DETAILED ACTION
Claims 1-20 are pending.
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 6 February 2025 and 13 June 2025 have been considered by the examiner.
Claim Objections
Claim 20 is objected to because of the following informalities:
Lines 1-2 recite “wherein the computer-implemented further”. This appears to be a typographical error and should be “wherein the computer-implemented method further” (emphasis added).
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 25 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Analysis for claim 1 is provided in the following. Claim 1 is reproduced in the following (annotation added):
A computer-implemented method for processing medical images, the method comprising:
receiving image data for a patient;
based on the received image data, determining:
a patient-specific epicardial adipose tissue (EAT) metric or a patient-specific pericoronary adipose tissue (PCAT) metric; and
at least one other patient-specific metric; and
using the EAT metric or the PCAT metric, and the at least one other patient-specific metric, to determine a risk score for the patient or to classify a disease state of the patient.
Step 1: Evaluating whether the claim belongs to one of the statutory categories.
Claim 1 recites at least one step or act. Thus, the claim is directed to a process, which is one of the statutory categories of invention (Step 1: YES)
Step 2A Prong One: Evaluating whether the claim recites a judicial exception (an abstract idea enumerated in 2019 PEG, a law of nature, or a natural phenomenon). If no exception is recited, the claim is eligible. This concludes the eligibility analysis. If the claim recites an exception, go to Step 2A Prong Two.
Claim 1 recites an abstract idea of a mental process. At least steps b, bi, bii, and c are recited at a high level of generality such that they could be practically performed by a human (The courts consider a mental process (thinking) that “can be performed in the human mind, or be a human using a pen and paper” to be an abstract idea.). These concepts fall into the “mental processes” group of abstract ideas, which is observation, evaluation and/or judgement.
Steps b, bi, and bii can be performed in the human mind since the EAT metric or PCAT metric and other patient-specific metric can be interpreted as an observation that can be made by visually looking at the image, such as the thickness of the EAT or PCAT and plaque (i.e. other patient-specific metric). The claim does not impose any limits on how the metrics are determined.
Step c can be performed in the human mind since a risk score or disease classification can be determined, mentally, corresponding to the metrics/thicknesses of the EAT or PCAT and plaque. The claim does not impose any limits on how the risk score is determined or how the disease is classified.
MPEP, 2106.04 (a) (2) III (C): Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). The limitations, interpreted under their broadest reasonable interpretation and in consistence with the specification, cover performance of the limitations in the mind or by generic computer components. See MPEP 2106.04 and the 2019 PEG. (Step 2A Prong One YES)
Step 2A Prong Two: Evaluating whether the claim recites additional elements that integrate the exception into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into practical application. If the answer to (a) is YES and (b) is NO, go to Step 2B; if the answer to (a) and (b) is YES, go to PATHWAY B, i.e., the claim is not directed to a judicial exception and the claim is eligible.
The 2019 PEG defines the phrase “integration into a practical application” to require an additional element or a combination of additional elements in the claim to apple, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
Limitations that are indicative of integration into a practical application when recited in a claim with a judicial exception include:
Improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a);
Applying or using a judicial exception to affect a particular treatment or prophylaxis for disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05©; and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018.
Limitations that are not indicative of integration into a practical application when recited in a claim with a judicial exception include:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f);
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g); and
Generally linking the use of the judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h).
[Examiners should note that revised Step 2A excludes consideration of whether claim elements represent well-understood, routine, conventional activity. The question of whether claim elements represent only well-understood, routine, conventional activity is considered at Step 2B and is not a consideration in Step 2A.]
Step a can be regarded as an additional element recited in claim 1. This additional element, i.e., receiving image data for a patient, does not integrate the exception into a practical application of the exception. Note even if the specification discloses that the invention pertains to an improvement in the technology, the claim must be evaluated to ensure the claim itself reflects the improvement in technology. It is also important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. Therefore, the additional elements do not recite an improvement. (Step 2A Prong Two NO)
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
Step a can be regarded as an additional element recited in claim 1. This additional element of step a (i.e. receiving image data for a patient) is mere data gathering that is recited at a high level of generality, which is considered insignificant extra-solution activities and amounts to automating a manual human activity, and this limitation does not impose any meaningful limits on the claim.
Using the broadest reasonable interpretation of the claim, the additional elements, taken individually and in combination, do not result in the claim, as a whole, amounting to significantly more than the abstract idea itself. See MPEP 2106.05. (Step 2B: NO) The claim is not eligible.
Claim 2 recites “the computer-implemented method of claim 1, wherein the at least one other patient-specific metric includes a vessel geometry, a vessel morphology, a plaque characteristic, or a hemodynamic measurement”. The limitation is directed to the abstract idea of mental processes which are mere automation of manual processes. The similar examination analysis as applied to claim 1 is applied to claim 2. No additional elements are recited. Accordingly, claim 2 does not have eligible subject matter.
