DETAILED ACTION
This is responsive to amendments filed on 07/07/2025 in which claims 1-24 and 29-36 are presented for examination; Claims 1, 7-8, 12-13, and 23-24 have been amended. Claim 26 have been cancelled. Claims 34-36 have been newly added.
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
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 1-24 and 29-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1: Is the claim to a process, machine, manufacture or composition of matter?” Yes, it’s a method.
Step 2a Prong 1 (judicial exception)
Step 2A (1): “Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes , the claim comes under mental processes.
Claim 1 recites:
“A method of using a prioritization function to assist in determination of a clinical intervention for a subject as part of a clinical trial, the method comprising:(a) obtaining a dataset for a treatment set of subjects and a reference set of subjects, wherein the treatment set of subjects receives one or more of a plurality of clinical intervention during the clinical trial , and wherein the reference set of subjects does not receive one or more of the plurality of clinical intervention during the clinical trial; (b) receiving, on a computing device, a plurality of treatment outcomes for the treatment set of subjects and the reference set of subjects, wherein the plurality of treatment outcomes are transmitted over a computer network; (c) presenting to a user, via a user interface on an electronic display of the computing device, a representation of the plurality of treatment outcomes(d) selecting, by the user via the user interface on the electronic display, a prioritization function that assigns ranked values to each of the plurality of treatment outcomes, wherein the ranked values are selected by the user based at least in part on (1)subject-level efficacies and subject-level adverse effects of individual treatment outcomes of the plurality of treatment outcomes, on the subject and (2) personalized preference of the subject; (e) performing by a computer processor a set of pairwise comparisons between a first subject selected from the treatment set of subjects and a second subject selected from the reference set of subjects, at least in part by performing a prioritized comparison of the plurality of treatment outcomes between the first subject and the second subject based at least in part on the ranked values of the prioritization function , wherein the prioritized comparison is subject to a clinical threshold, wherein the clinical threshold comprises a minimal threshold for a positive treatment outcome; (f) generating a plurality of net treatment benefit of the plurality of clinical intervention for the subject, based at least in part on the set of pairwise comparisons (g) electing the clinical intervention from among the plurality of clinical interventions based at least in part on the plurality of net treatment benefits; and (h) administering the clinical intervention to the subject. as part of the clinical trial , wherein the clinical intervention comprises a net treatment benefit that is above the clinical threshold .
All the limitations above are abstract idea related to the mental process (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)) with the exception of bold and underlined limitations. Claim language pertains to comparing two set of subjects , one receiving intervention and other without it, which can easily be done with pen and paper. Obtaining treatment outcomes and comparing based on prioritization (ranking , adverse effects etc.) , which again can be done with pen and paper. Net treatment benefit is determined by looking at the recorded treatment outcomes , which can be done mentally or on paper. A clinical intervention can be chosen from among multiple, by analyzing net treatment benefits. The net benefit of intervention can be written on paper, and analyzed. Also, ranking based on preference and comparing can be done on paper.
Step 2A(2): Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. NO
The claim does recite additional elements; however they don’t integrate the exception into a practical application of the exception.
receiving on a computing device ((Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) )
transmitted over a computer network(Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) )
user interface Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
electronic display(Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
computing device (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
a computer processor (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
administering the selected clinical intervention to the subject (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) )
Step 2B: evaluate whether the claim recites additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception? NO
As discussed previously with respect to Step 2A Prong Two, the additional elements in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
Regarding the claim limitations:
receiving on a computing device ((the courts have recognized the computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (“i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information”); See, MPEP 2106.05 (d)(II)
transmitted over a computer network ((the courts have recognized the computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (“i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information”); See, MPEP 2106.05 (d)(II)
“ administering the selected clinical intervention to the subject” is well‐understood, routine, and conventional functions that is performed routinely. For example, a person based on experience know whether they respond better to Advil or tylanol, and based on their experience, they can choose to take drug that works better for them. Simply, put administering an intervention is nothing more than using the treatment, which is done by all the patients.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Dependent claims 2-24, and 29-36 further narrow the abstract idea recited above with regard to claim 1; in addition, claims contain additional elements of “a medical device” , “storing an outcome”, “remotely accessing the dataset.”
device (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
storing an outcome(Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
remotely accessing the dataset (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) )
Under step 2A, prong two, the above recited units/devices don’t integrate the exception into a practical application of the exception as merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)
As discussed previously with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
Regarding claim limitation “remotely accessing the dataset” the courts have recognized the computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (“i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information”); See, MPEP 2106.05 (d)(II)
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-11, 15-18, 20-24 and 29-30 are rejected under 35 U.S.C. 103 as being unpatentable over Marc Buysea,b∗† ( “Generalized pairwise comparisons of prioritized outcomes in the two-sample problem”, 2nd March 2010 ) in view of O’Connor (“Strategies to Prioritize Clinical Options in Primary Care”, 2017) and further in view of Athreya et al. (US 20250006332 A1)
Regarding claim 1, Buysea,b∗† teaches a method of using a prioritization function to assist in determination of a clinical intervention for a subject as part of a clinical trial, the method comprising(see 4.3, pg. 3250):
(a) obtaining a dataset for a treatment set of subjects and a reference set of subjects (pg. 3247, 3. Generalized pairwise comparisons, “We are interested in the general situation of two groups of individuals (whom we call ‘patients’ in the clinical trial examples) to be compared in terms of one or more outcome measures (or ‘endpoints’) observed at one or more occasions for each individual. Formally, the outcome measures of interest are captured by random variables, the values of which are the individual outcomes. We assume that one group of n individuals is exposed to an intervention or treatment (labeled ‘T ’), while the other group of m individuals serves as a control (labeled ‘C’).)
wherein the treatment set of subjects receives one or more of a plurality of clinical interventions during the clinical trial, and wherein the reference set of subjects does not receives one or more of a plurality of clinical interventions during the clinical trial(pg. 3247, 3. Generalized pairwise comparisons, “We are interested in the general situation of two groups of individuals (whom we call ‘patients’ in the clinical trial examples) to be compared in terms of one or more outcome measures (or ‘endpoints’) observed at one or more occasions for each individual. Formally, the outcome measures of interest are captured by random variables, the values of which are the individual outcomes. We assume that one group of n individuals is exposed to an intervention or treatment (labeled ‘T ’), while the other group of m individuals serves as a control (labeled ‘C’).; Also, see Pg. 3255 that teach plurality of interventions (“35 per cent for patients receiving pegaptanib vs 45 per cent for patients receiving sham injections”))
(b) [receiving on a computing device] , a plurality of treatment outcomes for the treatment set of subjects and the reference set of subjects, [wherein the plurality of treatment outcomes are transmitted over a computer network] (pg. 3247, 3. Generalized pairwise comparisons, “Pairwise comparisons require consideration of pairs of individuals, one taken from group T and the other taken from group C. The outcomes of these two individuals are compared and the pair is said to be ‘favorable’ if the outcome of the individual in group T is better than the outcome of the individual in group C, ‘unfavorable’ if the outcome of the individual in group T is worse than the outcome of the individual in group C, ‘neutral’ if there is no difference between the outcomes of the two individuals, or ‘uninformative’ if it cannot be determined which of the two individuals has a better outcome (e.g. if the outcome is missing for at least one of the two individuals) Note: here, group C refers to treatment set and group T refers to reference set.)
(d) [selecting by the user via the user interface on an electronic display] a prioritization function that assigns ranked values to each of the plurality of treatment outcomes (pg. 3249, 4. Prioritized outcomes, “Generalized pairwise comparisons can be extended to several outcomes arising from successive thresholds of a single outcome measure (Section 4.1), from repeated observations of a single outcome measure (Section 4.2), or from several outcome measures (Section 4.3). We will consider the extension to several outcome measures when an ordering of the multivariate space can be defined by prioritizing the variables.
