DETAILED ACTION
Notices to Applicant
This communication is a Non-Final Office Action on the merits. Claims 1-12, 19, and 23 as filed 12/11/2025, are currently pending and have been considered below.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 on 12/11/2025 has been entered.
Claim Rejections - 35 USC § 112
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 1-12, 19, and 23 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.
Claims 1, 19, and 23 each recite the limitation “thereby creating a technological solution that improves clinical trial management beyond generic data analysis,“ however, the present Application Specification fails to provide support for this limitation as claimed. Accordingly, claims 1-12, 19, and 23 are rejected for failing to comply with the written description requirement.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12, 19, and 23 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the received responses" in lines 7. There is insufficient antecedent basis for this limitation in the claim. independent claims 19 and 23 recite the same limitation of "the received responses" each lacking antecedent basis in the claims.
Accordingly, claims 1-12, 19, and 23 are rejected as being indefinite.
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 1-12, 19, and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Claims 1-12 are drawn to a computer-implemented method for compliance and adherence in clinical trials, which is within the four statutory categories (i.e. method).
Independent Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites:
1. A computer-implemented method for compliance and adherence in clinical trials, comprising, executing on a processor the steps of:
receiving and storing in a memory, subject data of a subject,
using the subject data, determining one or more first questions of a questionnaire using a machine learning model trained on textual datas trained to identify clinical trial compliance patterns using historical drop out data from multiple healthcare facilities;
generating an input dataset from the subject data and the received responses and storing the input dataset to the memory;
predicting a subject predicted profile by applying a trained mathematical model that applies facility-specific weighting algorithms that account for environmental factors unique to clinical trial settings on the input dataset thereby creating a technological solution that improves clinical trial management beyond generic data analysis and wherein the subject predicted profile comprises one of: that the subject is predicted to be non-compliant, that the subject is predicted to be non-adherent, and that the subject is predicted to drop out, wherein the subject predicted profile comprises a probability score indicating likelihood of non-compliance, non-adherence, or drop-out, wherein the probability score exceeds a predetermined confidence threshold of at least 80%;
using the predicted profile, generating a customized subject message;
transmitting, in real-time, the customized subject message to a computing device;
determining if a predetermined configuration requirement has been met, then:
receiving and storing in the memory, healthcare facility data of the healthcare facility;
determining, performance information of the subjects using the healthcare facility data and the predicted profile;
generating a customized health practitioner message based on the determined performance information; and
transmitting, in real-time, the customized health practitioner message to a computer device associated with a health practitioner.
The above limitations, as drafted, is a method that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the above bolded elements, for example “executing on a processor,” “receiving and storing in a memory” “using a machine learning model trained on textual datas trained to identify clinical trial compliance patterns using historical drop out data from multiple healthcare facilities,” and “transmitting, in real-time, the customized subject message to a computing device” nothing in the claim precludes the steps from practically being performed in the mind. For example, but for the above bolded language, using subject data for determining one or more first questions of a questionnaire for generating an input dataset from the subject data and the received responses; predicting a subject predicted profile by applying a trained mathematical model that applies facility-specific weighting algorithms that account for environmental factors unique to clinical trial settings on the input dataset and wherein the subject predicted profile comprises one of: that the subject is predicted to be non-compliant, that the subject is predicted to be non-adherent, and that the subject is predicted to drop out, wherein the subject predicted profile comprises a probability score indicating likelihood of non-compliance, non-adherence, or drop-out, wherein the probability score exceeds a predetermined confidence threshold of at least 80%; generating a customized subject message using the predicted profile; determining if a predetermined configuration requirement has been met, then: determining, performance information of the subjects using the healthcare facility data and the predicted profile and generating a customized health practitioner message based on the determined performance information in the context of this claim encompasses observation, evaluation, judgment, and opinion of data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Further, the claim limitation of “predicting a subject predicted profile by applying a trained mathematical model that applies facility-specific weighting algorithms that account for environmental factors unique to clinical trial settings,” recites the abstract idea of a “Mathematical Concept” as a mathematical formula or equations or calculations. