DETAILED ACTION
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 .
Status of Claims
This action is in reply to an application filed on 12/11/2024. Claims 1-20 are currently pending and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea), and does not include additional elements that either: 1) integrate the abstract idea into a practical application, or 2) that provide an inventive concept — i.e. element that amount to significantly more than the abstract idea. The Claims are directed to an abstract idea because, when considered as a whole, the plain focus of the claims is on an abstract idea.
STEP 1
The claims are directed to a system and method which are included in the statutory categories of invention.
STEP 2A PRONG ONE
The claims recite the abstract idea (based on claim 12) of:
A method for autonomously managing an experimental study using real-world data, the method comprising: generating or receiving a trial protocol, wherein the trial protocol specifies participant criteria, one or more interventions, an outcome, and a duration of the experimental study; assessing eligibility of the one or more participants against the participant criteria specified in the trial protocol; facilitating recruitment of the one or more participants through one or more digital channels; and continuously analyzing the real-world data associated with the one or more participants during the experimental study to perform one or more of adaptive trial adjustments, detecting one or more anomalies, and recording one or more outcomes.
The claims, as illustrated by the limitations of Claim 1 above, recite an abstract idea within the “certain methods of organizing human activity” grouping — managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions.
The claims recite managing a clinical trial with real-world data. Managing a clinical trial with real-world data is a process that merely organizes human activity, as it involves generating a trial protocol, assessing eligibility of participants, facilitating recruitment of participants, continuously analyzing data, and making adaptive trial adjustments. As such, the claims are directed to an abstract idea within the category of certain methods of organizing human activity.
The dependent claims 2, 3, 5-11, 13-16, 18-20 recite further abstract ideas within the category of certain methods of organizing human activity, such as 2 refine the protocol using machine learning algorithms based on an interim result of the experimental study; 3 the recruitment is performed dynamically by assessing the eligibility using real-time physiological data and probabilistic models; 5 enroll the one or more participants based on the participant criteria; 6 the experimental study is a digitally conducted A/B test, the one or more interventions comprising at least a first intervention and a second intervention, wherein both the first intervention and the second intervention are delivered to at least one of the one or more participants such that at least one same participant receive both the first intervention and the second intervention; 7 the one or more outcomes comprises a first outcome corresponding to the first intervention and a second outcome corresponding to the second intervention, wherein analyze the first outcome and the second outcome to generate a comparative report; 8 implement a controlled comparative testing mechanism by allocating the one or more participants into at least two groups, wherein each group receives distinct interventions from the one or more interventions defined in the trial protocols; and collect and analyze the real-world data from participants in each group, including the outcomes and the real-world time comprising physiological parameters, to evaluate comparative effectiveness of the distinct interventions; 9 generate an adaptive trial protocol based on an analysis from the at least two groups to optimize an intervention efficacy; 10 one of either receive a trial protocol specifying one or more of participant recruitment criteria, intervention types, outcome measures, and trial duration, or autonomously generate the trial protocol to analyze historical study data and multi-dimensional input criteria; facilitate the recruitment of one or more participants by integrating with the one or more digital channels, including one or more of a mobile application, a wearable device, and a social networking server; dynamically assess participant eligibility by processing at least one of real-time physiological measurements and participant-reported inputs; automatically enroll the one or more participants into the experimental study when their data satisfies the participant recruitment criteria specified in the trial protocol; and execute models for analyzing the real-world data during the experimental study to generate one or more trial outcomes; 11 apply predictive models to monitor safety and efficacy of one or more interventions, detect one or more anomalies, and predict one or more adverse events; 13 the analysis is conducted using one or more models configured for real-time data processing and decision-making; 14 adjusting the trial protocol based on at least one of the one or more anomalies and the one or more adverse events; 15 presenting real-time insights and the one or more trial outcomes through an interactive dashboard; 16 the recruitment of the one or more participants is performed dynamically by assessing the eligibility using at least one of real-time physiological data and probabilistic models; 18 implementing a controlled comparative testing mechanism by allocating the one or more participants into at least two groups, wherein each group receives distinct interventions from the one or more interventions defined in the trial protocols; and collecting and analyzing the real-world data from participants in each group, including the outcomes and the real-world data comprising physiological parameters, to evaluate comparative effectiveness of the distinct interventions; 19 generating an adaptive trial protocol based on an analysis from the at least two groups to optimize an intervention efficacy; 20 automatically allocating the one or more participants to the experimental study matching their eligibility, wherein the allocation includes multi-group intervention comparisons.
