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 .
Claims 1-21 are pending. Claims 1-21 are rejected herein.
Priority
The application claims priority to provisional application 63/375800. The priority date is 15 September 2022.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Statutory Categories
Claims 1,12 and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a medical system, a method and a non-transitory computer-readable storage medium.
Step 2A Prong One: The Abstract Idea
The limitations of […] determining the plurality of treatment pathways; determining, for each respective treatment pathway of the plurality of treatment pathways, one or more respective predicted effectiveness indicators associated with the respective treatment pathway, one or more respective predicted risks of at least one adverse event associated with performing a medical procedure of the respective treatment pathway, and a respective confidence level associated with at least one of the respective predictions; and output for display that plurality of treatment pathways, the one or more respective predicted effectiveness indicators associated with the respective treatment pathway, and the respective confidence level associated with at least one of the respective predictions for each respective treatment pathway of the plurality of treatment pathways; [obtaining live angiogram imaging data of a coronary vasculature of a patient during a percutaneous coronary intervention (PCI) procedure;] determining a live reading based at least in part on the live angiogram imaging data, the live reading comprising one or more live predicted effectiveness indicators associated with the PCI procedure, one or more live risk of at least one adverse event associated with the PCI procedure, a live confidence level associated with at least one of the respective predictions for the PCI procedure, a live suggested device parameters for the PCI procedure; and output for display one of the plurality of treatment options and the live reading […], as drafted, is a process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for recitation of generic computer components. That is, other than reciting a medical system comprising: memory, processing circuitry communicatively coupled to the memory and a processor, nothing in the claim precludes the step from practically being performed in the mind. For example, this claim encompasses a person thinking about determining treatment pathways, determining effectiveness indicators associated with the respective treatment pathway, and determining predicted risks and a confidence levels associated with at least one of the respective predictions and outputting for display the plurality of treatment pathways in the manner described in the identified abstract idea, supra. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of a memory, processing circuitry and non-transitory computer-readable storage medium storing instructions and processor that implements the identified abstract idea. These are not described by the applicant and are recited at a high-level of generality such that they amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim further recites the additional element of obtaining live angiogram imaging data of a coronary vasculature of a patient during a percutaneous coronary intervention (PCI) procedure;…. This additional element was left in brackets as part of the abstract idea to preserve conceptual continuity. MPEP 2106.05(g) indicates that mere data gathering includes performing clinical tests on individuals to obtain input for an equation, see re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989). MPEP 2106.04(d)(I) indicates that extra-solution data gathering activity cannot provide a practical application. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application.
Step 2B: Significantly More
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a memory, processing circuitry and non-transitory computer-readable storage medium storing instructions and processor to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of obtaining live angiogram imaging data of a coronary vasculature of a patient during a percutaneous coronary intervention (PCI) procedure; was considered extra-solution activity. This has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field. MPEP 2016.05(d)(II) indicates determining the level of a biomarker in blood by any means (Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1362, 123 USPQ2d 1081, 1088 (Fed. Cir. 2017) has been held by the courts to be well-understood, routine, conventional activity. Obtaining the live angiogram imaging data represents determining the level of a biomarker in blood by any means (even if this is determining any expression of a biomarker in the blood via radio contrast agent). Well-understood, routine, conventional activity cannot provide an inventive concept (“significantly more”). As such the claim is not patent eligible.
Dependent Claims and Dependent Additional Elements
Claims 2-9,11-21 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 2 merely describe(s) determining a recommended treatment pathway of the plurality of treatment pathways; and output for display an indication of the recommended treatment pathway. Claim(s) 3 merely describe(s) determining, using a machine learning algorithm, an indicator, predicted risks, and confidence levels associated with one of the predictions. Claim 4 and 16 merely describes generates a 3D model of the patient’s vasculature use the model as input to a machine learning algorithm. Claim 5 and 17 merely describes determining the one or more respective predicted effectiveness indicators, risk, and confidence levels associated with at least one of the respective predictions and running a plurality of simulations. Claim 6 and 18 merely describes determining the one or more respective predicted effectiveness indicators is based on certain performance prediction. Claim 7 and 19 merely describes the predicted effectiveness indicators associated with the respective treatment pathway comprises at least one of a respective predicted fractional flow reserve (FFR) value, a respective predicted quality of life improvement, or at least one respective predicted readmission rate. Claim 8 merely describes each of the plurality of treatment pathways further comprises at least one of a respective availability or cost. Claim 9 merely describes determining a plurality of treatment options of the selected treatment pathway, each of the plurality of treatment options comprising one or more respective predicted effectiveness indicators associated with the respective treatment option, one or more respective predicted risk associated with the respective treatment option, a respective confidence level associated with at least one of the respective predictions for the respective treatment option, and suggested parameters for the respective treatment option; and output for display the plurality of treatment options of the selected treatment pathway, and the one or more respective predicted effectiveness indicators associated with the respective treatment option, the one or more respective predicted risk associated with the respective treatment option, the respective confidence level associated with at least one of the respective predictions for the respective treatment option, and the suggested parameters for the respective treatment option. Claim 11 merely describes determining at least one of a ghosted preview of a PCI procedure, a graphical predicted FFR, a graphical predicted risk of rupture, or a graphical predicted probability of a successful outcome; and output for display, during the PCI procedure, at least one of the ghosted preview of the procedure, the graphical predicted FFR the graphical predicted risk of rupture, or the graphical predicted probability of a successful outcome. Claim 13 merely describes determining a recommended treatment pathway of the plurality of treatment pathways; and outputting for display an indication of the recommended treatment pathway. Claim 14 merely describes wherein determining the one or more respective predicted effectiveness indicators associated with the respective treatment pathway, the one or more respective predicted risks associated with the respective treatment pathway, and the respective confidence level associated with at least one of the respective predictions comprises executing a machine learning algorithm. Claim 20 merely describes the plurality of treatment pathways further comprises at least one of a respective inventory availability or cost. Claim 21 merely describes obtaining first fluoroscopy with certain characteristics, generating a 3D model based on certain data; and determining the plurality of treatment pathways based on model, wherein the live angiogram imaging data comprises second fluoroscopy with contrast imaging data captured at a second frame rate, lower than the first frame rate.
The dependent claims recite processing circuitry, which is analyzed the same as the generic computer part(s) in the independent claims and does not provide a practical application or significantly more for the same reasons.
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.
Claim(s) 1,2,5,7-8,11-13,15,19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO 2014/049527 A2 (hereafter Dadlani Mahtani) in view of WO 2016/092420 A1 (hereafter Anderson) in view of Tzimas (Coronary CT angiography to guide percutaneous coronary intervention).
Regarding Claim 1
Dadlani Mahtani teaches:
A medical system comprising: a memory configured to store a plurality of treatment pathways; and processing circuitry communicatively coupled to the memory, the processing circuitry being configured to: determine the plurality of treatment pathways; [Dadlani Mahtani teaches at pg. 7 line 21-23 teaches the components of the IT infrastructure suitably include processors executing computer executable instructions embodying the foregoing functionality, where the computer executable instructions are stored on memories associated with the processors. Dadlani Mahtani teaches at pg. 7 line 12-17 that for example, the DSS acquires the patient’s medical records from the patient information system, clinical evidences on outcomes and prognosis for the appropriate population form the medical information system, the clinical models and algorithms, patient values, lifestyle regimes, and preference input by the patient, and displays the quantitative evaluation and comparison of the choices of treatment and pathways. Collectively, this teaches a medical system comprising: a memory configured to store a plurality of treatment pathways; and processing circuitry communicatively coupled to the memory, the processing circuitry being configured to: determine the plurality of treatment pathways.]
