DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) were submitted on 1/10/2025, 1/29/2026, and 4/20/2026. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character 2030 in Fig. 2 has been used to designate both “lesion classifications” and “one or more sensors”. Similarly, reference character 7030 in Fig. 7 has been used to designate both “lesion classifications” and “one or more sensors”. 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. 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.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: 130 in ¶0066 and 22000 in ¶0246. 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. 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.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 2030 in Fig. 2 when referring to “one or more sensors”, 7030 in Fig. 7 when referring to “one or more sensors”, 14004 in Fig. 4, and 22070 in Fig. 22.
Regarding 2030 and 7030, the Examiner notes that while the specification makes reference to “lesion classifications (2030/7030)”, no reference appears to be made to “one or more sensors (2030/7030)” in the corresponding sections. Therefore, any update to the numbering should be accompanied by appropriately updated text in the specification.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) 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. 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.
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 8, 9, and 20 are 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.
Regarding claim 8, the claim recites “a selected at least one treatment technique” and “a selected at least one medical instrument” [Emphasis added]. The term selected is indefinite as it is unclear how the selection is made. For example, does selected refer to being selected by the machine learning algorithm, by the user (separately from the treatment strategy), or corresponding to the treatment strategy selected in claim 6? For examination purposes, the Examiner will interpret selected as corresponding to the selected treatment strategy.
Regarding claims 9 and 20, the claims also recite “a selected at least one treatment technique” and “a selected at least one medical instrument” [Emphasis added] as recited in claim 8. Therefore, they are rejected with the same reasoning as for claim 8 above. For examination purposes, the Examiner will interpret selected as corresponding to the selected treatment strategy.
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 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 12, and 15, with claim 1 being exemplary, recite:
“(a) memory configured to store at least one computer vision model and at least one machine learning model; and (b) processing circuitry communicatively coupled to the memory, the processing circuitry being configured to: (c) receive diagnostic imaging data of at least a portion of a vasculature of a patient generated during a cardiac diagnostic procedure; (d) execute the at least one computer vision model to determine characteristics of a lesion in the vasculature based on the received diagnostic imaging data; and (e) execute the at least one machine learning model to determine at least one treatment strategy based on the determined characteristic of the lesion, the at least one treatment strategy comprising at least one treatment technique and at least one medical instrument” [Emphasis added].
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that the independent claim 1 is directed to an abstract idea as shown below:
STEP 1: Do the claims fall within one of the statutory categories? YES. Independent claims 1, 12, and 15 are directed to a system, method, and non-transitory computer readable medium, respectively.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? YES. Independent claims 1, 12, and 15 are directed towards a mental process (i.e. an abstract idea).
Regarding claim 1, 12, and 15, limitations (d) and (e), in an emphasized claim 1 above, recite “determine characteristics of a lesion in the vasculature based on the received diagnostic imaging data” and “determine at least one treatment strategy based on the determined characteristic of the lesion, the at least one treatment strategy comprising at least one treatment technique and at least one medical instrument”. These are decisions that a doctor or medical practitioner would typically perform when examining a patient for cardiovascular issues. Therefore, these limitations can be performed by the human mind and amount to mental processes.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? NO. Independent claims 1, 2, and 15 do not recite additional elements that integrate the judicial exception into a practical application.
Regarding claims 1, 12, and 15, limitation (c), in an emphasized claim 1 above, recites receiving medical images, which falls under insignificant extra-solution activity since it is merely data gathering (see MPEP §2106.05(g)). Limitations (a), (b), (d), and (e) recites a memory, processing circuitry, a computer vision model, and a machine learning model, which amounts to no more than a recitation of the words "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP §2106.05(f)). Since the claim merely recites the application of a machine learning technique, the combination of elements does not identify technological improvements to the functioning of a machine learning model.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO. Independent claims 1, 2, and 15 do not recite additional elements that amount to significantly more than the judicial exception.
Regarding claim 1, 12, and 15, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and in combination, the above recited additional elements from claims 1, 12, and 15 do not add significantly more (also known as an “inventive concept”) to the exception. Rather, the additional elements disclosed above perform well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP §2106.05(d).
Therefore, independent claims 1, 12, and 15 are directed towards an abstract idea without a practical application or significantly more.
Regarding claim 2 and 13, with claim 2 being exemplary, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the processing circuitry is further configured to output the determined at least one treatment strategy for display falls under data output (see MPEP §2106.05(g)).