Claim 3 recites “the computer-implemented method of claim 1, wherein the image data is
a first image data, the EAT metric is a first EAT metric, the PCAT metric is a first PCAT metric, and the at least one other patient-specific metric is at least a first other patient-specific metric, and wherein, before determining the risk score or classifying the disease state of the patient, the computer-implemented method further comprises:
receiving second image data for the patient;
based on the received secondimage data, determining:
a second patient-specific EAT metric or a second patient-specific PCAT metric; and
at least a second other patient-specific metric; and
using the second EAT metric or the second PCAT metric, and the second other patient-specific metric, to determine the risk score for the patient or to classify the disease state of the patient.
Steps b, bi, bii, and c are directed to the abstract idea of mental processes which are mere automation of manual processes. This additional element of step a (i.e. receiving image data for a patient) is mere data gathering that is recited at a high level of generality, which is considered insignificant extra-solution activities and amounts to automating a manual human activity, and this limitation does not impose any meaningful limits on the claim. The similar examination analysis as applied to claim 1 is applied to the steps of claim 3. Accordingly, claim 3 does not have eligible subject matter.
Claim 4 recites “the computer-implemented method of claim 1, wherein the received
image data is used to generate a three-dimensional model of a vasculature of the patient.” The limitation is directed to the abstract idea of mental processes which are mere automation of manual processes. The similar examination analysis as applied to claim 1 is applied to claim 4. No additional elements are recited. Accordingly, claim 4 does not have eligible subject matter.
Claim 5 recites “the computer-implemented method of claim 4, further comprising
generating a display image of the three-dimensional model, wherein the display image includes a color-coded indicator of the risk score or the disease state.” The limitation is directed to the abstract idea of mental processes which are mere automation of manual processes. The additional element of generating a “display image” is mere data outputting that is recited at a high level of generality, which is considered insignificant extra-solution activities and amounts to automating a manual human activity, and this limitation does not impose any meaningful limits on the claim. The similar examination analysis as applied to claim 1 and 4 is applied to claim 5. No additional elements are recited. Accordingly, claim 5 does not have eligible subject matter.
Claim 6 recites “the computer-implemented method of claim 1, wherein the disease state
is lschemia with Non-Obstructive Coronary Arteries (INOCA).” The limitation is directed to the abstract idea of mental processes which are mere automation of manual processes. The similar examination analysis as applied to claim 1 is applied to claim 6. No additional elements are recited. Accordingly, claim 6 does not have eligible subject matter.
Claim 7 recites “the computer-implemented method of claim 1, wherein the risk score is
predictive of a fractional flow reserve (FFR) value.” The limitation is directed to the abstract idea of mental processes which are mere automation of manual processes. The similar examination analysis as applied to claim 1 is applied to claim 7. No additional elements are recited. Accordingly, claim 7 does not have eligible subject matter.
Claim 8 recites “the computer-implemented method of claim 1, wherein both the EAT
metric and the PCAT metric are used to determine the risk score for the patient or to
classify the disease state of the patient.” The limitation is directed to the abstract idea of mental processes which are mere automation of manual processes. The similar examination analysis as applied to claim 1 is applied to claim 8. No additional elements are recited. Accordingly, claim 8 does not have eligible subject matter.
Independent claim 9 is directed to a machine, which is a statutory category of invention. Similar analysis is applicable as applied above to the method of claim 1. Claim 9 further recites other additional elements of “a data storage device storing instructions for medical image processing” and “a processor configured to execute the instructions to perform operations comprising:”. These elements are recited at a high level of generality such that they amount to no more than mere generic computer system elements. MPEP, 2106.04 (a) (2) III (C): Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). The remainder of the claim is identical to claim 1. The similar examination analysis as applied to claim 1 is applied to the remaining steps of claim 9. Accordingly claim 9 does not have eligible subject matter.
Similarly, independent claim 17 recites “a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform a computer-implemented method for medical image processing, the method comprising:” These elements are recited at a high level of generality such that they amount to no more than mere generic computer system elements. MPEP, 2106.04 (a) (2) III (C): Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). The remainder of the claim is identical to claim 1. The similar examination analysis as applied to claim 1 is applied to the remaining steps of claim 9. Accordingly claim 9 does not have eligible subject matter.
Similar analysis as applied to:
claim 2 is applied to claims 10 and 18,
claim 3 is applied to claim 16,
claim 4 is applied to claims 11 and 19,
claim 5 is applied to claims 12 and 20,
claim 6 is applied to claim 13,
claim 7 is applied to claim 14, and
claim 8 is applied to claim 15.
Therefore, claims 10-16 and 18-20 do not have eligible subject matter.
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, 4, 6 and 8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Antoniades et al. (U.S. Patent No. US 11393137 B2) (“Antoniades”).