STEP 2A PRONG TWO
The claims recite additional elements beyond those that encompass the abstract idea above including:
Independent claim 1:
software-as-a-service (SaaS)-based system
a processor to:
Dependent claim 2:
the processor is to
using machine learning algorithms
Dependent claim 5:
the processor is to
Dependent claim 7:
the processor is to
computer executable
Dependent claim 8:
the processor is to
Dependent claim 9:
the processor is to
Dependent claim 10:
the processor is to
from a user interface
by applying one or more machine learning algorithms
computational
Dependent claim 11:
the processor is to
Independent claim 12:
through an adaptive software-as-a-service (SaaS)-based system
Dependent claim 13:
computational
However, these additional elements do not integrate the abstract idea into a practical application of that idea in accordance with considerations laid out by the Supreme Court or the Federal Circuit. (see MPEP 2106.05 a-c and e) The additional elements integrate the abstract idea into a practical application when they: improve the functioning of a computer or improving any other technology, apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, apply the judicial exception with, or by use of, a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. The additional limitations do not integrate the abstract idea into a practical application when they merely serve to link the use of the abstract idea to a particular technological environment or field of use — i.e. merely uses the computer as a tool to perform the abstract idea; or recite insignificant extra-solution activity (see MPEP 2106.05 f - h).
The processor, software, user interface, and machine learning algorithms are recited at a high level of generality such that it amounts to no more than instructions to apply the abstract idea using generic computer components. These elements merely add instructions to implement the abstract idea on a computer, and generally link the abstract idea to a particular technological environment. Nothing in the claim recites specific limitations directed to an improved processor, software, user interface, and machine learning algorithms. Similarly, the specification is silent with respect to these kinds of improvements. A general purpose computer that applies a judicial exception to computer functions, as is the case here, does not qualify as a particular machine, nor does the recitation of a basic computer impose meaningful limits in the claimed process. (see Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17 (Fed. Cir. 2014)). As such, the additional elements recited in the claims do not integrate the abstract study management process into a practical application of that process.
STEP 2B
The additional elements identified above do not amount to significantly more than the abstract study management process. The additional structural elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generic computer structure. Because the specification describes these additional elements in general terms, without describing particulars, Examiner concludes that the claim limitations may be broadly, but reasonably construed, as reciting basic computer components and techniques. The specification describes the elements in a manner that indicates that they are sufficiently straightforward such that the specification does not need to describe the particulars in order to satisfy U.S.C. 112. Considered as an ordered combination, the limitations recited in the claims add nothing that is not already present when the steps are considered individually.
The limitations recited in the dependent claims, in combination with those recited in the independent claims add nothing that integrates the abstract idea into a practical application, or that amounts to significantly more. For example, dependent claim limitations 2 refine the protocol using machine learning algorithms based on an interim result of the experimental study; 3 the recruitment is performed dynamically by assessing the eligibility using real-time physiological data and probabilistic models; 5 enroll the one or more participants based on the participant criteria; 6 the experimental study is a digitally conducted A/B test, the one or more interventions comprising at least a first intervention and a second intervention, wherein both the first intervention and the second intervention are delivered to at least one of the one or more participants such that at least one same participant receive both the first intervention and the second intervention; 7 the one or more outcomes comprises a first outcome corresponding to the first intervention and a second outcome corresponding to the second intervention, wherein analyze the first outcome and the second outcome to generate a comparative report; 8 implement a controlled comparative testing mechanism by allocating the one or more participants into at least two groups, wherein each group receives distinct interventions from the one or more interventions defined in the trial protocols; and collect and analyze the real-world data from participants in each group, including the outcomes and the real-world time comprising physiological parameters, to evaluate comparative effectiveness of the distinct interventions; 9 generate an adaptive trial protocol based on an analysis from the at least two groups to optimize an intervention efficacy; 10 one of either receive a trial protocol specifying one or more of participant recruitment criteria, intervention types, outcome measures, and trial duration, or autonomously generate the trial protocol to analyze historical study data and multi-dimensional input criteria; facilitate the recruitment of one or more participants by integrating with the one or more digital channels, including one or more of a