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 .
Response to Amendment
In the amendment filed on October 1, 2025, the following has occurred: claim(s) 1, 12, 18 have been amended. Now, claim(s) 1-20 are pending.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1: Step 2A Prong One
Claim 1 recite(s)
validating the trained machine learning framework using a second subset of the external data;
extracting a baseline covariate based on validating the trained machine learning framework;
determining a first prognostic score for a first subject of a second set of subjects based on the baseline covariate, wherein the first prognostic score is above a threshold indicating the first subject is approved for remote diagnostic audiometric testing;
classifying the first subject as a clinical trial subject based on the first prognostic score;
determining a second prognostic score for a second subject of a second set of subjects based on the baseline covariate, wherein the second prognostic score is below the threshold indicating the second subject is ineligible for remote diagnostic audiometric testing;
excluding the second subject as a clinical trial subject based on the second prognostic score; and
conducting the remote diagnostic audiometric testing on the first subject but not the second subject
These limitations, as drafted given the broadest reasonable interpretation, but for the
recitation of generic computer components, encompass managing interactions between people, including following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. For example, but for the generic computer component of “the trained machine learning framework”, the claim recites a Certain Method of Organizing Human Activity. For example, the claim encompasses a user following instructions to validate a trained machine learning framework by inputting a second subset of the external data to the computer component, a user following instructions to extract a baseline covariate, a user following instructions to determine a first prognostic score for a first subject of a second set of subjects based on the baseline covariate, a user following instructions to classify the first subject as a clinical trial subject based on the first prognostic score, a user following instructions to determine a second prognostic score for a second subject of a second set of subjects based on the baseline covariate, a user following instructions to exclude the second subject as a clinical trial subject based on the second prognostic score as the second prognostic score is below a threshold, and a user following instructions to conduct the remote diagnostic audiometric testing on the first subject, but not the second subject. These steps could be accomplished by a user following rules or instructions.
Claim 1: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining element amounts to no more than general purpose computer components programmed to perform the abstract idea, generally linking the use of an abstract idea to a particular technological environment or field of use, and insignificant extra-solution activity.
Claim 1, directly or indirectly, recite the following generic computer component “the trained machine learning framework” is recited at a high degree of generality. As set forth in the MPEP 2106.05(f) "merely including instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claim recites “training a machine learning framework based on a first subset of the external data to generate a trained machine learning framework;” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claim recites “receiving external data including respective observed outcome data for a first set of subjects;” at a high degree of generality, amount to no more than mere data gathering. As set forth in 2106.05(g), the addition of insignificant extra-solution activity does not amount to an inventive concept.
Claim 1: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to
significantly more than the judicial exception because as discussed above with respect to
integration into a practical application, the additional elements are recited at a high level of generality.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer configured to perform above identified functions amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea
into a patent-eligible invention.”)
Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2106.05(h)).
Additionally, receiving or transmitting data over a network does not amount to significantly more than the abstract idea (See MPEP 2106.05(d)).
Claims 2-11 incorporate the abstract idea identified above and recite additional
limitations that expand on the abstract idea. For example, claims 2-4 further describe the external data. Similarly, claims 5-6 further describe the generic computer components. Similarly, claims 7-8 further describe the validating the trained machine learning framework. Similarly, claims 9-10 further describe the extracting the baseline covariate. Finally, claim 11 further describes determining the prognostic score for the first subject. Therefore, these claims recite limitations that fall into the Certain Methods of Organizing Human Activity grouping of abstract ideas.
Dependent claims 2-11 recite additional subject matter which amount to limitations
consisted with the additional elements in independent claim 1 (such as claims 5-6 recite
additional limitations that amount to generic computer components.) Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application. The claims are not patent eligible.
Claims 12-17 recite the same functions as claims 1-11, but in system form and does not recite the steps of “further comprising harmonizing the external data” in claim 4, “wherein the machine learning framework is an ensemble framework” in claim 6, “wherein validating the trained machine learning framework includes determining a correlation between a predicted second subset outcome and an observed second subset outcome” in claim 7, “wherein validating the trained machine learning framework further includes comparing the correlation to a correlation threshold” in claim 8.
