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 .
Response to Amendments
Applicant’s amendment filed on 05/04/2026 are entered. Claims 1-8, 11-14, and 16-19 are pending in this application of which Claims 1, 11, and 16 are independent.
Response to Arguments
Applicant’s arguments in view of amendments, filed on 05/04/2026 have been fully considered and the examiner’s response is as follows:
Applicant’s arguments, Pg. 10, regarding Drawing Objections are considered and found persuasive. Therefore, the objections will be withdrawn.
Applicant’s arguments, Pg. 10-11, regarding Specification Objection of the Abstract are withdrawn due to applicant providing a corrected abstract.
Applicant’s arguments, Pg. 11, regarding 112 (b) are withdrawn due to corrections made.
Applicant’s arguments, Pg. 11, regarding 112 (d) are withdrawn due to claim 10 being cancelled.
Applicant’s arguments, Pg. 11-13, regarding 35 U.S.C 102 rejections are considered but are moot because new grounds of rejection, necessitated by applicant’s amendments.
Applicant’s arguments, Pg. 13, regarding 35 U.S.C 103 rejections are considered but are moot because new grounds of rejection, necessitated by applicant’s amendments.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 7, 11-12, 14, 16-17, and 19 are rejected under 35 U.S.C. 102 as being anticipated over BORDIN et al. WO 2019153039 A1 (2019) [herein “BORDIN”].
Regarding Claim 1, BORDIN teaches
A system for classifying aortic stenoses, comprising:a digital information repository configured to store an aortic valve area measurement, a mean transaortic pressure gradient measurement, and a peak aortic jet velocity measurement for a subject of interest;
“As a result of the probable disease state prediction, the method may comprise the further step of directing the measurement operator to record relevant measurement data to increase the confidence of the probable disease state prediction. The disease condition may comprise aortic stenosis.”. (0036).
“The classification algorithm disclosed below provides the system 500 with the ability to classify the likely severity of disease…”. (0187).
“Aggregate echocardiogram data from a wide range of clinics Australia-wide are stored in the National Echocardiogram Database Australia (NEDA). In its current form, NEDA consists of measurements taken from echo procedures and report texts that are an analysis from that procedure for many patients. Currently, the database has over 500k patients.”. (0007).
“The current clinically accepted guidelines for diagnosis of severe aortic stenosis (AS) in a patient recommend that the following three main criteria are used: 1. AS Jet Velocity > 4.0 m/s 2. AV Mean Gradient > 40 mmHg 3. AV Area < lcm.sup.2”. (0208)
“The measurements included in the imputation model include AR Peak Velocity; AR Pressure Half Time; AV Mean Gradient; AV Mean Velocity; AV Peak Velocity; AV Velocity Time Integral; Aorta at Sinotubular Diameter; Aorta at Sinuses Diameter; Aortic Root Diameter; Aortic Root Diameter MM; Ascending Aorta Diameter; Body Weight; Diastolic Slope; Distal Transverse Aorta Diameter; EFtext; Heart Rate; IVC Diameter Expiration; IVS Diastolic Thickness; IVS Diastolic Thickness MM; IVS Systolic Thickness MM; LA Area 4C View; LA Length 4C; LA Systolic Diameter LX; LA Systolic Diameter MM; LA Systolic Diameter Transverse; LV Diameter; LV Diastolic Area 2C; LV Diastolic Area 4C; LV Diastolic Area PSAX; LV Diastolic Diameter 4C; LV Diastolic Diameter MM; LV Diastolic Diameter PLAX; LV Diastolic Length 2C; LV Diastolic Length 4C; LV E’ Lateral Velocity; LV Epi Diastolic Area PSAX; LV Mass(C)d; LV Relative Wall Thickness; LV Systolic Area 2C; LV Systolic Area 4C; LV Systolic Diameter 4C; LV Systolic Diameter Base LX; LV Systolic Diameter MM; LV Systolic Diameter PLAX; LV Systolic Length 2C; LV Systolic Length 4C; LVOT Diameter; LVOT Mean Velocity; LVOT Peak Velocity; LVOT Velocity Time Integral; LVPW Diastolic Thickness; LVPW Diastolic Thickness MM; LVPW Systolic Thickness MM; MV A’ Velocity; MV Deceleration Time; MV E’ Velocity; MV Mean Velocity; MV Peak Velocity; MV Pressure Half Time; MV Velocity Time Integral; Mitral A Point Velocity; Mitral E Point Velocity; PV Peak Velocity; PV Velocity Time Integral; PatientAge; Pulm Vein Atrial Duration; Pulm Vein Atrial Reversal Velocity; Pulmonary Vein A Velocity; Pulmonary Vein Diastolic Velocity; Pulmonary Vein Systolic Velocity; RA Area; RA Systolic Diameter LX; RA Systolic Diameter Transverse; RApressuretext; RVOT Peak Velocity; Right Atrial Pressure; TR Peak Velocity; and Thoracic Aorta Diameter.”. (0214).