mobile application, a wearable device, and a social networking server; dynamically assess participant eligibility by processing at least one of real-time physiological measurements and participant-reported inputs; automatically enroll the one or more participants into the experimental study when their data satisfies the participant recruitment criteria specified in the trial protocol; and execute models for analyzing the real-world data during the experimental study to generate one or more trial outcomes; 11 apply predictive models to monitor safety and efficacy of one or more interventions, detect one or more anomalies, and predict one or more adverse events; 13 the analysis is conducted using one or more models configured for real-time data processing and decision-making; 14 adjusting the trial protocol based on at least one of the one or more anomalies and the one or more adverse events; 15 presenting real-time insights and the one or more trial outcomes through an interactive dashboard; 16 the recruitment of the one or more participants is performed dynamically by assessing the eligibility using at least one of real-time physiological data and probabilistic models; 18 implementing a controlled comparative testing mechanism by allocating the one or more participants into at least two groups, wherein each group receives distinct interventions from the one or more interventions defined in the trial protocols; and collecting and analyzing the real-world data from participants in each group, including the outcomes and the real-world data comprising physiological parameters, to evaluate comparative effectiveness of the distinct interventions; 19 generating an adaptive trial protocol based on an analysis from the at least two groups to optimize an intervention efficacy; 20 automatically allocating the one or more participants to the experimental study matching their eligibility, wherein the allocation includes multi-group intervention comparisons are directed to the abstract idea of certain methods of organizing human activity without integrating into a practical application or amounting to significantly more. Dependent claim limitations 4 the one or more digital channels comprises one of a mobile application, a wearable device, and a social networking server; 17 the one or more digital channels comprises one of a mobile application, a wearable device, and a social networking server merely serve to further narrow the abstract idea above. Therefore, the claims 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 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.
Claims 1-5, 10-14, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Muehlhausen (US 2022/0165370 A1) in view of Knight (US 2002/0099570 A1).
With regards to claim 1, Muehlhausen teaches an adaptive software-as-a-service (SaaS)-based system for autonomously managing an experimental study using real-world data, the system comprising: a processor to: generate or receive a trial protocol defining participant criteria, one or more interventions, one or more outcomes, and respective durations (see at least ¶ 0065, protocol is constructed; ¶ 0059, parameters for clinical trial such as women vs. men, geriatric vs a pediatric population [participant criteria], trial outcomes; ¶ 0033, 0064, adjustments to protocol and durations to perform certain criteria; ¶ 0021, complying with aspects of the protocol [interventions]); …and continuously analyze the real-world data associated with the one or more participants for one or more of adaptive trial adjustments, anomaly detection, and outcome generation (see at least ¶ 0021, data from patient is monitored and analyzed to detect anomalies and adjustments to the protocol).
Muehlhausen does not explicitly teach …facilitate recruitment of one or more participants via one or more digital channels based on assessment of eligibility against the participant criteria. Knight teaches … facilitate recruitment of one or more participants via one or more digital channels based on assessment of eligibility against the participant criteria (see at least ¶ 0055-0068, 0127, recruiting patients using a web based interface to ask questions to see whether they match trial criteria). It would have been obvious to one of ordinary skill in the art to combine the patient trial recruitment method of Knight with the trial protocol system of Muehlhausen with the motivation of optimization of clinical trials (Knight, ¶ 0002-0004).
With regards to claim 2, Muehlhausen teaches the system of claim 1, wherein the processor is to refine the protocol using machine learning algorithms based on an interim result of the experimental study (see at least ¶ 0021, adjust protocol from AI [machine learning algorithms] engine analyzing patient sensor and subjective data during the clinical trial).
With regards to claim 3, Knight teaches the system of claim 1, wherein the recruitment is performed dynamically by assessing the eligibility using real-time physiological data and probabilistic models (see at least figure 2, ¶ 0068-0072, patient presented with initial set of questions, then second set of questions which asks for various physiological parameters [answering as the questioning logic moves along interpreted as real-time], then match for trial determined, if patient indicates interest for trial, server presents a set of trial specific questions, answers compared to trial specific criteria, if a match exists patient presented with trial details, registration and data forms [question logic interpreted as probabilistic model]). It would have been obvious to one of ordinary skill in the art to combine the patient trial recruitment method of Knight with the trial protocol system of Muehlhausen with the motivation of optimization of clinical trials (Knight, ¶ 0002-0004).