The addition of “ a data storage device storing processor-readable instructions; and a processor operatively connected to the data storage device and configured to execute the instructions to perform operations that include:” amount to no more than general purpose computer components programmed to perform the abstract idea.
Therefore, these claims also recite an abstract idea that falls into the Certain Methods of
Organizing Human Activity grouping of abstract ideas as explained above. The claims are not
patent eligible.
Claim 18: Step 2A Prong One
Claim 18 recite(s)
validating the trained machine learning framework using a second subset of the external data;
determining a correlation between a second observed outcome data of the second subset of the external data to a predicted outcome data output by the trained machine learning framework based on the second subset of the external data, wherein the correlation indicates whether a subject is approved for remote diagnostic audiometric testing;
determining a reduced sample size for a study based on the correlation by excluding at least a first subject based on the correlation, while retaining at least a second subject based on the correlation; and
conducting the remote diagnostic audiometric testing on the second subject but not the first subject
These limitations, as drafted given the broadest reasonable interpretation, but for the
recitation of generic computer components, encompass managing interactions between people, including following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. For example, but for the generic computer component of “the trained machine learning framework”, the claim recites a Certain Method of Organizing Human Activity. For example, the claim encompasses a user following instructions to validate a trained machine learning framework by inputting a second subset of the external data to the computer component, a user following instructions to determine a correlation between a second observed outcome data of the second subset of the external data to a predicted outcome data output, a user following instructions to determine a reduced sample size for a study based on the correlation by excluding at least a first subject based on the correlation, and a user following instructions to conduct the remote diagnostic audiometric testing on the second subject. These steps could be accomplished by a user following rules or instructions.
Claim 18: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining element amounts to no more than general purpose computer components programmed to perform the abstract idea, generally linking the use of an abstract idea to a particular technological environment or field of use, and insignificant extra-solution activity.
Claim 18, directly or indirectly, recite the following generic computer component “the trained machine learning framework” is recited at a high degree of generality. As set forth in the MPEP 2106.05(f) "merely including instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claim recites “training a machine learning framework based on a first subset of the external data to generate a trained machine learning framework;” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application.
Additionally, the claim recites “receiving external data including respective observed outcome data for a first set of subjects;” at a high degree of generality, amount to no more than mere data gathering. As set forth in 2106.05(g), the addition of insignificant extra-solution activity does not amount to an inventive concept.
Claim 18: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements are recited at a high level of generality.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer configured to perform above identified functions amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Alice 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”)
Additionally, generally linking the abstract idea to a particular technological environment does not amount to significantly more than the abstract idea (See MPEP 2106.05(h)).
Additionally, receiving or transmitting data over a network does not amount to significantly more than the abstract idea (See MPEP 2106.05(d)).
Claims 19-20 incorporate the abstract idea identified above and recite additional
limitations that expand on the abstract idea. For example, claim 19 further describes the correlation. Finally, claim 20 further describes the reduced sample size. Therefore, these claims recite limitations that fall into the Certain Methods of Organizing Human Activity grouping of abstract ideas.
Dependent claims 19-20 does not recite additional subject matter as the dependent claims further describe claim limitations from independent claim 18. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application. The claims are not patent eligible.
Therefore, these claims also recite an abstract idea that falls into the Certain Methods of
Organizing Human Activity grouping of abstract ideas as explained above. The claims are not
patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ennist et al (U.S. Patent Pre-Grant Publication No. 2020/0105380) in view of Will et al. (U.S. Patent Pre-Grant Publication No. 2020/0251188).
