serum lactate levels and using the one or more classifiers to estimate the serum lactate level of the patient to predict hypoperfusion, lung disease and cardiac shock. However, Heldt fails to disclose predicting or detecting a type of circulatory shock such as a cardiogenic shock based in part on left ventricular ejection fraction as taught by Parlikar. Combining Heldt and Parlikar is helpful in providing an additional parameter in order to predict cardiogenic shock.
Nevertheless Heldt and Parlikar are silent regarding a trained classification model trained with data that includes PCSS and non PCSS patient groups as taught by Chen:
the trained classification model having been trained using training data including first patient medical information for a PCCS patient group and second patient medical information for a no PCCS patient group, (See at least [0003] via: “…The computer implemented method further includes clustering ... and digesting the plurality of electronic communication to form a positive learning data set and a negative learning data set to train a neural network. The computer implemented method further includes training the neural network on the positive learning data set and the negative learning data set, and preparing a positive neutral network model and a negative neural network model.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Heldt and Parlikar with Chen. Heldt and Parliker teach a method to quantitatively predict a patient's serum lactate level, comprising measuring arterial blood pressure and heart rate from the patient, computing estimates of one or more cardiovascular parameters from the measured arterial blood pressure and heart rate, providing one or more classifiers that have been trained on a training data set including a reference set of arterial blood pressure, heart rate, and serum lactate levels and using the one or more classifiers to estimate the serum lactate level of the patient to predict hypoperfusion, lung disease and cardiac shock, whereby the type of circulatory shock such as a cardiogenic shock based in part on left ventricular ejection fraction. However, Heldt and Parlikar fails to disclose training a neural network with positive and negative learning data as taught by Chen. Combining Heldt, Parlikar and Chen is helpful in providing an additional parameter in order to predict cardiogenic shock with a higher level of precision when using a neural network model.
Regarding claim 2: Heldt, Parlikar and Chen teach the invention as claimed and detailed above with respect to claim 1. Heldt also teaches:
wherein the medical information for the patient includes one or more of an electronic health record, a laboratory report, a medical procedure report, physician notes, and a medical imaging report. (See at least [0081] via: “… At step 806, the feature extractor 555 extracts patient features from the incoming heart rate and arterial blood pressure data. This stage also includes feature extraction from patient record, for example patient history, demographic information and other static parameters associated with the patient…”) In addition see figure 8, step 806 …
Regarding claim 3: Heldt, Parlikar and Chen teach the invention as claimed and detailed above with respect to claim 1. Heldt also teaches:
wherein the medical information includes structured data and unstructured data. (See at least [0086] via: “…FIG. 13 is a flowchart of filtering a clinical database for preprocessing training data to train the classifiers as discussed previously in the training process 202. In this example, the database is an instance of the MIMIC II database that includes extensive high resolution waveform data, such as arterial blood pressure and windowed average of heart rate windows derived from electrocardiograms (ECG) (step 1302). In this example, the MIMIC II database contains 32,608 patient records. From the 32,608 patient records in the MIMIC II database, the training stage filters the database to select those patient records that contain an indication of infection, which is based on the presence of an ICD-9 diagnosis codes (step 1304). About 9,708 patient records are filtered from the initial 32,608 patient records. The records are filtered again to obtain 1215 records of patients with matched waveform trends (step 1306). These 1215 records are split into two groups: those who had a lactate reading of at least 2.5 mmol/L in their record (positive class), and those whose lactate measurements were all below 2.5 mmol/L (negative class) (decision block 1308)…”). In addition see Example 1
Regarding claim 7: Heldt, Parlikar and Chen teach the invention as claimed and detailed above with respect to claim 1. Heldt also teaches:
wherein the risk assessment includes a numerical value, and outputting an indication of the risk assessment comprises displaying the numerical value and/or information based on the numerical value on a user interface. (See at least [0083] via: “… At step 810, the serum lactate level determined is used to stratify the risk of sepsis of the patient. The serum lactate level may be used to predict hypoperfusion, lung disease and cardiac shock...”; in addition see at least [0082] via: “…The likelihoods and/or most likely serum lactate level for the patient may be displayed to a user such as a clinician over the display renderer 562 within the user interface 560. In addition, the display renderer 562 may also display a confidence score representative of a confidence in the predicted diagnosis. In some embodiments, the display renderer may display, by means of an indicator, an indication of the most likely serum lactate level being over a predetermined threshold...”;). In addition see Figure 8, step 810
Regarding claim 8: Heldt, Parlikar and Chen teach the invention as claimed and detailed above with respect to claims 1 & 7. Heldt also teaches:
performing based on the numerical value, categorization of the patient into a risk group of a plurality of risk groups, wherein outputting the indication of the risk assessment comprises outputting an indication of the risk group for the patient. (See at least [0083] via: “…At step 810, the serum lactate level determined is used to stratify the risk of sepsis of the patient. The serum lactate level may be used to predict hypoperfusion, lung disease and cardiac shock. The estimated serum lactate level may provide some insight into the patient's vulnerability to risk. For example, a serum lactate level of less than 2.5 mmol/L may be classified as ‘low risk’, a serum lactate level between 2.5 mmol/L and 4 mmol/L may be ‘moderate risk’ and serum lactate level greater than 4 mmol/L may be classified as ‘high risk’ for sepsis...”), In addition see Figure 8, step 810
Regarding claim 9: Heldt, Parlikar and Chen teach the invention as claimed and detailed above with respect to claims 1, 7 & 8. Heldt also teaches:
wherein performing categorization of the patient into a risk group comprises determining whether the numerical value is above a threshold value; and classifying a high risk for PCCS when it is determined that the numerical value is above the threshold value. (See at least [0083] via: “…At step 810, the serum lactate level determined is used to stratify the risk of sepsis of the patient. The serum lactate level may be used to predict hypoperfusion, lung disease and cardiac shock. The estimated serum lactate level may provide some insight into the patient's vulnerability to risk. For example, a serum lactate level of less than 2.5 mmol/L may be classified as ‘low risk’, a serum lactate level between 2.5 mmol/L and 4 mmol/L may be ‘moderate risk’ and serum lactate level greater than 4 mmol/L may be classified as ‘high risk’ for sepsis...”), In addition see Figure 8, step 810
Regarding claim 11: Heldt, Parlikar and Chen teach the invention as claimed and detailed above with respect to claim 1. Heldt also teaches:
wherein the risk assessment is a categorization of the patient into a risk group of a plurality of risk groups, and outputting the indication of the risk assessment comprises outputting an indication of the risk group for the patient. (See at least [0083] via: “…At step 810, the serum lactate level determined is used to stratify the risk of sepsis of the patient. The serum lactate level may be used to predict hypoperfusion, lung disease and cardiac shock. The estimated serum lactate level may provide some insight into the patient's vulnerability to risk. For example, a serum lactate level of less than 2.5 mmol/L may be classified as ‘low risk’, a serum lactate level between 2.5 mmol/L and 4 mmol/L may be ‘moderate risk’ and serum lactate level greater than 4 mmol/L may be classified as ‘high risk’ for sepsis...”), In addition see Figure 8, step 810.