Also, pg. 3248 “3.1. Binary variable , “Assume that the outcome measure of interest is binary in nature. For reasons that will become clear later in this paper, it is convenient to denote this binary variable X in the treatment group and Y in the control group, with X =1 (or Y =1) indicating success, and X =0 (or Y =0) indicating failure. Table I displays the possible situations that can arise in the comparison of Xi , the outcome of the ith individual (i =1, ...,n) in group T with Y j , the outcome of the jth individual ( j =1, ...,m) in group C) Note: here assigning/denoting treatment group and control group with values is ranking them.
(e) performing by a computer processor a set of pairwise comparisons between a first subject selected from the treatment set of subjects and a second subject selected from the reference set of subjects(Pg. 3245, 2. Presentation of case studies, 2.1. A randomized trial in advanced colorectal cancer, “We will first illustrate generalized pairwise comparisons using data from a randomized trial of 420 patients with advanced colorectal cancer [3]. Patients were randomized to either a standard regimen of 5-fluorouracil and leucovorin (‘LV5FU2’), or to the same regimen plus oxaliplatin.” Note: here, standard regimen refers to reference set. See Athreya reference for computer processor. )
at least in part by performing a prioritized comparison of the plurality of treatment outcomes between the first subject and the second subject based at least in part on the ranked values of the prioritization function (pg. 3249, 4.2. Repeated observations, “Generalized pairwise comparisons can easily be extended to repeated observations of a variable capturing the outcome measure of interest if the different occasions at which the variable is potentially measured are prioritized. For instance, when the variable is measured repeatedly over time (longitudinal data), a later difference between the groups may be more relevant than an earlier one in so far as it reflects a sustained effect of the intervention or treatment over time. In this case, a later difference will take priority over an earlier difference in pairwise comparisons. The clinical trial in macular generation (Section 2.2) again provides an example of such a situation in which up to 10 longitudinal measurements of visual acuity, taken 6 weeks apart, are available for each patient.” Note: Also, see Table IV. );
wherein the prioritized comparison is subject to a clinical threshold, wherein the clinical threshold comprises a minimal threshold for a positive treatment outcome (pg. 3248 “ 3.2. Continuous variable, Assume now that the outcome measure of interest is captured by continuous variable X in the treatment group and Y in the control group. Assume further, without loss of generality, that larger values of X (and Y ) are preferable to smaller values of X (and Y ). In some applied settings, the difference between the values of these two variables may have to exceed a pre-specified threshold, denoted , to be considered meaningful. The threshold can be a function ofthe precision with which X (and Y ) is measured. In clinical trials, the threshold can also reflect a difference regardedas clinically relevant. Table II displays generalized pairwise comparisons of continuous variables with a threshold .In Section 7.2, pairwise comparisons will be shown to be equivalent to the Wilcoxon rank-sum test in the specialcase where = 0 :
Also, pg. 3256 “Using generalized pairwise comparisons, it is easy to define a threshold for differences in vision that clearly defines a better outcome (such as 15 letters of visual acuity), and estimate the proportion in favor of treatment beyond that threshold (Table VI).”)
(f) generating a plurality of net treatment benefits of the plurality of clinical interventions for the subject, based at least in part on the set of pairwise comparisons (pg. 3250, 4.3. Several outcome measures, “Generalized pairwise comparisons can also be extended to several outcome measures by prioritizing the variables that capture them in order to define a better outcome, just as the occasions were prioritized in the case of repeated observations of a single outcome measure. A better outcome is defined for each of these variables, and a better outcome overall is then defined as a better outcome for the variable with the highest priority, as in Section 4.2.”
Also, pg. 3250, 4.3. Several outcome measures, “The prioritized variables can be of different types. In advanced cancer, for instance, in addition to time to death and time to disease progression, the achievement of a ‘tumor response’ (defined for solid tumors as greater than 50 per cent shrinkage of the tumor surface area) may sometimes be a relevant indicator of treatment benefit, though the time to achieve such a response is generally unimportant since most responses are obtained soon after starting therapy.”
Also, Pg. 3254, “One advantage of over P(X>Y ) may be its easier interpretation. For instance, P(X,Y )=0.5 would be interpreted as meaning that the experiment provides no evidence that T differs from C in either direction. This situation would correspond to =0, which is a more direct and intuitively obvious way of expressing the (lack of) treatment benefit. Moreover, the cumulative proportions in favor of treatment for various thresholds, times of measurement, or other prioritized outcomes can help interpret any differences between the groups being compared, as will become evident in the analysis of the case studies in the next Section.” Also, see table on Pg. 3255 that shows comparison between Pegaptanib and Sham.)
wherein the clinical intervention comprises a net treatment benefit that is above the clinical threshold (Pg. 3248, “Assume now that the outcome measure of interest is captured by continuous variable X in the treatment group and Y in the control group. Assume further, without loss of generality, that larger values of X (and Y ) are preferable to smaller values of X (and Y ). In some applied settings, the difference between the values of these two variables may have to exceed a pre-specified threshold, denoted , to be considered meaningful. The threshold can be a function of the precision with which X (and Y ) is measured. In clinical trials, the threshold can also reflect a difference regarded as clinically relevant. Table II displays generalized pairwise comparisons of continuous variables with a threshold . In Section 7.2, pairwise comparisons will be shown to be equivalent to the Wilcoxon rank-sum test in the special case where =0.”)
Buysea,b∗† doesn’t explicitly teaches:
wherein the ranked values are selected by the user based at least in part on (1) subject-level efficacies and subject-level adverse effects of individual treatment outcomes of the plurality of treatment outcomes, on the subject and (2) a personalized preference of the subject;
(b) receiving on a computing device , [a plurality of treatment outcomes for the treatment set of subjects and the reference set of subjects], wherein the plurality of treatment outcomes are transmitted over a computer network;
(c) presenting to a user, via a user interface on an electronic display of the computing device, a representation of the plurality of treatment outcomes
(d) selecting by the user via the user interface on an electronic display [a prioritization function that assigns ranked values to each of the plurality of treatment outcomes]
(g) electing the clinical intervention from among the plurality of clinical interventions based at least in part on the plurality of net treatment benefits;
and(h) administering the clinical intervention to the subject as part of the clinical trial,
O’Connor teaches:
wherein the ranked values are selected by the user based at least in part on (1) subject-level efficacies and subject-level adverse effects of individual treatment outcomes of the plurality of treatment outcomes, on the subject and (2) a personalized preference of the subject (Pg. 11, “The potential of electronic health records (EHRs) to improve care has long been recognized but rarely been realized. Prototype EHR-linked, Web-based clinical decision support systems that identify and prioritize clinical options, however, save time, satisfy clinicians, empower patients, have high use rates, and improve care are now up and running in several large health care systems.15,16 Web services that include risk prediction equations can receive patient-specific data that are automatically sent from an EHR, perform the multiple computations needed to estimate the relative benefits of alternative treatment options, and display patient specific prioritized treatment options on the EHR screen within 1 second. Presenting clinical options to the patient facilitates patient-centered care and shared decision making by informing the patient of clinical options with the most potential benefit and then empowering the patient to select their preferred option(s). Many patients will continue to decline clinical options of high benefit, such as smoking cessation, colorectal cancer screening, or statin treatment. Then we must respect our patient’s preferences and remember that patient treatment preferences and readiness to change typically vary with time.23 Clinical decision support systems update and reprioritize evidence-based treatment options at each subsequent encounter, enabling patients to see progress in some areas and reconsider previous preferences in other areas.”);
(d) selecting by the user [via the user interface on an electronic display a prioritization function that assigns ranked values to each of the plurality of treatment outcomes] (Pg. 11, “The potential of electronic health records (EHRs) to improve care has long been recognized but rarely been realized. Prototype EHR-linked, Web-based clinical decision support systems that identify and prioritize clinical options, however, save time, satisfy clinicians, empower patients, have high use rates, and improve care are now up and running in several large health care systems.15,16 Web services that include risk prediction equations can receive patient-specific data that are automatically sent from an EHR, perform the multiple computations needed to estimate the relative benefits of alternative treatment options, and display patient specific prioritized treatment options on the EHR screen within 1 second. Presenting clinical options to the patient facilitates patient-centered care and shared decision making by informing the patient of clinical options with the most potential benefit and then empowering the patient to select their preferred option(s).”)