This shows an information repository that stores aortic valve area measurement, a mean transaortic pressure gradient measurement, and a peak aortic jet velocity.
a memory that stores instructions for an aortic stenosis classifier; and
“The classification algorithm disclosed below provides the system 500 with the ability to classify the likely severity of disease…”. (0187).
“With reference to Figure 7, an exemplary computing device 700 is illustrated. The exemplary computing device 700 can include, but is not limited to, one or more central processing units (CPUs) 701 comprising one or more processors 702, a system memory 703, and a system bus 704 that couples various system components including the system memory 703 to the processing unit 701.”. (0197).
“Computer storage media includes media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.”. (0198).
This shows memory storing instructions for an aortic stenosis classifier.
a processor and the instructions, when executed by the processor, cause the processor to:
“With reference to Figure 7, an exemplary computing device 700 is illustrated. The exemplary computing device 700 can include, but is not limited to, one or more central processing units (CPUs) 701 comprising one or more processors 702, a system memory 703, and a system bus 704 that couples various system components including the system memory 703 to the processing unit 701.”. (0197).
“Computer storage media includes media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.”. (0198).
This shows a processor and memory to execute a set of instructions.
obtain a set of risk factors of aortic stenosis progression determined based on measurements for a plurality of subjects with aortic stenoses, including aortic valve area measurements, mean transaortic pressure gradient measurements, and peak aortic jet velocity measurements for the plurality of subjects;
“This unique NEDA resource collates all echocardiographic measurement and report data contained in the echocardiographic database of participating centers.”. (0008).
“[0214] The measurements included in the imputation model include AR Peak Velocity; AR Pressure Half Time; AV Mean Gradient; AV Mean Velocity; AV Peak Velocity; AV Velocity Time Integral; Aorta at Sinotubular Diameter; Aorta at Sinuses Diameter; Aortic Root Diameter; Aortic Root Diameter MM; Ascending Aorta Diameter; Body Weight; Diastolic Slope; Distal Transverse Aorta Diameter; EFtext; Heart Rate; IVC Diameter Expiration; IVS Diastolic Thickness; IVS Diastolic Thickness MM; IVS Systolic Thickness MM; LA Area 4C View; LA Length 4C; LA Systolic Diameter LX; LA Systolic Diameter MM; LA Systolic Diameter Transverse; LV Diameter; LV Diastolic Area 2C; LV Diastolic Area 4C; LV Diastolic Area PSAX; LV Diastolic Diameter 4C; LV Diastolic Diameter MM; LV Diastolic Diameter PLAX; LV Diastolic Length 2C; LV Diastolic Length 4C; LV E’ Lateral Velocity; LV Epi Diastolic Area PSAX; LV Mass(C)d; LV Relative Wall Thickness; LV Systolic Area 2C; LV Systolic Area 4C; LV Systolic Diameter 4C; LV Systolic Diameter Base LX; LV Systolic Diameter MM; LV Systolic Diameter PLAX; LV Systolic Length 2C; LV Systolic Length 4C; LVOT Diameter; LVOT Mean Velocity; LVOT Peak Velocity; LVOT Velocity Time Integral; LVPW Diastolic Thickness; LVPW Diastolic Thickness MM; LVPW Systolic Thickness MM; MV A’ Velocity; MV Deceleration Time; MV E’ Velocity; MV Mean Velocity; MV Peak Velocity; MV Pressure Half Time; MV Velocity Time Integral; Mitral A Point Velocity; Mitral E Point Velocity; PV Peak Velocity; PV Velocity Time Integral; PatientAge; Pulm Vein Atrial Duration; Pulm Vein Atrial Reversal Velocity; Pulmonary Vein A Velocity; Pulmonary Vein Diastolic Velocity; Pulmonary Vein Systolic Velocity; RA Area; RA Systolic Diameter LX; RA Systolic Diameter Transverse; RApressuretext; RVOT Peak Velocity; Right Atrial Pressure; TR Peak Velocity; and Thoracic Aorta Diameter.”. (0214).
“… analysing the training dataset on the basis of predefined disease conditions in known patient records to form a disease model which predicts a probability of a disease condition”. (Abstract).“… using the measurement prediction protocols, computing prediction values for measurement data for the unpopulated data fields.”. (0024).
“… a predicted diagnosis on the basis of a set of pre-defined risk factors for various diseases relevant to the nature of the data source, for example where the data source comprises echocardiography measurements, the AI prediction engine 517 may provide predictions of a particular patient’s probability of possessing, or likely to subsequently possess, heart-related diseases such as, for example arterial stenosis or heart chamber or valve malfunction.”. (0193).
This shows a database of various patients with aortic stenosis storing aortic valve area, mean transaortic pressure gradients, and peak aortic jet velocity measurements, which the system analyses to determine a set if risk factors of aortic stenosis progression.
model, by the aortic stenosis classifier, at least parameters of aortic valve area, mean transaortic pressure gradient, and peak aortic jet velocity based on the set of risk factors;
“… obtain a trained model and measurement prediction protocols for populating unpopulated fields in the training data set; using the measurement prediction protocols to predict data for the unpopulated data fields; analysing the training dataset on the basis of predefined disease conditions in known patient records to form a disease model which predicts a probability of a disease condition”. (Abstract).
