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 .
Notice to Applications
This communication is in response to the Application filed on March 22, 2024.
Claims 2-21 are pending.
Information Disclosure Statement
The information disclosure statement(s) (IDS(s)) submitted on January 16, 2025 are in compliance with the provisions of 27 CFR 1.97. Accordingly, the information disclosure statements are being considered and attached by the examiner.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 2-21 are rejected under 35 U.S.C. 103 as being unpatentable of Min et al., US 20210209757 A1, (hereinafter “Min”) in view of Kim et al., US 20200150684 A1, (hereinafter “Kim”), Choi et al., US 20150164453 A1, (hereinafter “Choi”), and Chiu et al., US 20120157795 A1, (hereinafter “Chiu”).
Regarding claim 2, Min teaches a computer-implemented method of determining a medical facility for a subject based at least in part on image-based analysis of plaque from a medical image, the computer-implemented method comprising:
accessing, by a computer system, a medical image of a subject, the medical image comprising a representation of a portion of one or more coronary arteries([0119] “In some embodiments, the systems, devices, and methods described herein are configured to utilize non-invasive medical imaging technologies, such as a CT image for example, which can be inputted into a computer system configured to automatically and/or dynamically analyze the medical image to identify one or more coronary arteries and/or plaque within the same.”);
analyzing, by the computer system, the medical image to identify one or more coronary arteries, the one or more coronary arteries comprising one or more regions of plaque ([0119] “In some embodiments, the systems, devices, and methods described herein are configured to utilize non-invasive medical imaging technologies, such as a CT image for example, which can be inputted into a computer system configured to automatically and/or dynamically analyze the medical image to identify one or more coronary arteries and/or plaque within the same.”);
analyzing, by the computer system, the identified one or more coronary arteries and the one or more regions of plaque to generate a plurality of image-derived variables ([0120] “ Based on the identified features, in some embodiments, the system can be configured to generate one or more quantified measurements from a raw medical image, such as for example radiodensity of one or more regions of plaque, identification of stable plaque and/or unstable plaque, volumes thereof, surface areas thereof, geometric shapes, heterogeneity thereof, and/or the like. In some embodiments, the system can also generate one or more quantified measurements of vessels from the raw medical image, such as for example diameter, volume, morphology, and/or the like.” wherein a plurality of image-derived variables is quantified measurements);
applying, by the computer system, ([0120] “ Based on the identified features, in some embodiments, the system can be configured to generate one or more quantified measurements from a raw medical image, such as for example radiodensity of one or more regions of plaque, identification of stable plaque and/or unstable plaque, volumes thereof, surface areas thereof, geometric shapes, heterogeneity thereof, and/or the like. In some embodiments, the system can also generate one or more quantified measurements of vessels from the raw medical image, such as for example diameter, volume, morphology, and/or the like.” wherein a plurality of image-derived variables is quantified measurements)
wherein the computer system comprises a computer processor and an electronic storage medium ([0456] “wherein the computer system comprises a computer processor and an electronic storage medium.”).
Min does not specifically disclose an imaging modality on an ambulance.
However, Kim teaches an imaging modality on an ambulance ([0193] “Accordingly, an ambulance is capable of accurately and quickly transmitting and receiving emergency data (e.g., a route to a hospital, medical equipment measurement data, and a medical image) using beam information.” wherein an imaging modality on an ambulance is beam information).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include an image modality on an ambulance of Kim in the image-based plaque analysis method of Min so an ambulance can transmit and receive emergency data.
Min in view of Kim does not specifically disclose a machine learning algorithm to determine the risk of myocardial infarction, wherein the machine learning algorithm is trained based at least in part on the plurality of image-derived variables derived from medical images of other subjects with known risk of myocardial infarction; and
determining when the determined risk of myocardial infarction for the subject is above a predetermined threshold.
