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 1-20 are pending. Claims 1-20 are rejected herein.
Priority
The application claims priority to provisional application 63/375800. The priority date is 15 September 2022.
Information Disclosure Statement
The information disclosure received 6 June 2025 has been reviewed.
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 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 risk associated with 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 […], 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.
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”).
Dependent Claims and Dependent Additional Elements
Claims 2-11 and 13-14 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 10 merely describes during a percutaneous coronary intervention (PCI) procedure, determine a live reading, the live reading comprising one or more live predicted effectiveness indicators associated with the PCI procedure, one or more live risks associated with the PCI procedure, a live confidence level associated with at least one of the respective predictions for the PCI procedure, and live suggested device parameters for the PCI procedure; and output for display one of the plurality of treatment options and live reading. 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.
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 § 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2,5,11-13,15 is/are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by WO 2014/049527 A2 (hereafter Dadlani Mahtani).
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 associated with the respective treatment pathway, and a respective confidence level associated with at least one of the respective predictions; [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.]
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 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.]
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 teaches the medical system of claim 1. Dadlani Mahtani further teaches:
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 teaches the medical system of claim 1. Dadlani Mahtani further 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. [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 11
Dadlani Mahtani teaches the medical system of claim 1. Dadlani Mahtani further teaches:
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 teaches the method of claim 12. Dadlani Mahtani further teaches:
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 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) 3-4,14 and 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 US 2018/0078139 (hereafter Sanders).
Regarding Claim 3
Dadlani Mahtani teaches the medical system of claim 1. Dadlani Mahtani 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/Sanders teach the medical system of claim 3. Dadlani Mahtani/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 US-20220406460-A1 (hereafter Golan).
Regarding Claim 9
Dadlani Mahtani teaches the medical system of claim 1. Dadlani Mahtani further teaches:
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 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) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO 2014/049527 A2 (hereafter Dadlani Mahtani) in view of US-20220406460-A1 (hereafter Golan) in view of WO-2016092420-A1 (hereafter Anderson).
Regarding Claim 10
Dadlani Mahtani/Golan teach the medical system of claim 9. Dadlani Mahtani/Golan further teach:
and live suggested device parameters for the PCI procedure; [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. Golan teaches at Figure 3 displaying a notification that includes a device recommendation: catheter with length L1 and diameter D2. The placing of the catheter of a specific length and diameter is interpreted here as the description of the PCI procedure. The recommendation of timing of when to do a procedure and what device to used and the length and diameter of the catheter is interpreted as the suggested device parameters for the respective treatment option. This teaches and live suggested device parameters for the PCI procedure.]
Dadlani Mahtani/Golan may not explicitly teach:
wherein the processing circuitry is further configured to: during a percutaneous coronary intervention (PCI) procedure, determine a live reading, the live reading comprising one or more live predicted effectiveness indicators associated with the PCI procedure, one or more live risk associated with the PCI procedure,
a live confidence level associated with at least one of the respective predictions for the PCI procedure,
[…]
and output for display one of the plurality of treatment options and live reading.
