Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed ----12/18/2025 has been entered.
Status of Claims
Claims 1-8, 10-12, 14, 16-26 are currently pending and have been examined.
Claims 1-5, 10-12, 14, 16, 19-24 and 26 have been amended.
Claims 9, 13, 15 have been canceled.
Claims 1-8, 10-12, 14, 16-26 have been rejected.
Subject Matter Free of Prior Art – Claim Objection
Claim 14 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if the 35 USC § 101 and 35 USC § 112(a) rejections are overcome.
Drawings
The new drawing received on 12/18/2025 are acceptable, therefore, the previous drawing objection is withdrawn.
Previous Claim Objections
The amendment of claim 19 is acceptable, therefore, the previous claim objection of claim 19 is withdrawn.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 14 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 14 has been amended to now read, “the method of claim 1, wherein the aggregate score is indicative of (i) a magnitude of a vector defined, at least in part, by a set of estimated burden impact values and (ii) a weighted combination of the set of estimated burden impact values, wherein each of the set of estimated burden impact values correspond to each of the plurality of variables.” The Applicant’s specification does not provide proper support for an aggregate score that is indicative of both “a magnitude of a vector” and “a weighted combination” at the same time. The specification repeatedly discloses, “the aggregate metric can be indicative of at least one of (i) a magnitude of a vector defined, at least in part, by the set of estimated burdens values or estimated burden reduction values, or (ii) a weighted combination of the set of estimated burden values or estimated burden reduction values,” (Specification para 10, for example). Thus, there is no description of the algorithm or methodology as to how the Applicant would combine both a magnitude of a vector and a weighted combination together as they are both different types of metrics.
Thus, Claim 14 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
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 1-8, 10-12, 14, 16-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to an abstract idea without significantly more. Claims 1-8, 10-12, 14, 16-26 are directed to a system, method, or product which are one of the statutory categories of invention. (Step 1: YES).
Independent Claim 1 discloses a computer-implemented method performed by one or more processors of a computing device, the method comprising: processing population data obtained from a plurality of longitudinal data sources to identify a plurality of variables for estimating a burden impact for subject participation in a study comprising at least one protocol, wherein the population data is indicative of healthcare treatment of potential subjects for the study, wherein each of the plurality of longitudinal data sources comprising at least a portion of the population data collected for at least one of the potential subjects at multiple time instances, wherein at least one variable from the plurality of variables describes a potential subject for the study, and wherein each of the at least one variable representing a feature of the healthcare treatment for the potential subjects; generating, for the plurality of variables and by a machine learning model, a plurality of parameters for a plurality of functions, each variable in the plurality of variables having corresponding parameters in the plurality of parameters for a corresponding function in the plurality of functions, each function representing a relationship between the corresponding variable and an estimated burden impact that would be imposed on the potential subject by the at least one protocol of the study if the potential subject were to participate in the study, wherein generating the parameters for a function is based on a distribution of the at least one variable in the plurality of variables in a population comprising the potential subjects for the study, and wherein the trained machine learning model is trained to generate parameters of functions based on population data of the potential subject; determining, using the plurality of parameters for the function and for each individual of a plurality of individuals, an aggregate score that estimates a combined incremental burden for the individual based on subject-specific values for the plurality of variables absent participation of the potential subject in the study, wherein the subject-specific values comprise values for the at least one variable found in the population data; identifying, based on the aggregate score for each individual of the plurality of individuals, a subset of the plurality of individuals to be prioritized for recruitment for the study; and transmitting data indicative of the subset of the plurality of individuals to at least one computing device associated with conducting the study.
Independent Claim 21 discloses a system comprising: a computing device comprising: a memory configured to store instructions; and a processor configured to execute the instructions to perform operations comprising: processing population data obtained from a plurality of longitudinal data sources to identify a plurality of variables for estimating a burden impact for subject participation in a study comprising at least one protocol, wherein the population data is indicative of healthcare treatment of potential subjects for the study, wherein each of the plurality of longitudinal data sources comprising at least a portion of the population data collected for at least one of the potential subjects at multiple time instances, wherein at least one variable from the plurality of variables describes a potential subject for the study, and wherein each of the at least one variable representing a feature of the healthcare treatment for the potential subjects; generating, for the plurality of variables and by a machine learning model, a plurality of parameters for a plurality of functions, each variable in the plurality of variables having corresponding parameters in the plurality of parameters for a corresponding function in the plurality of functions, each function representing a relationship between the corresponding variable and an estimated burden impact that would be imposed on the potential subject by the at least one protocol of the study if the potential subject were to participate in the study, wherein generating the parameters for a function is based on a distribution of the at least one variable in the plurality of variables in a population comprising the potential subjects for the study, and wherein the trained machine learning model is trained to generate parameters of functions based on population data of the potential subject; determining, using the plurality of parameters for the function and for each individual of a plurality of individuals, an aggregate score that estimates a combined incremental burden for the individual based on subject-specific values for the plurality of variables absent participation of the potential subject in the study, wherein the subject-specific values comprise values for the at least one variable found in the population data; identifying, based on the aggregate score for each individual of the plurality of individuals, a subset of the plurality of individuals to be prioritized for recruitment for the study; and transmitting data indicative of the subset of the plurality of individuals to at least one computing device associated with conducting the study.
Independent Claim 22 discloses one or more non-transitory machine-readable storage devices having encoded thereon computer readable instructions for causing one or more processing devices to perform operations comprising: processing population data obtained from a plurality of longitudinal data sources to identify a plurality of variables for estimating a burden impact for subject participation in a study comprising at least one protocol, wherein the population data is indicative of healthcare treatment of potential subjects for the study, wherein each of the plurality of longitudinal data sources comprising at least a portion of the population data collected for at least one of the potential subjects at multiple time instances, wherein at least one variable from the plurality of variables describes a potential subject for the study, and wherein each of the at least one variable representing a feature of the healthcare treatment for the potential subjects; generating, for the plurality of variables and by a machine learning model, a plurality of parameters for a plurality of functions, each variable in the plurality of variables having corresponding parameters in the plurality of parameters for a corresponding function in the plurality of functions, each function representing a relationship between the corresponding variable and an estimated burden impact that would be imposed on the potential subject by the at least one protocol of the study if the potential subject were to participate in the study, wherein generating the parameters for a function is based on a distribution of the at least one variable in the plurality of variables in a population comprising the potential subjects for the study, and wherein the trained machine learning model is trained to generate parameters of functions based on population data of the potential subject; determining, using the plurality of parameters for the function and for each individual of a plurality of individuals, an aggregate score that estimates a combined incremental burden for the individual based on subject-specific values for the plurality of variables absent participation of the potential subject in the study, wherein the subject-specific values comprise values for the at least one variable found in the population data; identifying, based on the aggregate score for each individual of the plurality of individuals, a subset of the plurality of individuals to be prioritized for recruitment for the study; and transmitting data indicative of the subset of the plurality of individuals to at least one computing device associated with conducting the study.
Independent Claim 23 discloses a method comprising: determining, using a plurality of parameters for a plurality of functions and for each individual of a plurality of individuals, an aggregate score that estimates a combined incremental burden for the individual based on subject-specific values for a plurality of variables absent participation of a potential subject in a study, wherein the subject-specific values comprise values for at least one variable found in population data, wherein the subject-specific values comprise values for at least one variable found in population data, wherein each function represents relationship between the corresponding variable and an estimated burden impact values that would be imposed on the individual by at least one protocol of a study if the individual were to participate in the study, wherein the plurality of parameters for the plurality of functions generated by a trained machine learning model and each of the variables in the plurality of variables have corresponding parameters in the plurality of parameters for the plurality of functions, wherein the machine learning model is trained to generate parameters of functions based on the population data of potential subjects and wherein the plurality of variables are determined by processing population data from a plurality of longitudinal data sources, the population data being indicative of healthcare treatment of potential subjects for the study comprising the at least one protocol of the study and each of the plurality of longitudinal data sources comprising at least a portion of the population data collected for at least one of the potential subjects at one or more time instances; identifying, based on the aggregate score for each individual of the plurality of individuals, a subset of the plurality of individuals prioritized for recruitment for the study; and transmitting data indicative of the subset of the plurality of individuals to at least one computing device associated with conducting the study.
The examiner is interpreting the above bolded limitations as additional elements as further discussed below. The remaining un-bolded limitations are merely directed to determining mathematical functions (thus directed to a mathematical concept which is an abstract idea, specifically steps, “wherein generating the parameters for the function is based on a distribution of the at least one variable in the plurality of variables in a population” and “generate parameters of functions based on population data of potential subject” and “using the plurality of parameters for the functions” are directed to a mathematical concept) to determine an individual’s burden or burden reduction score based on subject specific values (directed to managing personal behavior or relationships or interactions between people and thus grouped as certain methods of organizing human activity which is an abstract idea). The limitations are considered together as a single abstract idea for further analysis. (Step 2A- Prong 1: YES. The claims are abstract).
