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 .
Status of Claims
This action is a final rejection
Claims 1-2, 4-7, 9-13, 15-19, 21-24, 26 are pending
Claim 1, 5, 9, 11-12, 15, 17, 21 were amended
Claims 3, 8, 14, 20, 25 were cancelled
Claim 26 was added
Claims 1-2, 4-7, 9-13, 15-19, 21-24, 26 are rejected under 35 USC § 101
Priority
Acknowledgement is made of Applicant’s claim for a domestic priority date of 11-27-2019
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 6-2-2022 (two of them) are 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 1-2, 4-7, 9-13, 15-19, 21-24, 26 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 1-2, 4-7, 9-13, 15-19, 21-24, 26 the claims recite an abstract idea of detecting and responding to patient neuromorbidity.
Independent Claims 1, 9 and 15 are rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claims 1, 9 and 15 recite a method, system and computer program product of detecting and responding to patient neuromorbidity.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES): The claimed invention:
receiving, …patient cohort data for a plurality of pediatric patients;
the patient cohort data comprising at least an age of each patient of the plurality of pediatric patients and;
cohort vital sign data including at least heart rate, Glasgow coma scale score (GCSS), systolic blood pressure (SBP), and body temperature;
cohort medication administration data including at least administration of a benzodiazepine medication and/or an opioid medication;
cohort body fluid data including at least levels of creatinine, blood urea nitrogen (BUN), and potassium;
selecting, … a plurality of features of the patient cohort data using a feature selection evaluating parameter comprising at least one of the following: a first value, a last value, a minimum value, a maximum value, a difference between the minimum value and the maximum value, a difference between the first value and the last value, a change in value over a period of time, a slope of change over a period of time, or a combination thereof;
wherein the plurality of features comprises:
a feature associated with patient age;
a feature associated with a patient heart rate a feature associated with a patient GCSS:
a feature associated with a patient SBP;
a feature associated with a patient body temperature a feature associated with administration of a medication to a patient a feature associated with a patient creatinine level:
a feature associated with a patient BUN level;
training… using the plurality of features and the patient cohort data, a patient classification model to provide a trained patient classification model, the trained patient classification model comprising a decision tree based machine learning wherein the neuromorbidity risk is associated with one or more selected from delirium, chronic pain, seizures, intracranial hemorrhage, stroke, impairment or loss of consciousness, seizures, ischemic stroke, intracerebral hemorrhage, cerebral edema, delirium, and neuromuscular weakness;
monitoring, with at least one processor and in real-time, a pediatric patient at risk for brain injury and located in an intensive care unit (ICU) to generate real-time pediatric patient dataset associated with a pediatric patient;
the pediatric patient dataset comprising at least:
patient age;
real-time patient vital sign data including at least patient heart rate, patient GCSS, patient SBP, and patient body temperature;
patient medication administration data including at least administration of a benzodiazepine medication and/or an opioid medication; and
patient body fluid data including at least patient creatinine level, patient BUN level, and patient potassium level;
receiving, with at least one processor, the real-time pediatric patient dataset based on monitoring the pediatric patient
generating…in real-time, a patient classification of the pediatric patient comprising a probability of the pediatric patient developing a neuromorbidity within a time period, wherein generating the patient classification of the pediatric patient comprises;
real-time pediatric patient dataset into the patient classification model and
outputting, by the trained patient classification model, the
in response to the probability of the patient developing a neuromorbidity satisfying a predetermined threshold, transmitting automatically, .. an alert … associated with at least one of the following: a physician, a nurse, an advanced practice provider of deteriorating brain health, a healthcare provider, or any combination thereof wherein the alert comprises a medical intervention for the pediatric patient based on the patient classification.
belong to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites: “detecting and responding to patient neuromorbidity”. 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 “detecting and responding to patient neuromorbidity”. (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).
Claims 1, 9 and 15 recite:
processor;
electronic health record system;
computing device;
trained patient classification model.