(g) electing the clinical intervention from among the plurality of clinical interventions based at least in part on the plurality of net treatment benefits (Pg. 11, “The potential of electronic health records (EHRs) to improve care has long been recognized but rarely been realized. Prototype EHR-linked, Web-based clinical decision support systems that identify and prioritize clinical options, however, save time, satisfy clinicians, empower patients, have high use rates, and improve care are now up and running in several large health care systems.15,16 Web services that include risk prediction equations can receive patient-specific data that are automatically sent from an EHR, perform the multiple computations needed to estimate the relative benefits of alternative treatment options, and display patient specific prioritized treatment options on the EHR screen within 1 second. Presenting clinical options to the patient facilitates patient-centered care and shared decision making by informing the patient of clinical options with the most potential benefit and then empowering the patient to select their preferred option(s). Many patients will continue to decline clinical options of high benefit, such as smoking cessation, colorectal cancer screening, or statin treatment. Then we must respect our patient’s preferences and remember that patient treatment preferences and readiness to change typically vary with time.23 Clinical decision support systems update and reprioritize evidence-based treatment options at each subsequent encounter, enabling patients to see progress in some areas and reconsider previous preferences in other areas.” Note: here, the selection is in part based in net benefit, as list is prioritized based on relative benefits, and the selection is left to user preference.);
and(h) administering the clinical intervention to the subject as part of the clinical trial(Pg. 11, “Presenting clinical options to the patient facilitates patient-centered care and shared decision making by informing the patient of clinical options with the most potential benefit and then empowering the patient to select their preferred option(s). Many patients will continue to decline clinical options of high benefit, such as smoking cessation, colorectal cancer screening, or statin treatment. Then we must respect our patient’s preferences and remember that patient treatment preferences and readiness to change typically vary with time.23 Clinical decision support systems update and reprioritize evidence-based treatment options at each subsequent encounter, enabling patients to see progress in some areas and reconsider previous preferences in other areas.” Note: the selected intervention have to be administered in order to see progress, and revisit the prioritization.),
It would have been obvious for a person of ordinary skill in the art to apply intervention prioritizing teachings of O’Connor into the teachings of Buysea,b,∗ at the time the application was filed in order to prioritize clinical options. (Pg. 11, “Several alternative methods are available to identify and prioritize evidence-based clinical options with the most potential benefit to a given patient at a given point in time.”)
Buysea,b∗† as modified by O’Connor does not explicitly teach:
b) receiving on a computing device , [a plurality of treatment outcomes for the treatment set of subjects and the reference set of subjects], wherein the plurality of treatment outcomes are transmitted over a computer network;
(c) presenting to a user, via a user interface on an electronic display of the computing device, a representation of the plurality of treatment outcomes
(d) [selecting by the user ]via the user interface on an electronic display [a prioritization function that assigns ranked values to each of the plurality of treatment outcomes]
Athreya teaches :
(b) receiving on a computing device , [a plurality of treatment outcomes for the treatment set of subjects and the reference set of subjects], wherein the plurality of treatment outcomes are transmitted over a computer network(para , “[0101] …..In some embodiments, memory 720 can have encoded thereon a server program for controlling operation of server 652. In such embodiments, processor 712 can execute at least a portion of the server program to transmit information and/or content (e.g., data, images, a user interface, treatment outcome reports) to one or more computing devices 650, receive information and/or content from one or more computing devices 650, receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone), and so on.”
(c) presenting to a user, via a user interface on an electronic display of the computing device, a representation of the plurality of treatment outcomes(para, “[0105]… In some embodiments, memory 728 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 722 to control the one or more inputs 724, and/or receive data from the one or inputs 724; present content (e.g., images, a user interface, a treatment outcome report) using a display; communicate with one or more computing devices 650;”
Also, para “[0098…..In such embodiments, processor 702 can execute at least a portion of the computer program to present content (e.g.,images, user interfaces, graphics, tables, treatment outcome reports), receive content from server 652, transmit information to server 652, and so on.”)
(d) [selecting by the user ]via the user interface on an electronic display [a prioritization function that assigns ranked values to each of the plurality of treatment outcomes](para, “[0105] In some embodiments, memory 728 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 722 to control the one or more inputs 724, and/or receive data from the one or inputs 724; present content (e.g., images, a user interface, a treatment outcome report) using a display; ….”)
It would have been obvious for a person of ordinary skill in the art to apply user interface teachings of Athreya into the teachings of Buysea,b,∗ as modified by O’Connor at the time the application was filed in order to display predicted treatment outcome and clinical course of action(para, “[0089] The systems and methods described in the present disclosure may be implemented using a computer, a tablet, a smart phone, or other computing device to receive, via a user interface (e.g., a touch screen, a keyboard), indications of symptom measures for an adolescent patient at respective different points in time. The computing device could then, based on the received symptom measures, generate a predicted treatment outcome or other clinical course of action and provide, via the user interface (e.g., a display), an indication of the predicted treatment outcome and/or clinical course of action.”)
Regarding claim 2, Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 1.
Buysea,b∗† further teaches wherein the clinical intervention comprises an intervention that may be compared between a case group and a control group (pg. 3247,” We are interested in the general situation of two groups of individuals (whom we call ‘patients’ in the clinical trial examples) to be compared in terms of one or more outcome measures (or ‘endpoints’) observed at one or more occasions for each individual. Formally, the outcome measures of interest are captured by random variables, the values of which are the individual outcomes. We assume that one group of n individuals is exposed to an intervention or treatment (labeled ‘T ’), while the other group of m individuals serves as a control (labeled ‘C’). Such a situation is typical of comparative trials where patients are allocated to treatment or control through a random mechanism, as in the two case studies described in Section 2 and analyzed in Section 9. As for other two-sample tests, there is no requirement that the two groups be formed by random allocation: they can also be formed by independent random sampling from two populations, or by any other sampling scheme under a condition of exchangeability of individuals under the null hypothesis that will be further discussed below [5].”)
Regarding claim 3, Buysea,b∗† as modified by O’Connor teaches the method of claim 1.
Buysea,b∗† further teaches wherein the clinical intervention is selected from the group consisting of a medication, a cell-based or gene therapy, a drug treatment, a medical device, a surgical intervention, a radiotherapy, radio isotopic/nuclear therapy, physical therapy, occupational therapy, phono audiological therapy, a rehabilitation intervention, a psychological intervention, an immunotherapy, a digital health intervention, and a behavioral intervention (Pg. 3254, “Recall first that the logrank test failed to show a significant survival benefit of the addition of oxaliplatin to LV5FU2 (Figure 2). The authors of the paper also reported that Gehan’s generalized Wilcoxon test just reached significance (P =0.05) [3]. This result is confirmed through pairwise comparisons (=10.1 per cent, P =0.05, top panel of Table V), which additionally show that pairwise differences in times to death exceed one year in 4.4 per cent of pairs (P =0.043), and 6 months in 8.3 per cent of pairs (P =0.038). Although these cumulative differences would not reach statistical significance after proper adjustment for multiplicity (see Section 6.4), they provide useful information not given by standard two-sample non-parametric statistics.”
Also, see Fig. 2)
Regarding claim 4, Buysea,b∗† as modified by O’Connor teaches the method of claim 1.
Buysea,b∗† further teaches wherein the plurality of treatment outcomes are measured by discrete variables, continuous variables, ordinal variables, or time-to-event variables (Pg. 3245, “wherein the treatment outcomes are measured by discrete variables, continuous variables, ordinal variables, or time-to-event variables.”)
Regarding claim 5, Buysea,b∗† as modified by O’Connor teaches the method of claim 1.