As per independent claim 1, Ennist discloses a method comprising:
receiving external data including respective observed outcome data for a first set of subjects (See Fig. 1, [0060]-[0061]: The method identifies one or more subgroups of patients that show an improved outcome relative to the outcome for the full trial population in the previous clinical trial and uses one or more characteristics of the subgroup(s) to screen candidates for the new clinical trial, which the Examiner is interpreting the data from the previous clinical trial to encompass external data including respective observed outcome data for a first set of subjects);
training a machine learning framework based on a first subset of the external data to generate a trained machine learning framework (See [0063]-[0064], [0068]-[0069]: The one or more predictive models may be built by training a learning machine on clinical data of patients having the condition for which the predictive model is built, which the Examiner is interpreting clinical data of patients having the condition to encompass a first subset of the external data as the clinical data may be obtained from one or more databases of historical clinical data);
extracting a baseline covariate based on validating the trained machine learning framework (See [0076], [0126]: Patient baseline records for patients that participated in a previous clinical trial are extracted from a clinical trial database, which the Examiner is interpreting patient baseline records to encompass a baseline covariate when combined with Will's disclosure of verify that the first machine learning model returns accurate results as described below, and the predictions are additional baseline data ("covariates") ([0126]));
determining a first prognostic score for a first subject of a second set of subjects based on the baseline covariate (See [0024], [0051]-[0052], [0068]: Patients are screened at the beginning of a clinical trial to predict, for example, a death risk score for each candidate patient, and allowing only those patients whose risk scores fall within the predetermined range to participate in the clinical trial, which the Examiner is interpreting a death risk score to encompass a first prognostic score, and clinical data associated with a candidate for the second clinical trial to encompass a first subject of a second set of subjects based on the baseline covariate), wherein the first prognostic score is above a threshold indicating the first subject is approved for remote diagnostic audiometric testing (See Fig. 1 and [0061]-[0062], [0087]-[0089], [0120]: A condition for which the method 100 may be used can be a disease, disorder, or any other condition for which an effective treatment is sought which the Examiner is interpreting a disease, disorder, or any other condition to encompass audiometric testing ([0120]: The future clinical trial client system may be a system that is remote from the enrollment screening system, which the Examiner is interpreting to encompass remote diagnostic), and interpreting the patients that fall within the threshold range to encompass the first prognostic score is above a threshold indicating the first subject is approved as there is an upper threshold and lower threshold);
classifying the first subject as a clinical trial subject based on the first prognostic score (See [0024], [0128], [0134]: Prediction based Trial Enrichment can be used, this type of study design is useful for either predictive enrichment (enrolling patients who are predicted to respond to the treatment being tested) or prognostic enrichment (enrolling patients who are homogeneous (expected to progress in a similar way), which allows the drug effect to stand out when those patients are randomized to different trial arms), which the Examiner is interpreting prognostic enrichment to encompass the claimed portion);
determining a second prognostic score for a second subject of a second set of subjects based on the baseline covariate (See Fig. 1B and [0084]-[0089]: One or more condition progression prediction values are generated for the candidate patient by feeding the candidate's clinical data into the predictive model, which the Examiner is interpreting a second prognostic score for a second set of subjects based on the baseline covariate as Steps 118 through 122 may be repeated for any number of candidate patients), wherein the second prognostic score is below the threshold indicating the second subject is ineligible for remote diagnostic audiometric testing (See [0084]-[0089]: The screening criteria may be upper and lower prediction value thresholds and candidate patients that have a prediction value that falls in the range defined by the upper and lower prediction value thresholds may be identified for enrollment in the clinical trial and those whose prediction values are outside of the range are identified for rejection from enrollment in the clinical trial, which the Examiner is interpreting rejection from enrollment in the clinical trial to encompass the second subject is ineligible for remote diagnostic audiometric testing);
excluding the second subject as a clinical trial subject based on the second prognostic score (See [0084]-[0089]: The screening criteria may be upper and lower prediction value thresholds and candidate patients that have a prediction value that falls in the range defined by the upper and lower prediction value thresholds may be identified for enrollment in the clinical trial and those whose prediction values are outside of the range are identified for rejection from enrollment in the clinical trial, which the Examiner is interpreting rejection from enrollment in the clinical trial to encompass excluding the second subject as a clinical trial subject based on the second prognostic score); and
conducting the remote diagnostic audiometric testing on the first subject but not the second subject (See [0089]-[0091]: The clinical trial is conducted and enrolled patients may be given treatments or placebos, according to well-known clinical trial methods, which the Examiner is interpreting the clinical trial is conducted and enrolled patients may be given treatments or placebos to encompass the claimed portion as the subject that fell outside of the threshold range is rejected from the clinical trial.)
