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 .
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the
references as applied to the claims below for the convenience of the applicant. Although
the specified citations are representative of the teachings in the art and are applied to the
specific limitations within the individual claim, other passages and figures may apply as well. It
is respectfully requested that, in preparing responses, the applicant fully consider the
references in their entirety as potentially teaching all or part of the claimed invention, as well as
the context of the passage as taught by the prior art or disclosed by the examiner. The entire
reference is considered to provide disclosure relating to the claimed invention. The claims &
only the claims form the metes & bounds of the invention. Office personnel are to give
the claims their broadest reasonable interpretation in light of the supporting disclosure.
Unclaimed limitations appearing in the specification are not read into the claim. Prior art was
referenced using terminology familiar to one of ordinary skill in the art. Such an approach is
broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are
provided with the cited references to assist the applicant to better understand how the
examiner interprets the applied prior art. Such comments are entirely consistent with the
intent & spirit of compact prosecution.
Drawings
The drawings are objected to because figures 6-9 do not sufficiently label the figures. It is suggested to label these figures with text as well (for example the axes). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The abstract of the disclosure is objected to because it fails to meet compliance with US Patents official policies. The abstract is not provided on a separate piece of paper. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7, and 9-10 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
For claims 1-7, and 9-10: These claims state “configured to” which do not positively recite whether or not the limitation is performed. A suggestion would be for example to change “a memory configured to…” to “a memory to…”. The examiner interprets the system performs these actions.
For claim 10: The claim states “classify the severity… based on the model each of the aortic valve area, the mean transaortic pressure gradient, and the peak aortic jet velocity”. However, claim 1 states “classify a severity … based on at least on the aortic valve area measurement, the mean transaortic pressure gradient measurement, and the peak aortic jet velocity measurement”. It is unclear whether the “the aortic valve area, the mean transaortic pressure gradient, and the peak aortic jet velocity” from claim 10 are the same as “aortic valve area measurement, the mean transaortic pressure gradient measurement, and the peak aortic jet velocity measurement” from claim 1. The change in language broadens claim 10 in comparison to claim 1. The examiner interprets that the applicant means these measurements gathered by the system from the digital information repository.
Also, for claim 10: The claim states “based on the model each of the aortic valve area, the mean transaortic pressure gradient, and the peak aortic jet velocity”. The phrase “based on the model each of the” seems incomplete or missing an operator. Correction is therefore required to clarify whether it means “based on the model of each of the” or provide another way to state it.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 10 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. It is unclear whether the “classify the severity … based on the model each of the aortic valve area, the mean transaortic pressure gradient, and the peak aortic jet velocity” from claim 10 are the same as “classify a severity … based on at least on the aortic valve area measurement, the mean transaortic pressure gradient measurement, and the peak aortic jet velocity measurement” of claim 1. The change in language broadens claim 10 in comparison to claim 1. Therefore, claim 10 does not further limit claim 1. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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, comprising:a digital information repository(s) 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 computing apparatus, comprising:a memory configured to store 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 configured to execute the stored instructions for the aortic stenosis classifier to classify a severity of an aortic stenosis of the subject of interest based at least on the aortic valve area measurement, the mean transaortic pressure gradient measurement, 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).
This shows the classifier classifying severity based on the mentioned criteria.
a display configured to 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(s) is further configured to store information about subjects with aortic stenoses, including at least aortic valve area measurements, mean transaortic pressure gradient measurements, and 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 processor is further configured to: extract information from the digital information repository(s) for 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 perform an analysis on the processed extracted data to determine a set of risk factors of aortic stenosis progression.
“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, comprising:obtaining information about a subject, including at least an aortic valve area measurement, a mean transaortic pressure gradient measurement, and a peak aortic jet velocity measurement for the subject;
“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 instructions for an aortic stenosis classifier;“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 obtaining an algorithm for an aortic stenosis classifier.
executing the instructions to classify a severity of an aortic stenosis of the subject of interest based at least on the aortic valve area measurement, the mean transaortic pressure gradient measurement, 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 subjects with aortic stenoses, including at least aortic valve area measurements, mean transaortic pressure gradient measurements, and 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 nonobviousness.
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 further includes an individual-level visualizer, and the processor is further configured to execute the instructions for the individual-level visualizer 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 cause the display monitor to 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 further includes a population-level visualizer, and the processor is further configured to: execute the instructions for the population-level visualizer 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 aortic valve area versus mean transaortic pressure gradient versus and 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 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
cause the display monitor to 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 processor is further configured 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
cause the display monitor to 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 processor is further configured to construct a two-dimensional graph including the aortic valve area and the peak aortic jet velocity and
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616
826
media_image2.png
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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
cause the display monitor to 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.
Claims 8-10, 15, and 20 are 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 subjects to determine initial risk factors associated with aortic valve area, mean transaortic pressure gradient and 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).
Regarding Claim 9, MATHIEU does not explicitly teach but BORDIN teaches
The system of claim 7, wherein the processor is further configured to model each of aortic valve area, mean transaortic pressure gradient and peak aortic jet velocity based on the set of risk factors and predict a severity of aortic stenosis of the subject of interest based on the models.
“…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).
“One embodiment provides a non-transitive carrier medium for carrying computer executable code that, when executed on a processor, causes the processor to perform a method as described herein.”. (0019).
“The panels on the right demonstrate the imputation error (imputed vs actual measurement), calculated after predicting while holding out each measurement plus any directly dependent variables. The mean errors (95% confidence interval) were as follows: LVOT dimension = O.OlOcm (-0.165 to 0.165), LVOT velocity time integral =-0.669cm (-6.019 to 4.249), Mean transaortic valve gradient = 0.068mmHg (-5.639 to 3.133), Aortic Valve Area =-0.056 (0.885, 0.664), p=ns for each imputed vs actual measurement.”. (0078).
“AS was evaluated using the peak aortic jet velocity, aortic mean gradient, and the aortic valve area defined by the Continuity equation…”. (0080).
This shows a processor configured to classify severity based on the models of AVA, pressure gradient, and jet velocity.
Regarding Claim 10, MATHIEU does not explicitly teach but BORDIN teaches
The system of claim 9, wherein the processor is further configured to classify the severity of an aortic stenosis of the subject of interest based on the model each of the aortic valve area, the mean transaortic pressure gradient, and the peak aortic jet velocity.
“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 severity classification based on modeled AVA, pressure gradient, and jet velocity.
Claim 15 recites substantially the same limitations as claim 9 except this claim is directed to a “computer-implemented method” Therefore, this claim is rejected under the same rationale as addressed above.
Claim 20 recites substantially the same limitations as claim 9 except this claim is directed to a “computer-readable storage medium” Therefore, this claim is rejected under the same rationale as addressed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20180153415 A1 by LEE et al, and WO 2014100733 A1 by MILLER et al.
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/N.E.M./Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189