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the above bolded additional elements, for example “executing on a processor,” “receiving and storing in a memory” “using a machine learning model trained on textual datas trained to identify clinical trial compliance patterns using historical drop out data from multiple healthcare facilities,” and “transmitting, in real-time, the customized subject message to a computing device” to perform the claim limitations. The additional elements in each of these steps are recited at a high-level of generality (i.e., a server in communication with the computing devices of all the subjects; a non-transitory computer readable medium e.g. a memory and at least one processor, and a computing device/computer device such as a mobile/portable device e.g. a smartphone, laptop, etc., or a desktop computer as they relate to general purpose computers and the server may use a machine learning (ML) model trained on textual data, such as a decision tree or large language model (Application Specification [0027], [0043], [0074], [0080]])). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). Further, the additional elements of “receiving and storing in a memory” and “transmitting, in real-time, the customized subject message to a computing device,” each are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the above bolded additional elements, for example “executing on a processor,” “receiving and storing in a memory” “using a machine learning model trained on textual datas trained to identify clinical trial compliance patterns using historical drop out data from multiple healthcare facilities,” and “transmitting, in real-time, the customized subject message to a computing device” are recited to perform the claim limitations, which amounts to no more than mere instructions to apply the abstract idea using generic computer components. (i.e., a server in communication with the computing devices of all the subjects; a non-transitory computer readable medium e.g. a memory and at least one processor, and a computing device/computer device such as a mobile/portable device e.g. a smartphone, laptop, etc., or a desktop computer as they relate to general purpose computers and the server may use a machine learning (ML) model trained on textual data, such as a decision tree or large language model. (Application Specification [0027], [0043], [0074], [0080]])). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP 2106.05(f). Further, the additional elements of “receiving and storing in a memory” and “transmitting, in real-time, the customized subject message to a computing device,” amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The claim is not patent eligible.
Dependent claims 2-12 include limitations of the independent claim and are directed to the same abstract idea as discussed above and incorporated herein. The dependent claims are rejected under 35 U.S.C. § 101 because they are directed to non-statutory subject matter. These additional claims recite what the data is and how it is analyzed. These information characteristics do not integrate the judicial exception into a practical application, and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Claims 6-8 and 10 each recite the additional element of “a graphical user interface”/”GUI” and claim 9 recites “a computer system,” however, each of these additional elements are recited at a high level such they amount to applying generic computer components to perform the abstract idea. (i.e. See Application Specification at [0027], [0081]) See MPEP 2106.05(f). Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore the dependent claims are rejected under 35 U.S.C. § 101.
Claim 19 is drawn to a non-transitory computer readable medium for compliance and adherence in clinical trials, which is within the four statutory categories (i.e. manufacture).
Independent Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 19 recites:
19. A non-transitory computer readable medium for compliance and adherence in clinical trials, comprising instructions stored thereon, that when executed on a processor, perform the steps of:
receiving and storing in a memory subject data of the subject;
using the subject data, determining one or more first questions of a questionnaire using a machine learning model trained on textual datas trained to identify clinical trial compliance patterns using historical drop out data from multiple healthcare facilities;
generating an input dataset from the subject data and the received responses and storing the input dataset to the memory;
predicting a subject predicted profile by applying a trained mathematical model that applies facility-specific weighting algorithms that account for environmental factors unique to clinical trial settings on the input dataset thereby creating a technological solution that improve clinical trial management beyond generic data analysis and wherein the subject predicted profile comprises one of: that the subject is predicted to be non-compliant, that the subject is predicted to be non-adherent, and that the subject is predicted to drop out, wherein the subject predicted profile comprises a probability score indicating likelihood of non-compliance, non-adherence, or drop-out, wherein the probability score exceeds a predetermined confidence threshold of at least 80%;
using the predicted profile, generating a customized subject message;
transmitting, in real-time, the customized subject message to a computing device;
determining if a predetermined configuration requirement has been met, then
i. receiving and storing in the memory, healthcare facility data of the healthcare facility;
ii. determining performance information of the subject using the healthcare facility data and the predicted profile;
iii. generating a customized health practitioner message based on the determined performance information; and
iv. transmitting, in real-time, the customized health practitioner message to a computer device associated with a health practitioner.
