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