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) 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 (MPEP 2106.05.f), (2) Adding insignificant extra- solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
Independent Claim 1 discloses the following additional elements:
A computer implemented method performed by one ore more processors of a computing device
A machine learning model, wherein the trained machine learning model is trained to generate parameters of functions based on population data of the potential subject
Transmitting data indicative of the subset of the plurality of individuals to at least one computing device associated with conducting the study
Independent Claim 21 discloses the following additional elements:
A computing device
A memory configured to store instructions
A processor configured to execute the instructions to perform operations
A machine learning model, wherein the trained machine learning model is trained to generate parameters of functions based on population data of the potential subject
Transmitting data indicative of the subset of the plurality of individuals to at least one computing device associated with conducting the study
Independent Claim 22 discloses the following additional elements:
One or more non-transitory machine-readable storage devices having encoded thereon computer readable instructions for causing one or more processing devices to perform operations
A machine learning model, wherein the trained machine learning model is trained to generate parameters of functions based on population data of the potential subject
Transmitting data indicative of the subset of the plurality of individuals to at least one computing device associated with conducting the study
Independent Claim 23 discloses the following additional elements:
A machine learning model, wherein the trained machine learning model is trained to generate parameters of functions based on population data of the potential subject
Transmitting data indicative of the subset of the plurality of individuals to at least one computing device associated with conducting the study
In particular, the computer-implemented method performed by one or more processors of a computing device (of claim 1), a machine learning model, wherein the trained machine learning model is trained to generate parameters of functions based on population data of the potential subject (of claims 1, 21-22, and 23), the computing device (of claim 21), memory configured to store instructions, processor configured to execute the instructions (of claim 21), the one or more non-transitory machine-readable storage devices having encoded thereon computer readable instructions for causing one or more processing devices to perform operations (of claim 22) are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it’ (or an equivalent) with the judicial exception.
Applicant’s specification states at paragraph 75 - The computing device 1200 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The mobile computing device 1250 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart-phones, AR devices, and other similar computing devices.
Thus, disclosing a general computer component that is performing as expected. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claims 1 and 21-23 further disclose the additional element of transmitting data indicative of the subset of the plurality of individuals to at least one computing device associated with conducting the study. This additional element amounts to insignificant extra-solution activity and, thus, does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Accordingly, claim(s) 1 and 21-23 are directed to an abstract idea(s) without a practical application. (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the computer-implemented method performed by one or more processors of a computing device (of claim 1), a machine learning model, wherein the trained machine learning model is trained to generate parameters of functions based on population data of the potential subject (of claims 1, 21-22, and 23), the computing device (of claim 21), memory configured to store instructions, processor configured to execute the instructions (of claim 21), the one or more non-transitory machine-readable storage devices having encoded thereon computer readable instructions for causing one or more processing devices to perform operations (of claim 22) amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more’). MPEP2106.05(I)(A) indicates that merely saying "apply it” or equivalent to the abstract idea cannot provide an inventive concept ("significantly more").
Further, transmitting data indicative of the subset of the plurality of individuals to at least one computing device associated with conducting the study (of claims 1 and 21-23) were considered insignificant extra-solution activity in Step 2A, Prong 2. Re-evaluating here in step 2B, these are also determined to be well-understood, routine, conventional activity in the field. The following court cases indicate that transmission of data is well-understood, routine, and conventional activity in the field when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Further, the prior art of record indicates that transmitting data indicative of the subset of the plurality of individuals to at least one computing device associated with conducting the study is well-understood, routine, and conventional activity (See Perlina US PG Pub 2025/0006317 A1 Paras 9-14, 19, 43, 151).
Accordingly, even in combination, this additional element does not provide significantly more. As such the independent claims 1 and 21-23 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
Dependent claim(s) 2-8, 10-12, 14, 16-20 and 24-26 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim 24 further narrows the abstract idea of a mathematical concept by determining the distribution of the at least one variable in the plurality of variables in the population comprising the potential subjects.
Dependent claim 26 further discloses the additional elements of transmitting data indicative of the patient profile to the at least one computing device associated with conducting the study (claim 26).
The machine learning model of claim 24 is recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea by adding the words ‘apply it’ (or an equivalent) with the judicial exception. Thus, this additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claims 26 further discloses the additional element of transmitting data indicative of the patient profile to the at least one computing device associated with conducting the study. This additional element amounts to insignificant extra-solution activity and, thus, does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of the machine learning model amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more’). MPEP2106.05(I)(A) indicates that merely saying "apply it” or equivalent to the abstract idea cannot provide an inventive concept ("significantly more").
Further, transmitting data indicative of the patient profile to the at least one computing device associated with conducting the study (of claim 26) was considered insignificant extra-solution activity in Step 2A, Prong 2. Re-evaluating here in step 2B, these are also determined to be well-understood, routine, conventional activity in the field. The following court cases indicate that transmission of data is well-understood, routine, and conventional activity in the field when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Further, the prior art of record indicates that transmitting data indicative of the patient profile to the at least one computing device associated with conducting the study is well-understood, routine, and conventional activity (See Cho KR 2019/0134315 A Paras 12, 36, 40).
Therefore, the dependent claims are also directed to an abstract idea.
Thus, Claims 1-8, 10-12, 14, 16-26 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 5, 8, 11-12, 16, and 20-24 are rejected under 35 U.S.C. 103 as being unpatentable over Cameron (Cameron, D., Willoughby, C., Messer, D., Lux, M., Getz, K., & Aitken, M. (2020, July 31). Assessing participation burden in clinical trials: Introducing the patient friction coefficient. Clinical Therapeutics) in view of Perlina (US PG Pub 2025/0006317 A1), further in view of Vold (US PG Pub 2024/0266009 A1) and Wang (Automatic inference of demographic parameters using generative adversarial networks).
Regarding Claim 1, Cameron discloses:
the method comprising:
generating, for the plurality of variables and by a trained machine learning model, a plurality of parameters for a plurality of , each variable in the plurality of variables having corresponding parameters in the plurality of parameters for a corresponding function in the plurality of functions, each function representing a relationship between the corresponding variable wherein … (Abstract discloses protocol design complexity, and associated study volunteer burden, negatively impact patient recruitment and retention as well as overall research and development productivity. The Clinical Trial Patient Friction Coefficient section discloses the PFC (equation 1 and 2) must account for a wide variety of protocol design elements that impact perceived burden [wherein the model utilizes the equations to generate a burden impact based on subject specific values for plurality of variables]. These elements include the frequency of visits, procedures to be performed, duration of required participation, travel distance to the research site, time of day to schedule a visit, and many others [subject specific values for the plurality of variables]… Inputs to the PFC are drawn from 3 primary and comprehensive sources. The first source is anonymized treatment pattern data from clinical practice to determine baseline burden. The second is a set of trial characteristics that represent the incremental burden of trial participation overall. Finally, a set of anonymized patient-level characteristics are used to “personalize” the burden of participation in that trial to individual patients or groups of patients (subject to privacy and regulatory considerations). For this last set, one might, for example, adjust upward the trial-level burden for an individual patient based on geography (ie, travel burden), or age (ie, based on increased potential to disrupt daily routines for those in their prime working years). All of these inputs will be initially derived in 1 or more of 3 unit-based parameters: cost, time, and degree of daily routine disruption. Next, these 3 parameters should be weighted at a subgroup level to account for differences in burden perception (which, as discussed subsequently, will likely need to be derived from additional research)—perhaps, by way of example, based on socioeconomic, demographic, or geographic factors—as well as the risk for each burden (ie, a probabilistic weighting as to perceived likelihood of the burden occurring). The resulting aggregation, t(x), for a given patient x of perceived trial burden (and, if relevant, the caregiver burden associated with patient x participation) can be expressed as: equation 1 [wherein equation 1 illustrates the weighted “resulting aggregation, t(x)” of the 3 parameters].)
using the plurality of parameters for the functionsand for each individual of a plurality of individuals, an aggregate score that estimates a combined incremental burden for the individual based on subject-specific values for the plurality of variables absent participation of the potential subject in the study, [[a]] patient friction coefficient (PFC). The Clinical Trial Patient Friction Coefficient section discloses the PFC (equation 1 and 2) must account for a wide variety of protocol design elements that impact perceived burden [wherein the model utilizes the equations to generate a burden impact based on subject specific values for at least one variable]. These elements include the frequency of visits, procedures to be performed, duration of required participation, travel distance to the research site, time of day to schedule a visit, and many others [subject specific values for the one or more variables. All of these inputs will be initially derived in 1 or more of 3 unit-based parameters: cost, time, and degree of daily routine disruption. Next, these 3 parameters should be weighted at a subgroup level to account for differences in burden perception (which, as discussed subsequently, will likely need to be derived from additional research)—perhaps, by way of example, based on socioeconomic, demographic, or geographic factors—as well as the risk for each burden (ie, a probabilistic weighting as to perceived likelihood of the burden occurring). Equation 1 discloses the risk for each burden (ie, a probabilistic weighting as to perceived likelihood of the burden occurring) [wherein equation 1 illustrates the weighted “resulting aggregation, t(x)” of the 3 parameters]. The resulting aggregation, t(x), [a set of estimated burden values] for a given patient x of perceived trial burden (and, if relevant, the caregiver burden associated with patient x participation) can be expressed as: Equation 1. where tb(1) through tb(i) are the individual time-based burdens on the patient and/or the caregiver; cb(1) through cb(i) are the individual cost-based burdens on the patient and/or the caregiver; db(1) through db(i) are the individual disruption-based burdens on the patient and/or the caregiver; and tw, cw, and dw represent the perception-based weighting of time-, cost-, and disruption-based burdens, respectively… Equation 2 further discloses the PFC(x) = t(x) – e(x) where the t(x) is the aggregation for a given patient x of perceived trial burden and the e(x) is the perceived burden for the existing treatment pathway. [Wherein e(x) discloses subject-specific values for the plurality of variables absent participation (by disclosing the values of the time, cost, and disruption based burdens and weightings of the existing treatment pathway e(x))].)