The above limitations, as drafted, is a manufacture that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the above bolded elements, for example “a non-transitory computer readable medium for compliance and adherence in clinical trials, comprising instructions stored thereon, that when executed on a processor, perform the steps,” “receiving and storing in a memory,” “using a machine learning model trained on textual datas trained to identify clinical trial compliance patterns using historical drop out data from multiple healthcare facilities;” and “transmitting, in real-time, the customized subject message to a computing device,” nothing in the claim precludes the steps from practically being performed in the mind. For example, but for the above bolded language, using subject data for determining one or more first questions of a questionnaire for generating an input dataset from the subject data and the received responses; predicting a subject predicted profile by applying a trained mathematical model that applies facility-specific weighting algorithms that account for environmental factors unique to clinical trial settings on the input dataset and wherein the subject predicted profile comprises one of: that the subject is predicted to be non-compliant, that the subject is predicted to be non-adherent, and that the subject is predicted to drop out, wherein the subject predicted profile comprises a probability score indicating likelihood of non-compliance, non-adherence, or drop-out, wherein the probability score exceeds a predetermined confidence threshold of at least 80%; generating a customized subject message using the predicted profile; determining if a predetermined configuration requirement has been met, then: determining, performance information of the subjects using the healthcare facility data and the predicted profile and generating a customized health practitioner message based on the determined performance information in the context of this claim encompasses observation, evaluation, judgment, and opinion of data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Further, the claim limitation of “predicting a subject predicted profile by applying a trained mathematical model that applies facility-specific weighting algorithms that account for environmental factors unique to clinical trial settings,” recites the abstract idea of a “Mathematical Concept” as a mathematical formula or equations or calculations Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the above bolded additional elements, for example “a non-transitory computer readable medium for compliance and adherence in clinical trials, comprising instructions stored thereon, that when executed on a processor, perform the steps,” “receiving and storing in a memory,” “using a machine learning model trained on textual datas trained to identify clinical trial compliance patterns using historical drop out data from multiple healthcare facilities;” and “transmitting, in real-time, the customized subject message to a computing device,” are recited to perform the claim limitations. The additional elements in each of these steps are recited at a high-level of generality (i.e., the server for communicating to each computing device and comprising a non-transitory computer readable medium e.g. a memory and at least one processor, and a computing device/computer device such as a mobile/portable device e.g. a smartphone, laptop, etc., or a desktop computer as they relate to a general purpose computers and the server may use a machine learning (ML) model trained on textual data, such as a decision tree or large language model (Application Specification [0027], [0043], [0074], [0080]])). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). Further, the additional elements of “receiving and storing in a memory” and “transmitting, in real-time, the customized subject message to a computing device,” each are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the above bolded additional elements, for example “a non-transitory computer readable medium for compliance and adherence in clinical trials, comprising instructions stored thereon, that when executed on a processor, perform the steps,” “receiving and storing in a memory,” “using a machine learning model trained on textual datas trained to identify clinical trial compliance patterns using historical drop out data from multiple healthcare facilities;” and “transmitting, in real-time, the customized subject message to a computing device,” to perform the claim limitations amounts to no more than mere instructions to apply the abstract idea using a generic computer component. (i.e., the server for communicating to each computing device and comprising a non-transitory computer readable medium e.g. a memory and at least one processor, and a computing device/computer device such as a mobile/portable device e.g. a smartphone, laptop, etc., or a desktop computer as they relate to a general purpose computers and the server may use a machine learning (ML) model trained on textual data, such as a decision tree or large language model (Application Specification [0027], [0043], [0074], [0080]])). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP 2106.05(f). Further, the additional elements of “receiving and storing in a memory” and “transmitting, in real-time, the customized subject message to a computing device,” amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The claim is not patent eligible.
Claim 23 is drawn to a system for compliance and adherence in clinical trials, which is within the four statutory categories (i.e. machine).