Regarding claim 1, Antoniades discloses a computer-implemented method for processing medical images (col. 9 lines 22-25), the method comprising:
receiving image data for a patient (col. 8 lines 9-17; col. 11 lines 24-27, wherein CT scans (i.e. image data) along arteries of the patient are obtained);
based on the received image data, determining:
a patient-specific epicardial adipose tissue (EAT) metric (col. 10 lines 4-14 & col. 10 line 60 - col. 11 line 13, wherein the epicardial adipose tissue (EpAT) volume and EpAT fat attenuation index both correspond to an EAT metric and are determined) or a patient- specific pericoronary adipose tissue (PCAT) metric (col. 8 lines 18-23, wherein "perivascular" corresponds to "pericoronary" according to [0027] of the Specification; col. 10 line 60 - col. 11 line 13, wherein perivascular adipose tissue (PVAT) fat attenuation index, perivascular water index, and volumetric perivascular characterization index all correspond to PCAT metric and are determined); and
at least one other patient-specific metric (col. 9 lines 57-63, wherein fibrous plaque index (FPi) is defined; col. 9 line 64 - col. 10 line 3, wherein calcium index (Calcium-i) is defined; col. 10 line 60 - col. 11 line 13 & col. 16 lines 26-29, wherein FPi and Calcium-i are determined and correspond to other patient-specific metric); and
using the EAT metric or the PCAT metric, and the at least one other patient- specific metric, to determine a risk score for the patient or to classify a disease state of the patient (Fig. 6; col. 11 line 64 - col. 12 line 7, wherein the OxScore (i.e. risk score) is calculated with EpAT volume (i.e. EAT metric), PVAT attenuation index (i.e. PCAT metric), fibrous plaque index, and calcium index (i.e. other patient-specific metric) as independent variables).
Regarding claim 2, Antoniades discloses the computer-implemented method of claim 1. Antoniades additionally discloses wherein the at least one other patient-specific metric includes a vessel geometry, a vessel morphology, a plaque characteristic , or a hemodynamic measurement (col. 10 line 60 - col. 11 line 13; col. 16 lines 26-29, wherein the FPi (i.e. other patient-specific metric) is defined by dividing the total volume of fibrous plaque (i.e. plaque characteristic) by the volume of the respective vessel segment (i.e. vessel geometry).
Regarding claim 4, Antoniades discloses the computer-implemented method of claim 1. Antoniades additionally discloses wherein the received image data is used to generate a three-dimensional model of a vasculature of the patient (Fig. 1A; col. 4 lines 55-65, wherein the coronary CT angiography images were reconstructed in 3 dimensions (i.e. three-dimensional model of a vasculature)).
Regarding claim 6, Antoniades discloses the computer-implemented method of claim 1. Claim 1 only requires either to “determine a risk score for the patient or to classify a disease state of the patient” in lines 9-10 (emphasis added). Since Antoniades teaches determining “a risk score for the patient” (Fig. 6; col. 11 line 64 - col. 12 line 7, wherein the OxScore (i.e. risk score) is calculated with EpAT volume (i.e. EAT metric), PVAT attenuation index (PCAT metric), fibrous plaque index, and calcium index (i.e. other patient-specific metric) as independent variables), then any limitation with respect to the disease state in claim 6 is also met, because claim 1 requires only the option of “determine a risk score for the patient” or the other option of “classify a disease state of the patient”.
Regarding claim 8, Antoniades discloses the computer-implemented method of claim 1. Antoniades additionally discloses wherein both the EAT metric and the PCAT metric are used to determine the risk score for the patient (Fig. 6; col. 11 line 64 - col. 12 line 7, wherein the OxScore (i.e. risk score) is calculated using both the EpAT volume (i.e. EAT metric), PVAT attenuation index (PCAT metric) as independent variables) or to classify the disease state of the patient.
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.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Antoniades et al. (U.S. Patent No. US 11393137 B2) (“Antoniades”) in view of Min et al. (U.S. Publication No. US 2022/0392065 A1) (“Min 2022”).
Regarding claim 3, Antoniades discloses the computer-implemented method of claim 1.
Antoniades discloses wherein the image data is a first image data (col. 8 lines 9-17; col. 11 lines 24-27, wherein CT scans correspond to the first image data), the EAT metric is a first EAT metric (col. 10 lines 4-14 & col. 10 line 60 - col. 11 line 13, wherein the EpAt volume and EpAT fat attenuation correspond to the first EAT metric), the PCAT metric is a first PCAT metric (col. 8 lines 18-23, wherein “perivascular” corresponds to “pericoronary” according to [0027] of the Specification; col. 10 line 60 - col. 11 line 13, wherein perivascular adipose tissue (PVAT) fat attenuation index, perivascular water index, and volumetric perivascular characterization index all correspond to first PCAT metric ), and the at least one other patient-specific metric is at least a first other patient-specific metric (col. 10 line 60 - col. 11 line 13 & col. 16 lines 26-29, wherein FPi and Calcium-i correspond to first other patient-specific metric),
receiving (col. 8 lines 9-17; col. 11 lines 24-27, wherein CT scans (i.e. image data) along arteries of the patient are obtained);
based on the received
a (col. 10 lines 4-14 & col. 10 line 60 - col. 11 line 13, wherein the epicardial adipose tissue (EpAT) volume and EpAT fat attenuation index both correspond to an EAT metric and are determined) or a (col. 8 lines 18-23, wherein "perivascular" corresponds to "pericoronary" according to [0027] of the Specification; col. 10 line 60 - col. 11 line 13, wherein perivascular adipose tissue (PVAT) fat attenuation index, perivascular water index, and volumetric perivascular characterization index all correspond to PCAT metric and are determined); and
at least a (col. 9 lines 57-63, wherein fibrous plaque index (FPi) is defined; col. 9 line 64 - col. 10 line 3, wherein calcium index (Calcium-i) is defined; col. 10 line 60 - col. 11 line 13 & col. 16 lines 26-29, wherein FPi and Calcium-i are determined and correspond to other patient-specific metric); and
using the (Fig. 6; col. 11 line 64 - col. 12 line 7, wherein the OxScore (i.e. risk score) is calculated with EpAT volume (i.e. EAT metric), PVAT attenuation index (i.e. PCAT metric), fibrous plaque index, and calcium index (i.e. other patient-specific metric) as independent variables).