While Cameron discloses the above limitations, it does not fully disclose the following limitation that Perlina discloses:
A computer-implemented method performed by one or more processors of a computing device, (Para 153 discloses a computing device, the computing device 1210 may be implemented using hardware (e.g., in a desktop computer), software (e.g., in a virtual machine or the like), or a combination of software and hardware and Paras 157-158 discloses the computing device may comprise a processor (processing instructions for execution within the computing device) and a memory).)
identifying, based on the aggregate score (Perlina Abstract discloses there remains a need for techniques to inform healthcare providers or pharmaceutical companies of the most risky subset of profiles to potentially exclude from the trial and to select an optimal cohort of patients who will have increased chances of responding to the therapy and/or decreased chances of experiencing toxicity in the clinical trial. The present invention addresses this and other needs. Para 8 discloses providing a recommendation for clinical trial eligibility of the patient when the matching score for the patient passes, meets or exceeds requirements of the threshold score. Para 50 discloses threshold requirements may be established in order to identify the most at-risk patients that may need to be excluded as those patients whose scores, for example, fall below the 5.sup.th percentile while patients scoring at 5.sup.th percentile or above would be considered as having met (or passed) the threshold requirements.)
transmitting data indicative of the subset of the plurality of individuals to at least one computing device associated with computing the study (Paras 9-14 disclose a system for assessing eligibility of a patient to a clinical trial of a drug, the system comprising: a server; a computing device in communication with the server over a network, the computing device including a processor and a memory, the memory bearing computer executable code configured to perform the steps of: a-c where c discloses providing a recommendation for clinical trial eligibility of the patient when the matching score for the patient passes, meets, or exceeds requirements of the threshold score. Para 19 and FIG. 3 discloses an exemplary system output. The report generated by the system contains (but not limited to) de-identified patients' results with scores for each arm of the trial. Horizontal arrow in Arm2 result of patient ID 14 points to an example of a score that may result in deeming the patient as ineligible, because the set threshold requirement was not met, i.e., in this example, the score of 0 is below 5.sup.th percentile threshold. Para 43 discloses identifying a group of patients within the population having matching scores above a predetermined threshold to participate in the clinical trial. Para 151 discloses a computing device 1210 of the computer system 1200 is a network device operated by one or more users (e.g., physician or patient) in the system shown in FIG. 1. [computing device associated with computing the study].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron with the clinical trial optimization as taught by Perlina in order to identify the most at-risk patients that may need to be excluded as those patients whose scores, for example, fall below the 5.sup.th percentile while patients scoring at 5.sup.th percentile or above would be considered as having met (or passed) the threshold requirements (Para 50).
While the combination of Cameron and Perlina discloses the above limitations, Cameron Clinical Trial Patient Friction Coefficient discloses, “For this last set, one might, for example, adjust upward the trial-level burden for an individual patient based on geography (ie, travel burden), or age (ie, based on increased potential to disrupt daily routines for those in their prime working years),” and Perlina para 24 discloses, “a patient may visit a physician, who obtains and records informative medical and demographics data (e.g. age, gender, histological diagnosis, previous treatment(s), current treatment, medical history and comorbidities, familial history and inherited syndromes, and the like),” the combination does not fully disclose the use of longitudinal data sources that ---Vold discloses:
processing, a plurality of variables burden impact for subject participation in a study comprising at least one protocol, wherein the population data is indicative of healthcare treatment of potential subjects for the study, wherein each of the plurality of longitudinal data sources comprising at least a portion of the population data collected for at least one of the potential subjects at multiple time instances, wherein [[the]] at least one variable from the plurality of variables describes a potential subject for the study, and wherein each of the at least one variable representing a feature of the healthcare treatment for the potential subjects; (Para 22 discloses examples of data types that can be included in EHRs include medication history (e.g., current prescriptions, concomitant medications, medication classes, medication codes, and/or ontological terms [healthcare treatment]), disease conditions (e.g., pre-existing conditions, co-morbidities, symptoms, diagnoses, and/or prognoses), laboratory test results or clinical data (e.g., biomarkers, genomic variants, medical images, and/or sequencing data), and free-text observations and other notes (e.g., by a clinician). In some instances, EHRs include longitudinal data, such as information collected over multiple visits to a healthcare provider or over a period of time. Para 23 discloses EHRs are useful for obtaining information for clinical trials, such as to evaluate study feasibility, coordinate subject recruitment and enrollment, and facilitate pre- and post-trial data collection. In particular, EHR data is useful for pre-screening patients for eligibility in clinical research (e.g., by age, gender, diagnosis, medications, biomarkers, and/or other demographic or health-related factors). Similarly, EHR data can be used to exclude ineligible patients, thus reducing overall screening burden for clinical trials, misallocation of trial resources, and the potentially harmful effects of enrolling an ineligible patient in a study. Para 28 discloses there is a need in the art to improve the quality of data ingestion and label generation, increase the generalizability of EHR data analysis, and to reduce the computational complexity and resources required by currently available techniques. Para 338 discloses a computer system including one or more processors, memory, and one or more programs, where the one or more programs are stored in the memory and are configured to be executed by the one or more processors. In some embodiments, the one or more programs include instructions for a method of determining a relationship between a first subject and a first health entity for use in clinical decision-making.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron and the clinical trial optimization as taught by Perlina with the longitudinal data from electronic health records as taught by Vold in order to obtain information for clinical trials, such as to evaluate study feasibility, coordinate subject recruitment and enrollment, and facilitate pre- and post-trial data collection… [and[ reduce overall screening burden for clinical trials, misallocation of trial resources, and the potentially harmful effects of enrolling an ineligible patient in a study (Vold Para 23).
While the combination of Cameron, Perlina, and Vold discloses the above limitations and Perlina further discloses, “real world data can be leveraged using this system to understand failure a clinical trial (see e.g., FIG. 2). In some embodiments, the entire distribution of scored drug or drug combination of interest applied to individual patients can be provided, and then a user can determines a threshold within the population for study in a clinical trial,“ (Perlina Para 137), the combination does not fully disclose the following limitation that Wang discloses:
generating parameters for the function is based on a distribution of the at least one variable in the plurality of variables in a population comprising the potential subjects for the study, and wherein the trained machine learning model is trained to generate parameters of functions based on population data of the potential subjects… wherein the subject-specific values comprise values for the at least one variable found in the population data (The Abstract section discloses here, we develop a novel approach to estimating parameters in population genetic models that automatically adapts to data from any population. Our method, pg‐gan, is based on a generative adversarial network [machine learning model] that gradually learns to generate realistic synthetic data. We demonstrate that our method is able to recover input parameters in a simulated isolation‐with‐migration model… Section 2.1 Generator discloses each new set of parameters is proposed by sampling from a normal distribution around each current value, with variance based on the temperature. This allows the algorithm to explore the parameter space quickly in the beginning and refine the estimates towards the end of GAN training… Out of the several candidate proposals, we choose the one that minimizes ℒ𝐺(Θ). Then, we compare this loss to the loss of the previous iteration. If the proposal reduces or maintains the generator loss, we always accept it. If not, we use the simulated annealing temperature to help define a threshold for acceptance.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron, the clinical trial optimization as taught by Perlina and the longitudinal data from electronic health records as taught by Vold with the automatic inference of demographic parameters using generative adversarial networks as taught by Wang in order to estimate parameters that automatically adapt to data from any parameters (Wang Abstract).
Regarding Claim 2, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. The combination of Cameron, Perlina, Vold, and Wang discloses the following limitation that Cameron further discloses:
The method of claim 1, wherein the at least one variable includes a travel distance to a trial location associated with the study. (The Clinical Trial Patient Friction Coefficient section discloses the PFC must account for a wide variety of protocol design elements that impact perceived burden. These elements include the frequency of visits, procedures to be performed, duration of required participation, travel distance to the research site, time of day to schedule a visit, and many others [at least one variable]… Finally, a set of anonymized patient-level characteristics are used to “personalize” the burden of participation in that trial to individual patients or groups of patients (subject to privacy and regulatory considerations). For this last set, one might, for example, adjust upward the trial-level burden for an individual patient based on geography (ie, travel burden), or age (ie, based on increased potential to disrupt daily routines for those in their prime working years).)