Claim 9 recites:
Claim 15 recites:
A computer program product comprising at least one non-transitory computer-readable medium including program instructions, executed by at least one processor;
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 (0006-0010). (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,
Claims 1, 9 and 15 recite:
processor;
electronic health record system;
computing device;
trained patient classification model.
Claim 9 recites:
Claim 15 recites:
A computer program product comprising at least one non-transitory computer-readable medium including program instructions, executed by at least one processor;
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 (0006-0010). (refer to MPEP 2106.05(f)) Accordingly, the claim does 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 “detecting and responding to patient neuromorbidity”. These claim limitations include:
Claims 2, 10 and 16: wherein
the patient vital sign data further includes at least one of patient diastolic blood pressure, patient mean blood pressure, and patient blood oxygen level;
the patient medication administration data further comprises at least one of an anti-delirium medication and an anti-psychotic medication; and
the patient body fluid data further comprises at least one of patient bicarbonate level, patient chloride level, patient glucose level, patient lactate level, patient partial carbon dioxide pressure, patient blood pH, patient platelet count, patient sodium level, patient white blood cell count, and patient partial thromboplastin time
Claims 5, 11 and 17:
further comprising converting… values of the plurality of features from time series data to vector space representations prior to training the patient classification model, wherein converting values of the plurality of features from time series data to vector space representations comprises:
converting categorical laboratory values according to first value, second to last value, last value, time since last value, and indicators of a test order status; and
converting values of continuous variable features by summarizing time-dependent aspects of the values of continuous variable features, wherein the time-dependent aspects of the values of continuous variable features comprise an absolute difference between two measurements, a slope and a percent change between two measurements, and a comparison between most recent values with previous, apex, nadir, and baseline values
Claims 6, 12 and 18: further comprising, repeating, at a time interval, the following:
Receiving.. a new pediatric patient dataset from the record of the pediatric patient;
generating, .. by inputting the new pediatric patient dataset into the patient classification model, a new patient classification of the pediatric patient comprising a new probability of the patient developing a neuromorbidity over a subsequent time period; and
in response to the new probability of the pediatric patient developing a neuromorbidity satisfying the predetermined threshold, transmitting, .. an alert … associated with at least one of the following: the physician, the nurse, the advanced practice provider of deteriorating brain health, the healthcare provider, or any combination thereof;
Claims 7, 13 and 19: wherein the pediatric patient dataset comprises a value indicative of levels of at least one brain-specific biomarker, selected from ubiquitin carboxyl-terminal hydrolase L1 (UCH-L1), glial fibrillary acidic protein (GFAP), myelin basic protein (MBP), neuron specific enolase (NSE), S100b, neurofilament light chain (NFL), Tau, phosphorylated Tau (pTau), cleaved Tau (cTau), 150 kDa breakdown product of a-II-spectrin (SBDP150), or any combination thereof and wherein the patient classification is based at least partly on the value indicative of levels of the at least one brain-specific biomarker;
Claim 21: A method of treating a patient having increased risk of development of a neromorbidity, comprising;
receiving, the patient classification of the patient or the alert; and
increasing monitoring of the patient for development of the neuromorbidity and/or treating the patient for the neuromorbidity when the patient is classified as having increased risk of developing a neuromorbidity or an alert is transmitted indicating the patient as having increased risk of developing a neuromorbidity
Claims 22, 23, 24: wherein:
the patient cohort data further comprises cohort diagnostic data including at least data relating to computed tomography, magnetic resonance imaging, an electroencephalogram, and pupillary reaction; and
the pediatric patient dataset comprises patient diagnostic data including at least a patient computed tomography scan, a patient magnetic resonance imaging scan, a patient electroencephalogram, and a patient pupillary reaction
Claim 26: wherein the patient classification of the pediatric patient comprising a probability of the pediatric patient developing a neuromorbidity within a time period of 24 hours following monitoring of the pediatric patient
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 4: wherein the patient classification model comprises a machine-learning model executing at least one of the following techniques:
Multivariate Adaptive Regression Splines (MARS);
random forest;
logistic regression
support vector machines;
naive Bayes;
or any combination thereof;
Claims 5, 11 and 17:
at least one processor;
training the patient classification model.