While Ennist teaches a method for extracting a baseline covariate, Ennist may not explicitly teach validating the trained machine learning framework using a second subset of the external data.
Will teaches a method for validating the trained machine learning framework using a second subset of the external data (See [0024]: The labeled data in the first training data set may be used to initially train the first machine learning model, and a user may test the initially trained first machine learning model using the unlabeled data in the first training data set to verify that the first machine learning model returns accurate results (e.g., a recommended set of clinical trials including one or more trials that a patient actually enrolled in), which the Examiner is interpreting the unlabeled data to encompass a second subset of the external data, and verify that the first machine learning model returns accurate results to encompass validating the trained machine learning framework.)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Ennist to include validating the trained machine learning framework using a second subset of the external data as taught by Ennist since the combination of the two references is merely combining prior art elements according to known methods to yield predictable results (KSR rational A). It can be seen that each element claimed is present in either Ennist or Will. A user testing the initially trained first machine learning model using the unlabeled data in the first training data set to verify that the first machine learning model returns accurate results does not change or affect the one or more predictive models may be built by training a learning machine on clinical data of patients having the condition for which the predictive model is built. Since the functionalities of the elements in Ennist and Will do not interfere with each other, the results of the combination would be predictable.
Claim(s) 12 mirrors claim 1 only within a different statutory category, and is rejected for the same reason as claim 1.
The addition of “a data storage device storing processor-readable instructions; and a processor operatively connected to the data storage device and configured to execute the instructions to perform operations that include:” are encompassed by Ennist in [0057]-[0058]: “The present invention also relates to a device for performing the operations herein. This device may be specially constructed for the required purposes, or it may comprise a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a non-transitory, computer readable storage medium, such as, but not limited to, any type of disk, including floppy disks, USB flash drives, external hard drives, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs), or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus.”
As per claim 2, Ennist/Will discloses the method of claim 1 as described above. Ennist further teaches wherein the external data is received from one of a publicly available source, a previous clinical trial source, or a previously generated data source (See [0107]-[0108], [0112]: This data (training data ([0107])) may be retrieved from disease database (such as database described above with respect to method ), which may be a third-party database or a local database that maintains clinical information for patients suffering from the condition, which the Examiner is interpreting a third-party database to encompass a previously generated data source, interpreting disease database to encompass a previous clinical trial source.)
Claim(s) 13 mirrors claim 2 only within a different statutory category, and is rejected for the same reason as claim 2.
As per claim 3, Ennist/Will discloses the method of claim 1 as described above. Ennist further teaches wherein the external data further includes feature data of a plurality of features (See [0108]-[0109]: Patient clinical data may also come from patient registries, which generally are databases of observational patient data, which the Examiner is interpreting observational patient data to encompass feature data of a plurality of features as the features are described by the Applicant in the Specification in [0003] as attributes.)
Claim(s) 14 mirrors claim 3 only within a different statutory category, and is rejected for the same reason as claim 3.
As per claim 4, Ennist/Will discloses the method of claim 1 as described above. Ennist further teaches further comprising harmonizing the external data (See [0073]: The data from the database may be harmonized or screened for generating training data having a consistent content and/or format from one patient to the next.)
As per claim 5, Ennist/Will discloses the method of claim 1 as described above. Ennist further teaches wherein the machine learning framework comprises one or more machine learning models (See [0074]-[0075]: The predictive model is selected from among a set of available predictive models.)
Claim(s) 15 mirrors claim 5 only within a different statutory category, and is rejected for the same reason as claim 5.
As per claim 6, Ennist/Will discloses the method of claim 1 as described above. Ennist further teaches wherein the machine learning framework is an ensemble framework (See [0106]-[0107]: Model builder is configured to build one or more predictive models for predicting progression of a condition in patients, and the model builder may include one or more learning machines that can be trained to predict the condition progression of a patient based on the patient's clinical records, which the Examiner is interpreting one or more learning machines to encompass an ensemble framework.)