Regarding claim 3 and 14, with claim 3 being exemplary, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the at least one determined treatment strategy further comprises an indication of a predicted degree of success of a use of the at least one treatment technique and the at least one medical instrument falls under data output (see MPEP §2106.05(g)).
Regarding claim 4 and 16, with claim 4 being exemplary, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the processing circuitry is further configured to, in response to user input, execute a first simulation of a first medical procedure using the at least one treatment technique and the at least one medical instrument falls under a mental process as a human could simulate a procedure by planning out each step (see MPEP §2106.04(a)(2)(III)).
Regarding claim 5 and 17, with claim 5 being exemplary, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the first simulation is based, at least in part, on the received diagnostic imaging data falls under data input (see MPEP §2106.05(g)).
Regarding claim 6 and 18, with claim 6 being exemplary, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The claim recites: (a) receive user input of a selected at least one treatment strategy; (b) receive user input amending the selected at least one treatment strategy; and (c) amend the selected at least one treatment strategy based on the user input amending the selected at least one treatment strategy to generate at least one amended treatment strategy [Emphasis added]. Limitations (a) and (b) fall under data input (see MPEP §2106.05(g)). Limitation (c) falls under a mental process as adjusting a treatment strategy can be done by the human mind (see MPEP §2106.04(a)(2)(III)).
Regarding claim 7 and 19, with claim 7 being exemplary, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the processing circuitry is further configured to execute a second simulation of a second medical procedure using the at least one amended treatment strategy falls under a mental process as a human could simulate a procedure by planning out each step (see MPEP §2106.04(a)(2)(III)).
Regarding claim 8 and 20, with claim 8 being exemplary, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the at least one amended treatment strategy comprises at least one of a selected at least one treatment technique or a selected at least one medical instrument falls under selecting a data source/type (see MPEP §2106.05(g)).
Regarding claim 9, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the at least one amended treatment strategy does not comprise at least one of a selected at least one treatment technique or a selected at least one medical instrument falls under selecting a data source/type (see MPEP §2106.05(g)).
Regarding claim 10, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the at least one machine learning model is trained on data collected from past medical procedures comprising at least one of past imaging data, past tracked motion of medical instruments, past controller data, or past lesion classification falls under selecting a data source/type (see MPEP §2106.05(g)).
Regarding claim 11, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the computer vision model is trained on a plurality of lesions in past imaging data falls under selecting a data source/type (see MPEP §2106.05(g)).
Claim Rejections - 35 USC § 102
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 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.
Claims 1-3, 6, 8, 11-15, 18, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Passerini et al. (US 2021/0085397) (IDS) (hereafter, “Passerini”).
Regarding claim 1, Passerini discloses a medical system comprising: memory (Fig. 13; ¶0098, memory 1310) configured to store at least one computer vision model (¶0056, features extracted from medical image data; ¶0059, vFFR is predicted at all measurement locations in the stenosis regions using a second trained regression model. The second trained regression model is a surrogate model that estimates vFFR values at each location. Examiner considers the feature extraction along with the surrogate model as the “computer vision model”) and at least one machine learning model (Fig. 8; ¶0076 a machine learning model); and processing circuitry communicatively coupled to the memory (¶0098, controlled by the processor 1304 executing the computer program instructions), the processing circuitry being configured to: receive diagnostic imaging data of at least a portion of a vasculature of a patient generated during a cardiac diagnostic procedure (¶0050, Coronary CTA images ensure that the coronary vasculature is adequately imaged); execute the at least one computer vision model to determine characteristics of a lesion in the vasculature based on the received diagnostic imaging data (¶0003, CAD is typically caused by lesions, such as local narrowing, or stenosis; ¶0043, Another embodiment described herein utilizes a convolutional neural network (CNN) (FIG. 6) to compute the virtual FFR; ¶0044, machine-learning powered methods for the assessment of plaque vulnerability based on imaging data; ¶0046, Detection of lesions can be performed using a regressor trained with a recurrent neural network to estimate the stenosis severity based on the vessel radius Examiner considers vFFR the “characteristic”); and execute the at least one machine learning model to determine at least one treatment strategy based on the determined characteristic of the lesion (¶0073, predicted post-treatment vFFR pullback curves and/or predicted post-treatment lesion scores for each of the treatment option candidates can be compared to each other and to the estimated pre-treatment pullback curve and lesion scores to evaluate the effects of the different treatment option candidate and select an optimal treatment option candidate for the patient; ¶0076 a machine learning model to predict patient-specific geometric features of the post-PCI coronary arterial tree based on features extracted from the pre-PCI patient-specific coronary arterial tree anatomy), the at least one treatment strategy comprising at least one treatment technique and at least one medical instrument (¶0003, Percutaneous Coronary Intervention (PCI), which is a procedure that uses a catheter to place a metal or polymer stent in the coronary artery to open up the lumen; ¶0073, the treatment option candidates can be different PCI treatment options in which stents are placed at one or more locations in the coronary artery tree. Examiner considers the catheter a “medical instrument” and opening up the lumen a “treatment technique”).