Regarding Claim 5, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above The combination of Cameron, Perlina, Vold, and Wang discloses the following limitation that Perlina further discloses:
The method of claim 1, wherein, for each individual of the plurality of individuals, at least some of the subject-specific values for the at least one variable represent predicted future values for the individual. (Para 17 and FIG. 1 disclose an exemplary therapy matching system. The therapy matching and scoring system consists of curated knowledge base component that allows the algorithms to programmatically match an incoming cancer patient's profile against the relevant structured knowledge for each unique case. Inputs include but are not limited to cancer NGS testing report, diagnosis [predicted future value], trial drug(s) and their target(s). The scoring AI engine uses the relevant content, reasoning algorithms, and rules to produce matching scores for each patient in each arm of a trial. Scores can be used to establish thresholds for the population. Example discloses considerations for determining clinical trial eligibility for patients diagnosed with a proliferative, degenerative or inflammatory disease are discussed, where therapy comprises an immunotherapy.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron, the longitudinal data from electronic health records as taught by Vold, and the automatic inference of demographic parameters using generative adversarial networks as taught by Wang with the clinical trial optimization as taught by Perlina in order to identify the most at-risk patients that may need to be excluded as those patients whose scores, for example, fall below the 5.sup.th percentile while patients scoring at 5.sup.th percentile or above would be considered as having met (or passed) the threshold requirements (Para 50).
Regarding Claim 8, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. The combination of Cameron, Perlina, Vold, and Wang discloses the following limitation that Cameron further discloses:
The method of claim 1, wherein the corresponding functions are convex and monotonically increasing. (Cameron Abstract discloses this article proposes a new methodology to quantify patient burden: the clinical trial patient friction coefficient (PFC). Cameron Equation 1 discloses the risk for each burden (ie, a probabilistic weighting as to perceived likelihood of the burden occurring). The resulting aggregation, t(x), [a set of estimated burden values] for a given patient x of perceived trial burden (and, if relevant, the caregiver burden associated with patient x participation) can be expressed as: Equation 1. where tb(1) through tb(i) are the individual time-based burdens on the patient and/or the caregiver; cb(1) through cb(i) are the individual cost-based burdens on the patient and/or the caregiver; db(1) through db(i) are the individual disruption-based burdens on the patient and/or the caregiver; and tw, cw, and dw represent the perception-based weighting of time-, cost-, and disruption-based burdens, respectively. Wherein Equation 1 discloses various functions (tb, cb, db) that are convex and monotonically increasing.)
Regarding Claim 11, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. The combination of Cameron, Perlina, Vold, and Wang discloses the following limitation that Cameron further discloses:
The method of claim 1, wherein generating the plurality of parameters for the plurality of functions ; generating, for at least one function in the plurality of functions, at least some of the parameters an estimated burden output of the at least one function is zero, wherein the estimated burden output of the at least one function is zero when the subject-specific values the at least one variable specified by the protocol of the study. (The Clinical Trial Patient Friction Coefficient section discloses for this last set, one might, for example, adjust upward the trial-level burden for an individual patient based on geography (ie, travel burden), or age (ie, based on increased potential to disrupt daily routines for those in their prime working years) All of these inputs will be initially derived in 1 or more of 3 unit-based parameters: cost, time, and degree of daily routine disruption. Next, these 3 parameters should be weighted at a subgroup level to account for differences in burden perception (which, as discussed subsequently, will likely need to be derived from additional research)—perhaps, by way of example, based on socioeconomic, demographic, or geographic factors—as well as the risk for each burden (ie, a probabilistic weighting as to perceived likelihood of the burden occurring)…. Equation 2 further discloses the PFC(x) = t(x) – e(x) where the t(x) is the aggregation for a given patient x of perceived trial burden and the e(x) is the perceived burden for the existing treatment pathway. [Wherein if the values match pretreatment and for the perceived trial burden, then the estimated burden would be zero based on Equation 2].)
Regarding Claim 12, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. Cameron discloses the aggregate metric representative of the set of estimated burden values and the Clinical Trial Patient Friction Coefficient section and Equation 1 disclose, “the risk for each burden (ie, a probabilistic weighting as to perceived likelihood of the burden occurring). The resulting aggregation, t(x), [a set of estimated burden values] for a given patient x of perceived trial burden (and, if relevant, the caregiver burden associated with patient x participation) can be expressed as: Equation 1. where tb(1) through tb(i) are the individual time-based burdens on the patient and/or the caregiver; cb(1) through cb(i) are the individual cost-based burdens on the patient and/or the caregiver; db(1) through db(i) are the individual disruption-based burdens on the patient and/or the caregiver; and tw, cw, and dw represent the perception-based weighting of time-, cost-, and disruption-based burdens, respectively.” However, Cameron does not fully disclose the following limitation that Perlina discloses:
The method of claim 1, wherein identifying the subset of the plurality of individuals for the study comprises: determining for [[of]] the aggregate score. (Abstract discloses there remains a need for techniques to inform healthcare providers or pharmaceutical companies of the most risky subset of profiles to potentially exclude from the trial and to select an optimal cohort of patients who will have increased chances of responding to the therapy and/or decreased chances of experiencing toxicity in the clinical trial. The present invention addresses this and other needs. Para 8 discloses providing a recommendation for clinical trial eligibility of the patient when the matching score for the patient passes, meets or exceeds [is above a threshold] requirements of the threshold score. Para 50 discloses threshold requirements may be established in order to identify the most at-risk patients that may need to be excluded as those patients whose scores, for example, fall below the 5.sup.th percentile while patients scoring at 5.sup.th percentile or above would be considered as having met (or passed) the threshold requirements.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron, the longitudinal data from electronic health records as taught by Vold, and the automatic inference of demographic parameters using generative adversarial networks as taught by Wang with the clinical trial optimization as taught by Perlina in order to identify the most at-risk patients that may need to be excluded as those patients whose scores, for example, fall below the 5.sup.th percentile while patients scoring at 5.sup.th percentile or above would be considered as having met (or passed) the threshold requirements (Para 50).
Regarding Claim 16, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. The combination of Cameron, Perlina, Vold, and Wang discloses the following limitation that Cameron further discloses:
The method of claim 1, wherein identifying the subset of the plurality of individuals to be prioritized for recruitment for the study comprises: identifying a specified number of the plurality of individuals or a specified percentage of the plurality of individuals based on a ranking of the plurality of individuals according to the aggregate score (Perlina para 5 discloses providing a recommendation for clinical trial eligibility of the patient when the matching score for the patient passes, meets or exceeds requirements of the threshold score [specified number being one patient]. Para 50 discloses identifying the most at-risk patients that may need to be excluded as those patients whose scores, for example, fall below the 5.sup.th percentile while patients scoring at 5.sup.th percentile or above would be considered as having met (or passed) the threshold requirements.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron with the clinical trial optimization as taught by Perlina in order to identify the most at-risk patients that may need to be excluded as those patients whose scores, for example, fall below the 5.sup.th percentile while patients scoring at 5.sup.th percentile or above would be considered as having met (or passed) the threshold requirements (Para 50).
Regarding Claim 20, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. The combination of Cameron, Perlina, Vold, and Wang discloses the following limitation that Cameron further discloses:
The method of claim 1, further comprising comparing the protocol of the study with at least one other study protocol, wherein comparing the protocol of the study with the at least one other study protocol comprises: comparing, for each individual of the plurality of individuals, [[the]] a set of estimated burden impact values corresponding to each of the plurality of variables (Applying the PFC: Anticipating and Reducing Trial Burden for Patients section discloses since PFC values are comparable across trials, by ascribing a PFC to a trial, one calls out the burden as an essential variable. By habitually performing this activity, the concept of patient participation burden can be leveraged at the center of trial-design discussions and vernacular, bringing necessary focus to potential superfluous design elements…. the conclusion section discloses to vet the PFC concept: “On a sponsor side, use it to compare potential trials to ones that have gone well or not gone well from a patient viewpoint, and … pull whatever it is to lower the score.” Over time, the clinical trial PFC can be used prospectively during protocol planning and design to reduce, and ultimately avoid entirely, inefficient practices and to better predict clinical trial performance.)
Regarding claim 21, the claim is directed to the system implementing the computer implemented method of claim 1 and further recite a computing device comprising a memory configured to store instructions and a processor configured to execute the instructions to perform operations(e.g., see Perlina Para 153 teaching a computing device and Paras 157-158 teaching the computing device may comprise a processor (processing instructions for execution within the computing device) and a memory) and are similarly rejected.
Regarding claim 22, the claim is directed to the system implementing the computer implemented method of claim 1 and further recite one or more non-transitory machine-readable storage devices having encoded thereon computer readable instructions for causing one or more processing devices to perform operations (e.g., see Perlina Para 153 teaching a computing device, the computing device 1210 may be implemented using hardware (e.g., in a desktop computer), software (e.g., in a virtual machine or the like), or a combination of software and hardware and Paras 157-158 teaching the computing device may comprise a processor (processing instructions for execution within the computing device) and a memory) and are similarly rejected.