Regarding claim 17: Heldt, Parlikar and Chen teach the invention as claimed and detailed above with respect to claim 1. Heldt also teaches:
providing a user interface configured to display values for the one or more features; (See at least [0082] via: “…The likelihoods and/or most likely serum lactate level for the patient may be displayed to a user such as a clinician over the display renderer 562 within the user interface 560. In addition, the display renderer 562 may also display a confidence score representative of a confidence in the predicted diagnosis. In some embodiments, the display renderer may display, by means of an indicator, an indication of the most likely serum lactate level being over a predetermined threshold..”)
receiving user input via the user interface to change one or more of the values for the one or more features; (See at least [0035] via: “…The training stage 202 receives a set of training input data and provides a set of trained classifiers to the testing stage 204. The set of training input data includes a set of training heart rate and arterial blood pressure data recorded from a first group of patients and a set of the patients' serum lactate levels. In some embodiments, the set of training input data may contain gaps in some segments. For example, for some patients in the training data, the demographic information may be incomplete or may be implausible (an 800 year old man, for example). In that case, the training input data may also include synthetic data. The synthetic data may be generated by users or testers associated with the process for the purposes of covering correlations between heart rate and arterial blood pressure and serum lactate levels that are not covered in data from the first group of patients. In some embodiments, the training data may include solely synthetic data if the users associated with the training stage may wish to train the classifiers on a very specific set of characteristics and correlations that are not available in historic data of the first group of patients. For example, the user associated with the training stage may be a clinician (such as a doctor or a nurse) or a researcher at a hospital. In the process of importing the training input data, the user may identify gaps in the training data and might want to add some synthetic training data to fill these gaps. The gaps may include certain values or ranges of heart rate and arterial blood pressure that are not part of the training data…”)
simulating a risk assessment that the patient is likely to develop PCCS based, at least in part, on the changed one or more values, to generate a simulated risk assessment; (See at least [0035] via: “…the training data may include solely synthetic data if the users associated with the training stage may wish to train the classifiers on a very specific set of characteristics and correlations that are not available in historic data of the first group of patients. For example, the user associated with the training stage may be a clinician (such as a doctor or a nurse) or a researcher at a hospital. In the process of importing the training input data, the user may identify gaps in the training data and might want to add some synthetic training data to fill these gaps…all patients who have been recuperating post cardiac arrests are grouped together in one training data set, and all patients below the age of 50 are grouped together in a different training data set. The evolution of patients' serum lactate levels over the next few hours is very predictive of outcome. The risk of sepsis for these groups will be influenced by their preexisting conditions. Therefore classifiers are trained on specific groups to yield better results in predicting serum lactate level for a wide range of patients…”) and
displaying, on the user interface, the simulated risk assessment. (See at least [0082] via: “…The likelihoods and/or most likely serum lactate level for the patient may be displayed to a user such as a clinician over the display renderer 562 within the user interface 560. In addition, the display renderer 562 may also display a confidence score representative of a confidence in the predicted diagnosis. In some embodiments, the display renderer may display, by means of an indicator, an indication of the most likely serum lactate level being over a predetermined threshold..”)
Regarding claim 18: Heldt, Parlikar and Chen teach the invention as claimed and detailed above with respect to claim 1. Heldt also teaches:
wherein outputting an indication of the risk assessment comprises outputting a cumulative score associated with the risk assessment. (See at least [0037] via: “…. In particular, the application stage 206 may aggregate votes from the validated classifiers operating on the patient data to determine a predicted serum lactate level associated with the patient. The predicted serum lactate level may be provided by the system 200 to a user such as a medical professional…”)
Regarding claim 19: Heldt teaches:
A method of training a risk model to predict whether a patient is likely to develop post-cardiotomy cardiogenic shock (PCCS), the method comprising: (See at least [0063] via: “…FIG. 6 is a flowchart of a method used by the training stage 202 to train a set of classifiers on a set of training data. … In certain implementations, the serum lactate levels are a biomarker for patient risk-stratification of sepsis. …”; in addition see at least [0083] via: “…At step 810, the serum lactate level determined is used to stratify the risk of sepsis of the patient. The serum lactate level may be used to predict hypoperfusion, lung disease and cardiac shock...”). In addition see Figure 6 and Figure 8 step 810.
receiving a dataset of patient medical information; (See at least [0064] via: “…The method 600 includes the steps of receiving a dataset of heart rate and arterial blood pressure (step 602),..”) In addition see Figure 6, step 602.
selecting, from the dataset of patient medical information, training data based on PCCS criteria and defined data fields 312, the preprocessor prepares the training data that may be used to train the classifiers. The preprocessing may involve partitioning the data into various sets and removing outlier values.…”; in addition see at least [0035] via: “…The training stage 202 receives a set of training input data and provides a set of trained classifiers to the testing stage 204. The set of training input data includes a set of training heart rate and arterial blood pressure data recorded from a first group of patients and a set of the patients' serum lactate levels..”; in addition see at least [0046] via: “…The testing stage 204 receives testing input data and a set of N trained classifiers over the receiver 432. The receiver 432 may provide an interface with a data source, which may transmit testing heart rate and arterial blood pressure and corresponding serum lactate levels to the testing stage 204. The testing heart rate and arterial blood pressure may be recorded from a second group of patients (i.e., which may be different from the first group of patients making up the set of training data used in the training stage 202), and the serum lactate levels of the second group of patients may be known and transmitted to the receiver 432. In particular, there may be K patients in the second group of patients, such that the testing heart rate and arterial blood pressure include K different sets of values..”). In addition see figure 6.
training the risk model using the selected training data; (See at least [0063] via: “…Classifiers are trained on a training set of heart rate and arterial blood pressure data and their corresponding features.…”). In addition see figure 6, step 610 and
outputting the trained risk model. (See at least [0070] via: “…When iteration parameter n has reached its final value, training is complete at step 620..”; in addition see at least [0083] via: “…At step 810, the serum lactate level determined is used to stratify the risk of sepsis of the patient. The serum lactate level may be used to predict hypoperfusion, lung disease and cardiac shock. …”; in addition see at least [0082] via: “…The likelihoods and/or most likely serum lactate level for the patient may be displayed to a user such as a clinician over the display renderer 562 within the user interface 560…”) In addition see Figure 6, step 620 and see Figure 8, step 810.