Regarding claim 2, in which claim 1 is incorporated, Passerini discloses wherein the processing circuitry is further configured to output the determined at least one treatment strategy for display (¶0089, after generating the post-PCI scenarios and the corresponding scores, the treatment scenarios and scores can be displayed on a display device).
Regarding claim 3, in which claim 1 is incorporated, Passerini discloses wherein the at least one determined treatment strategy further comprises an indication of a predicted degree of success of a use of the at least one treatment technique and the at least one medical instrument (¶0084-0085, a plaque vulnerability index is computed for each post-PCI scenario … spot treatment A would resolve the flow limitation with the vFFR 1014 not becoming critical over the entire length of the coronary artery. In addition, spot treatment A would cause the plaque vulnerability index 1012 in the first stenosis to decrease as compared to the pre-PCI plaque vulnerability index 1002. However, due to increased blood flow in the vessel after PCI, spot treatment A would cause the plaque vulnerability index 1012 to increase for the second stenosis. This effect would not be mitigated by spot treatment B, which would only further reduce the plaque vulnerability index 1022 for the first stenosis. The complete coverage approach in image 1030 resolves the flow limitation, as shown by the predicted vFFR pullback curve 1034 and results in overall greater plaque stability (reduced plaque vulnerability), as shown by the predicted plaque vulnerability index 1032. Examiner considers the description and vulnerability index provided in ¶0085 an indication of success).
Regarding claim 6, in which claim 1 is incorporated, Passerini discloses wherein the processing circuitry is further configured to: receive user input of a selected at least one treatment strategy (¶0090, The interactive user interface allows the user to select different possible treatment options); receive user input amending the selected at least one treatment strategy (¶0090, The user interface 1110 includes controls 1112 that allow the user to select which stenosis segments should be considered for PCI. Examiner considers selecting the target sites as amending the treatment strategy); and amend the selected at least one treatment strategy based on the user input amending the selected at least one treatment strategy to generate at least one amended treatment strategy (¶0090, When the user inputs a selection of a which stenosis segments should be stented for a particular PCI treatment scenario, that PCI scenario can be automatically generated as scored (by predicting the post-PCI vFFR and/or the post-PCI plaque vulnerability index). The predicted vFFR pullback curve along the centerline can be displayed and/or selectable for different scenarios. The stenosis marker from that curve can be overlaid together with the centerline on the images. Examiner considers the updated predictions and graphics as generating an amended treatment strategy).
Regarding claim 8, in which claim 6 is incorporated, Passerini discloses wherein the at least one amended treatment strategy comprises at least one of a selected at least one treatment technique (¶0090, The user interface 1110 includes controls 1112 that allow the user to select which stenosis segments should be considered for PCI. Since the change in treatment strategy only involves changing the location of the procedure, it is still placing stents, which the Examiner considers the same technique. Since the limitation is recited in the alternative, Examiner considers this citation to fully disclose the limitation) or a selected at least one medical instrument.
Regarding claim 11, in which claim 1 is incorporated, Passerini discloses wherein the computer vision model is trained on a plurality of lesions in past imaging data (¶0044, machine-learning powered methods for the assessment of plaque vulnerability based on imaging data; ¶0046, Detection of lesions can be performed using a regressor trained with a recurrent neural network to estimate the stenosis severity based on the vessel radius … lesion and scoring in a medical image. The RNN is trained to classify medical images, so its training data must be “past” medical images).