Independent Claim 23 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 23 recites:
23. A system for compliance and adherence clinical trials in the healthcare industry, the system comprising: a processor that generates a subject predicted profile by applying a trained mathematical model that applies facility-specific weighting algorithms that account for environmental factors unique to clinical trial settings on the input dataset thereby creating a technological solution that improves clinical trial management beyond generic data analysis and wherein the subject predicted profile comprises one of: that the subject is predicted to be non-compliant, that the subject is predicted to be non- adherent, and that the subject is predicted to drop out, wherein the subject predicted profile comprises a probability score indicating likelihood of non-compliance, non-adherence, or drop-out wherein the probability score exceeds a predetermined confidence threshold of at least 80%;
a memory for storing subject data of a subject;
wherein the processor further using the subject data, determines one or more first questions of a questionnaire using a machine learning model trained on textual datas trained to identify clinical trial compliance patterns using historical drop out data from multiple healthcare facilities;
to generate an input dataset from the subject data and the received responses and using the predicted profile, generates a customized subject message;
transmits, in real-time, the customized subject message; and
determines if a predetermined configuration requirement has been met;
determines performance information of the subject using the healthcare facility data and the predicted profile;
generates a customized health practitioner message based on the determined performance information; and
transmits, in real-time, the customized health practitioner message to a computer device associated with a health practitioner; and
wherein the memory further stores the input dataset; and
stores healthcare facility data of the healthcare facility.
The above limitations, as drafted, is a machine that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the above bolded elements of “a processor,” “a memory for storing” “using a machine learning model trained on textual data s trained to identify clinical trial compliance patterns using historical drop out data from multiple healthcare facilities,” “transmits, in real-time, the customized subject message,” and “a computing device” nothing in the claim precludes the steps from practically being performed in the mind. For example, but for the above bolded language, using subject data for determining one or more first questions of a questionnaire for generating an input dataset from the subject data and the received responses; predicting a subject predicted profile by applying a trained mathematical model that applies facility-specific weighting algorithms that account for environmental factors unique to clinical trial settings on the input dataset and wherein the subject predicted profile comprises one of: that the subject is predicted to be non-compliant, that the subject is predicted to be non-adherent, and that the subject is predicted to drop out, wherein the subject predicted profile comprises a probability score indicating likelihood of non-compliance, non-adherence, or drop-out, wherein the probability score exceeds a predetermined confidence threshold of at least 80%; generating a customized subject message using the predicted profile; determining if a predetermined configuration requirement has been met, then: determining, performance information of the subjects using the healthcare facility data and the predicted profile and generating a customized health practitioner message based on the determined performance information in the context of this claim encompasses observation, evaluation, judgment, and opinion of data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Further, the claim limitation of “predicting a subject predicted profile by applying a trained mathematical model that applies facility-specific weighting algorithms that account for environmental factors unique to clinical trial settings,” recites the abstract idea of a “Mathematical Concept” as a mathematical formula or equations or calculations Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the above bolded additional elements, for example “a processor,” “a memory for storing” “using a machine learning model trained on textual data s trained to identify clinical trial compliance patterns using historical drop out data from multiple healthcare facilities,” “transmits, in real-time, the customized subject message,” and “a computing device” to perform the claim limitations. The additional elements in each of these steps are recited at a high-level of generality (i.e., a server in communication with the computing devices of all the subjects; a non-transitory computer readable medium e.g. a memory and at least one processor, and a computing device/computer device such as a mobile/portable device e.g. a smartphone, laptop, etc., or a desktop computer as they relate to general purpose computers and the server may use a machine learning (ML) model trained on textual data, such as a decision tree or large language model. (Application Specification [0027], [0043], [0074], [0080]])). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). Further, the additional elements of transmitting, in real-time, the customized subject message to a computing device is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the above bolded additional elements, for example “a processor,” “a memory for storing” “using a machine learning model trained on textual data s trained to identify clinical trial compliance patterns using historical drop out data from multiple healthcare facilities,” “transmits, in real-time, the customized subject message,” and “a computing device” are recited to perform the claim limitations amounts to no more than mere instructions to apply the abstract idea using a generic computer component. (i.e., a server in communication with the computing devices of all the subjects; a non-transitory computer readable medium e.g. a memory and at least one processor, and a computing device/computer device such as a mobile/portable device e.g. a smartphone, laptop, etc., or a desktop computer as they relate to general purpose computers and the server may use a machine learning (ML) model trained on textual data, such as a decision tree or large language model. (Application Specification [0027], [0043], [0074], [0080]])). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP 2106.05(f). Further, the additional elements of transmitting, in real-time, the customized subject message to a computing device amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The claim is not patent eligible.