As per claim 7, Ennist/Will discloses the method of claim 1 as described above. Ennist further teaches wherein validating the trained machine learning framework includes determining a correlation between a predicted second subset outcome and an observed second subset outcome (See [0108], [0129]: Patient clinical data may also come from patient registries, which generally are databases of observational patient data, not from an interventional clinical trial, prediction based virtual controls can be used to identify the patient's disease progression is observed, and compared to the predicted disease course, which the Examiner is interpreting to encompass the claimed portion when combined with Will.)
As per claim 8, Ennist/Will discloses the method of claims 1 and 8 as described above. Ennist further teaches wherein validating the trained machine learning framework further includes comparing the correlation to a correlation threshold (See [0083]-[0084]: Upper and lower predicted condition progression values for the identified subset of clinical trial data are established as upper and lower screening thresholds for screening candidates for a future clinical trial, which the Examiner is interpreting upper and lower screening thresholds to encompass a correlation threshold.)
As per claim 9, Ennist/Will discloses the method of claim 1 as described above. Ennist further teaches wherein extracting the baseline covariate includes determining a most relied upon feature of a plurality of features (See [0126]: The more predictive a baseline covariate is of the measured outcome of the trial (the trial “endpoint” that is measured), the more the adjustment will boost the study power (the likelihood that the study will be able to detect a real treatment effect if it is there), which the Examiner is interpreting the more predictive a baseline covariates is of the measured outcome to encompass a most relied upon feature of a plurality of features.)
Claim(s) 16 mirrors claim 9 only within a different statutory category, and is rejected for the same reason as claim 9.
As per claim 10, Ennist/Will discloses the method of claim 1 as described above. Ennist further teaches wherein extracting the baseline covariate includes determining a feature of a plurality of external data features that meets a weight threshold (See [0102]-[0103], [0126]: A future clinical trial could be designed that would include only patients whose baseline characteristics yield predictions that fall within the identified threshold boundaries, which the Examiner is interpreting baseline characteristics to encompass a feature of a plurality of external data features.)
Claim(s) 17 mirrors claim 10 only within a different statutory category, and is rejected for the same reason as claim 10.
As per claim 11, Ennist/Will discloses the method of claim 1 as described above. Ennist further teaches wherein determining the prognostic score for the first subject includes providing a participant feature to one of a prognostic algorithm or a prognostic machine learning model (See [0134]-[0135]: Healthcare patient screening and the data is sent to the prediction engine which returns a preliminary analysis based on limited data, which the Examiner is interpreting the prediction engine to encompass a prognostic algorithm, and the patient entered information to encompass a participant feature.)
As per independent claim 18, Ennist discloses a method comprising:
receiving external data including respective observed outcome data for a first set of subjects (See Fig. 1, [0060]-[0061]: The method identifies one or more subgroups of patients that show an improved outcome relative to the outcome for the full trial population in the previous clinical trial and uses one or more characteristics of the subgroup(s) to screen candidates for the new clinical trial, which the Examiner is interpreting the data from the previous clinical trial to encompass external data including respective observed outcome data for a first set of subjects);
training a machine learning framework based on a first subset of the external data to generate a trained machine learning framework (See [0063]-[0064], [0068]-[0069]: The one or more predictive models may be built by training a learning machine on clinical data of patients having the condition for which the predictive model is built, which the Examiner is interpreting clinical data of patients having the condition to encompass a first subset of the external data as the clinical data may be obtained from one or more databases of historical clinical data);
determining a correlation between a second observed outcome data of the second subset of the external data to a predicted outcome data output by the trained machine learning framework based on the second subset of the external data (See [0108], [0129]: Patient clinical data may also come from patient registries, which generally are databases of observational patient data, not from an interventional clinical trial, prediction based virtual controls can be used to identify the patient's disease progression is observed, and compared to the predicted disease course, which the Examiner is interpreting the comparison of the observed patient's disease progression and the predicted disease course to encompass a correlation between a second observed outcome data of the second subset of the external data to a predicted outcome data output by the trained machine learning framework based on the second subset of the external data as the observational patient data is interpreted to encompass the second subset of the external data), wherein the correlation indicates whether a subject is approved for remote diagnostic audiometric testing (See [0088]: The screening criteria may be upper and lower prediction value thresholds and candidate patients that have a prediction value that falls in the range defined by the upper and lower prediction value thresholds may be identified for enrollment in the clinical trial and those whose prediction values are outside of the range are identified for rejection from enrollment in the clinical trial, which the Examiner is interpreting identified for enrollment in the clinical trial to encompass a subject is approved for remote diagnostic audiometric testing);