Nevertheless Heldt is silent selecting training data that includes left ventricle ejection fraction and/or total bilirubin level as taught by Parlikar:
wherein the training data includes ...left ventricle ejection fraction and/or total bilirubin information. (See at least [0022] via: “…the invention relates to a system for predicting or detecting circulatory shock comprising a blood pressure measuring device, a processor, a display, a user interface, and a memory storing computer executable instructions, which when executed by the processor cause the processor to receive measurements of arterial blood pressure from the blood pressure device, compute mean arterial blood pressure from the received arterial blood pressure measurements, receive or compute estimates of at least one of heart rate, total peripheral resistance, cardiac output, stroke volume, ejection fraction, ventricular end-diastolic volume, and cardiac contractility, and classify each of these estimates as one of low, normal, or high, and predict or detect a type of circulatory shock based in part on one or more values of the received measurements and estimates. ..”; in addition see at least [0023] via: “… the type of shock includes one of septic shock, hypovolemic shock, anaphylactic shock, hemorrhagic shock, and cardiogenic shock. In one embodiment, the type of circulatory shock is determined to be … (e) cardiogenic shock if the mean arterial blood pressure is low, total peripheral resistance is high, ejection fraction is low, end-diastolic volume is high, cardiac output is low…”; in addition see at least [0059] via: “…the data set is comprised of measurements of central arterial blood pressure (cABP) measured at the aorta, carotid arterial blood pressure (carABP), and femoral arterial blood pressure (fABP), all sampled at 250 Hz with 16 bit resolution. In addition, there are intermittent echocardiography measurements of heart rate, left ventricular end-systolic volume and left ventricular end-diastolic volume, from which one can compute left ventricular ejection fraction using the EF and LVEDV methods..”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Heldt with Parlikar. Heldt teaches method to quantitatively predict a patient's serum lactate level, comprising measuring arterial blood pressure and heart rate from the patient, computing estimates of one or more cardiovascular parameters from the measured arterial blood pressure and heart rate, providing one or more classifiers that have been trained on a training data set including a reference set of arterial blood pressure, heart rate, and serum lactate levels and using the one or more classifiers to estimate the serum lactate level of the patient to predict hypoperfusion, lung disease and cardiac shock. However, Heldt fails to disclose predicting or detecting a type of circulatory shock such as a cardiogenic shock based in part on left ventricular ejection fraction as taught by Parlikar. Combining Heldt and Parlikar is helpful in providing an additional parameter in order to predict cardiogenic shock.
Nevertheless Heldt and Parlikar are silent regarding selecting a training data set with data that includes PCSS and non PCSS patient groups as taught by Chen:
wherein the training data includes first patient medical information for a PCCS patient group and second patient medical information for a no PCCS patient group, (See at least [0003] via: “…The computer implemented method further includes clustering ... and digesting the plurality of electronic communication to form a positive learning data set and a negative learning data set to train a neural network. The computer implemented method further includes training the neural network on the positive learning data set and the negative learning data set, and preparing a positive neutral network model and a negative neural network model.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Heldt and Parlikar with Chen. Heldt and Parliker teach a method to quantitatively predict a patient's serum lactate level, comprising measuring arterial blood pressure and heart rate from the patient, computing estimates of one or more cardiovascular parameters from the measured arterial blood pressure and heart rate, providing one or more classifiers that have been trained on a training data set including a reference set of arterial blood pressure, heart rate, and serum lactate levels and using the one or more classifiers to estimate the serum lactate level of the patient to predict hypoperfusion, lung disease and cardiac shock, whereby the type of circulatory shock such as a cardiogenic shock based in part on left ventricular ejection fraction. However, Heldt and Parlikar fails to disclose training a neural network with positive and negative learning data as taught by Chen. Combining Heldt, Parlikar and Chen is helpful in providing an additional parameter in order to predict cardiogenic shock with a higher level of precision when using a neural network model.
Regarding claim 20: Heldt, Parlikar and Chen teach the invention as claimed and detailed above with respect to claim 19. Heldt also teaches:
defining a plurality of PCCS criteria; (See at least [0059] via: “…The feature extractor555 extracts features from the heart rate windows and arterial blood pressure. The features may be characteristics of the heart rate window and arterial blood pressure that are directly correlated to serum lactate levels. Features may be indicative of heart health, and examples of features include.. shock index …”; in addition see at least [0093] via: “… the best classifier extracted from the data from the MIMIC II database used the following features: the median systolic blood pressure, the log ratio of the median heart rate over the first two hours to the median heart rate over the last two, the log ratio of median systolic blood pressure over the first two hours to the last two, and the slope term of the robust linear fit to systolic blood pressure…”) and
generating the at least two risk groups of patients based on the PCCS criteria. (See at least [0035] via: “…The training stage 202 receives a set of training input data and provides a set of trained classifiers to the testing stage 204. The set of training input data includes a set of training heart rate and arterial blood pressure data recorded from a first group of patients and a set of the patients' serum lactate levels..”; in addition see at least [0036] via: “…The testing stage 204 receives the set of trained classifiers from the training stage 202 and a set of testing input data. The set of testing input data includes a set of testing heart rate and arterial blood pressure recorded from a second group of patients and a set of the patients' serum lactate levels…”; in addition see at least [0086] via: “…About 9,708 patient records are filtered from the initial 32,608 patient records. The records are filtered again to obtain 1215 records of patients with matched waveform trends (step 1306). These 1215 records are split into two groups: those who had a lactate reading of at least 2.5 mmol/L in their record (positive class), and those whose lactate measurements were all below 2.5 mmol/L (negative class) (decision block 1308)..”)