With regards to claim 4, Muehlhausen teaches the system of claim 1, wherein the one or more digital channels comprises one of a mobile application, a wearable device, and a social networking server (see at least ¶ 0026, wearable).
With regards to claim 5, Knight teaches the system of claim 1, wherein the processor is to enroll the one or more participants based on the participant criteria (see at least ¶ 0070, patient completes registration form for enrollment after qualifying for trial through answers to questions). It would have been obvious to one of ordinary skill in the art to combine the patient trial recruitment method of Knight with the trial protocol system of Muehlhausen with the motivation of optimization of clinical trials (Knight, ¶ 0002-0004).
With regards to claim 10, Muehlhausen teaches the system of claim 1, wherein the processor is to: one of either receive, from a user interface, a trial protocol specifying one or more of participant recruitment criteria, intervention types, outcome measures, and trial duration, or autonomously generate the trial protocol by applying one or more machine learning algorithms to analyze historical study data and multi-dimensional input criteria (see at least ¶ 0065, protocol is constructed; ¶ 0059, parameters for clinical trial such as women vs. men, geriatric vs a pediatric population [participant criteria]); …and execute computational models for analyzing the real-world data during the experimental study to generate one or more trial outcomes (see at least ¶ 0021, data from patient is monitored and analyzed to detect compliance and anomalies [outcomes]).
Knight teaches …facilitate the recruitment of one or more participants by integrating with the one or more digital channels, including one or more of a mobile application, a wearable device, and a social networking server (see at least figures 3-10, ¶ 0055-0068, 0127, recruiting patients using a web based interface to ask questions to see whether they match trial criteria, where the patients interact with clinical trial providers [social networking]); dynamically assess participant eligibility by processing at least one of real-time physiological measurements and participant-reported inputs (see at least figure 2, ¶ 0068-0072, patient presented with initial set of questions, then second set of questions which asks for various physiological parameters, then match for trial determined, if patient indicates interest for trial, server presents a set of trial specific questions, answers compared to trial specific criteria, if a match exists patient presented with trial details, registration and data forms [dynamic assessment]); automatically enroll the one or more participants into the experimental study when their data satisfies the participant recruitment criteria specified in the trial protocol (see at least ¶ 0070, patient completes registration form for enrollment after qualifying for trial through answers to questions). It would have been obvious to one of ordinary skill in the art to combine the patient trial recruitment method of Knight with the trial protocol system of Muehlhausen with the motivation of optimization of clinical trials (Knight, ¶ 0002-0004).
With regards to claim 11, Muehlhausen teaches the system of claim 10, wherein the processor is to apply predictive models to monitor safety and efficacy of one or more interventions, detect one or more anomalies, and predict one or more adverse events (see at least ¶ 0021, AI monitors incoming patient data for compliance and to detect any anomalies which may relate to side effects or other undesirable effects of following the protocol).
With regards to claim 12, Muehlhausen teaches a method for autonomously managing an experimental study using real-world data through an adaptive software-as-a-service (SaaS)-based system, the method comprising: generating or receiving a trial protocol, wherein the trial protocol specifies participant criteria, one or more interventions, an outcome, and a duration of the experimental study (see at least ¶ 0065, protocol is constructed; ¶ 0059, parameters for clinical trial such as women vs. men, geriatric vs a pediatric population [participant criteria], trial outcomes; ¶ 0033, 0064, adjustments to protocol and durations to perform certain criteria; ¶ 0021, complying with aspects of the protocol [interventions]); …and continuously analyzing the real-world data associated with the one or more participants during the experimental study to perform one or more of adaptive trial adjustments, detecting one or more anomalies, and recording one or more outcomes (see at least ¶ 0021, data from patient is monitored and analyzed to detect anomalies and adjustments to the protocol).