Regarding claim 12, Passerini discloses a method comprising: receiving, by processing circuitry (¶0098, controlled by the processor 1304 executing the computer program instructions), diagnostic imaging data of at least a portion of a vasculature of a patient generated during a cardiac diagnostic procedure (¶0050, Coronary CTA images ensure that the coronary vasculature is adequately imaged); executing, by the processing circuitry, at least one computer vision model to determine characteristics of a lesion in the vasculature based on the received diagnostic imaging data (¶0003, CAD is typically caused by lesions, such as local narrowing, or stenosis; ¶0043, Another embodiment described herein utilizes a convolutional neural network (CNN) (FIG. 6) to compute the virtual FFR; ¶0044, machine-learning powered methods for the assessment of plaque vulnerability based on imaging data; ¶0046, Detection of lesions can be performed using a regressor trained with a recurrent neural network to estimate the stenosis severity based on the vessel radius Examiner considers vFFR the “characteristic”); and executing, by the processing circuitry, at least one machine learning model to determine at least one treatment strategy based on the determined characteristic of the lesion (¶0073, predicted post-treatment vFFR pullback curves and/or predicted post-treatment lesion scores for each of the treatment option candidates can be compared to each other and to the estimated pre-treatment pullback curve and lesion scores to evaluate the effects of the different treatment option candidate and select an optimal treatment option candidate for the patient; ¶0076 a machine learning model to predict patient-specific geometric features of the post-PCI coronary arterial tree based on features extracted from the pre-PCI patient-specific coronary arterial tree anatomy), the at least one treatment strategy comprising at least one treatment technique and at least one medical instrument (¶0003, Percutaneous Coronary Intervention (PCI), which is a procedure that uses a catheter to place a metal or polymer stent in the coronary artery to open up the lumen; ¶0073, the treatment option candidates can be different PCI treatment options in which stents are placed at one or more locations in the coronary artery tree. Examiner considers the catheter a “medical instrument” and opening up the lumen a “treatment technique”).
Regarding claim 13, in which claim 12 is incorporated, Passerini discloses further comprising outputting, by the processing circuitry, the determined at least one treatment strategy for display (¶0089, after generating the post-PCI scenarios and the corresponding scores, the treatment scenarios and scores can be displayed on a display device).
Regarding claim 14, in which claim 12 is incorporated, Passerini discloses wherein the at least one determined treatment strategy further comprises an indication of a predicted degree of success of a use of the at least one treatment technique and the at least one medical instrument (¶0084-0085, a plaque vulnerability index is computed for each post-PCI scenario … spot treatment A would resolve the flow limitation with the vFFR 1014 not becoming critical over the entire length of the coronary artery. In addition, spot treatment A would cause the plaque vulnerability index 1012 in the first stenosis to decrease as compared to the pre-PCI plaque vulnerability index 1002. However, due to increased blood flow in the vessel after PCI, spot treatment A would cause the plaque vulnerability index 1012 to increase for the second stenosis. This effect would not be mitigated by spot treatment B, which would only further reduce the plaque vulnerability index 1022 for the first stenosis. The complete coverage approach in image 1030 resolves the flow limitation, as shown by the predicted vFFR pullback curve 1034 and results in overall greater plaque stability (reduced plaque vulnerability), as shown by the predicted plaque vulnerability index 1032. Examiner considers the description and vulnerability index provided in ¶0085 an indication of success).
Regarding claim 15, Passerini discloses a non-transitory computer-readable storage medium (¶0025, a non-transitory computer readable medium) storing instructions, which, when executed, cause processing circuitry (¶0098, controlled by the processor 1304 executing the computer program instructions) to: receive diagnostic imaging data of at least a portion of a vasculature of a patient generated during a cardiac diagnostic procedure (¶0050, Coronary CTA images ensure that the coronary vasculature is adequately imaged); execute at least one computer vision model to determine characteristics of a lesion in the vasculature based on the received diagnostic imaging data (¶0003, CAD is typically caused by lesions, such as local narrowing, or stenosis; ¶0043, Another embodiment described herein utilizes a convolutional neural network (CNN) (FIG. 6) to compute the virtual FFR; ¶0044, machine-learning powered methods for the assessment of plaque vulnerability based on imaging data; ¶0046, Detection of lesions can be performed using a regressor trained with a recurrent neural network to estimate the stenosis severity based on the vessel radius Examiner considers vFFR the “characteristic”); and execute at least one machine learning model to determine at least one treatment strategy based on the determined characteristic of the lesion (¶0073, predicted post-treatment vFFR pullback curves and/or predicted post-treatment lesion scores for each of the treatment option candidates can be compared