Claims 6, 12 and 18:
at least one processor;
computing device;
Claim 21: computing device comprising the computer program product;
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, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible. The claims include:
Claim 4: wherein the patient classification model comprises a machine-learning model executing at least one of the following techniques:
Multivariate Adaptive Regression Splines (MARS);
random forest;
logistic regression
support vector machines;
naive Bayes;
or any combination thereof;
Claims 5, 11 and 17:
at least one processor;
training the patient classification model.
Claims 6, 12 and 18:
at least one processor;
computing device;
Claim 21: computing device comprising the computer program product;
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
Shaffer (RU 2603601 C2) - METHODS AND SYSTEMS FOR IDENTIFYING PATIENTS WITH MILD COGNITIVE IMPAIRMENT WITH RISK OF TRANSITION TO ALZHEIMER'S DISEASE – teaches: Training data are received from part of group of patients. Set of solution trees are adjusted on training data. Patient data are received from one or more patients of group, patient data therein are independent from training data. Any patient data are classified using adjusted set of solution trees for obtaining threshold values for patient. Threshold value of patient represents number of solution trees in adjusted set of solution trees, which can classify patient with mild cognitive impairment as passing to Alzheimer's disease. Method is implemented by means of computer.EFFECT: group of inventions allows early detection of Alzheimer's disease
Gallagher (US 20190008583 A1) - METHODS FOR TREATING DEPRESSION IN PATIENTS VIA RENAL NEUROMODULATION – teaches: Methods for treating depression and for reducing a risk associated with developing depression in patients via therapeutic renal neuromodulation and associated systems are disclosed herein. Sympathetic nerve activity can contribute to several cellular and physiological conditions associated with depression as well as an increased risk of developing depression. One aspect of the present technology is directed to methods for improving a patient's calculated risk score corresponding to a depression status in the patient. Other aspects are directed to reducing a likelihood of developing depression in patients presenting one or more depression risk factors. Renal sympathetic nerve activity can be attenuated to improve a patient's depression status or risk of developing depression. The attenuation can be achieved, for example, using an intravascularly positioned catheter carrying a therapeutic assembly configured to use, e.g., electrically-induced, thermally-induced, and/or chemically-induced approaches to modulate the renal sympathetic nerve.
Van Eyk (WO 2019133717 A1) - NOVEL BIOMARKERS AND METHODS FOR DIAGNOSING AND EVALUATING TRAUMATIC BRAIN INJURY – teaches: methods for diagnosing and evaluating a subject that has sustained or may have sustained an injury to the head, such as a traumatic brain injury (TBI). In particular, the present disclosure identifies various biomarkers, the detection and/or differential expression of which can be used to assess the presence or absence of a TBI in a subject, and can be used as a basis for diagnosing a subject as having a specific type of TBI (e.g., severe TBI or subclasses of mild TBI). The various TBI biomarkers can be detected individually or in combination and can be used as an important diagnostic, prognostic, and/or TBI risk stratification tool as part of assessing a subject's TBI status.