Regarding claim 21: Heldt, Parlikar and Chen teach the invention as claimed and detailed above with respect to claim 19. Heldt also teaches:
receiving input via a user interface regarding the data fields to define; and defining the data fields based, at least in part, on the received input. (See at least [0035] via: “…The training stage 202 receives a set of training input data and provides a set of trained classifiers to the testing stage 204. The set of training input data includes a set of training heart rate and arterial blood pressure data recorded from a first group of patients and a set of the patients' serum lactate levels. For example, the user associated with the training stage may be a clinician (such as a doctor or a nurse) or a researcher at a hospital….. The training data may include windowed averages of heart rate extracted from electrocardiogram (ECG) signals and second by second moving averages of arterial blood pressure obtained through a catheter. In some embodiments, the heart rate and arterial blood pressure data may be partitioned using different time windows. For example, the heart rate may be averaged over multiple seconds or minutes 5 seconds, 10 seconds, 60 seconds, and 5 minutes and so on. Similarly, the arterial blood pressure may be averaged over multiple seconds or minutes like 5 seconds, 10 seconds, and 60 seconds and so on. The time window of averaging these parameters may be manually specified by a user associated with the training input data. In some embodiments, it may be preferable to create longer partition of time windows of the training data …”; in addition see at least [0038] via: “… FIG. 3 is an illustrative block diagram 300 of a training stage 202 for training a set of classifiers on a set of training heart rate and arterial blood pressure. The training stage 202 includes a database 310, a receiver 312, a preprocessor 314, a feature extractor 316, a subset selector 320, a classifier tuner 322, and a user interface 324 that includes a display renderer 326 …”) )
Regarding claim 22: Heldt, Parlikar and Chen teach the invention as claimed and detailed above with respect to claim 19. Heldt also teaches:
validating the trained model using at least some patient medical information not used to train the model, wherein outputting the trained risk model comprises outputting the validated trained model. (See at least [0014] via: “…FIG. 8 is a flow diagram depicting a process, at the application stage, for using validated (trained and tested) classifiers to determine a serum lactate level associated with heart rate and arterial blood pressure, according to an illustrative embodiment…”). In addition see figure 2
Regarding claim 29 Heldt teaches:
A computer-implemented system for simulating a likelihood that a patient will develop post-cardiotomy cardiogenic shock (PCCS), the system comprising: (See at least [0003] via: “… System and methods are disclosed herein for predicting a patient's serum lactate level...”; in addition see at least [0083] via: “…. The serum lactate level may be used to predict hypoperfusion, lung disease and cardiac shock.…”; in addition see at least [0002] via: “….Serum lactate has, for instance, been shown to be an independent predictor of major complications after cardiac surgery; it is capable of stratifying patients by risk for developing shock..”). In addition see fig. 8.
a display (See at least [0038] via: “…FIG. 3 is an illustrative block diagram 300 of a training stage 202 for training a set of classifiers on a set of training heart rate and arterial blood pressure. The training stage 202 includes ...a user interface 324 that includes a display renderer 326. ..”.
at least one hardware computer processor; (See at least [0025] via: “…The computing device 100 comprises at least one communications interface unit, an input/output controller 110, system memory, and one or more data storage devices. The system memory includes at least one random access memory (RAM 102) and at least one read-only memory (ROM 104). All of these elements are in communication with a central processing unit (CPU 106) to facilitate the operation of the computing device 600 …”) and
at least one non-transitory computer readable medium encoded with a plurality of instructions that, when processed by the at least one hardware computer processor perform a method, the method comprising: (See at least [0032] via: “…The term “computer-readable medium” as used herein refers to any non-transitory medium that provides or participates in providing instructions to the processor of the computing device 100 (or any other processor of a device described herein) for execution…)
extracting one or more features from medical information for the patient; (See at least [0079] via: “…At step 802, a the receiver 552 receives a patient's data including heart rate and arterial blood pressure...”; in addition see at least [0081] via: “…At step 806, the feature extractor 555 extracts patient features from the incoming heart rate and arterial blood pressure data. This stage also includes feature extraction from patient record, for example patient history, demographic information and other static parameters associated with the patient…”), In addition see at least Figure 8, step 802 and see Figure 8, step 806.
providing the one or more features as input to a trained classification model configured to output a risk assessment that the patient is likely to develop PCCS; (See at least [0078] via: “…FIG. 8 is a flowchart of a method used by the application stage 206 to apply validated classifiers (i.e. received from testing save 204) to a patient's heart rate and arterial blood pressure data to predict a serum lactate level of the patient. The method 800 includes the steps of receiving patient data (step 802), preprocessing patient data (step 804), and extracting features from the preprocessed data (step 806) to estimate a serum lactate level for a patient (808). The estimated serum lactate level is used to stratify risk of sepsis of a patient (step 810)..”; in addition see at least [0081] via: “…At step 806, the feature extractor 555 extracts patient features from the incoming heart rate and arterial blood pressure data. This stage also includes feature extraction from patient record, for example patient history, demographic information and other static parameters associated with the patient..”; in addition see at least [0082] via: “…At step 808, the classifiers vote on the heart rate, arterial blood pressure and other extracted features of the patient. In particular, the record of the patient received at 802 may correspond to a patient with an unknown serum lactate level, and it is desirable to use the systems and methods described herein to determine the serum lactate level for the patient...”; in addition see at least [0083] via: “…At step 810, the serum lactate level determined is used to stratify the risk of sepsis of the patient. The serum lactate level may be used to predict hypoperfusion, lung disease and cardiac shock...”; in addition see Figure 8, steps 808 and step 810.
providing, on the display, a user interface configured to display values for at least some of the one or more features (See at least [0083] via: “…At step 810, the serum lactate level determined is used to stratify the risk of sepsis of the patient. The serum lactate level may be used to predict hypoperfusion, lung disease and cardiac shock. …”; in addition see at least [0082] via: “…The likelihoods and/or most likely serum lactate level for the patient may be displayed to a user such as a clinician over the display renderer 562 within the user interface 560…”) in addition see Figure 8, step 810...”)
receiving user input via the user interface to change one or more of the values displayed on the user interface; (See at least [0082] via: “... At step 808, the classifiers vote on the heart rate, arterial blood pressure and other extracted features of the patient. In particular, the record of the patient received at 802 may correspond to a patient with an unknown serum lactate level, and it is desirable to use the systems and methods described herein to determine the serum lactate level for the patient. The vote aggregator 558 then aggregates the votes to determine the likelihoods of the serum lactate levels of the patient. The voting collection and aggregation process is described in more detail in relation to FIG. 7, and may include the steps of 702-714 to determine a most likely serum lactate level for the patient. The likelihoods and/or most likely serum lactate level for the patient may be displayed to a user such as a clinician over the display renderer 562 within the user interface 560. In some embodiments, the serum lactate level may be classified along a specific threshold. For example, the serum lactate level of a patient may be classified as less than or greater than 2.5 mmol/L. In addition, the display renderer 562 may also display a confidence score representative of a confidence in the predicted diagnosis. In some embodiments, the display renderer may display, by means of an indicator, an indication of the most likely serum lactate level being over a predetermined threshold. In some embodiments, the indicator may include an audible sound that is played if the most likely serum lactate level is above a predetermined threshold...)
simulating, using the trained classification model, a risk assessment that the patient will develop PCCS based, at least in part, on the changed one or more values, to generate a simulated risk assessment; (See at least [0083] via: “...At step 810, the serum lactate level determined is used to stratify the risk of sepsis of the patient. The serum lactate level may be used to predict hypoperfusion, lung disease and cardiac shock. The estimated serum lactate level may provide some insight into the patient's vulnerability to risk. For example, a serum lactate level of less than 2.5 mmol/L may be classified as ‘low risk’, a serum lactate level between 2.5 mmol/L and 4 mmol/L may be ‘moderate risk’ and serum lactate level greater than 4 mmol/L may be classified as ‘high risk’ for sepsis. In some embodiments, serum lactate measurements of 4 mmol/L or greater may be associated with mortality rates of 38% in patients with infections, whereas serum lactate levels less than 2.5 mmol/L, and serum lactate levels between 2.5 and 4 mmol/L, may be associated with mortality rates of 15% and 25%, respectively...”) and
displaying, on the user interface, an indication of the simulated risk assessment (See at least [0083] via: “…At step 810, the serum lactate level determined is used to stratify the risk of sepsis of the patient. The serum lactate level may be used to predict hypoperfusion, lung disease and cardiac shock. …”; in addition see at least [0082] via: “…The likelihoods and/or most likely serum lactate level for the patient may be displayed to a user such as a clinician over the display renderer 562 within the user interface 560…”) in addition see Figure 8, step 810.