However, as indicated by the double strikethroughs above, Antoniades fails to teach wherein, before determining the risk score or classifying the disease state of the patient, the computer-implemented method further comprises:
receiving second image data for the patient;
based on the received second image data, determining:
a second patient-specific EAT metric or a second patient-specific PCAT metric; and
at least a second other patient-specific metric; and
using the second EAT metric or the second PCAT metric, and the second other patient-specific metric to determine the risk score for the patient or to classify the disease state of the patient, specifically (emphasis added).
Min 2022, on the other hand, teaches analyzing medical images of the arteries taken at a second point in time to identify and determine the plaque parameters, vascular parameters, atherosclerosis parameters, and perilesional tissue parameters from the medical image, similarly to the images taken at a first point in time, and determining a risk factor based on the set of images taken at the second point in time. More specifically and as it relates to the applicant’s claims, Antoniades in view of Min 2022 discloses wherein, before determining the risk score or classifying the disease state of the patient, the computer-implemented method further comprises:
receiving second image data for the patient (Min 2022, [1322-1323], wherein medical images including arteries are received from a second point in time (i.e. second image data));
based on the received second image data, determining:
a second patient-specific EAT metric or a second patient-specific PCAT metric (Min 2022, [1322-1323], wherein vascular parameters, atherosclerosis parameters, and perilesional tissue parameters, which correspond to second EAT metric or second PCAT metric when combined with Antoniades’ teaching of EAT metrics (Antoniades, col. 10 lines 4-14 & col. 10 line 60 - col. 11 line 13) and PCAT metrics (Antoniades, col. 10 line 60 - col. 11 line 13), are determined); and
at least a second other patient-specific metric (Min 2022, [1322-1323], wherein plaque parameters, which correspond to second other patient-specific metric when combined with Antoniades’ teaching of fibrous plaque index (Antoniades, col. 10 line 60 - col. 11 line 13 & col. 16 lines 26-29), are determined); and
using the second EAT metric or the second PCAT metric, and the second other patient-specific metric to determine the risk score for the patient or to classify the disease state of the patient (Min 2022, [1325] and [1438-1439], wherein the risk factor (i.e. risk score) using the parameters (i.e. second EAT metric or the second PCAT metric, and the second other patient-specific metric) determined from the patient’s medical images at a second point in time is determined).
Min 2022 is combinable with Antoniades because they are from the same art of image processing.
The suggestion/motivation for doing so would have been to monitor the progression, stabilization, or regression of the disease and more accurately reflect the risk/state of the patient at the second point in time (Min 2022, [1325] and [1438-1439]).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate wherein, before determining the risk score or classifying the disease state of the patient, the computer-implemented method further comprises: receiving second image data for the patient; based on the received second image data, determining: a second patient-specific EAT metric or a second patient-specific PCAT metric; and at least a second other patient-specific metric; and using the second EAT metric or the second PCAT metric, and the second other patient-specific metric to determine the risk score for the patient or to classify the disease state of the patient, as taught by Min 2022, into the computer-implemented method, as taught by Antoniades, to obtain the invention as specified in claim 3.
Claims 5, 7, 9-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Antoniades et al. (U.S. Patent No. US 11393137 B2) (“Antoniades”) in view of Min et al. (U.S. Publication No. US 2023/0289963 A1) ("Min 2023").
Regarding claim 5, Antoniades discloses the computer-implemented method of claim 4.
Although Antoniades discloses generating a three-dimensional model (Fig. 1A; col. 4 lines 55-65), Antoniades fails to teach further comprising generating a display image of the three-dimensional model, wherein the display image includes a color-coded indicator of the risk score or the disease state. Min 2023, on the other hand, teaches generating a 3D representation of the coronary arteries for display and color-coding regions based on its risk level. More specifically and as it relates to the applicant’s claims, Min 2023 discloses further comprising generating a display image of the three-dimensional model, wherein the display image includes a color-coded indicator of the risk score or the disease state (Fig. 5A & [0159], wherein a 3D representation of the coronary arteries are generated; Fig. 13A & [0611] and [0622], wherein the regions of plaque are color-coded (i.e. color-coded indicator) based on the level of risk (i.e. risk score)).
Min 2023 is combinable with Antoniades because they are from the same art of image processing.
The suggestion/motivation for doing so would have been to improve visibility of the patient’s arteries depending on their areas of different levels of risk for treatment/outcome (Min 2023, [0611]).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate further comprising generating a display image of the three-dimensional model, wherein the display image includes a color-coded indicator of the risk score or the disease state, as taught by Min 2023, into the computer-implemented method, as taught by Antoniades, to obtain the invention as specified in claim 5.
Regarding claim 7, Antoniades discloses the computer-implemented method of claim 1.