determine, for each respective treatment pathway of the plurality of treatment pathways, one or more respective predicted effectiveness indicators associated with the respective treatment pathway, one or more respective predicted risks of at least one adverse event associated with performing a medical procedure of the respective treatment pathway, and a respective confidence level associated with at least one of the respective predictions; [Dadlani Mahtani teaches at pg. 10 for example, imaging results of patient data at different points or stages of treatment will be uploaded to the system and used to make a comparison with the expected outcome (images or pictures stored in the knowledge based of medical evidence) and produce a treatment effectiveness or progress score. Dadlani Mahtani teaches at pg. 7 line 12-17 that for example, the DSS acquires the patient’s medical records from the patient information system, clinical evidences on outcomes and prognosis for the appropriate population form the medical information system, the clinical models and algorithms, patient values, lifestyle regimes, and preference input by the patient, and displays the quantitative evaluation and comparison of the choices of treatment and pathways. Dadlani Mahtani teaches at pg. 4 line 14-15 that the output of the system is a quantitative evaluation and comparison of the alternative choices and a simple straightforward treatment recommendation. Dadlani Mahtani teaches at pg. 4 line 15-18 if the patient requests, the system will provide additional outputs including traditional educational materials, information and access to a large patient community, probabilities of all the alternative options to be the best, confidence intervals of all the estimations, and the evidences the computation is based on. The probabilities of all the alternative options to be best and confidence intervals of all the estimations teaches the respective confidence level associated with at least one of the respective predictions for each respective treatment pathway of the plurality of treatment pathways. Dadlani Mahtani teaches at pg. 4 line 19-22 the decision support system also enable patients to compare alternative choices on the same measure, such as allowing the patients to adjust for lifestyle regime and preferences, outcome parameters, patient pathways, QALYs, desired probability of an overall outcome or of a specific outcome parameter and the like.]
output for display the plurality of treatment pathways, the one or more respective predicted effectiveness indicators associated with the respective treatment pathway, and the respective confidence level associated with at least one of the respective predictions for each respective treatment pathway of the plurality of treatment pathways; [Dadlani Mahtani teaches at pg. 4 line 19-22 the decision support system also enable patients to compare alternative choices on the same measure, such as allowing the patients to adjust for lifestyle regime and preferences, outcome parameters, patient pathways, QALYs, desired probability of an overall outcome or of a specific outcome parameter, and the like. Dadlani Mahtani teaches at pg. 4 line 15-18 if the patient requests, the system will provide additional outputs including traditional educational materials, information and access to a large patient community, probabilities of all the alternative options to be the best, confidence intervals of all the estimations, and the evidences the computation is based on. Dadlani Mahtani teaches at pg. 7 line 12-17 that for example, the DSS acquires the patient’s medical records from the patient information system, clinical evidences on outcomes and prognosis for the appropriate population form the medical information system, the clinical models and algorithms, patient values, lifestyle regimes, and preference input by the patient, and displays the quantitative evaluation and comparison of the choices of treatment and pathways. The probabilities of all the alternative options to be best and confidence intervals of all the estimations teaches the respective confidence level associated with at least one of the respective predictions for each respective treatment pathway of the plurality of treatment pathways. Regardless, Dadlani Mahtani teaches at pg. 13 line 18-21 teaches the user (e.g. patient or healthcare professional) gets to specify the acceptable confidence intervals or to set an acceptable level of Overlap’ for the outcomes of the individual treatment choices, and the DSS chooses which additional test would allow that. Dadlani Mahtani teaches at pg. 13 line 21-26 teaches basically this is the reverse of the approach described above: instead of “if” you do advanced diagnostic test A, then J is the outcome and you narrow the confidence interval by Y%’, it would allow user to set want to reduce overlap, what advanced diagnostic tests do I need to do?”, or “the maximum range of the confidence interval that I am willing to live with is +/-X%, what are options should I consider?”, “or the maximum acceptable overlap is Z%, what additional diagnostic tests should be done to get closest to achieve this?”. The teaching above: “the maximum range of the confidence interval that I am willing to live with is +/-X%” teaches the confidence level. Collectively, Dadlani Mahtani teaches and output for display the plurality of treatment pathways, the one or more respective predicted effectiveness indicators associated with the respective treatment pathway, and the respective confidence level associated with at least one of the respective predictions for each respective treatment pathway of the plurality of treatment pathways.]
Dadlani Mahtani may not explicitly teach:
obtain live angiogram imaging data of a coronary vasculature of a patient during a percutaneous coronary intervention (PCI) procedure;
determine a live reading based at least in part on the live angiogram imaging data, the live reading comprising one or more live predicted effectiveness indicators associated with the PCI procedure,
one or more live risks of at least one adverse event associated with the PCI procedure,
and live suggested device parameters for the PCI procedure;
a live confidence level associated with at least one of the respective predictions for the PCI procedure,
Anderson teaches:
obtain live angiogram imaging data of a coronary vasculature of a patient during a percutaneous coronary intervention (PCI) procedure; [Anderson teaches at claim 1 obtaining physiologic measurements from at least one instrument positioned within the vessel of the patient and moved longitudinally through the vessel from a first position to a second position; obtaining image data of a vessel system; co-registering the physiologic measurements with the image data to produce co-registered physiologic measurements; and determining whether to perform a first surgical procedure or a second surgical procedure based on the co-registered physiologic measurements. Anderson teaches at Claim 1 wherein the image of the vessel system is at least one of a two-dimensional angiographic image, a three-dimensional angiographic image, or a computer tomography angiographic (CTA) image and the image data is stored in the memory. Anderson teaches at claim 10, the system of claim 1, wherein the first surgical procedure comprises a percutaneous coronary intervention and the second surgical procedure comprises a coronary artery bypass graft.]
determine a live reading based at least in part on the live angiogram imaging data, the live reading comprising one or more live predicted effectiveness indicators associated with the PCI procedure, [Anderson teaches at claim 10, the system of claim 1, wherein the first surgical procedure comprises a percutaneous coronary intervention and the second surgical procedure comprises a coronary artery bypass graft. Anderson teaches at pg. 14 the user interface 600 also includes a window 614 that shows a calculated pressure ratio (E.g., FFR, iFR, or otherwise). Anderson teaches at pg. 14 in the illustrated embodiment of Fig. 6, the window 614 shows an iFR pressure ratio value during a pullback. Anderson teaches at pg. 14 as shown in Fig. 6, the user interface 600 provided on the display 182 includes a button 626 to allow a clinician to go back to a “Live” mode (or to toggle between a “Live” mode and a ”Review” mode), in which the user interface 600, including pressure waveform plots 612 and 638, calculated pressure ratios, and/or windows are updated in real time as a procedure is being performed. The FFR and iFR are interpreted as one or more live effectiveness indicators associated with the PCI procedure.]
one or more live risks of at least one adverse event associated with the PCI procedure, [Anderson teaches at claim 10, the system of claim 1, wherein the first surgical procedure comprises a percutaneous coronary intervention and the second surgical procedure comprises a coronary artery bypass graft. Anderson teaches at pg. 14 as shown in Fig. 6, the user interface 600 provided on the display 182 includes a button 626 to allow a clinician to go back to a “Live” mode (or to toggle between a “Live” mode and a ”Review” mode), in which the user interface 600, including pressure waveform plots 612 and 638, calculated pressure ratios, and/or windows are updated in real time as a procedure is being performed. Anderson teaches at pg. 15 information regarding the segments including classification and associated severities, will be provided to a risk calculator. Anderson teaches at pg. 15 the risk calculator, whether a SYNTAX score calculator or another disease quantification score calculator, will then provide a disease quantification score. Anderson teaches at pg. 15 when the disease quantification score is above a threshold, the system 150 may recommend a CABG surgery is the appropriate intervention. Anderson teaches at pg. 15 when the disease quantification score is below the threshold the system 150 may recommend PCI. Anderson teaches at pg. 15 while risk calculators, such as the SYNTAX score calculator, have been available to clinicians previously, the system 150 provides for the automatic calculation and determination of the SYNTAX score or other such disease quantification score. Anderson teaches at pg. 15 accordingly, the disease quantification score will be displayed to the clinician during an assessment procedure. The one or more live risks displayed is the disease state calculated by the disease quantification score. The adverse event associated with the PCI procedure is CABG surgery.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the personalizing patient pathways based on individual preferences, lifestyle regime, and preferences on outcome parameters to assist decision making of Dadlani Mahtani to the devices, systems, and methods for vessel assessment and intervention recommendation of Anderson with the motivation improving diagnostic angiography imaging data presentation.