Buysea,b∗† further teaches wherein the plurality of treatment outcomes comprises a member selected from the group consisting of event-free survival time, progression-free survival time, overall survival time, another time to event, efficacy, safety, quality of life, a score (functional score, performance score, toxicity grade, behavioral score, a composite score, an index score, or a combination thereof), and a biomarker (chemical, genomic, epigenomic, gene expression, protein, metabolite, clinical test result corresponding to a disease) (Pg. 3256, “Hence, time-to-event variables such as time to disease worsening can be combined with binary variables such as toxicities, or continuous variables such as quality of life scores.”)
Regarding claim 6, Buysea,b∗† as modified by O’Connor teaches the method of claim 1.
Buysea,b∗† further teaches wherein the first subject is randomly selected from the treatment set of subjects, and wherein the second subject is randomly selected from the reference set of subjects (Pg. 3247, “We are interested in the general situation of two groups of individuals (whom we call ‘patients’ in the clinical trial examples) to be compared in terms of one or more outcome measures (or ‘endpoints’) observed at one or more occasions for each individual. Formally, the outcome measures of interest are captured by random variables, the values of which are the individual outcomes. We assume that one group of n individuals is exposed to an intervention or treatment (labeled ‘T ’), while the other group of m individuals serves as a control (labeled ‘C’). Such a situation is typical of comparative trials where patients are allocated to treatment or control through a random mechanism, as in the two case studies described in Section 2 and analyzed in Section 9. As for other two-sample tests, there is no requirement that the two groups be formed by random allocation: they can also be formed by independent random sampling from two populations, or by any other sampling scheme under a condition of exchangeability of individuals under the null hypothesis that will be further discussed below [5]”)
Regarding claim 7, Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 1.
Buysea,b∗† further teaches wherein (f)comprises determining or a comparing a difference in the plurality of treatment outcomes between the first subject and the second subject (Pg. 3247, “Pairwise comparisons require consideration of pairs of individuals, one taken from group T and the other taken from group C. The outcomes of these two individuals are compared and the pair is said to be ‘favorable’ if the outcome of the individual in group T is better than the outcome of the individual in group C, ‘unfavorable’ if the outcome of the individual in group T is worse than the outcome of the individual in group C, ‘neutral’ if there is no difference between the outcomes of the two individuals, or ‘uninformative’ if it cannot be determined which of the two individuals has a better outcome (e.g. if the outcome is missing for at least one of the two individuals). For a pair to be considered favorable, unfavorable or neutral, a ‘better outcome’ must be defined for every possible pair of values of the variable of interest.”)
Regarding claim 8, Buysea,b∗† as modified by O’Connor Athreya teaches the method of claim 1.
Buysea,b∗† further teaches wherein( f )comprises comparing each of the plurality of treatment outcomes between the first subject and the second subject (Pg. 3247, “Pairwise comparisons require consideration of pairs of individuals, one taken from group T and the other taken from group C. The outcomes of these two individuals are compared and the pair is said to be ‘favorable’ if the outcome of the individual in group T is better than the outcome of the individual in group C, ‘unfavorable’ if the outcome of the individual in group T is worse than the outcome of the individual in group C, ‘neutral’ if there is no difference between the outcomes of the two individuals, or ‘uninformative’ if it cannot be determined which of the two individuals has a better outcome (e.g. if the outcome is missing for at least one of the two individuals). For a pair to be considered favorable, unfavorable or neutral, a ‘better outcome’ must be defined for every possible pair of values of the variable of interest.”)
Regarding claim 9, Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 7.
Buysea,b∗† further teaches further comprising characterizing a pairwise comparison as a win, a loss, a tie, or an indeterminate comparison based at least in part on the difference in the plurality of treatment outcomes between the first subject and the second subject (Pg. 3247, “Pairwise comparisons require consideration of pairs of individuals, one taken from group T and the other taken from group C. The outcomes of these two individuals are compared and the pair is said to be ‘favorable’ if the outcome of the individual in group T is better than the outcome of the individual in group C, ‘unfavorable’ if the outcome of the individual in group T is worse than the outcome of the individual in group C, ‘neutral’ if there is no difference between the outcomes of the two individuals, or ‘uninformative’ if it cannot be determined which of the two individuals has a better outcome (e.g. if the outcome is missing for at least one of the two individuals). For a pair to be considered favorable, unfavorable or neutral, a ‘better outcome’ must be defined for every possible pair of values of the variable of interest.” Note: here, win is favorable, loss is unfavorable, and tie is neutral)
Regarding claim 10, Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 9.
Buysea,b∗† further teaches wherein a pairwise comparison is characterized as a win, a loss, or a tie based at least in part on the difference in the plurality of treatment outcomes being a positive difference greater than a threshold, a negative difference greater than a threshold, or a difference less than a threshold, respectively ((Pg. 3247, “Pairwise comparisons require consideration of pairs of individuals, one taken from group T and the other taken from group C. The outcomes of these two individuals are compared and the pair is said to be ‘favorable’ if the outcome of the individual in group T is better than the outcome of the individual in group C, ‘unfavorable’ if the outcome of the individual in group T is worse than the outcome of the individual in group C, ‘neutral’ if there is no difference between the outcomes of the two individuals, or ‘uninformative’ if it cannot be determined which of the two individuals has a better outcome (e.g. if the outcome is missing for at least one of the two individuals). For a pair to be considered favorable, unfavorable or neutral, a ‘better outcome’ must be defined for every possible pair of values of the variable of interest.” Note: here, win is favorable, loss is unfavorable, and tie is neutral.
Pg. 3248, “Assume now that the outcome measure of interest is captured by continuous variable X in the treatment group and Y in the control group. Assume further, without loss of generality, that larger values of X (and Y ) are preferable to smaller values of X (and Y ). In some applied settings, the difference between the values of these two variables may have to exceed a pre-specified threshold, denoted , to be considered meaningful. The threshold can be a function of the precision with which X (and Y ) is measured. In clinical trials, the threshold can also reflect a difference regarded as clinically relevant. Table II displays generalized pairwise comparisons of continuous variables with a threshold . In Section 7.2, pairwise comparisons will be shown to be equivalent to the Wilcoxon rank-sum test in the special case where =0.”)
Regarding claim 11, Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 1.
Buysea,b∗† further teaches further comprising determining a likelihood or a probability that the first subject has a better treatment outcome than the second subject, based at least in part on the set of pairwise comparisons (Pg. 3254, “8.4. as a general measure of treatment effect A large body of literature has been recently devoted to measures of treatment effect that do not depend on the type of variable considered [24, 27]. is one such measure, and is closely connected to the ‘probabilistic index’, denoted P(X>Y ), defined as the probability that an individual taken randomly from the treatment group has a better outcome than an individual taken randomly from the control group [28]. is a linear transformation of P(X>Y ): =2· P(X>Y )−1 (7) and these two measures of treatment effect are therefore strictly equivalent. One advantage of over P(X>Y ) may be its easier interpretation. For instance, P(X,Y )=0.5 would be interpreted as meaning that the experiment provides no evidence that T differs from C in either direction. This situation would correspond to =0, which is a more direct and intuitively obvious way of expressing the (lack of) treatment benefit. Moreover, the cumulative proportions in favor of treatment for various thresholds, times of measurement, or other prioritized outcomes can help interpret any differences between the groups being compared, as will become evident in the analysis of the case studies in the next Section.”)
Regarding claim 15, Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 1.
Buysea,b∗† further teaches wherein the treatment set of subjects and the reference set of subjects comprise subjects having a disease or disorder (Pg. 3245, “We will first illustrate generalized pairwise comparisons using data from a randomized trial of 420 patients with advanced colorectal cancer [3]. Patients were randomized to either a standard regimen of 5-fluorouracil and leucovorin (‘LV5FU2’), or to the same regimen plus oxaliplatin.”)
Regarding claim 16, Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 15.