Cohen (US 20180068083 A1) - METHODS AND MACHINE LEARNING SYSTEMS FOR PREDICTING THE LIKELIHOOD OR RISK OF HAVING CANCER – teaches: non-invasive methods and tests that measure biomarkers (e.g., tumor antigens) and collect clinical parameters from patients, and computer-implemented machine learning methods, apparatuses, systems, and computer-readable media for assessing a likelihood that a patient has a disease, relative to a patient population or a cohort population. In one embodiment, a classifier is generated using a machine learning system based on training data from retrospective data and subset of inputs (e.g. at least two biomarkers and at least one clinical parameter), wherein each input has an associated weight and the classifier meets a predetermined Receiver Operator Characteristic (ROC) statistic, specifying a sensitivity and a specificity, for correct classification of patients. The classifier may then be used to assesses the likelihood that a patient has cancer relative to a population by classify the patient into a category indicative of a likelihood of having cancer or into another category indicative of a likelihood of not having cancer
Albright (US 20190272922 A1) -MACHINE-LEARNING-BASED FORECASTING OF THE PROGRESSION OF ALZHEIMER'S DISEASE-teaches: a machine-learning algorithm is provided that uses current and past clinical data in order to accurately and precisely predict the future onset of mild cognitive impairment (MCI) and dementia for individual patients, thus enabling early identification of those having high risk for Alzheimer's disease. In particular, a newly defined “All-Pairs” technique combines data from each doctor's visit for each patient with data from each of the patient's other doctor's visits. By correlating clinical data obtained from patients at one time point with the progression of Alzheimer's disease in the future, the techniques herein are able to increase the likelihood of identifying Alzheimer's disease patients at early stages.
Matthews (WO 2017011746 A1) - SYSTEM AND METHODS FOR DETERMINING A BRAIN CONDITION OF A PATIENT SYBJECT TO MULTIPLE DISEASE STATES-teaches: identifying a brain condition of a patient subject to a plurality of disease states are provided, in some aspects, the method includes receiving imaging data associated with a patient's brain acquired using an imaging system, and constructing a classifier having signatures corresponding to a plurality of disease states. The method also includes applying the classifier to the imaging data to determine a degree to which the patient expresses at least one of the plurality of disease states, and determining a brain condition of the patient using the determined degree. The method further includes generating a report indicative of the brain condition of the patient.
Boussios (US 11862346 B1) - Identification Of Patient Sub-cohorts And Corresponding Quantitative Definitions Of Subtypes As A Classification System For Medical Conditions -teaches: A classification method and system for medical conditions based on the concept of subtypes, which are classes of patients whose medical fact patterns as analyzed in an N-dimensional space places them closer to other patients belonging to the same subtype than to patients who belong to different subtypes and, who share similar likelihood of certain specified outcomes. A computer system processes patient data for a plurality of patients from a set of patients called a cohort. The computer system processes the patient data for the cohort to group patients into sub-cohorts of similar patients, i.e., each sub-cohort includes patients who have similar medical fact patterns in their patient data. Patients in different sub-cohorts generally, but not necessarily, have significant differences in their patient data. The computer system generates quantitative definitions, describing the patients in the sub-cohorts.
Response to Arguments
Applicant's arguments filed 10-21-2025, have been fully considered but not found
persuasive.
Applicant amended claims 1, 5, 9, 11-12, 15, 17, 21, canceled claims 3, 8, 14, 20, 25 and added claim 26 as posted in the above analysis.
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 case it does for claims 1, 9 and 15 since the claims recite a method, system and non-transitory computer-readable medium for detecting and responding to patient neuromorbidity.
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 “detecting and responding to patient neuromorbidity”. 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 “detecting and responding to patient neuromorbidity”. (refer to MPP 2106.04(a)(2)). Accordingly the claims recite an abstract idea
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 offers no argument against the claims being classified as an abstract idea.