to each other and to the estimated pre-treatment pullback curve and lesion scores to evaluate the effects of the different treatment option candidate and select an optimal treatment option candidate for the patient; ¶0076 a machine learning model to predict patient-specific geometric features of the post-PCI coronary arterial tree based on features extracted from the pre-PCI patient-specific coronary arterial tree anatomy), the at least one treatment strategy comprising at least one treatment technique and at least one medical instrument (¶0003, Percutaneous Coronary Intervention (PCI), which is a procedure that uses a catheter to place a metal or polymer stent in the coronary artery to open up the lumen; ¶0073, the treatment option candidates can be different PCI treatment options in which stents are placed at one or more locations in the coronary artery tree. Examiner considers the catheter a “medical instrument” and opening up the lumen a “treatment technique”)
Regarding claim 18, in which claim 12 is incorporated, Passerini discloses receiving, by the processing circuitry (¶0098, controlled by the processor), user input of a selected at least one treatment strategy (¶0090, The interactive user interface allows the user to select different possible treatment options); receiving, by the processing circuitry, user input amending the selected at least one treatment strategy (¶0090, The user interface 1110 includes controls 1112 that allow the user to select which stenosis segments should be considered for PCI. Examiner considers selecting the target sites as amending the treatment strategy); and amending, by the processing circuitry, the selected at least one treatment strategy based on the user input amending the selected at least one treatment strategy to generate at least one amended treatment strategy (¶0090, When the user inputs a selection of a which stenosis segments should be stented for a particular PCI treatment scenario, that PCI scenario can be automatically generated as scored (by predicting the post-PCI vFFR and/or the post-PCI plaque vulnerability index). The predicted vFFR pullback curve along the centerline can be displayed and/or selectable for different scenarios. The stenosis marker from that curve can be overlaid together with the centerline on the images. Examiner considers the updated predictions and graphics as generating an amended treatment strategy).
Regarding claim 20, in which claim 18 is incorporated, Passerini discloses wherein the at least one amended treatment strategy comprises at least one of a selected at least one treatment technique (¶0090, The user interface 1110 includes controls 1112 that allow the user to select which stenosis segments should be considered for PCI. Since the change in treatment strategy only involves changing the location of the procedure, it is still placing stents, which the Examiner considers the same technique. Since the limitation is recited in the alternative, Examiner considers this citation to fully disclose the limitation) or a selected at least one medical instrument.
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 4, 5, 7, 9, 10, 16, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Passerini et al. (US 2021/0085397) (IDS) (hereafter, “Passerini”) in view of Rawlinson et al. (US 2020/0337773) (IDS) (hereafter, “Rawlinson”).
Regarding claim 4, in which claim 1 is incorporated, Passerini discloses wherein the processing circuitry is further configured to, in response to user input (¶0090, an interactive user interface may be presented to the user on a display device), [execute a first simulation] of a first medical procedure using the at least one treatment technique and the at least one medical instrument (¶0073, select an optimal treatment option candidate for the patient; ¶0003, Percutaneous Coronary Intervention (PCI), which is a procedure that uses a catheter to place a metal or polymer stent in the coronary artery to open up the lumen. Examiner considers the catheter a “medical instrument” and opening up the lumen a “treatment technique”).
However, Passerini fails to explicitly disclose execute a first simulation.
Rawlinson teaches execute a first simulation (¶0027, configured to simulate all possible treatment decisions in parallel).
Passerini and Rawlinson are analogous to the claimed invention because they are directed to machine learning based methods for providing treatment recommendations. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the simulations of Rawlinson into the recommendation/guidance system of Paserini. The suggestion/motivation for doing so would have been to improve patient outcomes, as suggested by Rawlinson at ¶0062, The system improves patient outcomes by reducing the number of sub-optimal treatment decisions.
This method of improving Passerini was within the ordinary ability of one of ordinary skill in the art based on the teachings of Rawlinson.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Passerini with the teachings of Rawlinson to obtain the invention as specified in claim 4.
Regarding claim 5, Passerini in view of Rawlinson discloses the medical system of claim 4.
However, Passerini fails to explicitly disclose wherein the first simulation is based, at least in part, on the received diagnostic imaging data.
Rawlinson teaches wherein the first simulation is based, at least in part, on the received diagnostic imaging data (¶0030, perform the predictive simulations … embodiments of the invention directly apply to coronary angiography. Examiner considers angiography as “diagnostic imaging data”).