Dadlani Mahtani/Anderson may not explicitly teach:
and live suggested device parameters for the PCI procedure;
a live confidence level associated with at least one of the respective predictions for the PCI procedure,
Tzimas teaches:
and live suggested device parameters for the PCI procedure; [Tzimas teaches at Figure 1 coronary CT angiography and CT derive fraction flow reserve for percutaneous coronary intervention planning. Tzimas teaches at Fig. 1 (G) bottom right of the picture a button indicator displaying “live.” Tzimas teaches at pg. 10 that CCTA 3D reconstruction software provides 3D vessel imaging with accurate evaluation of the coronary vasculature and burden calcification. Tzimas teaches at pg. 10 this preprocedural knowledge will can now be integrated in the catheterization laboratory, where CCTA data can be imported to the laboratory screen and electronically linked and synchronized to the C-arm, which is tracked in real time, thus providing a live 3D-reconstructed anatomic roadmap for guidewire advancement and adjustment during the procedure. Tzimas teaches at Figure 1 (C) multiplanar reconstructing image shows length of stents required to cover the complete lesion. The length of the stent is the live suggested device parameters for the PCI procedure.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the personalizing patient pathways based on individual preferences, lifestyle regime, and preferences on outcome parameters to assist decision making of Dadlani Mahtani to the devices, systems, and methods for vessel assessment and intervention recommendation of Anderson to the coronary CT angiography to guide percutaneous coronary intervention of Tzimas with the motivation of allowing for the prediction of the response to revascularization (Tzimas at the shaded Abstract box).
Dadlani Mahtani/Anderson/Tzimas may not explicitly teach:
a live confidence level associated with at least one of the respective predictions for the PCI procedure,
Anderson also teaches the following noted feature:
…PCI… [Anderson teaches at claim 10, the system of claim 1, wherein the first surgical procedure comprises a percutaneous coronary intervention and the second surgical procedure comprises a coronary artery bypass graft.]
Dadlani Mahtani also teaches the following noted feature:
a live confidence level associated with at least one of the respective predictions for the […] procedure, [Dadlani Mahtani teaches at pg. 10 line 20-23 for example, imaging results or patient data at different points or stages of the treatment will be uploaded to the system and used to make a comparisons with the expected outcome (images or pictures stored in the knowledge based of medical evidence) and produce a treatment effectiveness or progress score. Dadlani Mahtani teaches at pg. 4 line 15 -line 18 teaches if the patient requests, the system will provide additional outputs including traditional educational materials, information and access to a large patient community, probabilities of all the alternative options to be the best, confidence intervals of all the estimations, and the evidences the computation is based on. The probabilities and confidence intervals of all the estimations teach the confidence level. Dadlani Mahtani teaches at pg. 9 line 19-21 to accomplish this, the DSS utilizes the patient data, clinical models and algorithms, medical data, and the like to computer optimal patient pathways and/or treatment options for the patient given their current condition. Collectively, this teaches a live confidence level associated with at least one of the respective predictions for the respective treatment option.]
It would have been prima facie obvious to one of ordinary skill in the art at the time of the invention was made to combine the noted features of Anderson with the teaching of Dadlani Mahtani since the combination of the two references is merely simple substitution of one known element for another producing a predictable result (KSR rationale B). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the PCI procedure for the procedure on which the risk interval is based in the primary reference. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding Claim 12,15
Due to its similarity to Claim 1, Claim 12 and 15 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 1.
Regarding Claim 2
Dadlani Mahtani/Anderson/Tzimas teach the medical system of claim 1. Dadlani Mahtani/Anderson/Tzimas further teach:
wherein the processing circuitry is further configured to: determine a recommended treatment pathway of the plurality of treatment pathways; [Dadlani Mahtani teaches at pg. 7 line 12-17 that for example, the DSS acquires the patient’s medical records from the patient information system, clinical evidences on outcomes and prognosis for the appropriate population form the medical information system, the clinical models and algorithms, patient values, lifestyle regimes, and preference input by the patient, and displays the quantitative evaluation and comparison of the choices of treatment and pathways. Dadlani Mahtani teaches at pg. 6 line 31-pg.7 line 2 further, the clinical models and algorithms typically include recommendations for the various diagnosis and/or treatment options based on the state of the patient and the patient data.]
and output for display an indication of the recommended treatment pathway. [Dadlani Mahtani teaches at pg. 7 line 12-17 that for example, the DSS acquires the patient’s medical records from the patient information system, clinical evidences on outcomes and prognosis for the appropriate population form the medical information system, the clinical models and algorithms, patient values, lifestyle regimes, and preference input by the patient, and displays the quantitative evaluation and comparison of the choices of treatment and pathways. Dadlani Mahtani teaches at pg. 6 line 31-pg.7 line 2 further, the clinical models and algorithms typically include recommendations for the various diagnosis and/or treatment options based on the state of the patient and the patient data.]
Regarding Claim 5
Dadlani Mahtani/Anderson/Tzimas teach the medical system of claim 1. Dadlani Mahtani/Anderson/Tzimas further teach:
wherein as part of determining the one or more respective predicted effectiveness indicators associated with the respective treatment pathway, the one or more respective predicted risk associated with the respective treatment pathway, and the respective confidence level associated with at least one of the respective predictions, the processing circuitry is configured to run a plurality of simulations. [Dadlani Mahtani teaches at pg. 3 line 4-5 in accordance with another aspect, a method for personalization of patient pathways and treatment options is provided. Dadlani Mahtani teaches at pg. 7 line 12-17 that for example, the DSS acquires the patient’s medical records from the patient information system, clinical evidences on outcomes and prognosis for the appropriate population form the medical information system, the clinical models and algorithms, patient values, lifestyle regimes, and preference input by the patient, and displays the quantitative evaluation and comparison of the choices of treatment and pathways. Dadlani Mahtani teaches at pg. 3 line 5-12 teaches the method includes receiving patient data representing a patient’s medical record, estimating probabilities of mortality and morbidity from the patient data, calculating probabilities of having long term impairments or disabilities based on the probabilities of morbidity, surveying the patient using time-trade-off to weight the possible long-term impairments or disabilities, calculating an expected quality-adjusted life years (QALYs) and confidence interval of alternative choices using the trade-off weighing the possible long-term impairments or disabilities, and displaying the alternative choices, QALYs, and confidence intervals in a graphical tool. Displaying the alternative choices in a graphical tool also teaches the processing circuitry is configured to run a plurality of simulations. Calculating the probabilities of having long term impairments or disabilities based on the probabilities of morbidity is determining the one or more respective predicted risk associated with the respective treatment pathway. Regardless, Dadlani Mahtani teaches at pg. 28 in various embodiments, the risk calculator will include one or more algorithms for calculating the likelihood of mortality, the likelihood of success when treating the lesion or stenosis, etc. Dadlani Mahtani teaches at pg. 10 14-19 teaches during or after treatment, patients can enter subjective data (e.g., fill in questionnaires) or patient reported outcomes, and clinicians will enter progress information with regard to the ailment (e.g., tumor reduction size), to compare how effective the treatment is (chosen patient pathway) compared to the expected recovery and side effects based on available evidence, to further understand and even graphically visualize the effectiveness and progress of the treatment. Dadlani Mahtani at pg. 13 line 18-21 teaches the user (e.g. patient or healthcare professional) gets to specify the acceptable confidence intervals or to set an acceptable level of Overlap’ for the outcomes of the individual treatment choices, and the DSS chooses which additional test would allow that. Dadlani Mahtani teaches at pg. 13 line 21-26 teaches basically this is the reverse of the approach described above: instead of “if” you do advanced diagnostic test A, then J is the outcome and you narrow the confidence interval by Y%’, it would allow user to set want to reduce overlap, what advanced diagnostic tests do I need to do?”, or “the maximum range of the confidence interval that I am willing to live with is +/-X%, what are options should I consider?”, “or the maximum acceptable overlap is Z%, what additional diagnostic tests should be done to get closest to achieve this?”. Dadlani Mahtani teaches at pg. 5 line 2-3 the decision support system also allows care providers to establish confidence interval limits prior to showing the results to the patient. The interval limits are the confidence level. The teaching above: “the maximum range of the confidence interval that I am willing to live with is +/-X%” teaches the respective confidence level associated with at least one of the respective predictions. This also teaches determining the one or more respective predicted effectiveness indicators associated with the respective treatment pathway. The predicted effective indicator is the confidence interval, +/-X%. Collectively, Dadlani Mahtani teaches wherein as part of determining the one or more respective predicted effectiveness indicators associated with the respective treatment pathway, the one or more respective predicted risk associated with the respective treatment pathway, and the respective confidence level associated with at least one of the respective predictions, the processing circuitry is configured to run a plurality of simulations.]