Muehlhausen does not explicitly teach …assessing eligibility of the one or more participants against the participant criteria specified in the trial protocol; facilitating recruitment of the one or more participants through one or more digital channels;. Knight teaches assessing eligibility of the one or more participants against the participant criteria specified in the trial protocol; facilitating recruitment of the one or more participants through one or more digital channels (see at least ¶ 0055-0068, 0127, recruiting patients using a web based interface to ask questions to assess whether they are eligible under the trial criteria). It would have been obvious to one of ordinary skill in the art to combine the patient trial recruitment method of Knight with the trial protocol system of Muehlhausen with the motivation of optimization of clinical trials (Knight, ¶ 0002-0004).
With regards to claim 13, Muehlhausen teaches the method of claim 12, wherein the analysis is conducted using one or more computational models configured for real-time data processing and decision-making (see at least ¶ 0021)
With regards to claim 14, Muehlhausen teaches the method of claim 12, comprising adjusting the trial protocol based on at least one of the one or more anomalies and the one or more adverse events (see at least ¶ 0021).
With regards to claim 16, Knight teaches the method of claim 12, wherein the recruitment of the one or more participants is performed dynamically by assessing the eligibility using at least one of real-time physiological data and probabilistic models (see at least figure 2, ¶ 0068-0072, patient presented with initial set of questions, then second set of questions which asks for various physiological parameters [answering as the questioning logic moves along interpreted as real-time], then match for trial determined, if patient indicates interest for trial, server presents a set of trial specific questions, answers compared to trial specific criteria, if a match exists patient presented with trial details, registration and data forms [question logic interpreted as probabilistic model]). It would have been obvious to one of ordinary skill in the art to combine the patient trial recruitment method of Knight with the trial protocol system of Muehlhausen with the motivation of optimization of clinical trials (Knight, ¶ 0002-0004).
With regards to claim 17, Muehlhausen teaches the method of claim 12, wherein the one or more digital channels comprises one of a mobile application, a wearable device, and a social networking server (see at least ¶ 0026, wearable).
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Muehlhausen (US 2022/0165370 A1) in view of Knight (US 2002/0099570 A1) in further view of Lim CY, In J. Considerations for crossover design in clinical study. Korean J Anesthesiol. 2021 Aug;74(4):293-299. doi: 10.4097/kja.21165. Epub 2021 Jul 30. PMID: 34344139; PMCID: PMC8342834.
With regards to claim 6, Muehlhausen fails to teach the system of claim 1, wherein the experimental study is a digitally conducted A/B test, the one or more interventions comprising at least a first intervention and a second intervention, wherein both the first intervention and the second intervention are delivered to at least one of the one or more participants such that at least one same participant receive both the first intervention and the second intervention. Lim teaches the system of claim 1, wherein the experimental study is a digitally conducted A/B test, the one or more interventions comprising at least a first intervention and a second intervention, wherein both the first intervention and the second intervention are delivered to at least one of the one or more participants such that at least one same participant receive both the first intervention and the second intervention (see at least Abstract and Introduction, clinical trials with AB/BA crossover design, where two treatment A and B are provided to subjects at different times such that one subject will receive both treatments). It would have been obvious to one of ordinary skill in the art to combine the crossover clinical trial design of Lim with the trial protocol system of Muehlhausen with the motivation of clinical trial efficiency (Lim, Introduction and Conclusion).
With regards to claim 7, Lim teaches the system of claim 6, wherein the one or more outcomes comprises a first outcome corresponding to the first intervention and a second outcome corresponding to the second intervention, wherein the processor is to analyze the first outcome and the second outcome to generate a computer executable comparative report (see at least Statistical model and SAS code, pages 295-297). It would have been obvious to one of ordinary skill in the art to combine the crossover clinical trial design of Lim with the trial protocol system of Muehlhausen with the motivation of clinical trial efficiency (Lim, Introduction and Conclusion).
Claims 8, 9, 15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Muehlhausen (US 2022/0165370 A1) in view of Knight (US 2002/0099570 A1) in further view of Xie, et al. (US 2021/0158906 A1).