Anderson teaches:
wherein the processing circuitry is further configured to: during a percutaneous coronary intervention (PCI) procedure, determine a live reading, the live reading comprising one or more live predicted effectiveness indicators associated with the PCI procedure, one or more live risk associated with the PCI procedure, [Anderson teaches at pg. 20 and pg. 21 as shown in Figure 6, the user interface provided on the display includes a button 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, including pressure waveform plots 612 and 628, calculated pressure ratios 636, and/or the windows 614, 670, 672 are updated in real time as a procedure is being performed. Anderson teaches at pg. 5 the physiological data, as collected real-time, is linked or co-registered to a schematic of the coronary arteries or an angiogram. Anderson teaches at pg. 5 the one or more embodiments described herein are also able to automatically make updates to the visual depiction based on the collected data as processed according to a risk calculator to provide a recommendation such as a particular intervention for a patient. Anderson teaches at pg. 5 for example the data will be processed to provide an objective recommendation of whether to perform a percutaneous coronary intervention (PCI) or a coronary artery bypass graft (CABG) surgery. Anderson teaches at pg. 5 one aspect of the present disclosure includes using a model of the patient’s vasculature, obtained from angiography, to automatically calculate a disease quantification score. Anderson teaches at pg. 5. one aspect of the present disclosure includes super-imposing real-time collected pressure and/or flow data (or other physiologic data) onto an angiogram, or a schematic of anatomy and representing the data in a way that helps a clinician determine how/where to intervene (including but not limited to using data to determine where to perform grafts (CABG planning) and PCI planning). Anderson teaches at pg. 5 in some embodiments, the collected physiology data will include real-time data obtained during a procedure. his teaches, during a percutaneous coronary intervention Collectively, Anderson teaches a (PCI) procedure, determine a live reading, the live reading comprising one or more live predicted effectiveness indicators associated with the PCI procedure.]
[…]
and output for display one of the plurality of treatment options and live reading. [Anderson teaches at pg. 33 the method includes determining whether to perform a first surgical procedure over a second surgical procedure, wherein the determining is based on the co-registered physiologic measurements. Anderson teaches at pg. 33 the determination will result in a recommendation of the intervention for the patient. Anderson teaches at pg. 33 for example, the intervention recommendation will be to perform either a CABG operation or a PCI operation. Anderson teaches at pg. 5 the physiological data, as collected real-time, is linked or co-registered to a schematic of the coronary arteries or an angiogram. Anderson teaches at pg. 5 the one or more embodiments described herein are also able to automatically make updates to the visual depiction based on the collected data as processed according to a risk calculator to provide a recommendation such as a particular intervention for a patient. Anderson teaches at pg. 5 for example the data will be processed to provide an objective recommendation of whether to perform a percutaneous coronary intervention (PCI) or a coronary artery bypass graft (CABG) surgery. Anderson teaches at pg. 5 one aspect of the present disclosure includes using a model of the patient’s vasculature, obtained from angiography, to automatically calculate a disease quantification score. Anderson teaches at pg. 5. one aspect of the present disclosure includes super-imposing real-time collected pressure and/or flow data (or other physiologic data) onto an angiogram, or a schematic of anatomy and representing the data in a way that helps a clinician determine how/where to intervene (including but not limited to using data to determine where to perform grafts (CABG planning) and PCI planning). This teaches, during a percutaneous coronary intervention (PCI) procedure, determine a live reading, the live reading comprising one or more live predicted effectiveness indicators associated with the PCI procedure. Anderson teaches at pg. 5 in some embodiments, the collected physiology data will include real-time data obtained during a 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 method and system for computer-aided decision guidance of Golan to the devices, systems, and methods for vessel assessment and intervention recommendation of Anderson with the motivation of the present disclosure relates generally to the assessment of vessels and, in particular, improving the assessment of the severity of a blockage or other restriction to the flow of fluid through a vessel and the treatment thereof.
Dadlani Mahtani/Golan/Anderson may not explicitly teach:
a live confidence level associated with at least one of the respective predictions for the PCI procedure, and live suggested device parameters for the PCI procedure;
Dadlani Mahtani 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. 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 […] procedure.]
Anderson teaches the following noted feature:
…PCI… [Anderson teaches at pg. 33 for example, the intervention recommendation will be to perform either a CABG operation or a PCI operation.]
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 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 of the secondary reference(s) for the general procedure means of the primary reference. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Claim(s) 6 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO 2014/049527 A2 (hereafter Dadlani Mahtani) in view of US 2022/0183915 A1 (hereafter Augustine).
Regarding Claim 6
Dadlani Mahtani teaches the medical system of claim 1. Dadlani Mahtani 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 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 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 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) 7-8,19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO 2014/049527 A2 (hereafter Dadlani Mahtani) in view of Siebert (Measurement of fractional flow reserve to guide decisions for percutaneous coronary intervention).