Regarding Claim 7
Dadlani Mahtani/Anderson/Tzimas teaches the medical system of claim 1. Dadlani Mahtani/Anderson/Tzimas further teach:
wherein the one or more respective predicted effectiveness indicators associated with the respective treatment pathway comprises at least one of a respective predicted fractional flow reserve (FFR) value, a respective predicted quality of life improvement, or at least one respective predicted readmission rate. [Tzimas teaches at Figure 2 Item A after virtual percutaneous coronary intervention (PCI), the predicted FFR was 0.89.]
Regarding Claim 19
Due to its similarity to Claim 7, Claim 19 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 7.
Regarding Claim 8
Dadlani Mahtani/Anderson/Tzimas teach the medical system of claim 1. Dadlani Mahtani/Anderson/Tzimas further teach:
wherein each of the plurality of treatment pathways further comprises a respective inventory availability or cost. [Dadlani Mahtani teaches that the present application also quantifies the alternative choices, and simplifies and shortens the shared decision making process, therefore reducing patients’ stress, increasing the patients’ satisfaction on their division, and ultimately reducing overall healthcare cost. Dadlani Mahtani teaches at pg. 14 in yet another embodiment, other factors like additional costs per advanced diagnostic test will be taken into account.
Regarding Claim 20
Due to its similarity to Claim 8, Claim 20 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 8.
Regarding Claim 11
Dadlani Mahtani/Anderson/Tzimas teaches the medical system of claim 1. Dadlani Mahtani/Anderson/Tzimas further teach:
wherein the processing circuitry is further configured to: determine at least one of a ghosted preview of a PCI procedure, a graphical predicted FFR, a graphical predicted risk of rupture, or a graphical predicted probability of a successful outcome; [Dadlani Mahtani teaches at pg. 6 line 31-pg.7 line 2 further, the clinical models and algorithms typically include recommendations for the various diagnosis and/or treatment options based on the state of the patient and the patient data. Dadlani Mahtani teaches at pg. 28 in various embodiments, the risk calculator will include one or more algorithms for calculating the likelihood of mortality, the likelihood of success when treating the lesion or stenosis, etc. This teaches determining a graphical predicted probability of a successful outcome.]
and output for display, during the PCI procedure, at least one of the ghosted preview of the procedure, the graphical predicted FFR, the graphical predicted risk of rupture, or the graphical predicted probability of a successful outcome. [Dadlani Mahtani teaches at pg. 34 the risk calculator will output a quantity that is an objective measure of the risk associated with the patient’s condition. This teaches and output for display the graphical predicted probability of a successful outcome.]
Regarding Claim 13
Dadlani Mahtani/Anderson/Tzimas teach the method of claim 12. Dadlani Mahtani/Anderson/Tzimas further teach:
further comprising: determining a recommended treatment pathway of the plurality of treatment pathways; [Dadlani Mahtani teaches at pg. 7 line 12-17 that for example, the DSS acquires the patient’s medical records from the patient information system, clinical evidences on outcomes and prognosis for the appropriate population form the medical information system, the clinical models and algorithms, patient values, lifestyle regimes, and preference input by the patient, and displays the quantitative evaluation and comparison of the choices of treatment and pathways. Dadlani Mahtani teaches at pg. 6 line 31-pg.7 line 2 further, the clinical models and algorithms typically include recommendations for the various diagnosis and/or treatment options based on the state of the patient and the patient data.]
and outputting for display an indication of the recommended treatment pathway. [Dadlani Mahtani teaches at pg. 7 line 12-17 that for example, the DSS acquires the patient’s medical records from the patient information system, clinical evidences on outcomes and prognosis for the appropriate population form the medical information system, the clinical models and algorithms, patient values, lifestyle regimes, and preference input by the patient, and displays the quantitative evaluation and comparison of the choices of treatment and pathways. This also teaches and outputting for display an indication of the recommended treatment pathway. Dadlani Mahtani teaches at pg. 6 line 31-pg.7 line 2 further, the clinical models and algorithms typically include recommendations for the various diagnosis and/or treatment options based on the state of the patient and the patient data. Dadlani Mahtani teaches at pg. 10 14-19 teaches during or after treatment, patients can enter subjective data (e.g., fill in questionnaires) or patient reported outcomes, and clinicians will enter progress information with regard to the ailment (e.g., tumor reduction size), to compare how effective the treatment is (chosen patient pathway) compared to the expected recovery and side effects based on available evidence, to further understand and even graphically visualize the effectiveness and progress of the treatment. Collectively, this teaches and outputting for display an indication of the recommended treatment pathway.]
Claim(s) 3,4,14,16,17 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO 2014/049527 A2 (hereafter Dadlani Mahtani) in view of WO 2016/092420 A1 (hereafter Anderson) in view of Tzimas (Coronary CT angiography to guide percutaneous coronary intervention) in view of US 2018/0078139 (hereafter Sanders).
Regarding Claim 3
Dadlani Mahtani/Anderson/Tzimas teach the medical system of claim 1. Dadlani Mahtani/Anderson/Tzimas may not explicitly teach:
wherein as part of determining the one or more respective predicted effectiveness indicators associated with the respective treatment pathway, the one or more respective predicted risks associated with the respective treatment pathway, and the respective confidence level associated with at least one of the respective predictions, the processing circuitry is configured to execute a machine learning algorithm.
Dadlani Mahtani teaches the following noted feature:
wherein as part of determining the one or more respective predicted effectiveness indicators associated with the respective treatment pathway, the one or more respective predicted risks associated with the respective treatment pathway, and the respective confidence level associated with at least one of the respective predictions, the processing circuitry is configured to execute a […] algorithm. [Dadlani Mahtani teaches at pg. 7 line 12-17 that for example, the DSS acquires the patient’s medical records from the patient information system, clinical evidences on outcomes and prognosis for the appropriate population form the medical information system, the clinical models and algorithms, patient values, lifestyle regimes, and preference input by the patient, and displays the quantitative evaluation and comparison of the choices of treatment and pathways. Dadlani Mahtani teaches at pg. 4 line 8-line 11 teaches the decision support system utilizes an algorithm to convert prognosis and clinical outcomes, such as probability of mortality and morbidities, int values that are directly meaningful for the patient in evaluating and comparing different choices from the patient’s perspective. Dadlani Mahtani teaches at pg. 4 line 19-22 the decision support system also enable patients to compare alternative choices on the same measure, such as allowing the patients to adjust for lifestyle regime and preferences, outcome parameters, patient pathways, QALYs, desired probability of an overall outcome or of a specific outcome parameter, and the like. Dadlani Mahtani teaches at pg. 4 line 15-18 if the patient requests, the system will provide additional outputs including traditional educational materials, information and access to a large patient community, probabilities of all the alternative options to be the best, confidence intervals of all the estimations, and the evidences the computation is based on. The probabilities of all the alternative options to be best and confidence intervals of all the estimations teaches the respective confidence level associated with at least one of the respective predictions for each respective treatment pathway of the plurality of treatment pathways. Regardless, Dadlani Mahtani teaches at pg. 13 line 18-21 teaches the user (e.g. patient or healthcare professional) gets to specify the acceptable confidence intervals or to set an acceptable level of Overlap’ for the outcomes of the individual treatment choices, and the DSS chooses which additional test would allow that. Dadlani Mahtani teaches at pg. 13 line 21-26 teaches basically this is the reverse of the approach described above: instead of “if” you do advanced diagnostic test A, then J is the outcome and you narrow the confidence interval by Y%’, it would allow user to set want to reduce overlap, what advanced diagnostic tests do I need to do?”, or “the maximum range of the confidence interval that I am willing to live with is +/-X%, what are options should I consider?”, “or the maximum acceptable overlap is Z%, what additional diagnostic tests should be done to get closest to achieve this?”. The teaching above: “the maximum range of the confidence interval that I am willing to live with is +/-X%” teaches the confidence level. Collectively, Dadlani Mahtani teaches and output for display the plurality of treatment pathways, the one or more respective predicted effectiveness indicators associated with the respective treatment pathway, and the respective confidence level associated with at least one of the respective predictions for each respective treatment pathway of the plurality of treatment pathways.]