However, Antoniades fails to teach wherein the risk score is predictive of a fractional flow reserve (FFR) value. Min 2023, on the other hand, teaches FFR being determined by the risk assessment. More specifically and as it relates to the applicant’s claims, Min 2023 discloses wherein the risk score is predictive of a fractional flow reserve (FFR) value (Fig. 13C blocks 1320 and 1322; [0637-0638], wherein FFR is defined as a flow characteristic within the vessel and is determined by the risk assessment (i.e. risk score)).
Min 2023 is combinable with Antoniades because they are from the same art of image processing.
The suggestion/motivation for doing so would have been to non-invasively determine the FFR (Min 2023, [0791-0792]).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate wherein the risk score is predictive of a fractional flow reserve (FFR) value, as taught by Min 2023, into the computer-implemented method, as taught by Antoniades, to obtain the invention as specified in claim 7.
Regarding claim 9, Antoniades discloses a system for processing medical images of a patient (col. 15. lines 21-23), comprising:
receiving medical images of the patient (col. 8 lines 9-17; col. 11 lines 24-27, wherein CT scans (i.e. medical images) along arteries of the patient are obtained);
based on the received medical images, determining:
a patient-specific epicardial adipose tissue (EAT) metric (col. 10 lines 4-14 & col. 10 line 60 - col. 11 line 13, wherein the epicardial adipose tissue (EpAT) volume and EpAT fat attenuation index both correspond to an EAT metric and are determined) or a patient- specific pericoronary adipose tissue (PCAT) metric (col. 8 lines 18-23, wherein "perivascular" corresponds to "pericoronary" according to [0027] of the Specification; col. 10 line 60 - col. 11 line 13, wherein perivascular adipose tissue (PVAT) fat attenuation index, perivascular water index, and volumetric perivascular characterization index all correspond to PCAT metric and are determined); and
at least one other patient-specific metric (col. 9 lines 57-63, wherein fibrous plaque index (FPi) is defined; col. 9 line 64 - col. 10 line 3, wherein calcium index (Calcium-i) is defined; col. 10 line 60 - col. 11 line 13 & col. 16 lines 26-29, wherein FPi and Calcium-i are determined and correspond to other patient-specific metric); and
using the EAT metric or the PCAT metric, and the at least one other patient-specific metric, to determine a risk score for the patient or to classify a disease state of the patient (Fig. 6; col. 11 line 64 - col. 12 line 7, wherein the OxScore (i.e. risk score) is calculated with EpAT volume (i.e. EAT metric), PVAT attenuation index (i.e. PCAT metric), fibrous plaque index, and calcium index (i.e. other patient-specific metric) as independent variables).
However, as indicated by the double strikethroughs above, Antoniades fails to teach a data storage device storing instructions for medical image processing; and a processor configured to execute the instructions to perform operations. Min 2023, on the other hand, teaches a system for processing medical images with a data storage device and a processor. More specifically and as it relates to the applicant’s claims, Min 2023 discloses a system for processing medical images of a patient (Fig. 1 element 105; [0120-0123] and [0124-0125]) comprising : a data storage device storing instructions for medical image processing (Fig. 4 elements 410; [0120] and [0144]); and a processor configured to execute the instructions to perform operations (Fig. 4 elements 404; [0120] and [0144]).
Min 2023 is combinable with Antoniades because they are from the same art of image processing.
The motivation for doing so would have been that doing so is combining prior art elements (the method of Antoniades and the system of Min 2023) according to known methods (it is well known in the art, as demonstrated by Min 2023, to use a data storage device and a processor for performing image processing) to yield predictable results (the result of performing the method of Antoniades with the aid of digital processing of Min 2023). Both the method of Antoniades and the system of Min 2023 operate in combination performing the same function as they did separately, the method of Antoniades continues to determine metrics to determine a risk score for the patient and the system of Min 2023 continues to perform image processing. Further, while Antoniades does not expressly disclose a data storage device or a processor, a person having ordinary skill in the art would understand based on the disclosure of Antoniades that such elements are in use for the described method.
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate a data storage device storing instructions for medical image processing and a processor configured to execute the instructions to perform operations, as taught by Min 2023, into the system disclosed by Antoniades.
Regarding claim 10, Antoniades and Min 2023 disclose the system of claim 9. Antoniades additionally discloses wherein the at least one other patient-specific metric includes a vessel geometry, a vessel morphology, a plaque characteristic , or a hemodynamic measurement (Antoniades, col. 10 line 60 - col. 11 line 13; col. 16 lines 26-29, wherein the FPi (i.e. other patient-specific metric) is defined by dividing the total volume of fibrous plaque (i.e. plaque characteristic) by the volume of the respective vessel segment (i.e. vessel geometry).
Regarding claim 11, Antoniades and Min 2023 disclose the system of claim 9. Antoniades additionally discloses wherein the received image data is used to generate a three-dimensional model of a vasculature of the patient (Antoniades, Fig. 1A; col. 4 lines 55-65, wherein the coronary CT angiography images were reconstructed in 3 dimensions (i.e. three-dimensional model of a vasculature)).
Regarding claim 12, Antoniades and Min 2023 disclose the system of claim 11.