With regards to claim 8, Muehlhausen fails to teach the system of claim 1, wherein the processor is to: implement a controlled comparative testing mechanism by allocating the one or more participants into at least two groups, wherein each group receives distinct interventions from the one or more interventions defined in the trial protocols; and collect and analyze the real-world data from participants in each group, including the outcomes and the real-world time comprising physiological parameters, to evaluate comparative effectiveness of the distinct interventions. Xie teaches the system of claim 1, wherein the processor is to: implement a controlled comparative testing mechanism by allocating the one or more participants into at least two groups, wherein each group receives distinct interventions from the one or more interventions defined in the trial protocols (see at least ¶ 0026, subjects assigned to drug arm or control arm); and collect and analyze the real-world data from participants in each group, including the outcomes and the real-world time comprising physiological parameters, to evaluate comparative effectiveness of the distinct interventions (see at least ¶ 0112, physiological data is collected from trial subjects; ¶ 0088, analysis of study data to recommend adjustments, modifications and adaptation to optimize the study, such as a primary objective of a trial may be directed towards assessing the efficacy of three different dose levels of a drug against a placebo. Based on analysis, it may become evident early in the trial that one of the dose levels is significantly more efficacious than either of the other two. As soon as that determination may be made at a statistically significant level and made available, it is advantageous to proceed further only with the most efficacious dose). It would have been obvious to one of ordinary skill in the art to combine the clinical trial efficacy determination of Xie with the trial protocol system of Muehlhausen with the motivation of optimization of effectiveness of a clinical trial (Xie, ¶ 0088).
With regards to claim 9, Xie teaches the system of claim 8, wherein the processor is to generate an adaptive trial protocol based on an analysis from the at least two groups to optimize an intervention efficacy (see at least ¶ 0088, analysis of study data to recommend adjustments, modifications and adaptation to optimize the study, such as a primary objective of a trial may be directed towards assessing the efficacy of three different dose levels of a drug against a placebo. Based on analysis, it may become evident early in the trial that one of the dose levels is significantly more efficacious than either of the other two. As soon as that determination may be made at a statistically significant level and made available, it is advantageous to proceed further only with the most efficacious dose). It would have been obvious to one of ordinary skill in the art to combine the clinical trial efficacy determination of Xie with the trial protocol system of Muehlhausen with the motivation of optimization of effectiveness of a clinical trial (Xie, ¶ 0088).
With regards to claim 15, Muehlhausen fails to teach the method of claim 12, comprising presenting real-time insights and the one or more trial outcomes through an interactive dashboard. Xie teaches the method of claim 12, comprising presenting real-time insights and the one or more trial outcomes through an interactive dashboard (see at least figures 14-16, ¶ 0088, real time statistical results of clinical study are displayed). It would have been obvious to one of ordinary skill in the art to combine the clinical trial efficacy determination of Xie with the trial protocol system of Muehlhausen with the motivation of optimization of effectiveness of a clinical trial (Xie, ¶ 0088).
With regards to claim 18, Muehlhausen fails to teach the method of claim 17, comprising: implementing a controlled comparative testing mechanism by allocating the one or more participants into at least two groups, wherein each group receives distinct interventions from the one or more interventions defined in the trial protocols; and collecting and analyzing the real-world data from participants in each group, including the outcomes and the real-world data comprising physiological parameters, to evaluate comparative effectiveness of the distinct interventions. Xie teaches the method of claim 17, implementing a controlled comparative testing mechanism by allocating the one or more participants into at least two groups, wherein each group receives distinct interventions from the one or more interventions defined in the trial protocols (see at least ¶ 0026, subjects assigned to drug arm or control arm); and collecting and analyzing the real-world data from participants in each group, including the outcomes and the real-world data comprising physiological parameters, to evaluate comparative effectiveness of the distinct intervention (see at least ¶ 0112, physiological data is collected from trial subjects; ¶ 0088, analysis of study data to recommend adjustments, modifications and adaptation to optimize the study, such as a primary objective of a trial may be directed towards assessing the efficacy of three different dose levels of a drug against a placebo. Based on analysis, it may become evident early in the trial that one of the dose levels is significantly more efficacious than either of the other two. As soon as that determination may be made at a statistically significant level and made available, it is advantageous to proceed further only with the most efficacious dose). It would have been obvious to one of ordinary skill in the art to combine the clinical trial efficacy determination of Xie with the trial protocol system of Muehlhausen with the motivation of optimization of effectiveness of a clinical trial (Xie, ¶ 0088).