The Examiner explains the method used to select the abstract idea, which 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, 9 and 15:
receiving, with at least one processor, patient cohort data for a plurality of pediatric patients from an electronic health record system the patient cohort data comprising at least an age of each patient of the plurality of pediatric patients and:
cohort vital sign data including at least heart rate, Glasgow coma scale score (GCSS), systolic blood pressure (SBP) (systolic blood pressure), and body temperature;
cohort medication administration data including at least administration of a benzodiazepine medication and/or an opioid medication; and
cohort body fluid data including at least levels of creatinine , blood urea nitrogen (BUN), and potassium
selecting, with at least one processor, a plurality of features of the patient cohort data using a feature selection evaluating parameter comprising at least one of the following: a first value, a last value, a minimum value, a maximum value, a difference between the minimum value and the maximum value, a difference between the first value and the last value, a change in value over a period of time, a slope of change over a period of time, or a combination thereof, wherein the plurality of features comprises:
a feature associated with patient age;
a feature associated with a patient heart rate a feature associated with a patient GCSS:
a feature associated with a patient SBP;
a feature associated with a patient body temperature a feature associated with administration of a medication to a patient a feature associated with a patient creatinine level:
a feature associated with a patient BUN level;
training, with at least one processor using the plurality of features and the patient cohort data, a patient classification model to provide a trained patient classification model, the trained patient classification model comprising a decision tree based machine learning model, the patient classification model configured to classify patients according to neuromorbidity risk, wherein the neuromorbidity risk is associated with one or more selected from delirium, chronic pain, seizures, intracranial hemorrhage, stroke, impairment or loss of consciousness, seizures, ischemic stroke, intracerebral hemorrhage, cerebral edema, delirium, and neuromuscular weakness;
monitoring, with at least one processor and in real-time, a pediatric patient at risk for brain injury and located in an intensive care unit (ICU) to generate, a real-time pediatric patient dataset associated with the pediatric patient, the pediatric patient dataset comprising at least:
patient age;
real-time patient vital sign data including at least patient heart rate, patient GCSS, patient SBP, and patient body temperature
patient medication administration data including at least administration of a benzodiazepine medication and/or administration of an opioid medication; and
patient body fluid data including at least patient creatinine level, patient BUN level (blood urea nitrogen level), and patient potassium level;
receiving, with at least one processor, the real-time pediatric patient dataset based on monitoring the pediatric patient;
generating, with at least one processor and in real-time, a patient classification of the pediatric patient comprising a probability of the pediatric patient developing a neuromorbidity within a time period, wherein generating the patient classification of the pediatric patient comprises:
inputting the real-time pediatric patient dataset into the trained patient classification model, and
outputting, by the trained patient classification model, the
in response to the probability of the pediatric patient developing a neuromorbidity satisfying a predetermined threshold, automatically transmitting, with at least one processor, an alert to a computing device associated with at least one of the following: a physician, a nurse, an advanced practice provider of deteriorating brain health, a healthcare provider, or any combination thereof, wherein the alert comprises a medical intervention for the pediatric patient based on the patient classification.
The selected abstract idea (boldened limitations) of claims 1, 9 and 15 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 “detecting and responding to patient neuromorbidity”. 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 “detecting and responding to patient neuromorbidity”. (refer to MPP 2106.04(a)(2)). Accordingly independent claims 7, 9 and 10 recite an abstract idea.
Step 2A Prong TWO
The Applicant argues that the invention is directed to a unique and unconventional method of detecting and responding to neuromorbidity of a pediatric patient using a specific machine learning model to determine a classification of the pediatric patient in an ICU setting based on real time information for the pediatric patient. The training of the machine learning model recited in the claims provides a particular machine, and thus represents a practical application. Furthermore the Applicant argues that the claims make use of a particularly trained machine, and that the claims improve the speed and accuracy of identification of risks of neuromorbidity in a pediatric patient population, and thus represent an improvement in the field and a practical application of any purported abstract idea.
The Examiner disagrees since the Applicant’s arguments are not persuasive. A colloquial interpretation what may be interpreted as a practical application is not enough to integrate the abstract idea into a practical application. The Examiner restates that claims 1, 9 and 15 do not integrate the abstract idea into a practical application. Neither claims 1, 9 and 15 recite additional elements that impose a meaningful limit on the abstract idea:
Claims 1, 9 and 15: recites the following additional elements:
processor;
electronic health record system;
computing device;
trained patient classification model.