Although Antoniades discloses generating a three-dimensional model (Fig. 1A; col. 4 lines 55-65), Antoniades fails to teach wherein the system is further configured to generate a display image of the three-dimensional model, and wherein the display image includes a color-coded indicator of the risk score or the disease state. Min 2023, on the other hand, teaches generating a 3D representation of the coronary arteries for display and color-coding regions based on its risk level. More specifically and as it relates to the applicant’s claims, Min 2023 discloses wherein the system is further configured to generate a display image of the three-dimensional model, and wherein the display image includes a color-coded indicator of the risk score or the disease state (Fig. 5A & [0159], wherein a 3D representation of the coronary arteries are generated; Fig. 13A & [0611] and [0622], wherein the regions of plaque are color-coded (i.e. color-coded indicator) based on the level of risk (i.e. risk score)).
Min 2023 is combinable with Antoniades because they are from the same art of image processing.
The suggestion/motivation for doing so would have been to improve visibility of the patient’s arteries depending on their areas of different levels of risk for treatment/outcome (Min 2023, [0611]).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate wherein the system is further configured to generate a display image of the three-dimensional model, and wherein the display image includes a color-coded indicator of the risk score or the disease state, as taught by Min 2023, into the system, as taught by Antoniades and Min 2023, to obtain the invention as specified in claim 12.
Regarding claim 13, Antoniades and Min 2023 disclose the system of claim 9. Claim 9 only requires either to “determine a risk score for the patient or to classify a disease state of the patient” in lines 11-12 (emphasis added). Since Antoniades teaches determining “a risk score for the patient” (Antoniades, Fig. 6; col. 11 line 64 - col. 12 line 7, wherein the OxScore (i.e. risk score) is calculated with EpAT volume (i.e. EAT metric), PVAT attenuation index (PCAT metric), fibrous plaque index, and calcium index (i.e. other patient-specific metric) as independent variables), then any limitation with respect to the disease state in claim 13 is also met, because claim 9 requires only the option of “determine a risk score for the patient” or the other option of “classify a disease state of the patient”.
Regarding claim 14, Antoniades and Min 2023 disclose the system of claim 9.
However, Antoniades fails to teach wherein the risk score is predictive of a fractional flow reserve (FFR) value. Min 2023, on the other hand, teaches FFR being determined by the risk assessment. More specifically and as it relates to the applicant’s claims, Min 2023 discloses wherein the risk score is predictive of a fractional flow reserve (FFR) value (Fig. 13C blocks 1320 and 1322; [0637-0638], wherein FFR is defined as a flow characteristic within the vessel and is determined by the risk assessment (i.e. risk score)).
Min 2023 is combinable with Antoniades because they are from the same art of image processing.
The suggestion/motivation for doing so would have been to non-invasively determine the FFR (Min 2023, [0791-0792]).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate wherein the risk score is predictive of a fractional flow reserve (FFR) value, as taught by Min 2023, into the system, as taught by Antoniades and Min 2023, to obtain the invention as specified in claim 14.
Regarding claim 15, Antoniades and Min 2023 disclose the system of claim 9. Antoniades additionally discloses wherein both the EAT metric and the PCAT metric are used to determine the risk score for the patient (Antoniades, Fig. 6; col. 11 line 64 - col. 12 line 7, wherein the OxScore (i.e. risk score) is calculated using both the EpAT volume (i.e. EAT metric), PVAT attenuation index (PCAT metric) as independent variables) or to classify the disease state of the patient.
Regarding claim 17, Antoniades discloses (col. 9 lines 22-25), the method comprising:
receiving image data for a patient (col. 8 lines 9-17; col. 11 lines 24-27, wherein CT scans (i.e. medical images) along arteries of the patient are obtained);
based on the received image data, determining:
a patient-specific epicardial adipose tissue (EAT) metric (col. 10 lines 4-14 & col. 10 line 60 - col. 11 line 13, wherein the epicardial adipose tissue (EpAT) volume and EpAT fat attenuation index both correspond to an EAT metric and are determined) or a patient- specific pericoronary adipose tissue (PCAT) metric (col. 8 lines 18-23, wherein "perivascular" corresponds to "pericoronary" according to [0027] of the Specification; col. 10 line 60 - col. 11 line 13, wherein perivascular adipose tissue (PVAT) fat attenuation index, perivascular water index, and volumetric perivascular characterization index all correspond to PCAT metric and are determined); and
at least one other patient-specific metric (col. 9 lines 57-63, wherein fibrous plaque index (FPi) is defined; col. 9 line 64 - col. 10 line 3, wherein calcium index (Calcium-i) is defined; col. 10 line 60 - col. 11 line 13 & col. 16 lines 26-29, wherein FPi and Calcium-i are determined and correspond to other patient-specific metric); and
using the EAT metric or the PCAT metric, and the at least one other patient-specific metric, to determine a risk score for the patient or to classify a disease state of the patient (Fig. 6; col. 11 line 64 - col. 12 line 7, wherein the OxScore (i.e. risk score) is calculated with EpAT volume (i.e. EAT metric), PVAT attenuation index (i.e. PCAT metric), fibrous plaque index, and calcium index (i.e. other patient-specific metric) as independent variables).