Buysea,b∗† further teaches wherein the disease or disorder is selected from the group consisting of allergic, articular, bone, cardiac, dermatologic, endocrinologic, gastrointestinal, gynecologic, hematologic, immunologic, infectious, neurologic, ophthalmic, otolaryngologic, pulmonary, psychiatric, renal, rheumatologic, urinary, and vascular disorders, as well as benign and malignant tumors, inborn errors of metabolism, obstetric conditions, and trauma, cancer, CVD, diabetes, and ophthalmic diseases (Pg. 3245, “We will first illustrate generalized pairwise comparisons using data from a randomized trial of 420 patients with advanced colorectal cancer [3]. Patients were randomized to either a standard regimen of 5-fluorouracil and leucovorin (‘LV5FU2’), or to the same regimen plus oxaliplatin.”)
Regarding claim 17, Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 1.
Buysea,b∗† further teaches wherein the plurality of treatment outcomes are obtained by performing a biomarker test on the treatment set of subjects and the reference set of subjects (Pg. 3247, “Visual acuity ranges from 0 (complete blindness) to 100 (perfect vision). Figure 3 illustrates the drop in mean visual acuity over time for two groups of patients: those randomized to receive a dose of 3 mg of pegaptanib (which had less favorable results than the approved dose of 0.3 mg and is used here for illustrative purposes) and those randomized to receive sham injections.”)
Regarding claim 18, Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 17.
Buysea,b∗† further teaches wherein the biomarker test comprises a laboratory test selected from the group consisting of biochemistry, hematology, coagulation, microbiology, molecular genetics, cytogenetics, flow cytometry, and pathology, imaging and radiology (X-rays, fluoroscopy, computed tomography, magnetic resonance imaging, ultrasound, echocardiography, positron- emission tomography, single-photon emission tomography, radionuclide imaging, optic coherence tomography, electrocardiography, electroencephalography, electromyography, evoked potential, audiometry, visual acuity testing, visual field testing, slit-lamp examination), and diagnostic, prognostic, predictive, and surrogate biomarkers, a blood test, a urine test, and a genetic test (Pg. 3247, “Visual acuity ranges from 0 (complete blindness) to 100 (perfect vision). Figure 3 illustrates the drop in mean visual acuity over time for two groups of patients: those randomized to receive a dose of 3 mg of pegaptanib (which had less favorable results than the approved dose of 0.3 mg and is used here for illustrative purposes) and those randomized to receive sham injections.”)
Regarding claim 20, Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 1.
Buysea,b∗† further teaches wherein the plurality of treatment outcomes comprise a plurality of endpoints (Pg. 3247, “The primary ‘endpoint’ of the trial, as defined by the EMEA and the FDA, was the proportion of patients losing at least 15 letters of visual acuity one year (54 weeks) after starting therapy.”
Also, Pg. 3247, “We are interested in the general situation of two groups of individuals (whom we call ‘patients’ in the clinical trial examples) to be compared in terms of one or more outcome measures (or ‘endpoints’) observed at one or more occasions for each individual.”)
Regarding claim 21, Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 20.
Buysea,b∗† further teaches wherein the plurality of endpoints are prioritized or ranked (Pg. 3249, “4. Prioritized outcomes Generalized pairwise comparisons can be extended to several outcomes arising from successive thresholds of a single outcome measure (Section 4.1), from repeated observations of a single outcome measure (Section 4.2), or from several outcome measures (Section 4.3). We will consider the extension to several outcome measures when an ordering of the multivariate space can be defined by prioritizing the variables. Wittkowski et al. [6] have developed a related approach based on a partial ordering of the multivariate space. Other extensions are possible but will not be pursued here.” Note: the outcomes observed are endpoints (see claim 20))
Regarding claim 22, Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 1.
Buysea,b∗† further teaches wherein the set of pairwise comparisons comprises all possible pairwise combinations of a subject selected from the treatment set and a subject selected from the reference set (Pg. 3248, “Assume that the outcome measure of interest is binary in nature. For reasons that will become clear later in this paper, it is convenient to denote this binary variable X in the treatment group and Y in the control group, with X =1 (or Y =1) indicating success, and X =0 (or Y =0) indicating failure. Table I displays the possible situations that can arise in the comparison of Xi , the outcome of the ith individual (i =1, ...,n) in group T with Yj , the outcome of the jth individual (j =1, ...,m) in group C.”)
Regarding claim 23, Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 1.
Buysea,b∗† further teaches wherein the prioritization function in (d) is selected by the subject based at least in part on thresholds of clinical relevance of individual treatment outcomes of the plurality of treatment outcomes (pg. 3249, 4. Prioritized outcomes, “Generalized pairwise comparisons can be extended to several outcomes arising from successive thresholds of a single outcome measure (Section 4.1), from repeated observations of a single outcome measure (Section 4.2), or from several outcome measures (Section 4.3). We will consider the extension to several outcome measures when an ordering of the multivariate space can be defined by prioritizing the variables.)
Regarding claim 24, Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 1.
Buysea,b∗† further teaches wherein the prioritization function in (d) comprises at least one of an ordering, a ranking, a set of weights, and a non-transitive ordering for individual treatment outcomes of the plurality of treatment outcomes (Pg. 3249, “We will consider the extension to several outcome measures when an ordering of the multivariate space can be defined by prioritizing the variables.”)
Regarding claim 30, Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 1.
O’Connor further teaches wherein the obtaining the dataset comprises remotely accessing the dataset (Pg. 11, “The potential of electronic health records (EHRs) to improve care has long been recognized but rarely been realized. Prototype EHR-linked, Web-based clinical decision support systems that identify and prioritize clinical options, however, save time, satisfy clinicians, empower patients, have high use rates, and improve care are now up and running in several large health care systems.15,16 Web services that include risk prediction equations can receive patient-specific data that are automatically sent from an EHR, perform the multiple computations needed to estimate the relative benefits of alternative treatment options, and display patient specific prioritized treatment options on the EHR screen within 1 second.”)
It would have been obvious for a person of ordinary skill in the art to apply intervention prioritizing teachings of O’Connor into the teachings of Buysea,b,∗ as modified by O’Connor and Athreya at the time the application was filed in order to prioritize clinical options. (Pg. 11, “Several alternative methods are available to identify and prioritize evidence-based clinical options with the most potential benefit to a given patient at a given point in time.”)
Claims 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Buysea,b∗† as modified by O’Connor and Athreya in view of Puhan et al. (“A framework for organizing and selecting quantitative approaches for benefit-harm assessment”, 2012)
Regarding claim 12, Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 1.
Buysea,b∗† as modified by O’Connor does not explicitly teach wherein (e) comprises comparing the plurality of treatment outcomes between the first subject and the second subject at least in part by comparing a net treatment benefit minus a net harm between the first subject and the second subject.
Puhan teaches wherein (d) comprises comparing the treatment outcomes between the first subject and the second subject at least in part by comparing a net treatment benefit minus a net harm between the first subject and the second subject (Pg. 7, “This is one of the most comprehensive approaches for benefit and harm assessment and considers various data sources to balance the benefits and harms of a treatment [3]. As described above, researchers can calculate the benefit and harm comparison metric as the sum of benefit and harm outcome rates per patient profile.”
Pg. 5, “The net benefit (benefit minus harm events) varied considerably and was positive for some profiles (as example above) but negative for others (e.g. black woman with age 50–59 years and a 5-year risk of invasive breast cancer of 4 percent).”)
It would have been obvious for a person of ordinary skill in the art to apply net benefit determining teachings of Puhan into the teachings of Buysea,b∗† as modified by O’Connor and Athreya at the time the application was filed in order to perform benefit-harm assessment. (Pg. 1, Method, “: We performed a review of the literature to identify quantitative approaches for benefit-harm assessment.”)
Regarding claim 13, Buysea,b∗† as modified by O’Connor, Athreya and Puhan teaches the method of claim 12.
Buysea,b∗† further teaches wherein the net treatment benefit comprises a member selected from the group consisting of event-free survival time, progression-free survival time, overall survival time, another time to event, efficacy, safety, quality of life, a score, and a biomarker (Pg. 3256, “Hence, time-to-event variables such as time to disease worsening can be combined with binary variables such as toxicities, or continuous variables such as quality of life scores.”)
Regarding claim 14, Buysea,b∗† as modified by O’Connor , Athreya and Puhan teaches the method of claim 12.