This shows the trained model modeling each of aortic valve area, mean transaortic pressure gradient, and peak aortic jet velocity and predicting disease on the basis of the predicted disease conditions and risk factors.
Classify, by the aortic stenosis classifier, a severity of an aortic stenosis of the subject of interest based at least on the modeled parameters, the aortic valve area measurement for the subject of interest, the mean transaortic pressure gradient measurement for the subject of interest, and the peak aortic jet velocity measurement for the subject of interest; and
“The classification algorithm disclosed below provides the system 500 with the ability to classify the likely severity of disease…”. (0187).
“With reference to Figure 7, an exemplary computing device 700 is illustrated. The exemplary computing device 700 can include, but is not limited to, one or more central processing units (CPUs) 701 comprising one or more processors 702, a system memory 703, and a system bus 704 that couples various system components including the system memory 703 to the processing unit 701.”. (0197).
“The classification algorithm disclosed below provides the system 500 with the ability to classify the likely severity of disease in a patient based on the imputed density function for a given measurement and established clinical thresholds for that measurement, from records in the data source 501 during model validation 513 and from measurements in production data source 519 whilst the system is in use.”. (0187).
“The measurements included in the imputation model include AR Peak Velocity; AR Pressure Half Time; AV Mean Gradient; AV Mean Velocity; AV Peak Velocity; AV Velocity Time Integral; Aorta at Sinotubular Diameter; Aorta at Sinuses Diameter; Aortic Root Diameter; Aortic Root Diameter MM…”. (0214).
This shows the classifier classifying severity based on the mentioned criteria and on the subject’s measured values against established clinical thresholds.
display the severity.
“Predicted measurements and disease risk factors are then presented to the user via interface 610 to a user display means 640, which may present the output predictions in any useful manner for interpretation by the user, for example the outputs may be presented in a graphical form for easy interpretation by the user.”. (0195).
This shows presenting the outputs to the user on a display.
Regarding Claim 2, BORDIN teaches
The system of claim 1, wherein the digital information repository is further configured to store information about the plurality of subjects with aortic stenoses, including at least the aortic valve area measurements, the mean transaortic pressure gradient measurements, and the peak aortic jet velocity measurements thereof, wherein the aortic stenosis classifier is trained with at least the aortic valve area measurements, the mean transaortic pressure gradient measurements, and the peak aortic jet velocity measurements of the subjects with aortic stenoses to provide a trained classifier.
“…obtain a trained model and measurement prediction protocols for populating unpopulated fields in the training data set; using the measurement prediction protocols to predict data for the unpopulated data fields; analysing the training dataset on the basis of predefined disease conditions in known patient records to form a disease model which predicts a probability of a disease condition…”. (Abstract).
“The methods of training and operating an AI-assisted echocardiography system as disclosed herein (e.g. methods 200, 300, 400 and 500 depicted in Figures 2, 3, 4 and 5 respectively may be implemented using a computer system 700, such as that shown in Figure 7 wherein the processes of Figures 2 to 5 may be implemented as software, such as one or more application programs executable within the computing device 700.”. (0196).
“The measurements included in the imputation model include AR Peak Velocity; AR Pressure Half Time; AV Mean Gradient; AV Mean Velocity; AV Peak Velocity; AV Velocity Time Integral; Aorta at Sinotubular Diameter; Aorta at Sinuses Diameter; Aortic Root Diameter; Aortic Root Diameter MM; Ascending Aorta Diameter; Body Weight; Diastolic Slope; Distal Transverse Aorta Diameter; EFtext; Heart Rate; IVC Diameter Expiration; IVS Diastolic Thickness; IVS Diastolic Thickness MM; IVS Systolic Thickness MM; LA Area 4C View; LA Length 4C; LA Systolic Diameter LX; LA Systolic Diameter MM; LA Systolic Diameter Transverse; LV Diameter; LV Diastolic Area 2C; LV Diastolic Area 4C; LV Diastolic Area PSAX; LV Diastolic Diameter 4C; LV Diastolic Diameter MM; LV Diastolic Diameter PLAX; LV Diastolic Length 2C; LV Diastolic Length 4C; LV E’ Lateral Velocity; LV Epi Diastolic Area PSAX; LV Mass(C)d; LV Relative Wall Thickness; LV Systolic Area 2C; LV Systolic Area 4C; LV Systolic Diameter 4C; LV Systolic Diameter Base LX; LV Systolic Diameter MM; LV Systolic Diameter PLAX; LV Systolic Length 2C; LV Systolic Length 4C; LVOT Diameter; LVOT Mean Velocity; LVOT Peak Velocity; LVOT Velocity Time Integral; LVPW Diastolic Thickness; LVPW Diastolic Thickness MM; LVPW Systolic Thickness MM; MV A’ Velocity; MV Deceleration Time; MV E’ Velocity; MV Mean Velocity; MV Peak Velocity; MV Pressure Half Time; MV Velocity Time Integral; Mitral A Point Velocity; Mitral E Point Velocity; PV Peak Velocity; PV Velocity Time Integral; PatientAge; Pulm Vein Atrial Duration; Pulm Vein Atrial Reversal Velocity; Pulmonary Vein A Velocity; Pulmonary Vein Diastolic Velocity; Pulmonary Vein Systolic Velocity; RA Area; RA Systolic Diameter LX; RA Systolic Diameter Transverse; RApressuretext; RVOT Peak Velocity; Right Atrial Pressure; TR Peak Velocity; and Thoracic Aorta Diameter.”. (0214).