However, as indicated by the double strikethroughs above, Antoniades fails to teach a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform a computer-implemented method for medical image processing. Min 2023, on the other hand, teaches non-transitory computer-readable medium and a processor. More specifically and as it relates to the applicant’s claims, Min 2023 discloses a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform a computer-implemented method for medical image processing (Fig. 4 elements 410 and 404; [0120] and [0144]).
Min 2023 is combinable with Antoniades because they are from the same art of image processing.
The motivation for doing so would have been that doing so is combining prior art elements (the method of Antoniades and the system of Min 2023) according to known methods (it is well known in the art, as demonstrated by Min 2023, to use a non-transitory computer-readable medium and a processor for performing image processing) to yield predictable results (the result of performing the method of Antoniades with the aid of digital processing of Min 2023). Both the method of Antoniades and the system of Min 2023 operate in combination performing the same function as they did separately, the method of Antoniades continues to determine metrics to determine a risk score for the patient and the system of Min 2023 continues to perform image processing. Further, while Antoniades does not expressly disclose a non-transitory computer-readable medium or a processor, a person having ordinary skill in the art would understand based on the disclosure of Antoniades that such elements are in use for the described method.
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate a non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform a computer-implemented method for medical image processing, as taught by Min 2023, into the method disclosed by Antoniades.
Regarding claim 18, Antoniades and Min 2023 disclose the non-transitory computer-readable medium of claim 17. Antoniades additionally discloses wherein the at least one other patient-specific metric includes a vessel geometry, a vessel morphology, a plaque characteristic , or a hemodynamic measurement (Antoniades, col. 10 line 60 - col. 11 line 13; col. 16 lines 26-29, wherein the FPi (i.e. other patient-specific metric) is defined by dividing the total volume of fibrous plaque (i.e. plaque characteristic) by the volume of the respective vessel segment (i.e. vessel geometry).
Regarding claim 19, Antoniades and Min 2023 disclose the non-transitory computer-readable medium of claim 17. Antoniades additionally discloses wherein the received image data is used to generate a three-dimensional model of a vasculature of the patient (Antoniades, Fig. 1A; col. 4 lines 55-65, wherein the coronary CT angiography images were reconstructed in 3 dimensions (i.e. three-dimensional model of a vasculature)).
Regarding claim 20, Antoniades and Min 2023 disclose the non-transitory computer-readable medium of claim 19.
Although Antoniades discloses generating a three-dimensional model (Fig. 1A; col. 4 lines 55-65), Antoniades fails to teach wherein the computer-implemented further involves generating a display image of the three-dimensional model, and wherein the display image includes a color-coded indicator of the risk score or the disease state. Min 2023, on the other hand, teaches generating a 3D representation of the coronary arteries for display and color-coding regions based on its risk level. More specifically and as it relates to the applicant’s claims, Min 2023 discloses wherein the system is further configured to generate a display image of the three-dimensional model, and wherein the display image includes a color-coded indicator of the risk score or the disease state (Fig. 5A & [0159], wherein a 3D representation of the coronary arteries are generated; Fig. 13A & [0611] and [0622], wherein the regions of plaque are color-coded (i.e. color-coded indicator) based on the level of risk (i.e. risk score)).
Min 2023 is combinable with Antoniades because they are from the same art of image processing.
The suggestion/motivation for doing so would have been to improve visibility of the patient’s arteries depending on their areas of different levels of risk for treatment/outcome (Min 2023, [0611]).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate wherein the computer-implemented further involves generating a display image of the three-dimensional model, and wherein the display image includes a color-coded indicator of the risk score or the disease state, as taught by Min 2023, into the non-transitory computer readable medium, as taught by Antoniades and Min 2023, to obtain the invention as specified in claim 20.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over and further in view of Antoniades et al. (U.S. Patent No. US 11393137 B2) (“Antoniades”) in view of Min et al. (U.S. Publication No. US 2023/0289963 A1) ("Min 2023") and further in view of Min et al. (U.S. Publication No. US 2022/0392065 A1) (“Min 2022”).