However, Choi teaches a machine learning algorithm to determine the risk of myocardial infarction, wherein the machine learning algorithm is trained based at least in part on the plurality of image-derived variables derived from medical images of other subjects with known risk of myocardial infarction ([0047] “Then, step 406 may include producing estimates of cardiac risk, including estimates of the probability of plaque rupture or probability of the event of myocardial infarction at lesions in the patient-specific geometric model. In one embodiment, the estimates are produced using a machine learning technique described in further detail in FIG. 4B.”) ([0050] “In other words, method 420 may be a process of training a prediction system using collected features in order to identify indications of acute myocardial infarction (MI) likelihood over time (if sufficiently large MI patient data were used for training) and/or plaque vulnerability or features of vulnerability measured from OCT, IVUS, and near-infrared spectroscopy (if a surrogate plaque vulnerability model was used for training).”); and
determining when the determined risk of myocardial infarction for the subject is above a predetermined threshold ([0039] “In one embodiment, the threshold value for a positive remodeling index to indicate the presence of positive remodeling is 1.05.” wherein a positive remodeling index is used to predict the determined risk of myocardial infarction and a predetermined threshold is 1.05).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the emergency response to plaque analysis method of Min in view of Kim to determine the risk of myocardial infarction of Choi because myocardial infarctions are often caused by the rupture of unstable atherosclerotic plaques in the coronary arteries. Therefore, image-based plaque analysis more accurately aids in characterizing and predicting the risk of myocardial infarction.
Min in view of Kim and Choi does not specifically disclose accessing a list of medical facilities comprising a cardiac catheterization lab and determining a medical facility for treating the subject from the list of medical facilities comprising a cardiac catheterization lab.
However, Chiu teaches accessing a list of medical facilities comprising a cardiac catheterization lab and determining a medical facility for treating the subject from the list of medical facilities comprising a cardiac catheterization lab ([0047] “In certain implementations, the emergency services database(s) 62 include a treatment facility database 90. An exemplary depiction of a treatment facility database 90 is provided in FIG. 3. Treatment facility database 90 includes information that enables the technologist or participating PSAP 70 to identify and select treatment facilities to which a patient should be transported in the case of a medical event. The process of identifying a correct treatment facility may be based on a number of factors, some of which may include the treatment facility's location, the treatment facility's equipment and capabilities, the time of the day, and the availability of certain treatment facility staff…Separate records may be provided for each distinct internal facility to better ensure that the patient is routed to the correct internal location. For example, a given hospital may have a surgery wing and a cardiac catheterization lab, which may be some distance from one another within the hospital.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a list of medical facilities comprising a cardiac catheterization lab of Chiu in the emergency response to risk of myocardial infarction method of Min in view of Kim and Choi because a cardiac catheterization lab is essential for treating myocardial infarction because it provides the necessary and immediate specialized imaging and tools. Therefore, transferring patients with a risk of myocardial infarction immediately to a medical facility with a cardiac catheterization lab improves their survival rates and reduces complications.
Regarding claim 3, Min in view of Kim, Choi, and Chiu teaches the computer-implemented method of Claim 2, wherein the plurality of image- derived variables comprising one or more of: percent atheroma volume of total plaque, total plaque volume, percent atheroma volume of low-density non-calcified plaque, percent atheroma volume of non-calcified plaque, percent atheroma volume, low- density non-calcified plaque volume, percent atheroma volume of total calcified plaque, non-calcified plaque volume, total calcified plaque volume, percent atheroma volume of total non-calcified plaque, percent atheroma volume of low-density calcified plaque, percent atheroma volume of high-density calcified plaque, total non-calcified plaque volume, low-density calcified plaque volume, percent atheroma volume of medium- density calcified plaque, high-density calcified plaque volume, medium-density calcified plaque volume, number of high-risk plaque regions, number of segments with calcified plaque, number of segments with non-calcified plaque, plaque area, plaque burden, necrotic core percentage, necrotic core volume, fatty fibrous volume, fatty fibrous percentage, dense calcium percentage, low-density calcium percentage, medium- density calcified percentage, high-density calcified percentage, vessel length, segment length, lesion length, lumen volume, number of chronic total occlusion (CTO), vessel volume, number of stenosis, severity of stenosis, remodeling index, minimum lumen diameter, maximum lumen diameter, mean lumen diameter, stenosis area percentage, stenosis diameter percentage, number of mild stenosis, number of moderate stenosis, number of zero stenosis, number of severe stenosis, presence of high-risk anatomy, presence of positive remodeling, number of severe stenosis excluding CTO, vessel area, lumen area, diameter stenosis percentage, reference lumen diameter before stenosis, perivascular fat attenuation, or reference lumen diameter after stenosis (Min - [0239] “In some embodiments, a patient-specific report generated by the system includes a quantified measure of various plaque and/or vascular morphology-related parameters shown within the vessel. In some embodiments, for each or some of the arteries included in the report, the system is configured to generate and/or derive from a medical image of the patient and include in a patient-specific report a quantified measure of the total plaque volume, total low-density or non-calcified plaque volume, total non-calcified plaque value, and/or total calcified plaque volume.”).