This shows training the system based on the mentioned criteria.
Regarding Claim 7, BORDIN teaches
The system of claim 2, wherein the instructions, when executed by the processor, further cause the processor to: extract information from the digital information repository for the plurality of subjects with aortic stenoses that do not have a prosthetic valve; process the extracted information to at least one of remove outliers, impute missing information, represent repeated measurements, or extract free text; and determine the set of risk factors of aortic stenosis progression based on the processed extracted information.
“This unique NEDA resource collates all echocardiographic measurement and report data contained in the echocardiographic database of participating centers. Each database is then remotely transferred into the Master NEDA Database via a“vendor-agnostic” , automated data extraction process that transfers every measurement for each echocardiogram performed into a standardized NEDA data format (according to the NEDA Data Dictionary ). Each individual contributing to NEDA is given a unique identifier along with their demographic profile (date of birth and sex) and all data recorded with their echocardiogram.”. (0008).
“The method may comprise the further step of (d) using the measurement prediction protocols, computing prediction values for measurement data for the unpopulated data fields.”. (0024).
“…using the measurement prediction protocols to predict data for the unpopulated data fields… predict a probable disease state for each patient record…”. (Abstract).
This shows a master database of various patients to be analyzed. This data may not have all values present for each patient so the method takes that into account and fills that data in to determine risk factors for the patient.
Regarding Claim 11, BORDIN teaches
A computer-implemented method for classifying aortic stenoses the method, comprising:obtaining information about a subject of interest, including at least an aortic valve area measurement, a mean transaortic pressure gradient measurement, and a peak aortic jet velocity measurement for the subject of interest;
“As a result of the probable disease state prediction, the method may comprise the further step of directing the measurement operator to record relevant measurement data to increase the confidence of the probable disease state prediction. The disease condition may comprise aortic stenosis.”. (0036).“The methods of training and operating an AI-assisted echocardiography system as disclosed herein…”. (0196).
“AS was evaluated using the peak aortic jet velocity, aortic mean gradient, and the aortic valve area defined by the Continuity equation.sup.13 (CE):”. (0080).
This shows collecting information such as peak aortic jet velocity, aortic mean gradient, and the aortic valve area.
obtaining a set of risk factors of aortic stenosis progression determined based on measurements for a plurality of subjects with aortic stenoses, including aortic valve area measurements, mean transaortic pressure gradient measurements, and peak aortic jet velocity measurements for the plurality of subjects;
“This unique NEDA resource collates all echocardiographic measurement and report data contained in the echocardiographic database of participating centers.”. (0008).
“The classification algorithm disclosed below provides the system 500 with the ability to classify the likely severity of disease in a patient based on the imputed density function for a given measurement and established clinical thresholds for that measurement, from records in the data source 501 during model validation 513 and from measurements in production data source 519 whilst the system is in use.”. (0187).
“[0214] The measurements included in the imputation model include AR Peak Velocity; AR Pressure Half Time; AV Mean Gradient; AV Mean Velocity; AV Peak Velocity; AV Velocity Time Integral; Aorta at Sinotubular Diameter; Aorta at Sinuses Diameter; Aortic Root Diameter; Aortic Root Diameter MM; Ascending Aorta Diameter; Body Weight; Diastolic Slope; Distal Transverse Aorta Diameter; EFtext; Heart Rate; IVC Diameter Expiration; IVS Diastolic Thickness; IVS Diastolic Thickness MM; IVS Systolic Thickness MM; LA Area 4C View; LA Length 4C; LA Systolic Diameter LX; LA Systolic Diameter MM; LA Systolic Diameter Transverse; LV Diameter; LV Diastolic Area 2C; LV Diastolic Area 4C; LV Diastolic Area PSAX; LV Diastolic Diameter 4C; LV Diastolic Diameter MM; LV Diastolic Diameter PLAX; LV Diastolic Length 2C; LV Diastolic Length 4C; LV E’ Lateral Velocity; LV Epi Diastolic Area PSAX; LV Mass(C)d; LV Relative Wall Thickness; LV Systolic Area 2C; LV Systolic Area 4C; LV Systolic Diameter 4C; LV Systolic Diameter Base LX; LV Systolic Diameter MM; LV Systolic Diameter PLAX; LV Systolic Length 2C; LV Systolic Length 4C; LVOT Diameter; LVOT Mean Velocity; LVOT Peak Velocity; LVOT Velocity Time Integral; LVPW Diastolic Thickness; LVPW Diastolic Thickness MM; LVPW Systolic Thickness MM; MV A’ Velocity; MV Deceleration Time; MV E’ Velocity; MV Mean Velocity; MV Peak Velocity; MV Pressure Half Time; MV Velocity Time Integral; Mitral A Point Velocity; Mitral E Point Velocity; PV Peak Velocity; PV Velocity Time Integral; PatientAge; Pulm Vein Atrial Duration; Pulm Vein Atrial Reversal Velocity; Pulmonary Vein A Velocity; Pulmonary Vein Diastolic Velocity; Pulmonary Vein Systolic Velocity; RA Area; RA Systolic Diameter LX; RA Systolic Diameter Transverse; RApressuretext; RVOT Peak Velocity; Right Atrial Pressure; TR Peak Velocity; and Thoracic Aorta Diameter.”. (0214).