Buysea,b∗† as modified by O’Connor and Puhan does not explicitly teach wherein the net harm comprises an adverse event grade selected from the group consisting of a side effect and a toxicity of the clinical intervention.
Puhan further teaches wherein the net harm comprises an adverse event grade selected from the group consisting of a side effect and a toxicity of the clinical intervention (Pg. 3, “The more complex approaches consider multiple outcomes for either benefit or harm, or both. Of note, although some approaches like the Number needed to treat (NNT) [12,13] and Number needed to harm (NNH) and (Quality-adjusted) Time without Symptoms and Toxicity (Q- TWiST) [14,15] are mostly used when there is a single benefit and a single harm outcome.”
Pg. 3, “We identified 16 approaches, which can be grouped into two broad categories (Figure 1): One category comprises simpler approaches that typically deal with a single outcome for benefit (e.g. prevention of myocardial infarction) and one outcome for harm (e.g. gastrointestinal bleeding).” Note: here gastrointestinal bleeding is side effect.)
It would have been obvious for a person of ordinary skill in the art to apply net benefit determining teachings of Puhan into the teachings of Buysea,b∗† as modified by O’Connor , Athreya and Puhan at the time the application was filed in order to perform benefit-harm assessment. (Pg. 1, Method, “: We performed a review of the literature to identify quantitative approaches for benefit-harm assessment.”)
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Buysea,b∗† as modified by O’Connor and Athreya in view of Qureshi et al. (US 20150019259 A1)
Regarding claim 19, Buysea,b∗† as modified by O’Connor teaches the method of claim 1.
Buysea,b∗† further teaches further comprising comparing the plurality of treatment outcomes between the first subject and the second subject for each of a plurality of clinical interventions (Pg. 3247, “Pairwise comparisons require consideration of pairs of individuals, one taken from group T and the other taken from group C. The outcomes of these two individuals are compared and the pair is said to be ‘favorable’ if the outcome of the individual in group T is better than the outcome of the individual in group C, ‘unfavorable’ if the outcome of the individual in group T is worse than the outcome of the individual in group C, ‘neutral’ if there is no difference between the outcomes of the two individuals, or ‘uninformative’ if it cannot be determined which of the two individuals has a better outcome (e.g. if the outcome is missing for at least one of the two individuals.”)
Buysea,b∗† as modified by O’Connor and Athreya doesn’t explicitly teach and prioritizing or ranking the plurality of clinical interventions for the subject.
Quereshi teaches and prioritizing or ranking the plurality of clinical interventions for the subject (“0091] In some embodiments, the invention provides a method comprising: a) searching a set of clinical data for an intervention used in treatment of a condition; b) determining by a processor of a computer system a use profile for the intervention in the treatment of the condition in the set of clinical data; and c) ranking based on the use profile of the intervention in the treatment of the condition the intervention against a plurality of interventions for treatment of the condition…”)
It would have been obvious for a person of ordinary skill in the art to apply ranking intervention teachings of Qureshi into the teachings of Buysea,b∗† as modified by O’Connor and Athreya at the time the application was filed in order to determine which intervention should be assigned to the subject. (“[0072] ….The ranking can then be used to determine when a subject should be given care and which interventions the subject should be assigned. A care manager can see this ranking as an alert, an e-mail, an online message, and a list.”)
Claim 31 are rejected under 35 U.S.C. 103 as being unpatentable over Marc Buysea,b∗† as modified by O’Connor and Athreya in view of Reitberg (US 20010044408 A1)
Regarding claim 31, Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 1.
Buysea,b∗† as modified by O’Connor and Athreya doesn’t explicitly teach wherein the clinical intervention is selected between an approved drug treatment and an experimental drug treatment.
Reitberg teaches wherein the clinical intervention is selected between an approved drug treatment and an experimental drug treatment (para, “[0072] Suitable drugs for evaluation include, without limitation, those agents currently approved for the above-identified conditions as well as agents awaiting approval and new chemical entities. For example, the drug can be selected from anti-asthmatic agents, dental agents, anti-epileptic agents, anti-psychotic agents, anti-depressants, cardiovascular agents, respiratory agents, antihypertensive agents, diabetic agents, steroidal and non-steroidal anti-inflammatory agents, opiates, narcotic and non-narcotic analgesics, hematologic agents, musculoskeletal agents, anti-anxiety agents, gastro-intestinal agents, dermatologic agents, and anti-allergy medications. Other categories not specifically mentioned are intended as well. Particular agents well suited for the methods of the present invention include methylphenidate, estrogen-containing agents, anti-asthmatic agents, cardioactive agents, and antidepressant agents.”)
It would have been obvious for a person of ordinary skill in the art to apply appropriate treatment determination teachings of Reitberg into the teachings of Buysea,b,∗ as modified by O’Connor and Athreya at the time the application was filed in order to determining appropriate treatment for illnesses. (See. Abstract)
Claim 32 is rejected under 35 U.S.C. 103 as being unpatentable over Marc Buysea,b∗† as modified by O’Connor in view of Wu et al. (“Clinical effects of psychological intervention and drug therapy against peptic ulcer”, 2012)
Regarding claim 32, Buysea,b∗† as modified by O’Connor teaches the method of claim 1.
Buysea,b∗† as modified by O’Connor doesn’t explicitly teach wherein the clinical intervention is selected between a drug treatment and a psychological intervention.
Wu teaches wherein the clinical intervention is selected between a drug treatment and a psychological intervention (Pg. 831, “These patients were randomly divided into trial group and control group with 48 cases in each. There was no statistics difference between the both groups (Table 1). Control group was given the routine medical advice and Tagamet (TSKF) 800 mg per evening p.o. for 6 weeks. On the basis of drug therapy identical with the control, trial group was additionally given the psychological interventions.”)
It would have been obvious for a person of ordinary skill in the art to apply treatment comparison teachings of Wu into the teachings of Buysea,b,∗ as modified by O’Connor and Athreya at the time the application was filed in order to evaluate efficacy of psychological intervention. (Pg.831, “The ulcer patients are usually accompanied with emotional disorders such as anxiety and depression, the exclusive drug therapy is not functioning properly and tends to be recurrent. This study aims to evaluate the efficacy of psychological intervention against ulcer from 96 recipients with peptic ulcer using mental intervention as well as drug therapy.”)
Claim 33 is rejected under 35 U.S.C. 103 as being unpatentable over Marc Buysea,b∗† as modified by O’Connor in view of Salas-Vega et al. (“Assessment of Overall Survival, Quality of Life, and Safety Benefits Associated With New Cancer Medicines”, 2017)
Regarding claim 33, Buysea,b∗† as modified by O’Connor teaches the method of claim 1.
Buysea,b∗† as modified by O’Connor doesn’t explicitly teach wherein the clinical intervention is selected between a first clinical intervention and a second clinical intervention, wherein the first clinical intervention is ranked higher in safety and ranked lower in quality of life than the second clinical intervention.
Salas-Vega teaches wherein the clinical intervention is selected between a first clinical intervention and a second clinical intervention, wherein the first clinical intervention is ranked higher in safety and ranked lower in quality of life than the second clinical intervention (Pg. 382, “Although 22 (42%) of 53 new medicines were associated with an increase in QoL, 24 (45%) were also associated with reduced patient safety. Of the 53 new cancer drugs, 42 (79%) were associated with at least some improvement in OS, QoL, or safety.”)
It would have been obvious for a person of ordinary skill in the art to apply treatment comparison teachings of Salas-Vega into the teachings of Buysea,b,∗ as modified by O’Connor and Athreya at the time the application was filed in order to assess overall Survival, Quality of Life, and Safety Benefits Associated With Medicines. (See, Title) Donis et al(“Treating cardiovascular complications of radiotherapy: a role for new pharmacotherapies”, 01 Mar 2018)
Claim 34 is rejected under 35 U.S.C. 103 as being unpatentable over Buysea,b∗† as modified by O’Connor and Athreya and in view of Schwalm et al.(“ Resource Effective Strategies to Prevent and Treat Cardiovascular Disease”, February 23, 2016)
Regarding claim 34 , Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 1.