The motivation for combining Min, Kim, Choi, and Chiu is the same motivation as used for claim 2.
Regarding claim 4, Min in view of Kim, Choi, and Chiu teaches the computer-implemented method of Claim 2, further comprising determining, by the computer system, a medical facility for treating the subject that is closest to a current location of the subject when the determined risk of myocardial infarction for the subject is below a predetermined threshold (Chiu - [0047] “In certain implementations, the emergency services database(s) 62 include a treatment facility database 90. An exemplary depiction of a treatment facility database 90 is provided in FIG. 3. Treatment facility database 90 includes information that enables the technologist or participating PSAP 70 to identify and select treatment facilities to which a patient should be transported in the case of a medical event. The process of identifying a correct treatment facility may be based on a number of factors, some of which may include the treatment facility's location, the treatment facility's equipment and capabilities, the time of the day, and the availability of certain treatment facility staff…Separate records may be provided for each distinct internal facility to better ensure that the patient is routed to the correct internal location. For example, a given hospital may have a surgery wing and a cardiac catheterization lab, which may be some distance from one another within the hospital.”) (Choi - [0039] “In one embodiment, the threshold value for a positive remodeling index to indicate the presence of positive remodeling is 1.05.” wherein a positive remodeling index is used to predict the determined risk of myocardial infarction and a predetermined threshold is 1.05).
The motivation for combining Min, Kim, Choi, and Chiu is the same motivation as used for claim 2.
Regarding claim 5, Min in view of Kim, Choi, and Chiu teaches the computer-implemented method of Claim 2, wherein determining the medical facility comprises:
accessing, by the computer system, availability of cardiac catheterization labs at one or more medical facilities on the list of medical facilities (Chiu - [0047] “In certain implementations, the emergency services database(s) 62 include a treatment facility database 90. An exemplary depiction of a treatment facility database 90 is provided in FIG. 3. Treatment facility database 90 includes information that enables the technologist or participating PSAP 70 to identify and select treatment facilities to which a patient should be transported in the case of a medical event. The process of identifying a correct treatment facility may be based on a number of factors, some of which may include the treatment facility's location, the treatment facility's equipment and capabilities, the time of the day, and the availability of certain treatment facility staff…Separate records may be provided for each distinct internal facility to better ensure that the patient is routed to the correct internal location. For example, a given hospital may have a surgery wing and a cardiac catheterization lab, which may be some distance from one another within the hospital.”); and
determining, by the computer system, as the medical facility a medical facility with highest availability of the cardiac catheterization lab within a predetermined distance from a current location of the subject (Chiu - [0047] “In certain implementations, the emergency services database(s) 62 include a treatment facility database 90. An exemplary depiction of a treatment facility database 90 is provided in FIG. 3. Treatment facility database 90 includes information that enables the technologist or participating PSAP 70 to identify and select treatment facilities to which a patient should be transported in the case of a medical event. The process of identifying a correct treatment facility may be based on a number of factors, some of which may include the treatment facility's location, the treatment facility's equipment and capabilities, the time of the day, and the availability of certain treatment facility staff…Separate records may be provided for each distinct internal facility to better ensure that the patient is routed to the correct internal location. For example, a given hospital may have a surgery wing and a cardiac catheterization lab, which may be some distance from one another within the hospital.”) (Chiu - [0050] “Using the patient's preliminary location information (e.g., GPS or radiolocation coordinates), in step 1010 a distance d.sub.1 is calculated from the preliminary location to the first scheduled location. In one embodiment, a straight line distance between the two locations is calculated. The distance d.sub.1 is preferably less than about 600 m, more preferably less than about 300 m, more preferably less than about 100 m, and even more preferably less than about 30 m. In step 1012, it is determined whether the calculated distance d.sub.1 is less (or no greater) than a selected distance.” wherein a predetermined distance is a selected distance).