Sanders teaches the following noted feature:
…machine learning… [Sanders teaches at Figure 4A Item 414A training a machine learning algorithm that will predict blood flow characteristics at one or more points of a model derived from image data feature vector(s) comprising features at the one or more points, using the associated features from step 412A. Sanders teaches at Figure 4A Item 416A outputting the trained machine learning algorithm to be used in method 400c of Fig. 4C.]
It would have been prima facie obvious to one of ordinary skill in the art at the time of the invention was made to combine the noted features of Sanders with teaching of Dadlani Mahtani since the combination of the two references is merely simple substitution of one known element for another producing a predictable result (KSR rationale B). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the machine learning of the secondary reference(s) for the manual identification means of the primary reference. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding Claim 4
Dadlani Mahtani/Anderson/Tzimas/Sanders teach the medical system of claim 3. Dadlani Mahtani/Anderson/Tzimas/Sanders further teach:
wherein as part of determining the one or more respective predicted effectiveness indicators associated with the respective treatment pathway, the one or more respective predicted risks associated with the respective treatment pathway, and the respective confidence level associated with at least one of the respective predictions, the processing circuitry is configured to: generate a three-dimensional (3D) model of the vasculature of a patient; [Sanders teaches at Figure 4A Item 404B determining and/or receiving a corresponding population-derived 3D anatomic model of at least the vasculature of interest. Sanders teaches at Figure 4A Item 404C generating a patient specific 3D anatomic model of at least the vasculature of interest.
and execute the machine learning algorithm, using input derived from the 3D model of the vasculature of the patient. [Sanders teaches at Figure 4A Item 404C generating a patient specific 3D anatomic model of at least the vasculature of interest. Sanders teaches at Figure 4A Item 406C applying boundary conditions to the 3D anatomic model. Sanders teaches at Figure 4A Item 408c determine blood flow characteristics at one or more points of the patient-specific 3D anatomic model using computational fluid dynamics (CFD). Sanders teaches at Figure 4A Item 410A creating feature vectors comprising one or more features at the one or more point of the patient specific 3D anatomic model, the population derived 3D anatomic model, and/or for each of the individual specific 3D anatomic model. Sanders teaches at Figure 4A Item 412A associate the feature vectors with the blood flow characteristics at the one or more points of the patient-specific 3D anatomic model, the population derived 3D anatomic model, and/or for each of the individual specific 3D anatomic models. Sanders teaches at Figure 4A Item 414A training a machine learning algorithm that will predict blood flow characteristics at one or more points of a model derived from image data feature vector(s) comprising features at the one or more points, using the associated features from step 412A. Sanders teaches at Figure 4A Item 416A outputting the trained machine learning algorithm to be used in method 400c of Fig. 4C. Collectively, Sanders teaches and execute the machine learning algorithm, using input derived from the 3D model of the vasculature of the patient.]
Regarding Claim 16
Due to its similarity to Claim 4, Claim 16 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 4.
Regarding Claim 14
Dadlani Mahtani teaches the method of claim 12. Dadlani Mahtani may not explicitly teach:
wherein determining the one or more respective predicted effectiveness indicators associated with the respective treatment pathway, the one or more respective predicted risks associated with the respective treatment pathway, and the respective confidence level associated with at least one of the respective predictions comprises executing a machine learning algorithm.
Dadlani Mahtani teaches the following noted feature:
wherein determining the one or more respective predicted effectiveness indicators associated with the respective treatment pathway, the one or more respective predicted risks associated with the respective treatment pathway, and the respective confidence level associated with at least one of the respective predictions comprises executing a […] algorithm. [Dadlani Mahtani teaches at pg. 3 line 4-5 in accordance with another aspect, a method for personalization of patient pathways and treatment options is provided. This teaches the processing circuitry is configured to run a plurality of simulations. Dadlani Mahtani teaches at pg. 3 line 5-12 teaches the method includes receiving patient data representing a patient’s medical record, estimating probabilities of mortality and morbidity from the patient data, calculating probabilities of having long term impairments or disabilities based on the probabilities of morbidity, surveying the patient using time-trade-off to weight the possible long-term impairments or disabilities, calculating an expected quality-adjusted life years (QALYs) and confidence interval of alternative choices using the trade-off weighing the possible long-term impairments or disabilities, and displaying the alternative choices, QALYs, and confidence intervals in a graphical tool. Calculating the probabilities of having long term impairments or disabilities based on the probabilities of morbidity is determining the one or more respective predicted risk associated with the respective treatment pathway. Dadlani Mahtani teaches at pg. 10 14-19 teaches during or after treatment, patients can enter subjective data (e.g., fill in questionnaires) or patient reported outcomes, and clinicians will enter progress information with regard to the ailment (e.g., tumor reduction size), to compare how effective the treatment is (chosen patient pathway) compared to the expected recovery and side effects based on available evidence, to further understand and even graphically visualize the effectiveness and progress of the treatment. Dadlani Mahtani teaches at pg. 13 line 18-21 teaches the user (e.g. patient or healthcare professional) gets to specify the acceptable confidence intervals or to set an acceptable level of Overlap’ for the outcomes of the individual treatment choices, and the DSS chooses which additional test would allow that. Dadlani Mahtani teaches at pg. 13 line 21-26 teaches basically this is the reverse of the approach described above: instead of “if” you do advanced diagnostic test A, then J is the outcome and you narrow the confidence interval by Y%’, it would allow user to set want to reduce overlap, what advanced diagnostic tests do I need to do?”, or “the maximum range of the confidence interval that I am willing to live with is +/-X%, what are options should I consider?”, “or the maximum acceptable overlap is Z%, what additional diagnostic tests should be done to get closest to achieve this?”. Dadlani Mahtani teaches at pg. 5 line 2-3 the decision support system also allows care providers to establish confidence interval limits prior to showing the results to the patient. The interval limits are the confidence level. The teaching above: “the maximum range of the confidence interval that I am willing to live with is +/-X%” teaches the respective confidence level associated with at least one of the respective predictions. This also teaches determining the one or more respective predicted effectiveness indicators associated with the respective treatment pathway. The predicted effective indicator is reflected by the confidence interval, +/-X%.]
Sanders teaches the following noted feature:
…machine learning algorithm…[Sanders teaches at Figure 4A Item 414A training a machine learning algorithm that will predict blood flow characteristics at one or more points of a model derived from image data feature vector(s) comprising features at the one or more points, using the associated features from step 412A. Sanders teaches at Figure 4A Item 416A outputting the trained machine learning algorithm to be used in method 400c of Fig. 4C. This teaches executing machine learning algorithm.]
It would have been prima facie obvious to one of ordinary skill in the art at the time of the invention was made to combine the noted features of Sanders with teaching of Dadlani Mahtani since the combination of the two references is merely simple substitution of one known element for another producing a predictable result (KSR rationale B). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the machine learning algorithm of the secondary reference(s) for the algorithm means of the primary reference. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding Claim 17
Due to its similarity to Claim 14, Claim 17 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 14.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO 2014/049527 A2 (hereafter Dadlani Mahtani) in view of WO 2016/092420 A1 (hereafter Anderson) in view of Tzimas (Coronary CT angiography to guide percutaneous coronary intervention) in view of US-20220406460-A1 (hereafter Golan).