Regarding Claim 23, Cameron discloses:
A method comprising:
determining, using a plurality of parameters for a plurality of functions an aggregate score that estimates a combined incremental burden for the individual based on subject-specific values for a plurality of variables absent participation of a potential subject in a study, wherein the subject-specific values comprise values for at least one variable found in population data, wherein each function represents a relationship between the corresponding variable and an estimated burden impact values that would be imposed on the individual by at least one [[a]] protocol of a study if the individual were to participate in the study, and each of the variables in the plurality of variables have corresponding parameters in the plurality of parameters for the plurality of functions, based on geography (ie, travel burden), or age (ie, based on increased potential to disrupt daily routines for those in their prime working years) [variables that describe the individual].)
While Cameron discloses the above limitations, it does not fully disclose the following limitation that Perlina discloses:
identifying, based on the aggregate score (Perlina Abstract discloses there remains a need for techniques to inform healthcare providers or pharmaceutical companies of the most risky subset of profiles to potentially exclude from the trial and to select an optimal cohort of patients who will have increased chances of responding to the therapy and/or decreased chances of experiencing toxicity in the clinical trial. The present invention addresses this and other needs. Para 8 discloses providing a recommendation for clinical trial eligibility of the patient when the matching score for the patient passes, meets or exceeds requirements of the threshold score. Para 50 discloses threshold requirements may be established in order to identify the most at-risk patients that may need to be excluded as those patients whose scores, for example, fall below the 5.sup.th percentile while patients scoring at 5.sup.th percentile or above would be considered as having met (or passed) the threshold requirements.)
transmitting data indicative of the subject of the plurality of individuals to at least one computing device associated with conducting the study. (Paras 9-14 disclose a system for assessing eligibility of a patient to a clinical trial of a drug, the system comprising: a server; a computing device in communication with the server over a network, the computing device including a processor and a memory, the memory bearing computer executable code configured to perform the steps of: a-c where c discloses providing a recommendation for clinical trial eligibility of the patient when the matching score for the patient passes, meets, or exceeds requirements of the threshold score. Para 19 and FIG. 3 discloses an exemplary system output. The report generated by the system contains (but not limited to) de-identified patients' results with scores for each arm of the trial. Horizontal arrow in Arm2 result of patient ID 14 points to an example of a score that may result in deeming the patient as ineligible, because the set threshold requirement was not met, i.e., in this example, the score of 0 is below 5.sup.th percentile threshold. Para 43 discloses identifying a group of patients within the population having matching scores above a predetermined threshold to participate in the clinical trial. Para 151 discloses a computing device 1210 of the computer system 1200 is a network device operated by one or more users (e.g., physician or patient) in the system shown in FIG. 1. [computing device associated with computing the study].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron with the clinical trial optimization as taught by Perlina in order to identify the most at-risk patients that may need to be excluded as those patients whose scores, for example, fall below the 5.sup.th percentile while patients scoring at 5.sup.th percentile or above would be considered as having met (or passed) the threshold requirements (Para 50).
While the combination of Cameron and Perlina discloses the above limitations, Cameron Clinical Trial Patient Friction Coefficient discloses, “For this last set, one might, for example, adjust upward the trial-level burden for an individual patient based on geography (ie, travel burden), or age (ie, based on increased potential to disrupt daily routines for those in their prime working years),” and Perlina para 24 discloses, “a patient may visit a physician, who obtains and records informative medical and demographics data (e.g. age, gender, histological diagnosis, previous treatment(s), current treatment, medical history and comorbidities, familial history and inherited syndromes, and the like),” the combination does not fully disclose the use of longitudinal data sources that ---Vold discloses:
and wherein the plurality of variables the study comprising the at least one protocol of the study and each of the plurality of longitudinal data sources comprising at least a portion of the population data collected for at least one of the potential subjects at one or more time instances; (Para 22 discloses examples of data types that can be included in EHRs include medication history (e.g., current prescriptions, concomitant medications, medication classes, medication codes, and/or ontological terms [healthcare treatment]), disease conditions (e.g., pre-existing conditions, co-morbidities, symptoms, diagnoses, and/or prognoses), laboratory test results or clinical data (e.g., biomarkers, genomic variants, medical images, and/or sequencing data), and free-text observations and other notes (e.g., by a clinician). In some instances, EHRs include longitudinal data, such as information collected over multiple visits to a healthcare provider or over a period of time. Para 23 discloses EHRs are useful for obtaining information for clinical trials, such as to evaluate study feasibility, coordinate subject recruitment and enrollment, and facilitate pre- and post-trial data collection. In particular, EHR data is useful for pre-screening patients for eligibility in clinical research (e.g., by age, gender, diagnosis, medications, biomarkers, and/or other demographic or health-related factors). Similarly, EHR data can be used to exclude ineligible patients, thus reducing overall screening burden for clinical trials, misallocation of trial resources, and the potentially harmful effects of enrolling an ineligible patient in a study. Para 28 discloses there is a need in the art to improve the quality of data ingestion and label generation, increase the generalizability of EHR data analysis, and to reduce the computational complexity and resources required by currently available techniques. Para 338 discloses a computer system including one or more processors, memory, and one or more programs, where the one or more programs are stored in the memory and are configured to be executed by the one or more processors. In some embodiments, the one or more programs include instructions for a method of determining a relationship between a first subject and a first health entity for use in clinical decision-making.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron and the clinical trial optimization as taught by Perlina with the longitudinal data from electronic health records as taught by Vold in order to obtain information for clinical trials, such as to evaluate study feasibility, coordinate subject recruitment and enrollment, and facilitate pre- and post-trial data collection… [and] reduce overall screening burden for clinical trials, misallocation of trial resources, and the potentially harmful effects of enrolling an ineligible patient in a study (Vold Para 23).
While the combination of Cameron, Perlina, and Vold discloses the above limitations and Perlina further discloses, “real world data can be leveraged using this system to understand failure a clinical trial (see e.g., FIG. 2). In some embodiments, the entire distribution of scored drug or drug combination of interest applied to individual patients can be provided, and then a user can determines a threshold within the population for study in a clinical trial,“ (Perlina Para 137), the combination does not fully disclose the following limitation that Wang discloses:
wherein the plurality of parameters for the plurality of functions is generated by a trained machine learning model …wherein the machine learning model is trained to generate parameters of functions based on the population data of potential subjects, (The Abstract section discloses here, we develop a novel approach to estimating parameters in population genetic models that automatically adapts to data from any population. Our method, pg‐gan, is based on a generative adversarial network [machine learning model] that gradually learns to generate realistic synthetic data. We demonstrate that our method is able to recover input parameters in a simulated isolation‐with‐migration model… Section 2.1 Generator discloses each new set of parameters is proposed by sampling from a normal distribution around each current value, with variance based on the temperature. This allows the algorithm to explore the parameter space quickly in the beginning and refine the estimates towards the end of GAN training… Out of the several candidate proposals, we choose the one that minimizes ℒ𝐺(Θ). Then, we compare this loss to the loss of the previous iteration. If the proposal reduces or maintains the generator loss, we always accept it. If not, we use the simulated annealing temperature to help define a threshold for acceptance.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron, the clinical trial optimization as taught by Perlina and the longitudinal data from electronic health records as taught by Vold with the automatic inference of demographic parameters using generative adversarial networks as taught by Wang in order to estimate parameters that automatically adapt to data from any parameters (Wang Abstract).
Regarding Claim 24, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. The combination of Cameron, Perlina, Vold, and Wang discloses the following limitation that Wang discloses:
The method of claim 1, wherein the processing comprises determining from the population data, the distribution of the at least one variable in the plurality of variables in the population comprising the potential subjects. (Section 2.1 Generator discloses each new set of parameters is proposed by sampling from a normal distribution around each current value, with variance based on the temperature. This allows the algorithm to explore the parameter space quickly in the beginning and refine the estimates towards the end of GAN training… Out of the several candidate proposals, we choose the one that minimizes ℒ𝐺(Θ). Then, we compare this loss to the loss of the previous iteration. If the proposal reduces or maintains the generator loss, we always accept it. If not, we use the simulated annealing temperature to help define a threshold for acceptance.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron, the clinical trial optimization as taught by Perlina and the longitudinal data from electronic health records as taught by Vold with the automatic inference of demographic parameters using generative adversarial networks as taught by Wang in order to estimate parameters that automatically adapt to data from any parameters (Wang Abstract).
Claim(s) 3 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Cameron (Cameron, D., Willoughby, C., Messer, D., Lux, M., Getz, K., & Aitken, M. (2020, July 31). Assessing participation burden in clinical trials: Introducing the patient friction coefficient. Clinical Therapeutics.) in view of Perlina (US PG Pub 2025/0006317 A1), further in view of Vold (US PG Pub 2024/0266009 A1), Wang (Automatic inference of demographic parameters using generative adversarial networks) and Harrison (US PG Pub 2010/0088245 A1).