Nevertheless Heldt is silent regarding extracting features including left ventricle ejection fraction and/or total bilirubin level from the received medical information as taught by Parlikar:
extracting one or more features including left ventricle ejection fraction and/or total bilirubin level. (See at least [0022] via: “…the invention relates to a system for predicting or detecting circulatory shock comprising a blood pressure measuring device, a processor, a display, a user interface, and a memory storing computer executable instructions, which when executed by the processor cause the processor to receive measurements of arterial blood pressure from the blood pressure device, compute mean arterial blood pressure from the received arterial blood pressure measurements, receive or compute estimates of at least one of heart rate, total peripheral resistance, cardiac output, stroke volume, ejection fraction, ventricular end-diastolic volume, and cardiac contractility, and classify each of these estimates as one of low, normal, or high, and predict or detect a type of circulatory shock based in part on one or more values of the received measurements and estimates. ..”; in addition see at least [0023] via: “… the type of shock includes one of septic shock, hypovolemic shock, anaphylactic shock, hemorrhagic shock, and cardiogenic shock. In one embodiment, the type of circulatory shock is determined to be … (e) cardiogenic shock if the mean arterial blood pressure is low, total peripheral resistance is high, ejection fraction is low, end-diastolic volume is high, cardiac output is low…”; in addition see at least [0059] via: “…the data set is comprised of measurements of central arterial blood pressure (cABP) measured at the aorta, carotid arterial blood pressure (carABP), and femoral arterial blood pressure (fABP), all sampled at 250 Hz with 16 bit resolution. In addition, there are intermittent echocardiography measurements of heart rate, left ventricular end-systolic volume and left ventricular end-diastolic volume, from which one can compute left ventricular ejection fraction using the EF and LVEDV methods..”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Heldt with Parlikar. Heldt teaches method to quantitatively predict a patient's serum lactate level, comprising measuring arterial blood pressure and heart rate from the patient, computing estimates of one or more cardiovascular parameters from the measured arterial blood pressure and heart rate, providing one or more classifiers that have been trained on a training data set including a reference set of arterial blood pressure, heart rate, and serum lactate levels and using the one or more classifiers to estimate the serum lactate level of the patient to predict hypoperfusion, lung disease and cardiac shock. However, Heldt fails to disclose predicting or detecting a type of circulatory shock such as a cardiogenic shock based in part on left ventricular ejection fraction as taught by Parlikar. Combining Heldt and Parlikar is helpful in providing an additional parameter in order to predict cardiogenic shock
Claim 6 is rejected under 35 U.S.C. 103 as being un-patentable by Heldt in view of Parlikar, in view of Chen, in further in view of Volosin et.al (US 20240074710 A1) hereinafter “Volosin”
Regarding claim 6: Heldt, Parlikar and Chen teach the invention as claimed and detailed above with respect to claim 1. Heldt also teaches:
receiving data indicating whether the patient developed PCCS; (See at least [0041] via: “…After the training data is preprocessed by the preprocessor 314, the feature extractor 316 extracts features from the remaining preprocessed heart rate and blood pressure values. The features may be characteristics of the heart rate and arterial blood pressure that are directly correlated to serum lactate levels. Features may be indicative of shock index..”)
However Heldt, Parlikar and Chen are silent the following limitation that is taught by Volosin:
retraining the trained classification model based, at least in part, on the received data. (See at least [0267] via: “… the training population can be updated by at least one of 1) adjusting one or more of the metrics in the training metrics, and 2) expanding the training metrics based on appending additional one or more subjects to the first plurality of subjects. The machine learning classifier models can be retrained based on the updated training metrics. For example, as additional patient metrics are determined from current patients and/or metrics from new patients are determined, the machine learning classifier can be retrained, e.g., on the increased number of metrics or on new, different metrics, to provide updated classifier models..”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Heldt, Parlikar and Chen with Volosin. Heldt teaches method to quantitatively predict a patient's serum lactate level, comprising measuring arterial blood pressure and heart rate from the patient, computing estimates of one or more cardiovascular parameters from the measured arterial blood pressure and heart rate, providing one or more classifiers that have been trained on a training data set including a reference set of arterial blood pressure, heart rate, and serum lactate levels and using the one or more classifiers to estimate the serum lactate level of the patient to predict hypoperfusion, lung disease and cardiac shock. However, Heldt fails to disclose retraining a classifier model as taught by Volosin. Combining Heldt and Volosin is useful in obtaining the most accurate classification by retraining the classifier after additional data such as additional one or more subjects to the first plurality of subjects are added to the initial data.
Claim 10 is rejected under 35 U.S.C. 103 as being un-patentable by Heldt, in view of Parlikar, in view of Chen in further view of Hann et.al (US 20130249695 A1) hereinafter “Hann”
Regarding claim 10: Heldt, Parlikar and Chen teach the invention as claimed and detailed above with respect to claim 1, 7 & 8. However Heldt, Parlikar and Chen are silent the following claim that is taught by Hann:
wherein outputting an indication of the risk group for the patient comprises displaying on a user interface a color coded indication of the risk group. (See at least [0005] via: “…receive patient data from an input device and calculate a risk level for the patient based on the patient data and assigned risk scores, and then outputs the risk level to a user on a display…can present groups of patients according to risk levels on an electronic screen to be viewed by multiple users, improving user's workflow and continuity of care between multiple users…”; in addition see at least [0007] via: “…can include a color-coded visual display configured to display risk levels corresponding to the correlated risk values..”; in addition see at least [0011] via: “…can include a device for submitting pressure ulcer prevention measures to nursing outcomes data registries, including a user interface configured to display only when a risk level of a patient is at a predetermined level,..”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Held, Parlikar and Chen with Hann. Heldt teaches method to quantitatively predict a patient's serum lactate level, comprising measuring arterial blood pressure and heart rate from the patient, computing estimates of one or more cardiovascular parameters from the measured arterial blood pressure and heart rate, providing one or more classifiers that have been trained on a training data set including a reference set of arterial blood pressure, heart rate, and serum lactate levels and using the one or more classifiers to estimate the serum lactate level of the patient to predict hypoperfusion, lung disease and cardiac shock. However, Heldt fails to disclose a color coded visual display as taught by Hann. Combining Heldt and Hann is useful to medical providers when reading a display of lactate levels in a patient whereby different displayed colors indicate different levels of risk for sepsis in a patient.