Passerini and Rawlinson are analogous to the claimed invention because they are directed to machine learning based methods for providing treatment recommendations. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the simulations of Rawlinson into the recommendation/guidance system of Paserini. The suggestion/motivation for doing so would have been to improve patient outcomes, as suggested by Rawlinson at ¶0062, The system improves patient outcomes by reducing the number of sub-optimal treatment decisions.
This method of improving Passerini was within the ordinary ability of one of ordinary skill in the art based on the teachings of Rawlinson.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Passerini with the teachings of Rawlinson to obtain the invention as specified in claim 5.
Regarding claim 7, in which claim 6 is incorporated, Passerini discloses wherein the processing circuitry is further configured to [execute a second simulation] of a second medical procedure using the at least one amended treatment strategy (¶0090, The user interface 1110 includes controls 1112 that allow the user to select which stenosis segments should be considered for PCI. Examiner considers the result of selecting the target sites as the amended treatment strategy and the execution of the strategy as the second medical procedure).
However, Passerini fails to explicitly disclose execute a second simulation.
Rawlinson teaches execute a second simulation (¶0054, alternate treatment plans for stenosis simulated by simulation software. Since Rawlinson can simulate multiple treatment plans, Examiner considers the simulation of the amended treatment strategy of Passerini as the “second simulation”).
Passerini and Rawlinson are analogous to the claimed invention because they are directed to machine learning based methods for providing treatment recommendations. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the simulations of Rawlinson into the recommendation/guidance system of Paserini. The suggestion/motivation for doing so would have been to improve patient outcomes, as suggested by Rawlinson at ¶0062, The system improves patient outcomes by reducing the number of sub-optimal treatment decisions.
This method of improving Passerini was within the ordinary ability of one of ordinary skill in the art based on the teachings of Rawlinson.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Passerini with the teachings of Rawlinson to obtain the invention as specified in claim 7.
Regarding claim 9, Passerini discloses the medical system of claim 6.
However, Passerini fails to explicitly disclose wherein the at least one amended treatment strategy does not comprise at least one of a selected at least one treatment technique or a selected at least one medical instrument.
Rawlinson teaches wherein the at least one amended treatment strategy does not comprise at least one of a selected at least one treatment technique (Fig. 5B; the location of (corrective) stents within the simulated geometry are represented by circles 504 and the location of a coronary arterial bypass graft (CABG) 508 is represented by a dotted line in the example treatment plans. Examiner considers the bypass graft as a different technique to the stents. Passerini selects a stent based treatment option and generates the amended treatment strategy by selecting among a set of presented options. In view of Rawlinson, those options include stents and bypass grafts. Therefore, choosing the graft would yield an amended strategy not comprising the same technique. Since the limitation is recited in the alternative, Examiner considers this citation to fully disclose the limitation) or a selected at least one medical instrument.
Passerini and Rawlinson are analogous to the claimed invention because they are directed to machine learning based methods for providing treatment recommendations. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the simulations of Rawlinson into the recommendation/guidance system of Paserini. The suggestion/motivation for doing so would have been to improve patient outcomes, as suggested by Rawlinson at ¶0062, The system improves patient outcomes by reducing the number of sub-optimal treatment decisions.
This method of improving Passerini was within the ordinary ability of one of ordinary skill in the art based on the teachings of Rawlinson.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Passerini with the teachings of Rawlinson to obtain the invention as specified in claim 9.
Regarding claim 10, in which claim 1 is incorporated, Passerini discloses wherein the at least one machine learning model is trained (¶0076, machine-learning method … an offline training stage prior to receiving/acquiring new medical image data) [on data collected from past medical procedures comprising at least one of past imaging data, past tracked motion of medical instruments, past controller data, or past lesion classification].
However, Passerini fails to explicitly disclose on data collected from past medical procedures comprising at least one of past imaging data, past tracked motion of medical instruments, past controller data, or past lesion classification
Rawlinson teaches on data collected from past medical procedures comprising at least one of past imaging data (¶0041, during the coronary catheterization of the patient … trained on manually annotated data angiogram frames. Examiner considers the catheterization as “past medical procedure” and the angiogram as “past imagining data”. Since the limitation is recited in the alternative, Examiner considers this citation to fully disclose the limitation), past tracked motion of medical instruments, past controller data, or past lesion classification
Passerini and Rawlinson are analogous to the claimed invention because they are directed to machine learning based methods for providing treatment recommendations. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the simulations of Rawlinson into the recommendation/guidance system of Paserini. The suggestion/motivation for doing so would have been to improve patient outcomes, as suggested by Rawlinson at ¶0062, The system improves patient outcomes by reducing the number of sub-optimal treatment decisions.