Regarding Claim 9
Dadlani Mahtani/Anderson/Tzimas teaches the medical system of claim 1. Dadlani Mahtani/Anderson/Tzimas further teach:
wherein in response to clinician input of a selected one of the plurality of treatment pathways, the processing circuitry is configured to: determine a plurality of treatment options of the selected treatment pathway, each of the plurality of treatment options comprising one or more respective predicted effectiveness indicators associated with the respective treatment option, [Dadlani Mahtani teaches at pg. 6 line 31-pg.7 line 2 further, the clinical models and algorithms typically include recommendations for the various diagnosis and/or treatment options based on the state of the patient and the patient data. Dadlani Mahtani teaches at pg. 7 line 12-17 that for example, the DSS acquires the patient’s medical records from the patient information system, clinical evidences on outcomes and prognosis for the appropriate population form the medical information system, the clinical models and algorithms, patient values, lifestyle regimes, and preference input by the patient, and displays the quantitative evaluation and comparison of the choices of treatment and pathways. The quantitative evaluation and comparison of the choices of treatment and pathways is each of the plurality of treatment options comprising one or more respective predicted effectiveness indicators associated with the respective treatment option.]
one or more respective predicted risks associated with the respective treatment option, [Dadlani Mahtani teaches at pg. 3 line 4-5 in accordance with another aspect, a method for personalization of patient pathways and treatment options is provided. Dadlani Mahtani teaches at pg. 3 line 5-12 teaches the method includes receiving patient data representing a patient’s medical record, estimating probabilities of mortality and morbidity from the patient data, calculating probabilities of having long term impairments or disabilities based on the probabilities of morbidity, surveying the patient using time-trade-off to weight the possible long-term impairments or disabilities, calculating an expected quality-adjusted life years (QALYs) and confidence interval of alternative choices using the trade-off weighing the possible long-term impairments or disabilities, and displaying the alternative choices, QALYs, and confidence intervals in a graphical tool. Calculating the probabilities of having long term impairments or disabilities based on the probabilities of morbidity is determining the one or more respective predicted risk associated with the respective treatment option.]
a respective confidence level associated with at least one of the respective predictions for the respective treatment option, [Dadlani Mahtani teaches at pg. 3 line 5-12 teaches the method includes receiving patient data representing a patient’s medical record, estimating probabilities of mortality and morbidity from the patient data, calculating probabilities of having long term impairments or disabilities based on the probabilities of morbidity, surveying the patient using time-trade-off to weight the possible long-term impairments or disabilities, calculating an expected quality-adjusted life years (QALYs) and confidence interval of alternative choices using the trade-off weighing the possible long-term impairments or disabilities, and displaying the alternative choices, QALYs, and confidence intervals in a graphical tool. Calculating probabilities of having long term impairments or disabilities based on the probabilities of morbidity and the confidence interval of alternative choices using the trade-off weighing the possible long-term impairments or disabilities teaches a respective confidence level associated with the at least one of the respective predictions for the respective treatment option.]
[…]
and the one or more respective predicted effectiveness indicators associated with the respective treatment option, [Dadlani Mahtani teaches at pg. 6 line 31-pg.7 line 2 further, the clinical models and algorithms typically include recommendations for the various diagnosis and/or treatment options based on the state of the patient and the patient data. Dadlani Mahtani teaches at pg. 7 line 12-17 that for example, the DSS acquires the patient’s medical records from the patient information system, clinical evidences on outcomes and prognosis for the appropriate population form the medical information system, the clinical models and algorithms, patient values, lifestyle regimes, and preference input by the patient, and displays the quantitative evaluation and comparison of the choices of treatment and pathways. The quantitative evaluation and comparison of the choices of treatment and pathways is the more respective predicted effectiveness indicators associated with the respective treatment option.]
the respective confidence level associated with at least one of the respective predictions for the respective treatment option, [Dadlani Mahtani teaches at pg. 4 line 15 -line 18 teaches if the patient requests, the system will provide additional outputs including traditional educational materials, information and access to a large patient community, probabilities of all the alternative options to be the best, confidence intervals of all the estimations, and the evidences the computation is based on. The probabilities and confidence intervals of all the estimations teach the confidence level. Dadlani Mahtani teaches at pg. 9 line 19-21 to accomplish this, the DSS utilizes the patient data, clinical models and algorithms, medical data, and the like to computer optimal patient pathways and/or treatment options for the patient given their current condition. Collectively, this teaches a live confidence level associated with at least one of the respective predictions for the respective treatment option.]
[…].
Dadlani Mahtani may not explicitly teach:
[…]
and suggested device parameters for the respective treatment option;
and output for display the plurality of treatment options of the selected treatment pathway,
[…]
and the suggested device parameters for the respective treatment option.
Golan teaches:
[…]
and suggested device parameters for the respective treatment option; [Golan teaches at Figure 2 Item 200 receiving a set. Golan teaches at Figure 2 Item S220 analyzing the set of data S220. Golan teaches at Figure 2 Item 230 determining a set of parameters associated with the set of data. Golan teaches at Figure 2 Item S240 triggering an output based on the set of parameters. Golan teaches at Figure 2 Item S240 recommending a procedure and/or other treatment. Golan teaches at Figure 2 Item S240 recommending a particular device. Golan teaches at Figure 2 Item S240 recommending features (e.g., timing) associated with a procedure. The recommendation of timing of when to do a procedure and what device to used is interpreted as the suggested device parameters for the respective treatment option.]
and output for display the plurality of treatment options of the selected treatment pathway, [Golan teaches at Figure 2 Item S240 triggering an output based on the set of parameters including recommending a procedure and/or other treatment. This teaches an output for display of the plurality of treatment options (the procedure and other treatment are a plurality of treatment options), of the selected treatment pathway. The selected treatment pathway is the recommended pathway.]
[…]
and the suggested device parameters for the respective treatment option. [Golan teaches at Figure 2 Item 200 receiving a set. Golan teaches at Figure 2 Item S220 analyzing the set of data S220. Golan teaches at Figure 2 Item 230 determining a set of parameters associated with the set of data. Golan teaches at Figure 2 Item S240 triggering an output based on the set of parameters. Golan teaches at Figure 2 Item S240 recommending a procedure and/or other treatment. Golan teaches at Figure 2 Item S240 recommending a particular device. Golan teaches at Figure 2 Item S240 recommending features (e.g., timing) associated with a procedure. The recommendation of timing of when to do a procedure and what device to used is interpreted as the suggested device parameters for the respective treatment option. Golan teaches at Figure 3 displaying a notification that includes a device recommendation: catheter with length L1 and diameter D2. This teaches the device recommendation, length and diameter are the device parameters.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the personalizing patient pathways based on individual preferences, lifestyle regime, and preferences on outcome parameters to assist decision making of Dadlani Mahtani to the devices, systems, and methods for vessel assessment and intervention recommendation of Anderson to the coronary CT angiography to guide percutaneous coronary intervention of Tzimas to the method and system for computer-aided decision guidance of Golan with the motivation conferring the benefit of helping physicians make fast and accurate decisions related to the treatment (e.g., surgery, drug administration, etc.) of patient experiencing an acute, time sensitive condition (e.g., stroke), which can in turn function to reduce waste, improve outcomes, and/or otherwise benefit the patient or users (Golan at para. [0018]).
Claim(s) 6,18 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO 2014/049527 A2 (hereafter Dadlani Mahtani) in view of WO 2016/092420 A1 (hereafter Anderson) in view of Tzimas (Coronary CT angiography to guide percutaneous coronary intervention) in view of US 2022/0183915 A1 (hereafter Augustine).
Regarding Claim 6
Dadlani Mahtani/Anderson/Tzimas teach the medical system of claim 1. Dadlani Mahtani/Anderson/Tzimas may not explicitly teach:
wherein the processing circuitry is configured to determine the one or more respective predicted effectiveness indicators associated with the respective treatment pathway based on a device performance prediction.
Dadlani Mahtani teaches the following noted feature:
…pathway… [Dadlani Mahtani teaches at pg. 3 line 4-5 in accordance with another aspect, a method for personalization of patient pathways and treatment options is provided.]
Augustine teaches the following noted feature:
wherein the processing circuitry is configured to determine the one or more respective predicted effectiveness indicators associated with the respective treatment […] based on a device performance prediction. [Augustine teaches at the Abstract modules for housing electronic and electromechanical medical equipment including a system to measure and record administration of one or more IV medications or fluids for IV administrations. The administration of one or more IV medications or fluids for IV administrations is the respective treatment. Augustine teaches at para. [0302] in some examples, the confidence calculator uses the fused estimate FE and determines an associated confidence level C for each fused estimate. Augustine teaches at para. [0302] the fused estimate with the higher confidence value is selected as the optimal estimate. Augustine teaches at para. [0302] these parameters are used to modify a sensor error model and prediction error model. Augustine teaches at para. [0302] the sensor error model is used to statistically characterize the nominal error contamination on the sensor measurements X. sub 1-Xsub 3. The nominal error contamination is the device performance. Augustine teaches at para. [0302] the prediction error model is used to statistically characterize the variability of the physiological parameter itself. This teaches the determining the one or more respective predicted effectiveness indicators associated with the respective treatment […]. Collectively Augustine teaches, determining the one or more respective predicted effectiveness indicators associated with the respective treatment […] is based on a device performance prediction.]