Buysea,b∗† as modified by O’Connor does not explicitly teach wherein the subject has cardiovascular disease, and wherein the selected clinical intervention comprises a drug treatment, a medical device, a surgical intervention, a radiotherapy, or a radioisotopic or nuclear therapy, capable of treating the cardiovascular disease of the subject.
Schwalm teaches wherein the subject has cardiovascular disease, and wherein the selected clinical intervention comprises a drug treatment, a medical device, a surgical intervention, a radiotherapy, or a radioisotopic or nuclear therapy, capable of treating the cardiovascular disease of the subject( pg. 749 “Resource-Efficient Management of Acute Presentations of CVD , “The significant reduction of CVD mortality in HIC by 50% to 75% since the 1970s has been attributed to different factors, including better management of acute ischemic heart disease events.5–7 Although the widespread implementation of primary percutaneous coronary intervention for acute MIs or a thrombolytic program for acute strokes (with computed tomography scans within 4 hours of symptom onset) may not be feasible or resource-efficient options in many LMICs, other acute interventions are needed. The use of aspirin and streptokinase for the acute, in-hospital management of ST-segment–elevation MI is considered to be cost-effective and could avert 335000 DALYs among patients 30 to 69 years of age in LMIC.87 Furthermore, clopidogrel, β-blockers, ACE inhibitors, diuretics, and statins for the management of acute coronary syndromes, stroke, and acute heart failure are considered essential medicines by the World Health Organization (Table 7)”
Note: see Table 7 below.
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It would have been obvious for a person of ordinary skill in the art to apply Cardiovascular disease treatment teachings of Schwalm into the teachings Buysea,b∗† as modified by O’Connor and Athreya at the time the application was filed in order to increase overall health costs. (pg. 749, “…. Ensuring that these evidence-based medications are available post-MI and keeping the costs to the patient low through the elimination of copayments results in improved medication adherence and rates of first major vascular events, without increasing overall health costs.”)
Claim 35 is rejected under 35 U.S.C. 103 as being unpatentable over Buysea,b∗† as modified by O’Connor and Athreya and in view of Sihvonen et al.(“ Music-based interventions in neurological rehabilitation”, August , 2017)
Regarding claim 35 , Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 1.
Buysea,b∗† as modified by O’Connor and Athreya does not explicitly teach wherein the subject has a neurological disease or disorder, and wherein the selected clinical intervention comprises a medication, a gene therapy, a cell- based therapy, a drug treatment, a medical device, a surgical intervention, a radiotherapy, radioisotopic/nuclear therapy, physical therapy, occupational therapy, phonoaudiological therapy, a rehabilitation intervention, a psychological intervention, an immunotherapy, a digital health intervention, or a behavioral intervention.
Sihvonen teaches wherein the subject has a neurological disease or disorder, and wherein the selected clinical intervention comprises a medication, a gene therapy, a cell- based therapy, a drug treatment, a medical device, a surgical intervention, a radiotherapy, radioisotopic/nuclear therapy, physical therapy, occupational therapy, phonoaudiological therapy, a rehabilitation intervention, a psychological intervention, an immunotherapy, a digital health intervention, or a behavioral intervention. (pg. 648 “Music-based interventions for stroke , Stroke is one of the leading causes of long-term disability worldwide.23 Of the major neurological disorders, the strongest evidence for efficacy of music-based interventions has been reported for stroke. We identified 16 randomised controlled trials that used music as an add-on therapy for stroke-related neurological and neuropsychiatric disturbances (table).24–39 The assessed outcomes included motor functions, such as gait and upper extremity function;24,25,27,30,31,34–39 language functions;26,28,29 cognitive functions, such as memory and attention;28,33 mood;28,33,36 and quality of life.30,36”
Also, pg. 656 “Since active music-based rehabilitation involves multiple components analogous to training and music learning (ie, iterated practice of movements coupled with auditory feedback and extensive cognitive processing), it is plausible that music-based neurological rehabilitation induces similar structural and functional neuroplastic changes seen in populations of healthy individuals that receive musical training.18,19 Indeed, some studies have reported memory-related plastic effects after music listening,28,32 as well as neural reorganisation after music supported therapy in patients recovering after a stroke.34 Other studies have provided further evidence of auditory related and motor-related neuroplasticity after music supported therapy78–80 and melodic intonation therapy81 in patients who have had a stroke.”
It would have been obvious for a person of ordinary skill in the art to apply neurological treatment teachings of Sihvonen into the teachings Buysea,b∗† as modified by O’Connor and Athreya at the time the application was filed in order to alleviate the manifestations of multiple sclerosis.(pg. 655, “Despite relatively low prevalence, patients with the disease require expensive medication and in most cases life-long rehabilitation.3 Multiple sclerosis treatments aim to ameliorate function after flare-up of an episode or to prevent new episodes. Only two randomised controlled trials62,63 have studied the effect of musical interventions in alleviating the manifestations of multiple sclerosis (table). Between these studies, outcomes were different, and the intervention was administered by a music therapist in only one study.”)
Claim 36 is rejected under 35 U.S.C. 103 as being unpatentable over Buysea,b∗† as modified by O’Connor and Athreya and in view of Chen et al.(“ Metronomic chemotherapy and immunotherapy in cancer treatment”, 26 January 2017)
Regarding claim 36 , Buysea,b∗† as modified by O’Connor and Athreya teaches the method of claim 1.
Buysea,b∗† as modified by O’Connor and Athreya does not explicitly teach wherein the subject has a tumor, and wherein the selected clinical intervention comprises a chemotherapy, a radiotherapy, an immunotherapy, or a surgical intervention.
Chen teaches wherein the subject has a tumor, and wherein the selected clinical intervention comprises a chemotherapy, a radiotherapy, an immunotherapy, or a surgical intervention(pg. 283, “Several strategies, including metronomic chemotherapy, have the potential for immune modulation to enhance host immunity to overcome the immunosuppressive defense that tumors elicit. Current data show that adding chemotherapeutic agents to immunotherapy can trigger host production of durable and effective tumor antigenespecific T lymphocytes and synergistically optimize the anti-tumor effects [30,31]. Although few pre-clinical and clinical studies have been conducted to evaluate the efficacy and safety of combined metronomic chemotherapy and immunotherapy, these preliminary data hint at the feasibility of translating this combination into clinical application of future cancer treatment.”
Also, pg. 286 “Metronomic chemotherapy is intended to induce tumor dormancy by virtue of its antiangiogenic activity [92]. When the remission is induced by chemotherapy, conventional chemotherapy may be followed by the metronomic scheduling of chemotherapeutic agents for the purpose of long-term maintenance, which implies induction of angiogenic dormancy [21,22].” )
It would have been obvious for a person of ordinary skill in the art to apply tumor clinical intervention teachings of Chen into the teachings Buysea,b∗† as modified by O’Connor and Athreya at the time the application was filed in order to optimize the anti-tumor effects.(pg. 283 “Several strategies, including metronomic chemotherapy, have the potential for immune modulation to enhance host immunity to overcome the immunosuppressive defense that tumors elicit. Current data show that adding chemotherapeutic agents to immunotherapy can trigger host production of durable and effective tumor antigenespecific T lymphocytes and synergistically optimize the anti-tumor effects “)
Response to Arguments
Applicant's arguments filed on 07/07/2025 have been fully considered but they are not persuasive.
Remarks - 35 USC § 101
In remarks, Pg. 9-10, applicant highlights elements such as computing device, user interface on electronic display etc.., and contends: “therefore, the instant claims recite specific elements that are not practical to be performed in the mind, at least because these elements necessitate (1) presenting a representation of treatment outcomes to a user via a user interface on an electronic display, (2) selecting a prioritization function by the user via the user interface on the electronic display, (3) performing pairwise comparisons by a computer processor, and (4) administering a clinical intervention to a user as part of a clinical trial. Consequently, with respect to the Step 2A, Prong One analysis, the instant claims do not recite a mental process.”