“… analysing the training dataset on the basis of predefined disease conditions in known patient records to form a disease model which predicts a probability of a disease condition”. (Abstract).“… using the measurement prediction protocols, computing prediction values for measurement data for the unpopulated data fields.”. (0024).
“… a predicted diagnosis on the basis of a set of pre-defined risk factors for various diseases relevant to the nature of the data source, for example where the data source comprises echocardiography measurements, the AI prediction engine 517 may provide predictions of a particular patient’s probability of possessing, or likely to subsequently possess, heart-related diseases such as, for example arterial stenosis or heart chamber or valve malfunction.”. (0193).
This shows a database of various patients with aortic stenosis storing aortic valve area, mean transaortic pressure gradients, and peak aortic jet velocity measurements, which the system analyses to determine a set if risk factors of aortic stenosis progression.
modeling, by the aortic stenosis classifier, at least parameters of aortic valve area, mean transaortic pressure gradient, and peak aortic jet velocity based on the set of risk factors;
“… obtain a trained model and measurement prediction protocols for populating unpopulated fields in the training data set; using the measurement prediction protocols to predict data for the unpopulated data fields; analysing the training dataset on the basis of predefined disease conditions in known patient records to form a disease model which predicts a probability of a disease condition”. (Abstract).
This shows the trained model modeling each of aortic valve area, mean transaortic pressure gradient, and peak aortic jet velocity and predicting disease on the basis of the predicted disease conditions and risk factors.
classifying, by the aortic stenosis classifier, a severity of an aortic stenosis of the subject of interest based at least on the modeled parameters, the aortic valve area measurement for the subject of interest, the mean transaortic pressure gradient measurement for the subject of interest, and the peak aortic jet velocity measurement for the subject of interest; and“The classification algorithm disclosed below provides the system 500 with the ability to classify the likely severity of disease in a patient based on the imputed density function for a given measurement and established clinical thresholds for that measurement, from records in the data source 501 during model validation 513 and from measurements in production data source 519 whilst the system is in use.”. (0187).
This shows execution of the classifier’s instructions based on the given values of interest.
visually presenting the classified severity.
“Predicted measurements and disease risk factors are then presented to the user via interface 610 to a user display means 640, which may present the output predictions in any useful manner for interpretation by the user, for example the outputs may be presented in a graphical form for easy interpretation by the user.”. (0195).
This shows presenting the output of the classifier to the user.
Regarding Claim 12, BORDIN teaches
The computer-implemented method of claim 11, further comprising:extracting information about the plurality of subjects with aortic stenoses, including at least the aortic valve area measurements, the mean transaortic pressure gradient measurements, and the peak aortic jet velocity measurements; and“A method for processing a sparsely populated data source comprising: retrieving data from a sparsely populated data source whose records comprise at least one unpopulated data field corresponding to a medical measurement”. (Abstract).
“Each patient record in the database usually does not contain a full set of measurements that can be possibly taken during an Echo procedure due to time constraints. Measurements are targeted to a subset of measurements that may be related to a suspected diagnosis or condition of the heart. This results in NEDA being a sparse data set with each patient echo record consisting of a subset of a full echo procedure leaving“blanks” for measurements that are not measured in a full procedure. Figure 1 shows a few real examples of different measurements present and missing in echo studies for a small randomly selected group of patients, being a typical example of sparse echo data where each row of the table is a record of the echo measurement available for a single patient.”. (0010).This shows extracting and gathering the mentioned measurements for training.
training the aortic stenosis classifier with at least the aortic valve area measurements, the mean transaortic pressure gradient measurements, and the peak aortic jet velocity measurements of the subjects with aortic stenoses.