Examiner Statement - 35 USC § 102/103
The closest prior art of record – U.S. Patent Application Pub. No. 2016/0342771 A1 (hereinafter “Deng et al.”) in view of U.S. Patent Application Pub. No. 2015/0213206 A1 (hereinafter “Amarasingham et al.”) and U.S. Patent Application Pub. No. 2018/0189457 A1 (hereinafter “Plummer et al.”) – does not teach the invention in the particular combination as claimed in the independent claims; therefore, the closest prior art of record does not anticipate or otherwise render the claimed invention obvious, specifically the disclosure of: “using the subject data, determining one or more first questions of a questionnaire using a machine learning model trained on textual datas trained to identify clinical trial compliance patterns using historical drop out data from multiple healthcare facilities; generating an input dataset from the subject data and the received responses and storing the input dataset to the memory; predicting a subject predicted profile by applying a trained mathematical model that applies facility-specific weighting algorithms that account for environmental factors unique to clinical trial settings on the input dataset thereby creating a technological solution that improves clinical trial management beyond generic data analysis and wherein the subject predicted profile comprises one of: that the subject is predicted to be non-compliant, that the subject is predicted to be non-adherent, and that the subject is predicted to drop out, wherein the subject predicted profile comprises a probability score indicating likelihood of non-compliance, non-adherence, or drop-out, wherein the probability score exceeds a predetermined confidence threshold of at least 80%; using the predicted profile, generating a customized subject message; … determining if a predetermined configuration requirement has been met, then: receiving and storing in the memory, healthcare facility data of the healthcare facility; determining, performance information of the subjects using the healthcare facility data and the predicted profile; generating a customized health practitioner message based on the determined performance information … ” as recited in independent claim 1, and substantially similarly recited in independent claims 19 and 23.
Deng et al. teaches handling self-efficacy, e.g., capability or motivation; For instance, patient answers questions related to adherence to care plan and engagement with hospital and care providers, filling out optional quiz on personal characteristics; a self-efficacy measurement component dynamically estimates patient current self-efficacy levels (assessment scores) related to a care plan for self-efficacy classification estimation and dynamically choosing the most appropriate instrument for customized patient engagement, e.g., based on a patient's latent adherence trait estimated from lifestyle and other clinical data. See Deng et al., [0014], [0019], [0020], [0023], [0028], [0051]. Amarasingham et al. and Plummer et al. fail to fill the gaps. Amarasingham et al. teaches a holistic hospital patient care and management system utilizing a disease component/risk logic module comprises a predictive modeling process that is adapted to predict the risk of being diagnosed with particular diseases or developing an adverse event of interest according to one or more predictive models, wherein the artificial intelligence model tuning process utilizes adaptive self-learning capability using machine learning technologies and may periodically retrain a selected predictive model for improving accurate outcome to allow for selection of the most accurate statistical methodology for a local health system or clinic. See Amarasingham et al. [0035], [0061], [0064]. Plummer et al. teaches dynamic question identification model facilitates identification of a subset of questions associated with a particular medical instrument (e.g., a form or questionnaire), which can be used to obtain data from a patient and then a patient score may be generated, based on the obtained data, and used to classify the patient into a particular class, wherein the questionnaire data stored in the questionnaire database may be obtained and used to generate a set of labeled training data for utilizing one or more machine learning algorithms. See Plummer et al., [0020], [0031]. However, the prior art fails to disclose the particular combination of limitations as recited in each of the independent claims in an obvious combination. Dependent claims 2-12 are hereby indicated as being free of prior art for at least the same rationale applied to the independent claims.
Response to Arguments
Applicant's arguments filed 12/11/2025 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed on 12/11/2025.
In the remarks, Applicant argues in substance that:
Regarding the 101 rejection of claims 1-12, 19, and 23, Applicant argues the claims recite significantly more than the abstract idea; and
Regarding the 103 rejection of claims 1-12, 19, and 23, Applicant argues the previously cited prior art fails to teach each of the newly amended limitations of the independent claims.