Claims 23 & 27 are rejected under 35 U.S.C. 103 as being un-patentable by Heldt in view of Parlikar, in view of Chen in further view of Burnett et.al (US 20210241871 A1) hereinafter “Burnett”
Regarding claim 23: Heldt, Parlikar and Chen teach the invention as claimed and detailed above with respect to claim 19. However, Heldt, Parlikar and Chen are silent the following claim that is taught by Burnett:
receiving an indication to update the trained risk model; and retraining the risk model in response to receiving the indication to update the trained risk model. (See at least [0069] via: “…the data selecting module 218 receives feedback from the machine-learning training module 220 that is used to adjust the historical patient data (e.g., adjust the number of patients and/or time period in which patients are included). In some embodiments, the data selecting module 216 adjusts the predetermined time period and/or the predetermined number of patients for the historical patient data such that a trained risk model meets a minimum accuracy rate. For instance, a trained risk model, as explained in detail below, may have an accuracy rate below the minimum accuracy rate and the machine-learning training system 300 can adjusts the predetermined time period and/or the predetermined number of patients for the historical patient data to retrain a risk model with an accurate rate above the minimum accuracy rate. The accuracy rate for a trained risk model is determined by machine-learning training module 220 ..”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Heldt with Burnett. Heldt teaches method to quantitatively predict a patient's serum lactate level, comprising measuring arterial blood pressure and heart rate from the patient, computing estimates of one or more cardiovascular parameters from the measured arterial blood pressure and heart rate, providing one or more classifiers that have been trained on a training data set including a reference set of arterial blood pressure, heart rate, and serum lactate levels and using the one or more classifiers to estimate the serum lactate level of the patient to predict hypoperfusion, lung disease and cardiac shock. However, Heldt fails to disclose retraining a risk model once the risk model reaches an accuracy rate below a minimum rate as taught by Burnett. Combining Heldt and Burnett is useful in retraining a risk model once the risk model reaches an accuracy rate below a minimum rate as a result of adding new patient data under evaluation.
Regarding claim 27: Heldt, Parlikar and Chen teach teaches the invention as claimed and detailed above with respect to claim 19. However, Heldt, Parlikar and Chen are silent the following claim that is taught by Burnett:
receiving additional medical information; retraining the trained risk model based on the additional medical information. (See at least [0016] via: “… the method includes periodically retraining the risk machine-learning system as new patient data, and data reflecting any readmissions from one or more post-acute care facilities to one or more acute care facilities, is collected. In some embodiments, the method includes, before receiving patient data for a plurality of patients, receiving historical patient data from at least one healthcare recording database. The method includes extracting training data from the historical patient data, utilizing the training data to train multiple risk machine-learning systems, selecting a risk machine-learning system of the multiple risk machine-learning systems that respectively determined risk scores above a predetermined accuracy rate, and storing the risk machine-learning system…”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Heldt with Burnett. Heldt teaches method to quantitatively predict a patient's serum lactate level, comprising measuring arterial blood pressure and heart rate from the patient, computing estimates of one or more cardiovascular parameters from the measured arterial blood pressure and heart rate, providing one or more classifiers that have been trained on a training data set including a reference set of arterial blood pressure, heart rate, and serum lactate levels and using the one or more classifiers to estimate the serum lactate level of the patient to predict hypoperfusion, lung disease and cardiac shock. However, Heldt fails to disclose retraining a risk model once the risk model reaches an accuracy rate below a minimum rate as taught by Burnett. Combining Heldt and Burnett is useful in retraining a risk model once the risk model reaches an accuracy rate below a minimum rate as a result of adding new patient data under evaluation.
Claim 28 is rejected under 35 U.S.C. 103 as being un-patentable by Heldt in view of Parlikar, in view of Chen in further view of Burnett, in further view of Mortazavi et.al (US 20180315507 A1) hereinafter “Mortazavi”
Regarding claim 28: Heldt, Parlikar and Chen teach the invention as claimed and detailed above with respect to claim 19 and Heldt, Parlikar Chen and Burnett teach the invention as claimed and detailed above with respect to claim 27. However, Heldt, Parlikar, Chen and Burnett are silent the following claim that is taught by Mortazavi:
wherein the additional medical information includes medical information for a plurality of patients at a medical facility on which cardiac surgery was performed. (See at least [0035] via: “…The disclosure details the personalized predictions of postoperative complications in cardiovascular procedure patients. It also covers the extraction of data from the EPIC electronic health record system [33] used by Yale-New Haven Hospital (Y-NHH). The cohort consisted of patients admitted to the Heart and Vascular Center (HVC) for cardiac procedures, with a primary principal procedure code for CABG, PCI or ICD. This study used all data available in the EHR from February, 2013 (the go-live date for EPIC at Y-NHH) through September, 2015. As prior data were stored on a different HER system, all visits from this date forward were considered first visits. Methods considered for this work considered data upon patient presentation at admission and collected from then forward..”; in addition see at least [0038] via: “… Data were extracted for each admission. Each visit's dataset consisted of data from admission time to either 24 hours or the start of patient's first procedure, whichever came first; this period of time was believed to be long enough to gather clinically relevant information on the patients to provide an understanding of patient risk prior to the procedure that resulted in the adverse event. .. The desired goal, therefore, was to create a dataset and system that would serve as a balance between early enough for appropriate decision making and late enough for considering a wide array of data. The following categories of information were gathered: [0039] Patient Information: Included features, such as age, gender, insurance, and admission information. [0040] Patient History: Included information, such as the patient problem list and admission diagnosis codes (ICD-9). [0041] Visit Information: Included primary principal procedure information, admission time, and attending staff information. [0042] Medical Information: Included medications prescribed, laboratory results, and patient vitals, including temperature, pulse oxygenation, systolic blood pressure, diastolic blood pressure, respiratory rate, and heart rate. [0043] Rothman Index: Rothman Index scores..”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Heldt, Parlikar Chen and Burnett with Mortazavi. Heldt teaches method to quantitatively predict a patient's serum lactate level, comprising measuring arterial blood pressure and heart rate from the patient, computing estimates of one or more cardiovascular parameters from the measured arterial blood pressure and heart rate, providing one or more classifiers that have been trained on a training data set including a reference set of arterial blood pressure, heart rate, and serum lactate levels and using the one or more classifiers to estimate the serum lactate level of the patient to predict hypoperfusion, lung disease and cardiac shock. However, Heldt fails to disclose obtaining medical data of patients at a medical facility in order to predict postoperative complications in cardiovascular procedure patients as taught by Mortazavi. Combining Heldt and Mortazavi is useful to medical practitioners in order to evaluate the cardiovascular risk level of a patient before and after a cardiovascular procedure by obtaining medical data of a patient before and after a cardiovascular procedure.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure, and is listed in the attached form PTO-892 (Notice of References Cited). Unless expressly noted otherwise by the Examiner, all documents listed on form PTO-892 are cited in their entirety.