“The measurements included in the imputation model include AR Peak Velocity; AR Pressure Half Time; AV Mean Gradient; AV Mean Velocity; AV Peak Velocity; AV Velocity Time Integral; Aorta at Sinotubular Diameter; Aorta at Sinuses Diameter; Aortic Root Diameter; Aortic Root Diameter MM; Ascending Aorta Diameter; Body Weight; Diastolic Slope; Distal Transverse Aorta Diameter; EFtext; Heart Rate; IVC Diameter Expiration; IVS Diastolic Thickness; IVS Diastolic Thickness MM; IVS Systolic Thickness MM; LA Area 4C View; LA Length 4C; LA Systolic Diameter LX; LA Systolic Diameter MM; LA Systolic Diameter Transverse; LV Diameter; LV Diastolic Area 2C; LV Diastolic Area 4C; LV Diastolic Area PSAX; LV Diastolic Diameter 4C; LV Diastolic Diameter MM; LV Diastolic Diameter PLAX; LV Diastolic Length 2C; LV Diastolic Length 4C; LV E’ Lateral Velocity; LV Epi Diastolic Area PSAX; LV Mass(C)d; LV Relative Wall Thickness; LV Systolic Area 2C; LV Systolic Area 4C; LV Systolic Diameter 4C; LV Systolic Diameter Base LX; LV Systolic Diameter MM; LV Systolic Diameter PLAX; LV Systolic Length 2C; LV Systolic Length 4C; LVOT Diameter; LVOT Mean Velocity; LVOT Peak Velocity; LVOT Velocity Time Integral; LVPW Diastolic Thickness; LVPW Diastolic Thickness MM; LVPW Systolic Thickness MM; MV A’ Velocity; MV Deceleration Time; MV E’ Velocity; MV Mean Velocity; MV Peak Velocity; MV Pressure Half Time; MV Velocity Time Integral; Mitral A Point Velocity; Mitral E Point Velocity; PV Peak Velocity; PV Velocity Time Integral; PatientAge; Pulm Vein Atrial Duration; Pulm Vein Atrial Reversal Velocity; Pulmonary Vein A Velocity; Pulmonary Vein Diastolic Velocity; Pulmonary Vein Systolic Velocity; RA Area; RA Systolic Diameter LX; RA Systolic Diameter Transverse; RApressuretext; RVOT Peak Velocity; Right Atrial Pressure; TR Peak Velocity; and Thoracic Aorta Diameter.”. (0214).
“… training dataset on the basis of predefined disease conditions in known patient records to form a disease model which predicts a probability of a disease condition…”. (Abstract).
This shows training the classifier with the mentioned measurements among others.
Claim 14 recites substantially the same limitations as claim 7 except this claim is directed to a “computer-implemented method” Therefore, this claim is rejected under the same rationale as addressed above.
Claim 16 recites substantially the same limitations as claims 1 and 11 except this claim is directed to a “computer-readable storage medium” Therefore, this claim is rejected under the same rationale as addressed above.
Claim 17 recites substantially the same limitations as claim 12 except this claim is directed to a “computer-readable storage medium” Therefore, this claim is rejected under the same rationale as addressed above.
Claim 19 recites substantially the same limitations as claim 7 except this claim is directed to a “computer-readable storage medium” Therefore, this claim is rejected under the same rationale as addressed above.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 3, 13, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over BORDIN et al. WO 2019153039 A1 (2019) [herein “BORDIN”] and over KIM et al. “Predicting Disease Progression in Patients with Bicuspid Aortic Stenosis Using Mathematical Modeling” (2019) [herein “KIM”].
Regarding Claim 3, BORDIN does not explicitly teach but KIM teaches
The system of claim 2, wherein the instructions, when executed by the processor, further cause the processor to construct a two-dimensional graph of aortic stenosis versus time based on historical aortic stenosis diagnoses…“Patients who showed an increase in mean pressure gradient (MPG) at their first visit relative to baseline (denoted as rapid progressors) showed a significantly faster disease progression overall... Our novel disease progression model for bicuspid AS significantly increased prediction power by including subsequent follow-up visit information rather than baseline information alone.”. (Abstract).
“Figure 3 shows model fits of MPG for individuals with at least three post-baseline measurements, indicating overall good agreement between observations and predictions, except for individuals with atypical trends.”. (3.4).
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This shows individual level graphical representation of aortic stenosis versus time.
KIM does not explicitly teach but BORDIN teaches
… and display the two-dimensional graph.
“The means for alerting the operator may comprise a visible notification on a display surface of the apparatus.”. (0037).
“The term,“real-time” , for example“displaying real-time data” , refers to the display of the data without intentional delay, given the processing limitations of the system and the time required to accurately measure the data.”. (0058).
This shows being able to display graphs and other data on a screen.
It would have been obvious to one skilled in the art before the effective filing date of the
claimed invention to incorporate the teachings of KIM’s individual level graphical representation with BORDIN’s system disease severity classification. The motivation for doing so would have been to “… to predict the progression of aortic stenosis (AS) and aortic dilatation (AD) in bicuspid aortic valve patients.”. (Abstract). Doing so would allow for an enhanced system to display visuals for disease severity classification at an individual level.
Claim 13 recites substantially the same limitations as claim 3 except this claim is directed to a “computer-implemented method” Therefore, this claim is rejected under the same rationale as addressed above.