The motivation for combining Min, Kim, Choi, and Chiu is the same motivation as used for claim 2.
Regarding claim 6, Min in view of Kim, Choi, and Chiu teaches the computer-implemented method of Claim 2, further comprising causing, by the computer system, generation of driving instructions for the ambulance to the medical facility (Kim - [0193] “According to an embodiment, it is necessary for an emergency vehicle such as an ambulance need to transmit and receive emergency data. In this case, at least one infrastructure may exist on a predicted route of the emergency vehicle, and a server may identify beam information available for the infrastructure and the vehicle using learned data. In this case, the emergency vehicle may need to in real time transmit and receive large capacity data, such as a route to a hospital, medical equipment measurement data, a medical image, etc. with respect to the infrastructure.” wherein driving instructions are a route to a hospital).
The motivation for combining Min, Kim, Choi, and Chiu is the same motivation as used for claim 2.
Regarding claim 7, Min in view of Kim, Choi, and Chiu teaches the computer-implemented method of Claim 6, further comprising causing, by the computer system, self-driving of the ambulance to the medical facility (Kim - [0193] “According to an embodiment, it is necessary for an emergency vehicle such as an ambulance need to transmit and receive emergency data. In this case, at least one infrastructure may exist on a predicted route of the emergency vehicle, and a server may identify beam information available for the infrastructure and the vehicle using learned data. In this case, the emergency vehicle may need to in real time transmit and receive large capacity data, such as a route to a hospital, medical equipment measurement data, a medical image, etc. with respect to the infrastructure.” wherein driving instructions are a route to a hospital) (Kim - [0092] “The autonomous vehicle 100b may be realized into a mobile robot, a vehicle, or an unmanned aerial vehicle, for example, through the application of AI technologies.” wherein the emergency vehicle can be an autonomous vehicle).
The motivation for combining Min, Kim, Choi, and Chiu is the same motivation as used for claim 2.
Regarding claim 8, Min in view of Kim, Choi, and Chiu teaches the computer-implemented method of Claim 2, further comprising transmitting, by the computer system, the determined risk of myocardial infarction for the subject and the medical image to the medical facility (Choi - [0047] “Then, step 406 may include producing estimates of cardiac risk, including estimates of the probability of plaque rupture or probability of the event of myocardial infarction at lesions in the patient-specific geometric model. In one embodiment, the estimates are produced using a machine learning technique described in further detail in FIG. 4B.”) (Min - [0434] “After analysis, in some embodiments, the analysis results, such as for example quantified plaque parameters, assessed risk of a cardiovascular event, generated report, annotated and/or derived medical images, and/or the like, can be transmitted back to the medical facility client system 1304 via the network 1308.”).
The motivation for combining Min, Kim, Choi, and Chiu is the same motivation as used for claim 2.
Regarding claim 9, Min in view of Kim, Choi, and Chiu teaches the computer-implemented method of Claim 2, further comprising generating, by the computer system, a weighted measure of the plurality of image-derived variables, wherein the risk of myocardial infarction is determined based at least in part on the weighted measure of the plurality of image-derived variables (Min - [0197] “In some embodiments, the system can be configured to generate a weighted measure of one or more vascular morphology parameters and/or quantified plaque parameters determined and/or derived from raw medical images.”) (Choi - [0047] “Then, step 406 may include producing estimates of cardiac risk, including estimates of the probability of plaque rupture or probability of the event of myocardial infarction at lesions in the patient-specific geometric model. In one embodiment, the estimates are produced using a machine learning technique described in further detail in FIG. 4B.”).
The motivation for combining Min, Kim, Choi, and Chiu is the same motivation as used for claim 2.
Regarding claim 10, Min in view of Kim, Choi, and Chiu teaches the computer-implemented method of Claim 2, wherein the imaging modality comprises computed tomography (CT) (Min - [0153] “In some embodiments, the medical image can be obtained using one or more modalities such as CT, Dual-Energy Computed Tomography (DECT), Spectral CT, x-ray, ultrasound, echocardiography, IVUS, MR, OCT, nuclear medicine imaging, PET, SPECT, NIRS, and/or the like.”).
The motivation for combining Min, Kim, Choi, and Chiu is the same motivation as used for claim 2.