It would have been prima facie obvious to one of ordinary skill in the art at the time of the invention was made to combine the noted features of Augustine with the teaching of Dadlani Mahtani since the combination of the two references is merely simple substitution of one known element for another producing a predictable result (KSR rationale B). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the treatment pathways of the Dadlani Mahtani for the treatment on which the device performance depends of Augustine. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding Claim 18
Dadlani Mahtani/Anderson/Tzimas teach the method of claim 12. Dadlani Mahtani/Anderson/Tzimas may not explicitly teach:
wherein determining the one or more respective predicted effectiveness indicators associated with the respective treatment pathway is based on a device performance prediction.
Augustine teaches:
wherein determining the one or more respective predicted effectiveness indicators associated with the respective treatment pathway is based on a device performance prediction. [Augustine teaches at the Abstract modules for housing electronic and electromechanical medical equipment including a system to measure and record administration of one or more IV medications or fluids for IV administrations. The administration of one or more IV medications or fluids for IV administrations is the respective treatment pathway. Augustine teaches at para. [0302] in some examples, the confidence calculator uses the fused estimate FE and determines an associated confidence level C for each fused estimate. Augustine teaches at para. [0302] the fused estimate with the higher confidence value is selected as the optimal estimate. Augustine teaches at para. [0302] these parameters are used to modify a sensor error model and prediction error model. Augustine teaches at para. [0302] the sensor error model is used to statistically characterize the nominal error contamination on the sensor measurements X. sub 1-Xsub 3. The nominal error contamination is the device performance prediction. Augustine teaches at para. [0302] the prediction error model is used to statistically characterize the variability of the physiological parameter itself. This teaches the determining the one or more respective predicted effectiveness indicators associated with the respective treatment pathway. Collectively Augustine teaches, determining the one or more respective predicted effectiveness indicators associated with the respective treatment pathway is based on a device performance prediction.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the personalizing patient pathways based on individual preferences, lifestyle regime, and preferences on outcome parameters to assist decision making of Dadlani Mahtani to the devices, systems, and methods for vessel assessment and intervention recommendation of Anderson to the coronary CT angiography to guide percutaneous coronary intervention of Tzimas to the relocation module and methods for surgical equipment of Augustine with the motivation of correcting manually inputted data, which is sporadic and prone to errors and omissions (Augustine at para. [0008]).
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO 2014/049527 A2 (hereafter Dadlani Mahtani) in view of WO 2016/092420 A1 (hereafter Anderson) in view of Tzimas (Coronary CT angiography to guide percutaneous coronary intervention) in view of WO 2021/055754 A1 (hereafter Gopinath).
Regarding Claim 21
Dadlani Mahtani/Anderson/Tzimas teach the medical system of claim 1. Dadlani Mahtani/Anderson/Tzimas may not explicitly teach:
to determine the plurality of treatment pathways, the processing circuitry is configured to: obtain first fluoroscopy with contrast imaging data captured at a first frame rate;
generate a 3d model based on the first fluoroscopy with contrast imaging data;
determine the plurality of treatment pathways based on the 3D model,
wherein the live angiogram image data comprises one or more live fluoroscopy with contrast imaging data captured at a second frame rate, lower than the first frame rate.
Gopinath teaches:
to determine the plurality of treatment pathways, the processing circuitry is configured to: obtain first fluoroscopy with contrast imaging data captured at a first frame rate; [Gopinath teaches at para. [0028] in one embodiment, the angiography and live fluoroscopy co-registration will be combined with the foregoing to allow intravascular markers and intravascular image data or parameters to be overlaid with or co-registered with live fluoroscopy. Gopinath teaches at para. [0025] in one embodiment, for each live fluoroscopy frame, the corresponding angiography frame is identified based on signal or timing parameter correlation, such as through ECG correlation. Gopinath teaches at para. [0077] the user may also configure the frame rate at which the successive fluoroscopic and angiographic images are shown. Gopinath teaches at para. [0077] for example, the user may configure the system so that it presents a series of three live fluoroscopic image frames followed by two successive angiographic image frames, or the user could configure the system to display one angiographic image within each series of four live fluoroscopic images. Gopinath teaches at para. [0077] based on the correlation of non-live and live signals described above, angiographic image frames can be combined with live fluoroscopy to reduce contrast solution usages. This teaches contrast is used. Collectively, Gopinath teaches obtain first fluoroscopy with contrast imaging data captured at a first frame rate.]
generate a 3d model based on the first fluoroscopy with contrast imaging data; [Gopinath teaches at para. [0011] the various software-based tools to address medical imaging problems and other related challenges and problems and parts of the foregoing will be used for medical application and other applications for displaying information relating to stents, blood vessels, and two and three-dimensional views thereof without limitation. The three dimensional views are generating a 3D model based on the first fluoroscopy with contrast imaging data. The fluoroscopy with contrast imaging data is taught elsewhere.]
determine the plurality of treatment pathways based on the 3D model, [Gopinath teaches at para. [0083] these methods of evaluation can include displaying one or more views of an artery from angiography or intravascular imaging relative to one or more live fluoroscopy image frames. See above, Gopinath teaches the 3D model. Gopinath teaches at para. [0083] the various fluoroscopy-based methods reduced geographic misses and help facilitate better imaging and artery-directed treatments.]
wherein the live angiogram image data comprises one or more live fluoroscopy with contrast imaging data captured at a second frame rate, lower than the first frame rate. [Gopinath teaches at pg. 2 para. [0077] for example, the user may configure the system so that it presents a series of three live fluoroscopic image frames followed by two successive angiographic image frames, or the user could configure the system to display one angiographic image within each series of four live fluoroscopic images. The claim requires live angiogram image data comprises one or more live fluoroscopy with contrast imaging data captured at a second frame rate that is lower than the first live fluoroscopy frame rate. The configuration of the system to display three live fluoroscopic image frames followed by two successive angiographic image frames teaches the required frame rate, lower than the first frame rate. Gopinath teaches at para. [0009] the system will also be configured to display one or more intravascular image frames or a subset thereof corresponding to one or more live angiography frames. Gopinath teaches at pg. 2 para. [0077] for example, one or more angiographic image frames will be inserted in between the live fluoroscopic image frames, so as to present the combined frames at a higher frame rate. Gopinath teaches at pg. 14 para. [0083] these methods of evaluation can include displaying one or more views of an artery from angiography or intravascular imaging relative to one or more live fluoroscopy image frames. Collectively, Gopinath teaches wherein the live angiogram image data comprises one or more live fluoroscopy with contrast imaging data captured at a second frame rate, lower than the first frame rate.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the personalizing patient pathways based on individual preferences, lifestyle regime, and preferences on outcome parameters to assist decision making of Dadlani Mahtani to the devices, systems, and methods for vessel assessment and intervention recommendation of Anderson to the coronary CT angiography to guide percutaneous coronary intervention of Tzimas to the systems and methods of combined imaging of Gopinath with the motivation of improving the ability to better diagnose, monitor, and treat coronary artery disease, which can be of life saving importance (Gopinath at para. [0003] on pg. 5).
Response to Arguments
35 U.S.C. 101 Argument Responses
Applicant argues that amended claim 1 is not directed to an abstract idea. A human mind cannot interface with a medical imager to obtain live angiogram imaging data during an ongoing surgical procedure.
The Examiner disagrees that the amended claim 1 is not directed to an abstract idea. Additionally, the limitations highlighted in Step 2A prong one in the 35 U.S.C. 101 rejection above could be practically performed in the human mind. Note that abstract ideas categorized as mental processes may or may not require the use of a computer. The Examiner is bound to broadest reasonable interpretation, “interfacing” wasn’t specifically used in the claims.