Claim 18 recites substantially the same limitations as claim 3 except this claim is directed to a “computer-readable storage medium” Therefore, this claim is rejected under the same rationale as addressed above.
Claims 4, 5, and 6 are rejected under 35 U.S.C. 103 as being unpatentable over BORDIN et al. WO 2019153039 A1 (2019) [herein “BORDIN”], over MINNERS et al. “Inconsistencies of echocardiographic criteria for the grading of aortic valve stenosis” (2008) [herein “MINNERS”], and over HIRSH et al. US 20090124867 A1 (2009) [herein “HIRSH”].
Regarding Claim 4, BORDIN and MINNERS do not teach but HIRSH teaches
The system of claim 2, wherein the instructions, when executed by the processor, further cause the processor to: construct a three-dimensional graph…
“The converted neurological and hemodynamic state of a patient are displayed on a screen as an index value and a three-dimensional vector… Therefore, a medical practitioner looks at the screen and quickly obtains the important and necessary information.”. (Abstract).
“The three-dimensional graph on the screen allows a clinician to process a great deal of hemodynamic information at one glance. The display substantially improves vigilance in cardiovascular monitoring in the perioperative period.”. (0161).
This shows constructing a three-dimensional graph for better information viewing of hemodynamic cardiovascular data.
BORDIN and HIRSH do not teach but MINNERS teaches…of the aortic valve area versus the mean transaortic pressure gradient versus and the peak aortic jet velocity, including:
“Clinical and echocardiography data of our study population are summarized…”. (Pg. 1044 Results).
“Current guidelines/recommendations define severe stenosis as an aortic valve area (AVA) ,1 cm2 (or ,0.6 cm2 adjusted for body surface area), mean pressure gradient (DPm) .40 mmHg, or peak flow velocity (Vmax) .4 m/s. We tested the consistency of the three criteria for the grading of aortic valve stenosis in 3483 echocardiography studies performed in 2427 patients with normal left ventricular (LV) systolic function and a calculated AVA of 2 cm2. We calculated curve fits for the relationship between AVA and DPm using the Gorlin equation and between AVA and Vmax based on the continuity equation for our study population.”. (Pg. 1043 Methods and results).
This shows a population level visualization of the MPG, AVA, and VMAX.
a data point for the aortic valve area measurement, the mean transaortic pressure gradient measurement, and the peak aortic jet velocity measurement for the subject of interest;
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(Figure 1,2, and 3 are attached for reference.)This shows data points for all three criteria for a subject of interest.
data points for the aortic valve area measurements, the mean transaortic pressure gradient measurements, and the peak aortic jet velocity measurements of the plurality of subjects with aortic stenoses; and“Clinical and echocardiography data of 3483 echocardiography studies in 2427 patients…”. (Table 1 Caption).
“Clinical and echocardiography data of our study population are summarized in Table 1.”. (Pg. 1044).
This shows data points for all three metrics for the population.
an aortic stenosis threshold plane identifying combinations of values of the aortic valve area, the mean transaortic pressure gradient and the peak aortic jet velocity that indicate severe aortic stenosis; and
“The parameters referred to in current guidelines/recommendations for the grading of the severity of aortic valve stenosis are aortic valve area (AVA), mean pressure gradient (DPm), and peak flow velocity (Vmax) 1–4 with cut-off values for severe aortic valve stenosis of an AVA ,1.0cm2, DPm .40 mmHg, and Vmax .4.0 m/s. In patients with normal left ventricular (LV) function, the three parameters should yield a consistent classification of a particular aortic stenosis as either mild, moderate, or severe.5–7”. (Pg. 1043).
“With a perioperative risk of up to 8.8%,11 it is essential that recommendations for the management of these patients are based on reliable parameters. We therefore tested the consistency of the three echocardiographic criteria AVA, DPm, and Vmax for the grading of aortic valve stenosis in patients with normal systolic LV function with special focus on the cut-off value for severe stenosis.”. (Pg. 1043).
HIRSH and MINNERS do not explicitly teach but BORDIN teaches
display the three-dimensional graph.
“The means for alerting the operator may comprise a visible notification on a display surface of the apparatus.”. (0037).
“The term,“real-time” , for example“displaying real-time data” , refers to the display of the data without intentional delay, given the processing limitations of the system and the time required to accurately measure the data.”. (0058).
This shows being able to display graphs and other data on a screen.
It would have been obvious to one skilled in the art before the effective filing date of the
claimed invention to incorporate the teachings of HIRSH’s graphical representation of patient data with BORDIN’s system disease severity classification. The motivation for doing so would have been to create a “… three-dimensional graph on the screen allows a clinician to process a great deal of hemodynamic information at one glance. The display substantially improves vigilance in cardiovascular monitoring in the perioperative period.”. (0161). Doing so would allow for an enhanced system to display three-dimensional visuals for disease severity classification.