This method of improving Passerini was within the ordinary ability of one of ordinary skill in the art based on the teachings of Rawlinson.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Passerini with the teachings of Rawlinson to obtain the invention as specified in claim 10.
Regarding claim 16, in which claim 12 is incorporated, Passerini discloses further comprising, in response to user input, (¶0090, an interactive user interface may be presented to the user on a display device), [executing a first simulation] of a first medical procedure using the at least one treatment strategy (¶0073, select an optimal treatment option candidate for the patient).
However, Passerini fails to explicitly disclose executing a first simulation.
Rawlinson teaches executing a first simulation (¶0027, configured to simulate all possible treatment decisions in parallel).
Passerini and Rawlinson are analogous to the claimed invention because they are directed to machine learning based methods for providing treatment recommendations. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the simulations of Rawlinson into the recommendation/guidance system of Paserini. The suggestion/motivation for doing so would have been to improve patient outcomes, as suggested by Rawlinson at ¶0062, The system improves patient outcomes by reducing the number of sub-optimal treatment decisions.
This method of improving Passerini was within the ordinary ability of one of ordinary skill in the art based on the teachings of Rawlinson.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Passerini with the teachings of Rawlinson to obtain the invention as specified in claim 16.
Regarding claim 17, Passerini in view of Rawlinson discloses the method of claim 16.
However, Passerini fails to explicitly disclose wherein the first simulation is based, at least in part, on the received diagnostic imaging data.
Rawlinson teaches wherein the first simulation is based, at least in part, on the received diagnostic imaging data (¶0030, perform the predictive simulations … embodiments of the invention directly apply to coronary angiography. Examiner considers angiography as “diagnostic imaging data”).
Passerini and Rawlinson are analogous to the claimed invention they because are directed to machine learning based methods for providing treatment recommendations. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the simulations of Rawlinson into the recommendation/guidance system of Paserini. The suggestion/motivation for doing so would have been to improve patient outcomes, as suggested by Rawlinson at ¶0062, The system improves patient outcomes by reducing the number of sub-optimal treatment decisions.
This method of improving Passerini was within the ordinary ability of one of ordinary skill in the art based on the teachings of Rawlinson.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Passerini with the teachings of Rawlinson to obtain the invention as specified in claim 17.
Regarding claim 19, in which claim 18 is incorporated, Passerini discloses further comprising executing, by the processing circuitry (¶0098, controlled by the processor), [a second simulation] of a second medical procedure using the at least one amended treatment strategy (¶0090, The user interface 1110 includes controls 1112 that allow the user to select which stenosis segments should be considered for PCI. Examiner considers the result of selecting the target sites as the amended treatment strategy and the execution of the strategy as the second medical procedure).
However, Passerini fails to explicitly disclose a second simulation.
Rawlinson teaches a second simulation (¶0054, alternate treatment plans for stenosis simulated by simulation software. Since Rawlinson can simulate multiple treatment plans, Examiner considers the simulation of the amended treatment strategy of Passerini as the “second simulation”).
Passerini and Rawlinson are analogous to the claimed invention they because are directed to machine learning based methods for providing treatment recommendations. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the simulations of Rawlinson into the recommendation/guidance system of Paserini. The suggestion/motivation for doing so would have been to improve patient outcomes, as suggested by Rawlinson at ¶0062, The system improves patient outcomes by reducing the number of sub-optimal treatment decisions.
This method of improving Passerini was within the ordinary ability of one of ordinary skill in the art based on the teachings of Rawlinson.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Passerini with the teachings of Rawlinson to obtain the invention as specified in claim 19.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yu et al. (US 2009/0234626) discloses a treatment recommendation system allowing user input in changing the treatment plan (¶0095, The user may select different treatment plans, types of treatment, and/or alter treatment parameters based on the probabilities).
Kunz et al. (US 2023/0131675) discloses a machine learning method for determining treatments (¶0037, using machine learning to determine a treatment or a treatment's effectiveness).
Rosenberg et al. (US 2023/0245748) discloses machine learning methods for adjusting treatment plans (¶0862, The input may be used to retrain the one or more machine learning models to determine subsequent treatment plans).
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/XIAOMAO DING/Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676
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