Furthermore, a human mind cannot computationally process live angiogram imaging data to dynamically determine live reading, including predicted effectiveness indicators, live risks of at least one adverse event, a live confidence level, and live suggested device parameters based on that imaging data. Because these physical and computational limitations cannot be practically performed in the human mind, the claim does not recite a mental process.
The Examiner disagrees. Doctors could complete the limitations outlined in the abstract idea in the 35 U.S.C. 101 rejection above in question in their mind during percutaneous coronary intervention procedures.
Applicant argues that the amended claims integrate any such concept into a practical application. By obtaining live angiogram imaging data during the procedure and determining a live reading based on that data, the system provides a specific, actionable technical improvement to a surgical environment.
The Examiner disagrees. As recited, obtaining live angiogram imaging data could be analyzed as part of the abstract idea. However, the limitation concerning obtaining live angiogram imaging data was analyzed as an additional element, and was found to be extra-solution activity at Step 2A prong two and well understood routine and conventional activity at Step 2B. As such this additional element does not integrate the judicial exception into a practical application of the exception or provide significantly more.
Generating live suggested device parameters based on live angiogram imaging data ties the data processing to a specific, practical application in medical imaging and surgical intervention, ensuring the claim is not a pre-emptive monopoly on a fundamental concept.
A successful pass through the Alice/May subject matter eligibility analysis framework resolves issues of pre-emption. This limitation was part of the abstract idea.
Applicant argues the ordered combination of elements in amended claim 1 provides an inventive concept that amounts to significantly more than any alleged abstract idea. The claim requires the specific, unconventional step of taking live angiogram imaging data during an ongoing procedure and dynamically generating live reading, including live suggested device parameters and live risks of adverse events, from the specific imaging data. This specific implementation provides a technological solution to the problem of intra-operative risk assessment and device selection, which goes well beyond generic computer functions or mere instructions to apply an exception.
The “intra-operative risk assessment and device selection” that is an argued improvement may not be solving a technical problem. Moreover, the technical solution recited does not address the problem of “intra-operative device selection.” For example, device selection is not recited in the claim, merely the displaying of the plurality of treatment options and live reading. While this information could be used by the doctor or operator to determine the correct device to use, that determination of which device to use during a procedure (device selection) is not present in the solution. A feature not recited in the claims cannot provide an inventive concept. The additional elements, considered alone and in combination, do not integrate the judicial exception into a practical application of the exception or provide significantly more.
35 U.S.C. 102 (a)(1) & (a)(2) Arguments
The arguments are moot, this rejection has been withdrawn in response to amendment.
35 U.S.C. 103 Arguments
Applicant argues that the applied references, alone or in any proper combination, fail to disclose or suggest the subject matter recited by Applicant’s claims, and there would have been no apparent reason that would have caused one of ordinary skill in the art to modify the medical systems, methods, and non-transitory computer-readable storage media described by the applied references to arrive at the claimed subject matter.
The Examiner disagrees, but the argument is moot. The original rejection has been changed in response to amendment.
In rejecting claim 1, the Office action did not specifically address the claimed one or more respective predicted risks.
The Examiner disagrees, but the argument is moot. The original rejection has been changed in response to amendment.
Applicant requests clarification as to whether the Office’s position with respect to the or more predicted risks for claim 1 is the same as the Office position for the one or more predicted risks is for claim 5.
The one or more respective predicted risk associated with the respective treatment pathway appears to be recited in both claim 1 and 5.
Applicant argues that the prior art fails to teach or suggest:
…obtaining live angiogram imaging data of a coronary vasculature of a patient during a percutaneous coronary intervention procedure;
New art has been incorporated to teach the limitation.
Determining a live reading based at least in part on the live angiogram imaging data, the live reading comprising one or more live predicted effectiveness indicators associated with the procedure, one or more live risks of at least one adverse event associated with the procedure, a live confidence level associated with at least one of the respective predictions for the procedure, and live suggested device parameters for the procedure, and outputting for display the live angiogram imaging data and the live reading, wherein the live reading is overlaid onto the live angiogram imaging data.
New art has been incorporated to teach relevant limitations. Note that the Examiner is bound to broadest reasonable interpretation and this compendium of alleged points of what the prior art fails to teach or suggest appears to be paraphrased. The actual limitations taught were included in the rejection.
Applicant argues that Anderson’s risk calculator simply calculates a disease quantification that is used to determine whether a CABG surgery or PCI is recommended. Anderson does not teach or suggest “the live reading comprising…one or more live risks of at least one adverse event associated with the PCI procedure.”
The CABG is interpreted as the at least one adverse event associated with the PCI procedure. Anderson teaches at pg. 2 the method where the indication of the recommended intervention identifies a CABG procedure when the functional disease quantification score is above a threshold. Anderson teaches at pg. 2 when the disease quantification score is below the threshold the system will recommend PCI.
The Office’s proposed combination is fundamentally flawed and fails to teach the claimed invention, specifically, Anderson does not computationally process the live angiogram imaging data itself to dynamically determine live risks of adverse events, a live confidence level, and live suggested device parameter for the procedure. Dadlani Mahtani’s determinations are based on static pre-operative data, not live angiogram imaging data.
The Examiner disagrees. See the updated rejection above for line by line explanations of how these limitations are taught and for how the references are combined. Additionally, the embodiments of Dadlani Mahtani are not restricted to the pre-operative phase as alleged by Applicant. For Example, Dadlani Mahtani teaches at pg. 10 for example, imaging results of patient data at different points or stages of treatment will be uploaded to the system and used to make a comparison with the expected outcome (images or pictures stored in the knowledge based of medical evidence) and produce a treatment effectiveness or progress score. The progress score teaches that the treatment could also be during the treatment.
Modifying Dadlani Mahtani to incorporate the live, intra-operative angiogram imaging and overlay features of Anderson would change the principle of operation of the prior art invention, which renders the combination improper under MPEP 2143.01(VI). Dadlani Mahtani is designed to operate a pre-operative consultation tool where a patient inputs lifestyle preferences to weigh long-term survival against quality of life.
The Examiner disagrees. For example, the embodiments of Dadlani Mahtani, by broadest reasonable interpretation, are not restricted to the pre-operative phase as alleged by Applicant. Dadlani Mahtani teaches at pg. 10 for example, imaging results of patient data at different points or stages of treatment will be uploaded to the system and used to make a comparison with the expected outcome (images or pictures stored in the knowledge based of medical evidence) and produce a treatment effectiveness or progress score. Dadlani Mahtani is not necessarily simply a pre-operative consultation tool and is used to teach limitations directed towards general patient treatment pathway analysis that are not even limited to the realm of angiography by broadest reasonable interpretation.
Attempting to re-architect Dadlani Mahtani to function as an intra-operative surgical tracking system that processes live angiogram imaging data to output live device parameters and live confidence levels during an active percutaneous coronary intervention procedure would fundamentally break the intended purpose and operation of Dadlani Mahtani, rending it unsatisfactory for its intended use. Therefore, Dadlani Mahtani, fails to teach or suggest the limitations of amended claim 1.
The Examiner disagrees with the Applicant’s portrayal of Dadlani Mahtani as being fundamentally misaligned with its application to a PCI procedure. Dadlani Mahtani teaches at pg. 10 for example, imaging results of patient data at different points or stages of treatment will be uploaded to the system and used to make a comparison with the expected outcome (images or pictures stored in the knowledge based of medical evidence) and produce a treatment effectiveness or progress score. Again, as shown in the answer above Dadlani Mahtani is not necessarily simply a pre-operative consultation tool and is used to teach limitations directed towards general patient treatment pathway analysis that are not even limited to the realm of angiography by broadest reasonable interpretation.
Applicant argues Siebert does not teach claim 8
The Examiner agrees with Applicant. Examiner has withdrawn Siebert from the rejection, the argument is moot. See the updated rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20200222018 A1 (hereafter van Walsum) teaches overlay dynamic roadmap on fluoroscopic image, which is tangentially related to the subject matter herein.
The following is tangentially related to the subjects of surgical guidance and angiography:
Tufaro et al. An automated software for real-time quantification of wall shear stress distribution in quantitative coronary angiography data, International Journal of Cardiology, Volume 357, 2022, Pages 14-19, ISSN 0167-5273.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/T.I.E./Examiner, Art Unit 3683
/CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683