Regarding Claim 3, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. The combination of Cameron, Perlina, Vold, and Wang does not fully disclose the following limitation that Harrison discloses:
The method of claim 1, wherein, for each individual of the plurality of individuals, the subject-specific values for the at least one variable are determined, at least in part, based on questionnaire data or electronic medical records (EMR) data. (Paras 44-45 disclose FIG. 1 is an example of a block diagram of portions of a system 100 for computer-assisted physician and patient recruitment for a clinical trial as well as clinical site selection (the system)… In the example of FIG. 1, patient data can be obtained in different formats, such as from different data storages, represented here by the patient database 145, which may be associated with different business organizational entities that may not be concerned about the compatibility of data formats or sharing of patient data between such business entities. In an example, the organizational entity providing the computer-assisted patient recruitment contracts with other organizational entities to obtain the patient data, and such other organizational entities include, among other things, Independent Practice Associations (IPAs), Preferred Provider Organizations (PPOs), Health Maintenance Organizations (HMOs), Practice Management Systems (PMS) companies, Electronic Medical Records (EMR) companies and others.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron, the clinical trial optimization as taught by Perlina, the longitudinal data from electronic health records as taught by Vold, and the automatic inference of demographic parameters using generative adversarial networks as taught by Wang with the systems and methods for developing studies such as clinical trials as taught by Harrison in order to determine whether the patient qualifies for participation in the study (Para 134).
Regarding Claim 17, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. The combination of Cameron, Perlina, Vold, and Wang does not fully disclose the following limitation that Harrison discloses:
The method of claim 1, further comprising, recruiting the subset of the plurality of individuals for the study. (Para 135-137 discloses FIG. 20 illustrates an example of handling patient referrals to clinical trial sites. In an example, the process 2000 has minimal direct interaction with the system 100 such that, after 2010, the referral process 2000 is completed between the clinical trial site and the patient… if the patient enrolls in the study at 2060 the study is performed at 2070. Performance of the actual study could involve many additional visits and last a period days, weeks, months or years, this is completely dependent on the individual clinical trial.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron, the clinical trial optimization as taught by Perlina, the longitudinal data from electronic health records as taught by Vold, and the automatic inference of demographic parameters using generative adversarial networks as taught by Wang with the systems and methods for developing studies such as clinical trials as taught by Harrison in order to determine whether the patient qualifies for participation in the study (Para 134).
Regarding Claim 18, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. The combination of Cameron, Perlina, Vold, and Wang does not fully disclose the following limitation that Harrison discloses:
The method of claim 1, further comprising treating at least a portion of the subset of the plurality of individuals in accordance with the protocol of the study. (Para 136 discloses if the patient enrolls in the study at 2060 the study is performed at 2070 [treating]. Performance of the actual study could involve many additional visits and last a period days, weeks, months or years, this is completely dependent on the individual clinical trial.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron, the clinical trial optimization as taught by Perlina, the longitudinal data from electronic health records as taught by Vold, and the automatic inference of demographic parameters using generative adversarial networks as taught by Wang with the systems and methods for developing studies such as clinical trials as taught by Harrison in order to determine whether the patient qualifies for participation in the study (Para 134) and perform the study after enrollment in order to treat the patient and identify the outcomes of the trial.
Claim(s) 4, 6-7, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Cameron (Cameron, D., Willoughby, C., Messer, D., Lux, M., Getz, K., & Aitken, M. (2020, July 31). Assessing participation burden in clinical trials: Introducing the patient friction coefficient. Clinical Therapeutics.) in view of Perlina (US PG Pub 2025/0006317 A1), further in view of Vold (US PG Pub 2024/0266009 A1) and Wang (Automatic inference of demographic parameters using generative adversarial networks) and Walpole (US PG Pub 2019/0206521 A1).
Regarding Claim 4, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. The combination of Cameron, Perlina, Vold, and Wang does not fully disclose the following limitation that Walpole discloses:
The method of claim 1, wherein, for each individual of the plurality of individuals, at least some of the subject-specific values for the at least one variable represents historical values for the individual. (Para 7 discloses the method may further include training a machine learning system with a training observations of historic patient factor data and historic patient burden data… The method may include inputting the factor data for the plurality of patients into the trained neural network and outputting, from the at least one output node, the patient burden index for each of the plurality of patients.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron, the clinical trial optimization as taught by Perlina, the longitudinal data from electronic health records as taught by Vold, and the automatic inference of demographic parameters using generative adversarial networks as taught by Wang with the intelligent planning, execution, and reporting of clinical trials as taught by Walpole in order to generate a rule base that associates the patient factors (including historic) with the patient burden index (Para 7) in order to improve the complex process of protocol design and avoid poorly constructed protocols, potentially numerous costly amendments, protocol deviations, delays in obtained appropriate data, and other problems (Paras 4-5).
Regarding Claim 6, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. The combination of Cameron, Perlina, Vold, and Wang does not fully disclose the following limitation that Walpole discloses:
The method of claim 1, wherein the estimated burden impact is inversely related to a likelihood that the potential subject satisfies a particular outcome with respect to the study. (Para 27 discloses the burden that patients undergo has a significant correlation to retention, that is, having patients stay in a clinical trial all the way to completion [wherein staying in a clinical trial is also adherence to a trial’s protocol]. Para 55 discloses data is available for the patient's financial burden, maximum waiting time, and ease of interaction with study staff. Further suppose that the best mathematical model projects these data to be associated with a probability of staying in the clinical trial to completion of 0.75. The sponsor may seek to increase this probability by searching over the possible adjustments that could be made for the patient, such as easing the interaction with the study staff, ensuring minimal waiting times, or reducing the patient's cost. The search over these adjustments yields a response surface representing potential new probabilities of completing the trial. The sponsor can then use this as a decision tool to determine if it is worth investing time or funds to improve the situation for this patient, and thereby improve the likelihood of their being retained to completion. Para 59 discloses the likelihood of withdrawing was inversely proportional to the number of interactions with study staff. Moreover, from the Phase 2 data, patients who were scheduled in the afternoon were twice as likely to withdraw from the study as were those who were scheduled in the morning [wherein adherence to the protocol would be increased interactions with study staff and attending an increased amount of afternoon visits].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron, the clinical trial optimization as taught by Perlina, the longitudinal data from electronic health records as taught by Vold, and the automatic inference of demographic parameters using generative adversarial networks as taught by Wang with the intelligent planning, execution, and reporting of clinical trials as taught by Walpole in order to generate a rule base that associates the patient factors (including historic) with the patient burden index (Para 7) in order to improve the complex process of protocol design and avoid poorly constructed protocols, potentially numerous costly amendments, protocol deviations, delays in obtained appropriate data, and other problems (Paras 4-5).
Regarding Claim 7, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. The combination of Cameron, Perlina, Vold, and Wang does not fully disclose the following limitation that Walpole discloses:
The method of claim 6, wherein the particular outcome comprises adherence to the at least one protocol of the study. (Para 27 discloses the burden that patients undergo has a significant correlation to retention, that is, having patients stay in a clinical trial all the way to completion [wherein staying in a clinical trial is also adherence to a protocol of the study]. Para 55 discloses data is available for the patient's financial burden, maximum waiting time, and ease of interaction with study staff. Further suppose that the best mathematical model projects these data to be associated with a probability of staying in the clinical trial to completion of 0.75. The sponsor may seek to increase this probability by searching over the possible adjustments that could be made for the patient, such as easing the interaction with the study staff, ensuring minimal waiting times, or reducing the patient's cost. The search over these adjustments yields a response surface representing potential new probabilities of completing the trial. The sponsor can then use this as a decision tool to determine if it is worth investing time or funds to improve the situation for this patient, and thereby improve the likelihood of their being retained to completion. Para 59 discloses the likelihood of withdrawing was inversely proportional to the number of interactions with study staff. Moreover, from the Phase 2 data, patients who were scheduled in the afternoon were twice as likely to withdraw from the study as were those who were scheduled in the morning [wherein adherence to the protocol would be increased interactions with study staff and attending an increased amount of afternoon visits].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron, the clinical trial optimization as taught by Perlina, the longitudinal data from electronic health records as taught by Vold, and the automatic inference of demographic parameters using generative adversarial networks as taught by Wang with the intelligent planning, execution, and reporting of clinical trials as taught by Walpole in order to improve success, and/or propose revised protocols 908 in an intelligent manner concurrent to each trial (Para 72).