A PHOSITA would have been further motivated to add MINNERS’s method of
graphical representation of patient data with cut offs for AVA, MPG, and Jet Velocity. MINNERS states “Evaluation of aortic valve stenosis as based on data obtained from two-dimensional (2D) and Doppler echocardiography plays a key role in the grading of aortic valve stenosis. The parameters referred to in current guidelines/recommendations for the grading of the severity of aortic valve stenosis are aortic valve area (AVA), mean pressure gradient (DPm), and peak flow velocity (Vmax) 1–4 with cut-off values for severe aortic valve stenosis of an AVA ,1.0cm2 , DPm .40 mmHg, and Vmax .4.0 m/s. In patients with normal left ventricular (LV) function, the three parameters should yield a consistent classification of a particular aortic stenosis as either mild, moderate, or severe.5–7.”. Combining with BORDINS’s teaching of a “…disease model for predicting a probable disease state for a patient undergoing a measurement procedure; and means for alerting the measurement operator of the predicted measurement data and probable disease state and for providing direction to the measurement operator for relevant measurement data to be collected on the basis of the predicted probable disease state." would lead to improved display of graphical patient data for aortic stenosis classification and treatment in a combination of BORDINDS-HIRSH-MINNERS by enabling a helpful three-dimensional representation.
Regarding Claim 5, HIRSH and BORDIN do not explicitly teach but MINNERS teaches
The system of claim 4, wherein the instructions, when executed by the processor, further cause the processor to construct a two-dimensional graph including the aortic valve area and the mean transaortic pressure gradient and
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Figure 1 shows a constructed two-dimensional graph of AVA and mmHg.
HIRSH and MINNERS do not explicitly teach but BORDIN teaches
display the two- dimensional graph.
“The means for alerting the operator may comprise a visible notification on a display surface of the apparatus.”. (0037).
“The term,“real-time” , for example“displaying real-time data” , refers to the display of the data without intentional delay, given the processing limitations of the system and the time required to accurately measure the data.”. (0058).
This shows being able to display graphs and other data on a screen.
Regarding Claim 6, HIRSH and BORDIN do not explicitly teach but MINNERS teaches
The system of claim 4, wherein the instructions, when executed by the processor, further cause the processor to construct a two-dimensional graph including the aortic valve area and the peak aortic jet velocity and
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Figure 1 shows a constructed two-dimensional graph of AVA and VMAX.
HIRSH and MINNERS do not explicitly teach but BORDIN teaches
display the two-dimensional graph.
“The means for alerting the operator may comprise a visible notification on a display surface of the apparatus.”. (0037).
“The term,“real-time” , for example“displaying real-time data” , refers to the display of the data without intentional delay, given the processing limitations of the system and the time required to accurately measure the data.”. (0058).
This shows being able to display graphs and other data on a screen.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over BORDIN et al. WO 2019153039 A1 (2019) [herein “BORDIN”] and over MATHIEU et al. WO 2013185214 A1 (2013) [herein “MATHIEU”].
Regarding Claim 8, BORDIN does not explicitly teach but MATHIEU teaches
The system of claim 7, wherein the analysis includes performing a univariate analysis for each subject of the plurality of subjects to determine initial risk factors associated with the aortic valve area, the mean transaortic pressure gradient and the peak aortic jet velocity, followed by a multivariate analysis of the initial risk factors to determine the set of risk factors associated with the aortic valve area, the mean transaortic pressure gradient and the peak aortic jet velocity.
“The present invention relates to the treatment and/or prevention of calcific aortic vascular disease (CAVD) and valve calcification.”. (Abstract).
“Variables with p values < 0.1 on univariate analysis were entered into the multivariate models. Age at implantation was forced into the models.”. (0143).
“Peak transprosthetic flow velocity was determined by continuous-wave Doppler. Mean transprosthetic gradient was calculated using the modified Bernoulli equation.
Bioprosthetic valve effective orifice area (EOA) was calculated using the standard continuity equation. The absolute and annualized changes in mean gradient and EOA were calculated as follows”. (0138).
This shows a univariate analysis followed by multivariate analysis by teaching identification of risk factors associated with AVA, pressure gradient, and jet velocity.
It would have been obvious to one skilled in the art before the effective filing date of the
claimed invention to incorporate the teachings of MATHIEU’s univariate and multivariate analysis of AVA, MPG, and Jet Velocity with BORDIN’s system disease severity classification. The motivation for doing so would have been to create “…a disease model for predicting a probable disease state for a patient undergoing a measurement procedure; and means for alerting the measurement operator of the predicted measurement data and probable disease state and for providing direction to the measurement operator for relevant measurement data to be collected on the basis of the predicted probable disease state.”. (0038).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-20180107791-A1 teaches disease detection from multimodal data and machine learning. A plurality of disease-specific features is extracted from the plurality of patient records. The plurality of disease-specific features is provided to a classifier. An indicator of a likely disease condition of the patient is received from the classifier.
US-20180107801-A1 teaches automatically detecting disease presence from combining disease-specific measurements with textual descriptions of disease and its severity in unstructured textual reports.
US-20180107792-A1 teaches automatically finding discrepancies in medical data.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/N.E.M./Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
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