In response to Applicant’s argument (a) regarding the 101 rejection, Examiner respectfully disagrees.
Applicant argues that the claims are directed to an improvement to the functioning of a computer or other technology or technical field. First, Applicant argues that the claim recites a machine learning model that is specifically “trained to identify clinical trial compliance patterns using historical dropout data from multiple healthcare faculties” such that this represents a significant departure from generic machine learning implementations as a result of cross-facility pattern recognition that creates a technical solution that addresses the specific technical problem of predicting clinical trial outcomes across diverse healthcare environments. See Remarks at pg. 7. Examiner respectfully disagrees. The above Applicant argument does not recite a technical solution to a technical problem, but rather, applies machine learning technology using generic computer components to perform an analysis to improve the abstract idea i.e. “predicting clinical trial outcomes across diverse healthcare environments”. That is, the additional elements of, for example, “a non-transitory computer readable medium for compliance and adherence in clinical trials, comprising instructions stored thereon, that when executed on a processor, perform the steps,” “receiving and storing in a memory,” “using a machine learning model trained on textual datas trained to identify clinical trial compliance patterns using historical drop out data from multiple healthcare facilities;” and “transmitting, in real-time, the customized subject message to a computing device,” to perform the claim limitations amounts to no more than mere instructions to apply the abstract idea using generic computer components (i.e., a server in communication with the computing devices of all the subjects; a non-transitory computer readable medium e.g. a memory and at least one processor, and a computing device/computer device such as a mobile/portable device e.g. a smartphone, laptop, etc., or a desktop computer as they relate to general purpose computers and the server may use a machine learning (ML) model trained on textual data, such as a decision tree or large language model. (Application Specification [0027], [0043], [0074], [0080]])). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP 2106.05(f).
Second, Applicant argues the recitation of “facility specific weighting algorithms that account for environmental factors unique to clinical trial settings” enables “sophisticated analysis” through “statistical analysis” including “correlation or regression analysis” such that the claims go beyond generic computer implementation. Examiner respectfully disagrees and submits this recitation of the claim limitations of “predicting a subject predicted profile by applying a trained mathematical model that applies facility-specific weighting algorithms that account for environmental factors unique to clinical trial settings,” recites the abstract idea of a “Mathematical Concept” as a mathematical formula or equations or calculations. That is, these limitations are abstract and not an additional element, wherein the additional element of a “processor” is invoked to perform the step of “predicting” via the mathematical model that applies the claimed algorithms i.e. the mathematical concepts.
Accordingly, the claims fail to recite a technical solution to a technical problem such that the claims recite an integration of the abstract idea into a practical application or significantly more than the abstract idea.
Therefore, the 101 rejection of claims 1-12, 19, and 23 as applied in the above Office Action is maintained.
In response to Applicant’s argument (b) regarding the 103 rejection, Applicant argues that the previously cited prior art fails to teach the currently amended limitation of the subject predicted profile comprises a probability score … wherein the probability score exceeds a predetermined confidence threshold of at least 80%. Examiner is persuaded, in particular, that the closest prior art of record fails to teach, in obvious combination, the particular combination as recited in currently amended claim 1 as discussed in the Examiner Statement in the above Office Action.
Accordingly, the 103 rejection of claims 1-12, 19, and 23 is withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
W.O. 2016/185460 A1 teaches for a predetermined number of repetitions, providing a computer based system to sense metadata concerning a patient's emotional activity based on voice analysis; using said system, obtaining metadata concerning said patient during said activity undertaken; analyzing said data; and from said analysis, diagnosing the patient's emotional activity ([052]);
U.S. Patent Application Pub. No. 2023/0200664 A1 teaches training a classification machine learning model to estimate patient medicine and diet adherence using generated features based on at least one health questionnaires, demographic data or historical treatment data of the patients ([0019]); and
U.S. Patent Application Pub. No. 2019/0392924 A1 teaches providing real-time advice on usefulness of one or more clinical/medical actions, that may be suggested by medical professionals and/or machine learning, and recommending additional useful actions to reduce the overall cost, improve productivity of healthcare, and also protect the patients from unnecessary and unsafe action ([0065]).
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/A.M.B./Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682