Bayes (WO 2020169751 A1) – Predicting Mortality Risk Among Patients Suffering From Cardiogenic Shock, Involves Determining Concentration Level Of Beta-2-microglobulin In Biological Sample Obtained From Patient- teaches: In vitro method for predicting mortality risk among patients suffering from cardiogenic shock involves determining the concentration level of at least β -2-microglobulin (B2MG) in a biological sample obtained from the patient, where an increased level of at least the protein B2MG with respect to the concentration level determined in control survivor patients suffering from cardiogenic shock, is an indication of mortality risk.
Response to Arguments
Applicant's arguments filed 1-9-2026, have been fully considered but not found
persuasive.
Applicant amended independent claims 1 as posted in the above analysis with additions underlined and deletions
In response to applicant's arguments regarding claim rejection under 35 U.S.C § 101.
Several steps are taken in the analysis as to whether an invention is rejected under 101. The first step is to determine if the claim falls within a statutory category. In this claims 1, 19 & 29 recite a method, system of predicting whether a patient is likely to develop post-cardiotomy cardiogenic shock (PCCS).
The second step under 2A prong one is to determine if the claims recite an abstract idea, which would be the case if the invention can be grouped as either: a) mathematical concepts; (b) mental processes; or (c) certain methods of organizing human activity (encompassing (i) fundamental economic principles, (ii) commercial or legal interactions or (iii) managing personal behavior or relationships or interactions between people). The current invention is classified as an abstract idea since it may be grouped as a mental process as it recites “identifying the risk of post-cardiotomy cardiogenic shock in patients”. Alternatively, the selected abstract idea belongs to certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “identifying the risk of post-cardiotomy cardiogenic shock in patients”.
The third step under 2A Prong Two is to determine if additional elements in the claim imposes a meaningful limit on the abstract idea in order to integrate it into a practical idea. The current invention does not represent a practical idea since the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a generic computer as a tool to implement the abstract idea.
the fourth step under 2B is to determine if additional elements of the claim provide an inventive concept. An invention may be classified as an inventive concept if a computer-implemented processes is determined to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic, and non-conventional even if generic computer operations on a generic computing device is used to implement the abstract idea. The current invention does not represent an inventive concept since the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a generic computer as a tool to implement the abstract idea.
Step 2A Prong ONE
The Applicant argues that the claims are not directed to an abstract idea, yet fails to elaborate further the reasoning to support that argument.
The Examiner disagrees since the Applicant’s arguments are not persuasive. The method used to select the abstract idea, is to strip the additional elements from the claims. As seen below the recited boldened words constitute the abstract idea after stripping the un-boldened additional elements of amended limitation of claims 1, 19 and 29:
Claim 1:
predicting whether a patient is likely to develop post-cardiotomy cardiogenic shock (PCCS), the method comprising:
receiving medical information for a patient;
extracting one or more features including left ventricle ejection fraction and/or total bilirubin level from the received medical information;
providing the one or more features as input to a trained classification model configured to output a risk assessment that the patient is likely to develop PCCS;
the trained classification model having been trained using training data including first patient medical information for a PCCS patient group and second patient medical information for a no PCCS patient group, wherein each of the first patient medical information and the second patient medical information includes left ventricle ejection fraction and/or total bilirubin level information;
outputting an indication of the risk assessment, wherein outputting the indication of the risk assessment comprises displaying a visual indicator of the risk assessment in an electronic health record of the patient.
Claim 19:
training a risk model to predict whether a patient is likely to develop post-cardiotomy cardiogenic shock (PCCS), the method comprising:
receiving a dataset of patient medical information;
selecting, from the dataset of patient medical information, training data based on PCCS criteria and defined data fields, wherein the training data includes first patient medical information for a PCCS patient group and second patient medical information for a no PCCS patient group, wherein each of the first patient medical information and the second patient medical information includes left ventricle ejection fraction and/or total bilirubin information
training the risk model using the selected training data; and
outputting the trained risk model
Claim 29:
a display;
at least one hardware computer processor; and
at least one non-transitory computer readable medium encoded with a plurality of instructions that, when processed by the at least one hardware computer processor perform a method,
extracting one or more features including left ventricle ejection fraction and/or total bilirubin level from medical information for the patient;
providing the one or more features as input to a trained classification model configured to output a risk assessment that the patient is likely to develop PCCS;
providing, on the display, a user interface configured to display values for at least some of the one or more features
receiving user input via the user interface to change one or more of the values displayed on the user interface;
simulating, using the trained classification model, a risk assessment that the patient will develop PCCS based, at least in part, on the changed one or more values, to generate a simulated risk assessment; and
displaying, on the user interface, an indication of the simulated risk assessment;
The selected abstract idea (boldened limitations) of claims 1, 19, 29 can be implemented by pencil and paper and thus belong to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites “identifying the risk of post-cardiotomy cardiogenic shock in patients”. Alternatively, the selected abstract idea belongs to certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “identifying the risk of post-cardiotomy cardiogenic shock in patients”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
Step 2A Prong TWO
The Applicant argues that even if he claims recite an abstract idea the claimed subject matter is directed to a practical application based on the amendments but offers no further arguments.
The Examiner disagrees since the Applicant’s arguments are not persuasive. Most of the added amendments constitute additional elements that do not impose a meaningful limit on the abstract idea.
The Examiner restates that claims 1, 19 and 29 do not integrate the abstract idea into a practical application. Neither claims 1, 19 or 29 recite additional elements that impose a meaningful limit on the abstract idea:
Claim 1 recites the following additional elements:
trained classification model;
the trained classification model having been trained using training data.
electronic health record.
Claim 19 recites the following additional elements:
training the risk model;
trained risk model.
Claim 29 recites the following additional elements:
a display
at least one hardware computer processor; and
at least one non-transitory computer readable medium encoded with a plurality of instructions that, when processed by the at least one hardware computer processor perform a method
providing, on the display, a user interface configured to display values for at least some of the one or more features
user interface
trained classification model.