Regarding Claim 7
Dadlani Mahtani teaches the medical system of claim 1. Dadlani Mahtani may not explicitly 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.
Siebert teaches:
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. [Siebert teaches at pg. 5 and pg. 6, under 5.1 Methods, teaches the development of the German Coronary Arter Disease Outcome Model (German CADOM), a decision-analytic Markov model, to estimate the long term effectiveness and cost effectiveness of FFR measurement to guide the decision on PCI in the context of the German healthcare system. This teaches 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.]
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 measurement of fractional flow reserve to guide decisions for percutaneous coronary intervention of Siebert with the motivation of proving the necessity of PCI for patients (Siebert at the Abstract-Background).
Regarding Claim 8
Dadlani Mahtani teaches the medical system of claim 1. Dadlani Mahtani may not explicitly teach:
wherein each of the plurality of treatment pathways further comprises at least one of a respective inventory availability or cost.
Siebert teaches:
wherein each of the plurality of treatment pathways further comprises at least one of a respective inventory availability or cost. [Siebert teaches at pg. 5 and pg. 6, under 5.1 Methods, teaches the development of the German Coronary Arter Disease Outcome Model (German CADOM), a decision-analytic Markov model, to estimate the long term effectiveness and cost effectiveness of FFR measurement to guide the decision on PCI in the context of the German healthcare system. This teaches 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. Additionally, this teaches wherein each of the plurality of treatment pathway further comprises at least one of a respective cost.]
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 measurement of fractional flow reserve to guide decisions for percutaneous coronary intervention of Siebert with the motivation of proving the necessity of PCI for patients (Siebert at the Abstract-Background).
Regarding Claim 19
Dadlani Mahtani teaches the method of claim 12. Dadlani Mahtani may not explicitly 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.
Siebert teaches:
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. [Siebert teaches at pg. 5 and pg. 6, under 5.1 Methods, teaches the development of the German Coronary Arter Disease Outcome Model (German CADOM), a decision-analytic Markov model, to estimate the long term effectiveness and cost effectiveness of FFR measurement to guide the decision on PCI in the context of the German healthcare system. This teaches 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.]
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 measurement of fractional flow reserve to guide decisions for percutaneous coronary intervention of Siebert with the motivation of estimating the long-term effectiveness and cost-effectiveness of FFR measurement in the context of the German healthcare system (Siebert at the Abstract-Methods).
Regarding Claim 20
Dadlani Mahtani teaches the method of claim 12. Dadlani Mahtani may not explicitly teach:
wherein each of the plurality of treatment pathways further comprises at least one of a respective inventory availability or cost.
Siebert teaches:
wherein each of the plurality of treatment pathways further comprises at least one of a respective inventory availability or cost. [Siebert teaches at pg. 5 and pg. 6, under 5.1 Methods, teaches the development of the German Coronary Arter Disease Outcome Model (German CADOM), a decision-analytic Markov model, to estimate the long term effectiveness and cost effectiveness of FFR measurement to guide the decision on PCI in the context of the German healthcare system. This teaches wherein each of the plurality of treatment pathways further comprises cost.]
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 measurement of fractional flow reserve to guide decisions for percutaneous coronary intervention of Siebert with the motivation of estimating the long-term effectiveness and cost-effectiveness of FFR measurement in the context of the German healthcare system (Siebert at the Abstract-Methods).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2014058738 A1 (hereafter Yeskel) teaches predictive analysis of medical treatment pathways.
Zlotnick et al. Classification and treatment of coronary artery bifurcation lesions: putting the Medina classification to the test, Cardiovascular Revascularization Medicine, Volume 13, Issue 4, 2012, Pages 228-233, ISSN 1553-8389 (hereafter Zlotnick). Zlotnick teaches about MEDINA classifications.
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/T.I.E./Examiner, Art Unit 3683
/CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683
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