Regarding Claim 25, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. The combination of Cameron, Perlina, Vold, and Wang does not fully disclose the following limitation that Walpole discloses:
The method of claim 1, wherein estimating a burden impact comprises estimating a reduction in burden that would be imposed on the potential subject by the at least one protocol of the study if the potential subject were to participate in the study. (Para 7 discloses the method may further include generating a rule base that associates the patient factors with the patient burden index. The rule base may include fuzzy rules. The method may further include modifying the protocol to reduce the patient burden index [estimating a burden impact]. Para 43 discloses different treatments induce more or less discomfort, fatigue, or other factors that affect patient disposition. Alternative choices for clinical trial design can affect patient burden directly. For example, burden could be reduced in a trial by eliminating an extra blood draw, or reducing the number of required site visits. Para 85 discloses the method 1100 may further include modifying the protocol to reduce the patient burden index. See further: Para 42, 44, 50).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron, the clinical trial optimization as taught by Perlina, the longitudinal data from electronic health records as taught by Vold, and the automatic inference of demographic parameters using generative adversarial networks as taught by Wang with the intelligent planning, execution, and reporting of clinical trials as taught by Walpole in order to identify alternative choices for clinical trial design that can affect patient burden directly (such as reducing burden by eliminating an extra blood draw, or reducing the number of required site visits) (Walpole Para 43).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Cameron (Cameron, D., Willoughby, C., Messer, D., Lux, M., Getz, K., & Aitken, M. (2020, July 31). Assessing participation burden in clinical trials: Introducing the patient friction coefficient. Clinical Therapeutics) in view of Perlina (US PG Pub 2025/0006317 A1), further in view of Vold (US PG Pub 2024/0266009 A1), Wang (Automatic inference of demographic parameters using generative adversarial networks), and Cowie (Electronic health records to facilitate clinical research).
Regarding Claim 10, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. The combination of Cameron, Perlina, Vold, and Wang discloses the following limitation that Cameron further discloses:
The method of claim 1, wherein determining the corresponding functions comprises determining one or more parameters of the corresponding functions based on (Cameron Abstract discloses this article proposes a new methodology to quantify patient burden: the clinical trial patient friction coefficient (PFC). The Clinical Trial Patient Friction Coefficient section discloses the PFC must account for a wide variety of protocol design elements that impact perceived burden. These elements include the frequency of visits, procedures to be performed, duration of required participation, travel distance to the research site, time of day to schedule a visit, and many others [subject matter expertise as to what impacts burden]… Finally, a set of anonymized patient-level characteristics are used to “personalize” the burden of participation in that trial to individual patients or groups of patients (subject to privacy and regulatory considerations). For this last set, one might, for example, adjust upward the trial-level burden for an individual patient based on geography (i.e., travel burden), or age (i.e., based on increased potential to disrupt daily routines for those in their prime working years). All of these inputs will be initially derived in 1 or more of 3 unit-based parameters: cost, time, and degree of daily routine disruption. Next, these 3 parameters should be weighted at a subgroup level to account for differences in burden perception (which, as discussed subsequently, will likely need to be derived from additional research)—perhaps, by way of example, based on socioeconomic, demographic, or geographic factors—as well as the risk for each burden (ie, a probabilistic weighting as to perceived likelihood of the burden occurring). The resulting aggregation, t(x), for a given patient x of perceived trial burden (and, if relevant, the caregiver burden associated with patient x participation) can be expressed as: equation 1 [wherein equation 1 illustrates the weighted “resulting aggregation, t(x)” of the 3 parameters].)
While Cameron discloses the above limitations, the combination of Cameron, Perlina, Vold, and Wang does not fully disclose the following limitation that Cowie discloses:
wherein determining the corresponding functions comprises determining one or more parameters of the corresponding functions based and [[or]] (System capabilities section discloses EHRs for use in clinical research need a flexible architecture to accommodate studies of different interventions or disease states. EHR systems may be capable of matching eligibility criteria to relevant data fields and flagging potential trial subjects to investigators. Patient questionnaires and surveys can be linked to EHRs to provide additional context to clinical data. Pre-population of eCRFs has been proposed as a potential role for EHRs, but the proportion of fields in an EHR that can be mapped to an eCRF varies substantially across systems.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron, the clinical trial optimization as taught by Perlina, the longitudinal data from electronic health records as taught by Vold, and the automatic inference of demographic parameters using generative adversarial networks as taught by Wang with the EHRs and questionnaires and surveys of the electronic health records to facilitate clinical research as taught by Cowie in order to provide additional context to clinical data while flagging potential trial subjects.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Cameron (Cameron, D., Willoughby, C., Messer, D., Lux, M., Getz, K., & Aitken, M. (2020, July 31). Assessing participation burden in clinical trials: Introducing the patient friction coefficient. Clinical Therapeutics) in view of Perlina (US PG Pub 2025/0006317 A1), further in view of Vold (US PG Pub 2024/0266009 A1), Wang (Automatic inference of demographic parameters using generative adversarial networks), Teare (Teare, M.D., Dimairo, M., Shephard, N. et al. Sample size requirements to estimate key design parameters from external pilot randomised controlled trials: a simulation study. Trials 15, 264 (2014)) and Harrison (US PG Pub 2010/0088245 A1).
Regarding Claim 19, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. The combination of Cameron, Perlina, Vold, and Wang does not fully disclose the following limitation that Teare discloses:
The method of claim 1, further comprising: determining that the identified subset of the plurality of individuals to be prioritized for recruitment for the study is below a threshold size; (Teare Methods section discloses using the estimate of the SD, along with other information, such as the minimum clinically important difference in outcomes between groups, and Type I and Type II errors levels, to calculate the required sample size [threshold size of individuals] (using the significance thresholds approach) for the definitive RCT)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron, the clinical trial optimization as taught by Perlina, the longitudinal data from electronic health records as taught by Vold, and the automatic inference of demographic parameters using generative adversarial networks as taught by Wang with the sample size requirements to estimate key design parameters from external pilot randomised controlled trials as taught by Teare in order to identify the impact of increasing the pilot sample size on the precision and bias and predicted power (Methods).
While Teare discloses the above limitation, the combination of Cameron, Perlina, Vold, Wang, and Teare does not fully disclose the following limitation that Harrison discloses:
and altering the protocol of the study with respect to at least some of the plurality of variables (Para 141-144 discloses protocol organization analyzes each clinical trial protocol criteria provided by the sponsor in relationship to the entire patient or physician population. Analyzing each clinical trial criteria individually allows the system 100 to suggest potential modifications to enhance study size, increase patient enrollment [increase size of identified subset and enrollment], or improve the study's likelihood of success. Once obtained, the eligible patient cohort is saved at 2235 for use in the analysis performed at 2240. The analysis at 2240 is performed on each individual clinical trial protocol criterion with the eligible patient cohort 2235 as input. Any criteria utilized to obtain the eligible patient cohort are not re-analyzed. The analysis results are added to the result list at 2250. The individual protocol criterion result list is saved at 2255 for use in the organization at 2270… At 2260, the system 100 can determine whether there are additional criteria to analyze. If there are additional protocol criteria to analyze, control loops back to 2240 and the process 2200 proceeds to analyze the next protocol criterion. If there are no more protocol criteria to analyze, the process 2200 proceeds to organize the protocol at 2270.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron, the clinical trial optimization as taught by Perlina, the longitudinal data from electronic health records as taught by Vold, the automatic inference of demographic parameters using generative adversarial networks as taught by Wang, and the sample size requirements to estimate key design parameters from external pilot randomised controlled trials as taught by Teare with the systems and methods for developing studies such as clinical trials as taught by Harrison in order to determine whether the patient qualifies for participation in the study (Para 134) and perform the study after enrollment in order to treat the patient and identify the outcomes of the trial.
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Cameron (Cameron, D., Willoughby, C., Messer, D., Lux, M., Getz, K., & Aitken, M. (2020, July 31). Assessing participation burden in clinical trials: Introducing the patient friction coefficient. Clinical Therapeutics) in view of Perlina (US PG Pub 2025/0006317 A1), further in view of Vold (US PG Pub 2024/0266009 A1), Wang (Automatic inference of demographic parameters using generative adversarial networks), and Cho (KR 20190134315 A).
Regarding Claim 26, this claim recites the limitations of Claim 1 and as to those limitations is
rejected for the same basis and reasons as disclosed above. While Cameron discloses an aggregate score (See Clinical Trial Patient Friction Coefficient discloses, “the resulting aggregation, t(x), for a given patient,”), the combination of Cameron, Perlina, Vold, and Wang does not fully disclose the following limitation that Cho discloses:
The method of claim 1, further comprising: identifying, based on [[the]] the aggregate score (Para 12 discloses one embodiment of the present invention is to classify and analyze patient information to build patient information hubs and patient maps and to provide information of patients suitable for clinical trials. Para 31 discloses the AI-based clinical subject matching system can visualize patient data provided through the patient information hub in real time to display and analyze a patient map on a dashboard [transmitting data indicative of the patient profile]. The AI-based clinical subject matching system can apply the analyzed results to the report format and provide them to pharmaceutical companies [a computing device associated with conducting the study]. Para 36 discloses as shown in FIG. 3, the AI-based clinical subject matching system may automatically search for patient data in real time through a platform in each hospital to conduct a clinical trial, recruiting patients suitable for the clinical trial, and constructing a patient group. Para 40 discloses the AI-based clinical subject matching system extracts variables for the participation and completion probability of the patients' clinical trials by applying machine learning, and selects the subject-oriented clinical subjects who can complete the clinical trials by indexing them.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of assessing participation burden in clinical trials: introduction the patient friction coefficient as taught by Cameron, the clinical trial optimization as taught by Perlina, the longitudinal data from electronic health records as taught by Vold, and the automatic inference of demographic parameters using generative adversarial networks as taught by Wang with the system for matching subjects clinical trials based on artificial intelligence as taught by Cho in order to avoid delays, abandonments, or failures because the selection criteria of the test subjects for clinical trials are unclear (Cho Para 9), to classify and analyze patient information to build patient information hubs and patient maps and to provide information of patients suitable for clinical trials (Cho Para 12), and improve clinical trial success rate by saving the cost and time of patient recruitment (Cho Para 19).