Claim 9: recites the following additional elements:
at least one processor
Claims 15: recites the following additional elements:
A computer program product comprising at least one non-transitory computer-readable medium including program instructions, executed by at least one processor
The additional elements as recited above for claims 1, 9 and 15 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. (refer to MPEP 2106.05(f)). Support for this can be found in the specification, paragraphs (0086-0089). 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
The Applicant argues that the present claims are directed to computer-implemented methods, and systems, that make use of machine learning models, trained using a specific set of patient data, and which provide a probability of a patient developing a neuromorbidity over a time period. The claimed methods and system, and the speed and accuracy of the same, greatly improve the ability of medical personnel to proactively assess and potentially treat patients with respect to predicted neuromorbidities, which may improve patient outcomes (see para. [0063] of the present application). The Applicant further argues that the claims clearly represent an improvements in speed and accuracy that provides significantly more than any purported abstract idea. Furthermore, the Applicant argues that the amended claims thus recite a specific combination of particular machine learning training (classification analysis) performed on a particular dataset which is not disclosed in or suggested by the art of record, and thus was not routine or conventional in the art as of the time of filing of the present application.
The Applicant thus submits that the claims as amended are directed to eligible subject matter. Withdrawal of the rejection is therefore requested.
The Examiner disagrees since the Applicant’s arguments are not persuasive. 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. (refer to MPEP 2106.05(f)). Support for this can be found in the specification, paragraphs (0006-0010). 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 the following amended limitations of claims 1, 9 and 15 are not taught by Shaffer, Gallagher, Van Eyk, or Cohen, alone or in combination:
selecting, with at least one processor, a plurality of features of the patient cohort data using a feature selection evaluating parameter comprising at least one of the following: a first value, a last value, a minimum value, a maximum value, a difference between the minimum value and the maximum value, a difference between the first value and the last value, a change in value over a period of time, a slope of change over a period of time, or a combination thereof, wherein the plurality of features comprises:
a feature associated with patient age;
a feature associated with a patient heart rate a feature associated with a patient GCSS:
a feature associated with a patient SBP;
a feature associated with a patient body temperature a feature associated with administration of a medication to a patient a feature associated with a patient creatinine level:
a feature associated with a patient BUN level
training, with at least one processor using the plurality of features and the patient cohort data, a patient classification model to provide a trained patient classification model, the trained patient classification model comprising a decision tree based machine learning wherein the neuromorbidity risk is associated with one or more selected from delirium, chronic pain, seizures, intracranial hemorrhage, stroke, impairment or loss of consciousness, seizures, ischemic stroke, intracerebral hemorrhage, cerebral edema, delirium, and neuromuscular weakness;
generating, with at least one processor and in real-time, a patient classification of the pediatric patient comprising a probability of the pediatric patient developing a neuromorbidity within a time period, wherein generating the patient classification of the pediatric patient comprises:
inputting the real-time pediatric patient dataset into the trained patient classification model, and
outputting, by the trained patient classification model, the
in response to the probability of the pediatric patient developing a neuromorbidity satisfying a predetermined threshold, automatically transmitting, with at least one processor, an alert to a computing device associated with at least one of the following: a physician, a nurse, an advanced practice provider of deteriorating brain health, a healthcare provider, or any combination thereof, wherein the alert comprises a medical intervention for the pediatric patient based on the patient classification
Therefore, the references fail to disclose or suggest all of the claim limitations of claims 1, 9 and 15.
The Examiner agrees with the Applicant, Furthermore the Examiner has not found additional art that would in combination with the previously cited references successfully teach the added amended limitations. As a result, the Examiner withdraws the 35 USC §103 rejection. Prior art reference of record that is most closely related to the claim limitation recited above is listed under “Prior Art Made of Record”
For reasons of record and as set forth above, the examiner maintains the rejection of claims 1-2, 4-7, 9-13, 15-19, 21-24, 26 as being directed to a judicial exception without significantly more, and thereby being directed to non-statutory subject matter under 35 USC §101. In reaching this decision, the Examiner considered all evidence presented and all arguments actually made by Applicant.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571)270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PIERRE L MACCAGNO/Examiner, Art Unit 3687
/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684