The additional elements as recited above for claims 1, 19 and 29 amount to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs [0039-0041]. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In order to integrate the abstract idea into a practical idea the Applicant could demonstrate at least one of the conditions enumerated below applies:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
The Applicant has not demonstrated any of the above listed conditions. As a result, the Examiner restates the rejection of the invention under 35 USC §101.
Step 2B
Similar to the analysis under Step 2A Prong Two, the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs [0039-0041] (refer to MPEP 2106.05(f)). The use of generic computer components, in combination, do not perform functions that are not merely generic, and non-conventional even if the generic computer operations on a generic computing device is used to implement the abstract idea. Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
In order evaluate whether the claim recites additional elements that amount to an inventive concept what could be shown is:
Adding a specific limitation (unconventional other than what is well-understood, routine, conventional (WURC) activity in the field - see MPEP 2106.05(d)
The Applicant has not demonstrated the above listed condition.
In response to applicant's arguments regarding claim rejection under 35 U.S.C § 103.
The Applicant argues that it disagrees that it would have been obvious to have modified any purported combined system of Heldt and Parlikar based on the teachings of Chen to teach the following limitations:
“the trained classification model having been trained using training data including first patient medical information for a PCCS patient group and second patient medical information for a no PCCS patient group” of claim 1;
“the training data includes first patient medical information for a PCCS patient group and second patient medical information for a no PCCS patient group” of claim 19
In addition the Applicant disagrees that Heldt teaches the following limitations of independent claim 29:
providing, on the display, a user interface configured to display values for at least some of the one or more features;
receiving user input via the user interface to change one or more of the values displayed on the user interface;
simulating, using the trained classification model, a risk assessment that the patient will develop PCCS based, at least in part, on the changed one or more values, to generate a simulated risk assessment; and
displaying, on the user interface, an indication of the simulated risk assessment"
Regarding 1 and 2 above the Applicant argues that Chen is neither in the same field of endeavor as the present application nor is Chen reasonably pertinent to the problem faced by the inventors of the present application. For at least these reasons, Chen is not analogous prior art, and therefore is not a reference proper to use in an obviousness rejection under 35 U.S.C. 103. Furthermore the Applicant argues that even assuming that Chen is analogous prior art, Chen when considered in combination with Heldt and Parlikar, fails to disclose the above listed limitations of points 1 and 2. Specifically none of the steps of Chen [0003] teach the above cited limitations 1 and 2.
Regarding 3, 4, 5 and 6 above, the Applicant argues that the paragraphs [0082]- [0083] of Heldt fails to teach the above listed limitations of points 3, 4, 5 and 6.
The Examiner disagrees with the Applicant’s arguments since they are not persuasive.
Regarding points 1 and 2: Whether Chen is or not an analogous prior art. In order for a reference to be proper for use in an obviousness rejection under 35 U.S.C. 103, the reference must be analogous art to the claimed invention. In re Bigio, 381 F.3d 1320, 1325, 72 USPQ2d 1209, 1212 (Fed. Cir. 2004). A reference is analogous art to the claimed invention if: (1) the reference is from the same field of endeavor as the claimed invention (even if it addresses a different problem); or (2) the reference is reasonably pertinent to the problem faced by the inventor (even if it is not in the same field of endeavor as the claimed invention). In this case Chen, although not from the same field of endeavor as the claimed invention, it is reasonably pertinent to the problem faced by the inventor. The limitation under discussion relates to a model using training for a PCCS patient group and a no PCCS patient group. This relates to Chen who teaches a model based on a positive and a negative learning data set. Hence Chen’s reference is reasonably pertinent to the problem faced by the inventor.
Furthermore although Chen does not teach the concept of PCCS, this is already taught by Heldt [0082] that teaches the concept of serum lactate levels which the Examiner notes is a well known fact that serum lactate levels can be used as a predictor of post-cardiotomy cardiogenic shock (PCCS) as recited in [0083] via “…At step 810, the serum lactate level determined is used to stratify the risk of sepsis of the patient. The serum lactate level may be used to predict hypoperfusion, lung disease and cardiac shock...”. Chen teaches in [0003] the concept of a model based on a positive and a negative learning data set which in combination with Heldt [0082] and [0083] teaches the limitation of 1 and 2 cited above.
The motivation for this combination is as follows: It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Heldt and Parlikar with Chen. Heldt and Parliker teach a method to quantitatively predict a patient's serum lactate level, comprising measuring arterial blood pressure and heart rate from the patient, computing estimates of one or more cardiovascular parameters from the measured arterial blood pressure and heart rate, providing one or more classifiers that have been trained on a training data set including a reference set of arterial blood pressure, heart rate, and serum lactate levels and using the one or more classifiers to estimate the serum lactate level of the patient to predict hypoperfusion, lung disease and cardiac shock, whereby the type of circulatory shock such as a cardiogenic shock based in part on left ventricular ejection fraction. However, Heldt and Parlikar fails to disclose training a neural network with positive and negative learning data as taught by Chen. Combining Heldt, Parlikar and Chen is helpful in providing an additional parameter in order to predict cardiogenic shock with a higher level of precision when using a neural network model.
Regarding point 3 it is taught by Heldt [0082] “The likelihoods and/or most likely serum lactate level for the patient may be displayed to a user such as a clinician over the display renderer 562 within the user interface 560…”.
Regarding point 4 it is taught by Heldt [0082]: “the record of the patient received at 802 may correspond to a patient with an unknown serum lactate level... the steps of 702-714 to determine a most likely serum lactate level for the patient. The likelihoods and/or most likely serum lactate level for the patient may be displayed to a user such as a clinician over the display renderer 562 within the user interface 560..”
Regarding point 5, it is taught by Heldt [0083]: “At step 810, the serum lactate level determined is used to stratify the risk of sepsis of the patient... a serum lactate level of less than 2.5 mmol/L may be classified as ‘low risk’, a serum lactate level between 2.5 mmol/L and 4 mmol/L may be ‘moderate risk’ and serum lactate level greater than 4 mmol/L may be classified as ‘high risk’ for sepsis..”
Regarding point 6, it is taught by Heldt [0082]: “most likely serum lactate level for the patient may be displayed to a user such as a clinician over the display renderer 562 within the user interface 560…”. in addition see Figure 8, step 810.
Hence the rejection under 35 USC §103 is maintained
For reasons of record and as set forth above, the examiner maintains the rejection of claims 1-3, 6-11, 17-19, 21-23, 27-29 as being directed to a judicial exception without significantly more, and thereby being directed to non-statutory subject matter under 35 USC §101 in addition to being rejected under 35 USC §103. In reaching this decision, the Examiner considered all evidence presented and all arguments actually made by Applicant.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PIERRE L MACCAGNO whose telephone number is (571)270-5408. The examiner can normally be reached M-F 8:00 to 5:00.
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/PIERRE L MACCAGNO/Examiner, Art Unit 3687
/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684
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