The examiner agrees that recited hardware elements don’t recite abstract idea, and rather treated as additional elements, and these elements have been addressed in the 35 U.S.C. 101 analysis above.
In remarks, Pg. 11 applicant contends: “the claimed techniques are directed to improvements in the field of clinical interventions in clinical trials. Current approaches for determining clinical interventions in clinical trials face inefficiencies due to lack of patient-centric assessments. As explained in the specification, "[s]tatistical analysis of clinical data may be performed to identify treatment plans for subjects (e.g., patients in clinical trials) and to evaluate net benefits of treatment plans (e.g., net treatment benefits). Currently, there may be a lack of flexible approaches to identifying and evaluating net benefits of treatment plans (e.g., net treatment benefits). Thus, there exists a need for improved methods and systems which take into account personalized preferences [and perform] assessments at the patient-level or individual-level (e.g., using patient-level treatment outcomes and data)" (see paragraphs [0002]-[0003]).”
The above arguments does not present any technical improvement, rather it recites the abstract idea of mental process that can be accomplished via paper and pen or mind. For example, one can mentally or using paper and pen determine the net benefit of intervention. In fact, the majority of drugs have side effects, and when the medical doctor prescribes a drug, the doctor considers the benefits and the side effects, patient preferences, and the net benefit.
Pg. 11-12 further discuss and hashes further the abstract idea itself; please note that in step 2A, prong 2 additional elements are identified, and the remaining discussion is based on additional elements, and how the additional elements which are not abstract idea could integrate the recited abstract idea into practical application. The applicant is reciting the abstract analysis and providing a conclusory statement as to how the abstract idea provides improvement to the technical field. In fact, applicant already states (Pg. 9-14) that “the independent claim enables methods for improving the efficiency and effectiveness of a clinical intervention”, “the claimed methods allow for clinical trials to be performed with greater efficiency”, “further, the claimed methods allow for the collaboration of the patient and a clinician, such that the intervention aligns with the prioritization of the patient.” None, of these are technical fields, or improvement into the technical field. The applicant never discusses, what technical elements, are used to achieve these objects, and how these technical elements are improved. There is no technical detail in claims or specification with regard to processor, or display device etc.…
Remarks - 35 USC § 103
In remarks, Pg. 16, applicant contends: “however, O'Connor merely describes that (1) clinical interventions as well as (2) associated potential benefits are both presented at the same time to a patient, in order to allow the patient to select their preferred option of treatment. In particular, O'Connor is silent on evaluating net treatment benefits of clinical intervention options in a clinical trial, in which a prioritization function "assigns ranked values to each of the plurality of treatment outcomes, wherein the ranked values are selected by the user based at least in part on (1) subject-level efficacies and subject-level adverse effects of individual treatment outcomes of the plurality of treatment outcomes on the subject and (2) a personalized preference of the subject" and then the net treatment benefits are evaluated based on ranked values of the prioritization function. Further, O'Connor is silent on administering a clinical intervention to a subject as part of a clinical trial, where the clinical intervention is elected based at least in part on net treatment benefits that take into account a subject's subject-level efficacies, subject-level adverse effects, and personalized preferences.”
The amended claim language have been addressed in the prior art section above. Furthermore, O’Connor, Pg. 11 states:
“The potential of electronic health records (EHRs) to improve care has long been recognized but rarely been realized. Prototype EHR-linked, Web-based clinical decision support systems that identify and prioritize clinical options, however, save time, satisfy clinicians, empower patients, have high use rates, and improve care are now up and running in several large health care systems.15,16 Web services that include risk prediction equations can receive patient-specific data that are automatically sent from an EHR, perform the multiple computations needed to estimate the relative benefits of alternative treatment options, and display patient specific prioritized treatment options on the EHR screen within 1 second. Presenting clinical options to the patient facilitates patient-centered care and shared decision making by informing the patient of clinical options with the most potential benefit and then empowering the patient to select their preferred option(s). Many patients will continue to decline clinical options of high benefit, such as smoking cessation, colorectal cancer screening, or statin treatment. Then we must respect our patient’s preferences and remember that patient treatment preferences and readiness to change typically vary with time.23 Clinical decision support systems update and reprioritize evidence-based treatment options at each subsequent encounter, enabling patients to see progress in some areas and reconsider previous preferences in other areas.”)
As can be seen from the teaching above, the patients are informed of clinical options (clinical interventions) with most potential benefit (net treatment benefit). Further it teaches “display patient specific prioritized treatment options.” Thus, it explicitly teaches subject level ranking/prioritizing for intervention options.
Under the section, “No motivation or reasonable expectation of success” (Pg. 15-16), the applicant highlights the feature of claims are not disclosed, thus “in view of the deficiencies of the cited references, one of skill in the art would not have had motivation or a reasonable expectation of success to modify the cited references to arrive at the claims.” However, as discussed above the cited reference do teach the claimed features, furthermore please see prior art section for details that addresses each limitation including the amended limitations in detail. Also, note a single reference doesn’t teach all the claimed aspects, rather the cited references in combination teach the claimed language.
In remarks, Pg. 16, applicant contends: “the claims as amended are not obvious because the claimed methods satisfy a long-felt unmet need in the field of clinical trials.”
Basically, the applicant points out the ongoing major challenge discussed in the OConnor reference and states that claimed invention satisfy the challenge. The applicant further states that it is done by determining the net benefit. As disclosed with regard to above argument, the reference explicitly teaches determining the net benefit.
In remarks, Pg. 14 , applicant contends: In particular the cited art is silent on evaluating net treatment benefits of clinical intervention options in a clinical trial, in which (1) a prioritization function is selected "at least in part on (1) subject-level efficacies and subject-level adverse effects of individual treatment outcomes of the plurality of treatment outcomes on the subject and (2) a personalized preference of the subject" and then (2) the net treatment benefits are evaluated based on ranked values of the prioritization function.”
O, Connor, Pg. 10 states:
“There are several fundamentally sound reasons to prioritize clinical services at the patient level. First, the value of even very strongly evidence-based clinical services
varies across patients and with time. For example, the potential benefit of screening for colorectal, lung, cervical, and breast cancer varies up to tenfold based on patient-specific demographic, clinical, behavioral, and genetic factors.6 Likewise, the risks and benefits of intensive glucose control in patients with diabetes vary by age, comorbid conditions, cardiovascular risk, distance from personalized glycated hemoglobin (HbA1c) goal, and other factors. If an older patient with major comorbidities already on intensive glucose-lowering therapy is not at their personalized HbA1c goal, the
risks of further intensifying glucose therapy may well exceed the benefits. The ranks provided by Maciosek et al, which are based on overall population health benefit, must be further personalized to assess relative benefit of these services to an individual patient. et al, which are based on overall population health benefit, must be further personalized to assess relative benefit of these services to an individual patient.”
As can be seen the citation above explicitly teaches considering risks (adverse effects of individual treatment outcomes), and benefit (efficacies); in fact the example tells that net benefit (relative benefit) of intensifying glucose therapy (intervention) is negative, as the risk (adverse effect) exceed the benefit (efficacy). Also, it teaches that it is done on subject level, as it explicitly states “personalized to assess relative benefit of these services to an individual patient.
Pg. 11 states: “Presenting clinical options to the patient facilitates patient-centered care and shared decision making by informing the patient of clinical options with the most
potential benefit and then empowering the patient to select their preferred option(s). Many patients will continue to decline clinical options of high benefit, such as
smoking cessation, colorectal cancer screening, or statin treatment. Then we must respect our patient’s preferences and remember that patient treatment preferences
and readiness to change typically vary with time.23 Clinical decision support systems update and reprioritize evidence-based treatment options at each subsequent
encounter, enabling patients to see progress in some areas and reconsider previous preferences in other areas.”
Thus, the reference explicitly teaches a personalized preference of the subject.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/HUMA WASEEM/Examiner, Art Unit 3686
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686
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