With regards to claim 19, Xie teaches the method of claim 18, comprising generating an adaptive trial protocol based on an analysis from the at least two groups to optimize an intervention efficacy (see at least ¶ 0088, analysis of study data to recommend adjustments, modifications and adaptation to optimize the study, such as a primary objective of a trial may be directed towards assessing the efficacy of three different dose levels of a drug against a placebo. Based on analysis, it may become evident early in the trial that one of the dose levels is significantly more efficacious than either of the other two. As soon as that determination may be made at a statistically significant level and made available, it is advantageous to proceed further only with the most efficacious dose). It would have been obvious to one of ordinary skill in the art to combine the clinical trial efficacy determination of Xie with the trial protocol system of Muehlhausen with the motivation of optimization of effectiveness of a clinical trial (Xie, ¶ 0088).
With regards to claim 20, Knight teaches the method of claim 19, comprising automatically allocating the one or more participants to the experimental study matching their eligibility (see at least ¶ 0070, patient completes registration form for enrollment after qualifying for trial through answers to questions). It would have been obvious to one of ordinary skill in the art to combine the patient trial recruitment method of Knight with the trial protocol system of Muehlhausen with the motivation of optimization of clinical trials (Knight, ¶ 0002-0004).
Furthermore, Xie teaches the wherein the allocation includes multi-group intervention comparisons (see at least ¶ 0026, subjects assigned to drug arm or control arm; figures 2-7, 14-16, comparisons). It would have been obvious to one of ordinary skill in the art to combine the clinical trial efficacy determination of Xie with the trial protocol system of Muehlhausen with the motivation of optimization of effectiveness of a clinical trial (Xie, ¶ 0088).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Eteminan, et al. (US 2019/0206520 A1) which discloses a trial operations service suite in a clinical trial operations system includes: patient, clinician, investigator, and coordinator portals supporting corresponding applications and providing specific services for each type of participant, where the coordinator portal allows a coordinator user to develop, build, customize, and establish trial protocol and participant interaction using an application factory system embedded within a trial design aspect of the coordinator portal; security configuration and management authentication databases; operational databases including trial data including provisioning and authorization data describing trial participants, access logs recording participant activity, protected and de-identified health information and/or non-health provided by patients, clinicians, and associated sensors; a notification relay service and corresponding external notifications gateway providing essential communication between the system and users, between clinicians and patients, and between coordinators and all participants; and a secure communication relay service providing multi-media communication among participants, constrained by permissions as established by a particular coordinator user.
Kalathil (US 2015/0161336 A1) which discloses adequate patient enrollment and participation in different design stages of a clinical trial is facilitated and scaled by dynamically adjusting clinical trial criteria relative to characteristics and conditions of massive numbers of patients whose medical records have been aggregated in databases in compliance with patient privacy and confidentiality laws and regulations. Patient participation results without intervention by multiple providers of healthcare services, and by directly identifying and communicating with qualified patients while maintaining patient privacy and compliance requirements as required by law.
Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs; T Burnett, P Mozgunov, P Pallmann, SS Villar, GM Wheeler, T Jaki; BMC medicine, 2020
which discloses adaptive designs for clinical trials permit alterations to a study in response to accumulating data in order to make trials more flexible, ethical, and efficient. These benefits are achieved while preserving the integrity and validity of the trial, through the pre-specification and proper adjustment for the possible alterations during the course of the trial. Despite much research in the statistical literature highlighting the potential advantages of adaptive designs over traditional fixed designs, the uptake of such methods in clinical research has been slow. One major reason for this is that different adaptations to trial designs, as well as their advantages and limitations, remain unfamiliar to large parts of the clinical community. The aim of this paper is to clarify where adaptive designs can be used to address specific questions of scientific interest; we introduce the main features of adaptive designs and commonly used terminology, highlighting their utility and pitfalls, and illustrate their use through case studies of adaptive trials ranging from early-phase dose escalation to confirmatory phase III studies.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joey Burgess whose telephone number is (571)270-5547. The examiner can normally be reached Monday through Friday 9-6.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kambiz Abdi can be reached on 571-272-6702 The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSEPH D BURGESS/ Primary Examiner, Art Unit 3685
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