Additional Subject Matter Not Cited
US PG Pub 2023/0245732 A1 as taught by Bagga discloses methods and systems to predict, quantify, and present a patient's burden when participating in a clinical trial.
WO 2016/115143 A1 as taught by Bound discloses systems and methods for determining a clinical trial patient burden.
Response to Arguments
Applicant’s arguments filed 06/17/2025 with respect to 35 U.S.C. § 101 have been fully considered, but are not persuasive.
The Applicant argues that the recited following features are not directed to a mathematical function and therefore the claim does not recite the alleged abstract idea. The Examiner respectfully disagrees.
In regards to “generating, for the plurality of variables and by a trained machine learning model, a plurality of parameters for a plurality of functions, wherein generating the parameters for the function is based on a distribution of the at least one variable in the plurality of variables in a population comprising the potential subjects for the study, and wherein the trained machine learning model is trained to generate parameters of functions based on population data of potential subjects… determining, using the plurality of parameters for the functions and for each individual of a plurality of individuals, an aggregate score that estimates a combined incremental burden for the individual based on subject-specific values for the plurality of variables absent participation of the potential subject in the study, wherein the subject-specific values comprise values for the at least one variable found in the population data” the bolded limitations are specifically directed to a mathematical concept. Claim 8 clarifies that the functions are convex and monotonically increasing, where claim 1 discloses, “each function representing a relationship between the corresponding variable and the estimated burden impact…” Thus, the bolded limitations above (the distribution and the parameters of functions) are both directed to a mathematical concept. Further, the entire limitations were found to be directed to certain methods of organizing human activity as disclosed above. However, since the Applicant only argues that the claims are not directed to a mathematical concept, the Examiner has only responded in light of that argument. In conclusion, this argument is not persuasive.
The Applicant briefly points to Example 39 and that the training the neural network was not directed to a mathematical concept and that the “above-noted limitations” are similar to that of claim 39. The Examiner respectfully disagrees. In Example 39, the claim only discloses “training the neural network in a first stage using the first training set,” and “training the neural network in a second stage using the second training set.” As further clarified above, the claims of the instant application were found to be directed to a mathematical concept specifically because of the various limitations directed to, to paraphrase, parameters for a function based on a distribution of the at least one variable and the corresponding parameters in the plurality of parameters for a corresponding function in the plurality of functions where claim 8 clarifies that the functions are convex and monotonically increasing. In regards to the claims of Example 39, even though the claims of Example 39 disclose training a machine learning model, they were not found to be directed to a mathematical concept, certain method of organizing human activity, mental process and therefore does not recite any judicial exception. Training the machine learning model was not the reason that example 39 was eligible, the reason was because the claims were not found to be directed to an abstract idea. As such, the claims of the instant application are not similar to Example 39 and therefore this argument is not persuasive.
Applicant argues that the claims are not directed to a mental process. The Examiner submits that the abstract idea was not characterized as being directed to a mental process. The claimed invention was characterized as falling under Certain Methods of Organizing Human activity (see Final Office Action dated 09/19/2025 at Pg. 8). As such, this argument cannot be persuasive.
Further, the Applicant argues that any alleged abstract idea is integrated into a practical application and points to two limitations of claim 1 specifically. The Examiner respectfully disagrees.
In regards to, “identifying, based on the aggregate score for each individual of the plurality of individuals, a subset of the plurality of individuals to be prioritized for recruitment for the study,” this limitation was found to be a part of the abstract idea as it is directed to rules a user would follow to identify a subset of the plurality of individuals to be prioritized for recruitment for the study. Therefore, the features argued to provide an “improvement” are part of the abstract idea. Thus, any improvement is to the abstract idea. An improved abstract idea is still an abstract idea. Thus, this argument is not persuasive.
In regards to, “transmitting data indicative of the subset of the plurality of individuals to at least one computing device associated with conducting the study,” this limitation was found to be an additional element that amounts to insignificant extra-solution activity. As previously presented in step 2B, the following court cases indicate that transmission of data is well-understood, routine, and conventional activity in the field when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). As such, this limitation cannot provide a practical application and this argument is not persuasive.
The Applicant then argues that the claim as a whole integrates any alleged mental process into a practical application. As addressed above, the claims were not found to be directed to a mental process. However, to further prosecution, the Examiner disagrees that the abstract idea are integrated into a practical application. In particular, the Applicant argues that the claims are integrated into a practical application because the claims identify, “incremental burdens to potential subjects of a study ‘much earlier in the subject selection process’ and reducing drop out rates of subjects in clinical trials by providing ‘data indicative of the subset of the plurality of individuals’.” The Examiner respectfully disagrees. MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem. See, e.g., DDR Holdings, LLC. v. Hotels.com, L.P., 773 F.3d 1245, 1259 (Fed. Cir. 2014) (finding that claiming a website that retained the “look and feel” of a host webpage provided a technological solution to the problem of retention of website visitors by utilizing a website descriptor that emulated the “look and feel” of the host webpage, where the problem arose out of the internet and was thus a technical problem). Here, the Applicant’s argued problem is not a technological problem caused by the technological environment to which the claims are confined, the processors of a computing device. The problem of needing to identify “incremental burdens to potential subjects of a study ‘much earlier in the subject selection process’ and reducing drop out rates of subjects in clinical trials by providing ‘data indicative of the subset of the plurality of individuals’,” was not a problem caused by the processors of the computing device that is involved in the process. At best, Applicant’s identified problem is a business problem. Because no technological problem is present, the claims do not provide a practical application.
Further, the Applicant then argues that, “the computing devices involved in conducting the clinical trial are less likely to generate incomplete or unusable data from non-compliant patients.” The Examiner notes that the Applicant discloses that the computing devices “are less likely to generate incomplete or unusable data” after discussing identifying subjects who are less burdened and therefore more likely to adhere to clinical trial protocols. The computing device itself is not improved in any way, it is just receiving data from subjects who adhere to clinical trial protocols and therefore result in complete or usable data as the Applicant describes it. First, MPEP 2106.04(d)(1) states "the word 'improvements' in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B." Here, there is no improvement to the computing device nor is there an improvement to another technology. The computing device is still analyzing the data received from the subjects as expected. Merely identifying which subjects will generate complete and usable data does not result in the computing device itself being improved. Because neither type of element is present in the claims, an improvement to technology is not present and there is no practical application. Second, the problem of needing to identify subjects who are less burdened is not a problem caused by the processors of the computing device that is involved in the process. At best, Applicant’s identified problem is a business problem. Because no technological problem is present, the claims do not provide a practical application.
Further, the Applicant argues that the technology provides the advantage of enabling direct comparisons between the estimated burdens associated with different study protocols. The Examiner notes that this argument seems to be in regards to the limitations of claim 20. The Examiner notes that this limitation was found to further define/narrow the abstract idea and was found to not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination (see Final Office Action mailed 09/19/2025 at page 12). As such, the features argued to provide an “improvement” are part of the abstract idea. Thus, any improvement is to the abstract idea. An improved abstract idea is still an abstract idea. Thus, this argument is not persuasive. Further, the problem of needing to identify comparisons between estimated burdens associated with different study protocols is not a problem caused by the processors of the computing device that is involved in the process. At best, Applicant’s identified problem is a business problem. Because no technological problem is present, the claims do not provide a practical application.
The Applicant then argues that the claimed technology allows for improved protocol design. The Examiner notes that this argument seems to be in regards to the limitations of claim 19. The Examiner notes that this limitation was found to further define/narrow the abstract idea and was found to not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination (see Final Office Action mailed 09/19/2025 at page 12). As such, the features argued to provide an “improvement” are part of the abstract idea. Thus, any improvement is to the abstract idea. An improved abstract idea is still an abstract idea. Thus, this argument is not persuasive. Further, the need for identifying a threshold size subset and altering a protocol to try to increase the identified subset (where the subset is identified by comparing the aggregate score of burden to a threshold) is not a problem caused by the processors of the computing device that is involved in the process. At best, Applicant’s identified problem is a business problem. Because no technological problem is present, the claims do not provide a practical application.
Applicant’s arguments filed 12/18/2025 with respect to 35 U.S.C. § 103 have been fully
considered and are persuasive regarding the newly added limitations. Therefore, the previous 35 U.S.C.
§ 103 rejection has been withdrawn. However, upon further consideration, a new grounds of rejection under 35 U.S.C. § 103 necessitated by Applicant’s amendments as disclosed above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARA J MORICE DE VARGAS whose telephone number is (703)756-4608. The examiner can normally be reached M-F 8:30-5:30 pm.
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/SARA JESSICA MORICE DE VARGAS/Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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