Regarding claim 11, Min in view of Kim, Choi, and Chiu teaches the computer-implemented method of Claim 10, wherein the medical image comprises a coronary CT angiography (CCTA) (Min - [0311] “In some embodiments, the system is configured as a web-based software application that is intended to be used by trained medical professionals as an interactive tool for viewing and analyzing cardiac CT data for determining the presence and extent of coronary plaques (i.e., atherosclerosis) and stenosis in patients who underwent Coronary Computed Tomography Angiography (CCTA) for evaluation of coronary artery disease (CAD), or suspected CAD.”).
The motivation for combining Min, Kim, Choi, and Chiu is the same motivation as used for claim 2.
Regarding claim 12, Min in view of Kim, Choi, and Chiu teaches the computer-implemented method of Claim 2, wherein the imaging modality comprises one or more of CT, x-ray, ultrasound, echocardiography, MR imaging, optical coherence tomography (OCT), nuclear medicine imaging, positron-emission tomography (PET), single photon emission computed tomography (SPECT), or near-field infrared spectroscopy (NIRS) (Min - [0153] “In some embodiments, the medical image can be obtained using one or more modalities such as CT, Dual-Energy Computed Tomography (DECT), Spectral CT, x-ray, ultrasound, echocardiography, IVUS, MR, OCT, nuclear medicine imaging, PET, SPECT, NIRS, and/or the like.”).
The motivation for combining Min, Kim, Choi, and Chiu is the same motivation as used for claim 2.
Regarding claim 13, the claim recites similar limitations to claim 2 but in the form of a system comprising: a non-transitory computer storage medium configured to at least store computer executable instructions; and one or more computer hardware processors in communication with the non-transitory computer storage medium, the one or more computer hardware processors configured to execute the computer executable instructions to perform the method of claim 2 (Min - [0378] “For example, by one or more computer hardware processors in communication with the one or more non-transitory computer storage mediums, executing the computer-executable instructions stored on one or more non-transitory computer storage mediums.”). Therefore, claim 13 recites similar limitations to claim 2 and is rejected for similar rationale and reasoning (see the analysis for claim 2 above).
Regarding claim 14, the claim recites similar limitations to claim 3 but in the form of a system. Therefore, claim 14 recites similar limitations to claim 3 and is rejected for similar rationale and reasoning (see the analysis for claim 3 above).
Regarding claim 15, the claim recites similar limitations to claim 4 but in the form of a system. Therefore, claim 15 recites similar limitations to claim 4 and is rejected for similar rationale and reasoning (see the analysis for claim 4 above).
Regarding claim 16, the claim recites similar limitations to claim 5 but in the form of a system. Therefore, claim 16 recites similar limitations to claim 5 and is rejected for similar rationale and reasoning (see the analysis for claim 5 above).
Regarding claim 17, the claim recites similar limitations to claim 6 but in the form of a system. Therefore, claim 17 recites similar limitations to claim 6 and is rejected for similar rationale and reasoning (see the analysis for claim 6 above).
Regarding claim 18, the claim recites similar limitations to claim 7 but in the form of a system. Therefore, claim 18 recites similar limitations to claim 7 and is rejected for similar rationale and reasoning (see the analysis for claim 7 above).
Regarding claim 19, the claim recites similar limitations to claim 10 but in the form of a system. Therefore, claim 19 recites similar limitations to claim 10 and is rejected for similar rationale and reasoning (see the analysis for claim 10 above).
Regarding claim 20, the claim recites similar limitations to claim 11 but in the form of a system. Therefore, claim 20 recites similar limitations to claim 11 and is rejected for similar rationale and reasoning (see the analysis for claim 11 above).
Regarding claim 21, the claim recites similar limitations to claim 2 but in the form of a non-transitory computer readable medium having program instructions for causing a hardware processor to perform the method of claim 2 (Min - [0378] “For example, by one or more computer hardware processors in communication with the one or more non-transitory computer storage mediums, executing the computer-executable instructions stored on one or more non-transitory computer storage mediums.”). Therefore, claim 21 recites similar limitations to claim 2 and is rejected for similar rationale and reasoning (see the analysis for claim 2 above).
Conclusion
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/AMANDA H PEARSON/Examiner, Art Unit 2666
/MING Y HON/Primary Examiner, Art Unit 2666