Regarding claim 16, Antoniades and Min 2023 disclose the system of claim 9
Antoniades discloses wherein the image data are a first image data (col. 8 lines 9-17; col. 11 lines 24-27, wherein CT scans correspond to the first image data), the EAT metric is a first EAT metric (col. 10 lines 4-14 & col. 10 line 60 - col. 11 line 13, wherein the EpAt volume and EpAT fat attenuation correspond to the first EAT metric), the PCAT metric is a first PCAT metric (col. 8 lines 18-23, wherein “perivascular” corresponds to “pericoronary” according to [0027] of the Specification; col. 10 line 60 - col. 11 line 13, wherein perivascular adipose tissue (PVAT) fat attenuation index, perivascular water index, and volumetric perivascular characterization index all correspond to first PCAT metric ), and the at least one other patient-specific metric is at least a first other patient-specific metric (col. 10 line 60 - col. 11 line 13 & col. 16 lines 26-29, wherein FPi and Calcium-i correspond to first other patient-specific metric),
receiving (col. 8 lines 9-17; col. 11 lines 24-27, wherein CT scans (i.e. image data) along arteries of the patient are obtained);
based on the received
a (col. 10 lines 4-14 & col. 10 line 60 - col. 11 line 13, wherein the epicardial adipose tissue (EpAT) volume and EpAT fat attenuation index both correspond to an EAT metric and are determined), or
a (col. 8 lines 18-23, wherein "perivascular" corresponds to "pericoronary" according to [0027] of the Specification; col. 10 line 60 - col. 11 line 13, wherein perivascular adipose tissue (PVAT) fat attenuation index, perivascular water index, and volumetric perivascular characterization index all correspond to PCAT metric and are determined); and
at least a (col. 9 lines 57-63, wherein fibrous plaque index (FPi) is defined; col. 9 line 64 - col. 10 line 3, wherein calcium index (Calcium-i) is defined; col. 10 line 60 - col. 11 line 13 & col. 16 lines 26-29, wherein FPi and Calcium-i are determined and correspond to other patient-specific metric); and
using the (Fig. 6; col. 11 line 64 - col. 12 line 7, wherein the OxScore (i.e. risk score) is calculated with EpAT volume (i.e. EAT metric), PVAT attenuation index (i.e. PCAT metric), fibrous plaque index, and calcium index (i.e. other patient-specific metric) as independent variables).
However, as indicated by the double strikethroughs above, Antoniades fails to teach wherein, before determining the risk score or classifying the disease state of the patient, the processor is further configured to execute the instructions to perform operations comprising:
receiving second image data for the patient;
based on the received second image data, determining:
a second patient-specific EAT metric, or
a second patient-specific PCAT metric; and
at least a second other patient-specific metric; and
using the second EAT metric or the second PCAT metric, and the second other patient-specific metric to determine the risk score for the patient or to classify the disease state of the patient, specifically (emphasis added).
Min 2022, on the other hand, teaches analyzing medical images of the arteries taken at a second point in time to identify and determine the plaque parameters, vascular parameters, atherosclerosis parameters, and perilesional tissue parameters from the medical image, similarly to the images taken at a first point in time, and determining a risk factor based on the set of images taken at the second point in time. More specifically and as it relates to the applicant’s claims, Antoniades and Min 2023 in view of Min 2022 discloses wherein, before determining the risk score or classifying the disease state of the patient, the processor is further configured to execute the instructions to perform operations comprising:
receiving second image data for the patient (Min 2022, [1322-1323], wherein medical images including arteries are received from a second point in time (i.e. second image data));
based on the received second image data, determining:
a second patient-specific EAT metric (Min 2022, [1322-1323], wherein vascular parameters, atherosclerosis parameters, and perilesional tissue parameters, which correspond to second EAT metric when combined with Antoniades’ teaching of EAT metrics (Antoniades, col. 10 lines 4-14 & col. 10 line 60 - col. 11 line 13), are determined), or
a second patient-specific PCAT metric (Min 2022, [1322-1323], wherein vascular parameters, atherosclerosis parameters, and perilesional tissue parameters, which correspond to second PCAT metric when combined with Antoniades’ teaching of PCAT metrics (Antoniades, col. 10 line 60 - col. 11 line 13), are determined); and
at least a second other patient-specific metric (Min 2022, [1322-1323], wherein plaque parameters, which correspond to second other patient-specific metric when combined with Antoniades’ teaching of fibrous plaque index (Antoniades, col. 10 line 60 - col. 11 line 13 & col. 16 lines 26-29), are determined); and
using the second EAT metric or the second PCAT metric, and the second other patient-specific metric to determine the risk score for the patient or to classify the disease state of the patient (Min 2022, [1325] and [1438-1439], wherein the risk factor (i.e. risk score) using the parameters (i.e. second EAT metric or the second PCAT metric, and the second other patient-specific metric) determined from the patient’s medical images at a second point in time is determined).
Min 2022 is combinable with Antoniades and Min 2023 because they are from the same art of image processing.
The suggestion/motivation for doing so would have been to monitor the progression, stabilization, or regression of the disease and more accurately reflect the risk/state of the patient at the second point in time (Min 2022, [1325] and [1438-1439]).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate wherein, before determining the risk score or classifying the disease state of the patient, the processor is further configured to execute the instructions to perform operations comprising: receiving second image data for the patient; based on the received second image data, determining: a second patient-specific EAT metric or a second patient-specific PCAT metric; and at least a second other patient-specific metric; and using the second EAT metric or the second PCAT metric, and the second other patient-specific metric to determine the risk score for the patient or to classify the disease state of the patient, as taught by Min 2022, into the system, as taught by Antoniades and Min 2023, to obtain the invention as specified in claim 16.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Wilson et al. (U.S. Publication No. 2024/0169525 A1) teaches automatically segmenting PCAT corresponding to a patient from coronary computed tomography angiography images to determine risk factors for major adverse cardiovascular events by applying the segmented PCAT to a regression model ([0021-0023] and [0026]).
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RACHEL Y DANG whose telephone number is (571)438-9519. The examiner can normally be reached Monday - Thursday: 7am - 4:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Villecco can be reached at (571) 272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RACHEL Y DANG/Examiner, Art Unit 2661
/JOHN VILLECCO/Supervisory Patent Examiner, Art Unit 2661