determining a reduced sample size for a study based on the correlation (See [0133]: The treatment effect and RMSE of individual cells can be used as inputs to the Simulation Generator, thus providing a way to determine sample sizes for future trials for a desired power) by excluding at least a first subject based on the correlation, while retaining at least a second subject based on the correlation ((See [0084]-[0089]: The screening criteria may be upper and lower prediction value thresholds and candidate patients that have a prediction value that falls in the range defined by the upper and lower prediction value thresholds may be identified for enrollment in the clinical trial and those whose prediction values are outside of the range are identified for rejection from enrollment in the clinical trial, which the Examiner is interpreting rejection from enrollment in the clinical trial to encompass excluding the first subject based on the correlation, and interpreting a prediction value that falls in the range defined by the upper and lower prediction value thresholds may be identified for enrollment in the clinical trial to encompass retaining at least a second subject based on the correlation); and
conducting the remote diagnostic audiometric testing on the second subject but not the first subject (See [0089]-[0091]: The clinical trial is conducted and enrolled patients may be given treatments or placebos, according to well-known clinical trial methods, which the Examiner is interpreting the clinical trial is conducted and enrolled patients may be given treatments or placebos to encompass the claimed portion as the subject that fell outside of the threshold range is rejected from the clinical trial.)
While Ennist teaches the method as described above, Ennist may not explicitly teach validating the trained machine learning framework using a second subset of the external data.
Will teaches a method for validating the trained machine learning framework using a second subset of the external data (See [0024]: The labeled data in the first training data set may be used to initially train the first machine learning model, and a user may test the initially trained first machine learning model using the unlabeled data in the first training data set to verify that the first machine learning model returns accurate results (e.g., a recommended set of clinical trials including one or more trials that a patient actually enrolled in), which the Examiner is interpreting the unlabeled data to encompass a second subset of the external data, and verify that the first machine learning model returns accurate results to encompass validating the trained machine learning framework.)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed to modify the method of Ennist to include validating the trained machine learning framework using a second subset of the external data as taught by Ennist since the combination of the two references is merely combining prior art elements according to known methods to yield predictable results (KSR rational A). It can be seen that each element claimed is present in either Ennist or Will. A user testing the initially trained first machine learning model using the unlabeled data in the first training data set to verify that the first machine learning model returns accurate results does not change or affect the one or more predictive models may be built by training a learning machine on clinical data of patients having the condition for which the predictive model is built. Since the functionalities of the elements in Ennist and Will do not interfere with each other, the results of the combination would be predictable.
As per claim 19, Ennist/Will discloses the method of claim 18 as described above. Ennist further teaches wherein the correlation is based on a relationship between the second observed outcome data and the predicted outcome data (See [0108], [0129]: Patient clinical data may also come from patient registries, which generally are databases of observational patient data, not from an interventional clinical trial, prediction based virtual controls can be used to identify the patient's disease progression is observed, and compared to the predicted disease course, which the Examiner is interpreting to encompass the claimed portion.)
As per claim 20, Ennist/Will discloses the method of claim 18 as described above. Ennist further teaches wherein the reduced sample size is based on an original sample size of the study (See [0133]: The treatment effect and RMSE of individual cells can be used as inputs to the Simulation Generator, thus providing a way to determine sample sizes for future trials for a desired power.)
Response to Arguments
In the Remarks filed on October 1, 2025, the Applicant argues that the newly amended and/or added claims overcome the 35 U.S.C. 101 rejection(s) and 35 U.S.C. 103 rejection(s). The Examiner does not acknowledge that the newly added and/or amended claims overcome the 35 U.S.C. 101 rejection(s) and 35 U.S.C. 103 rejection(s).
The Applicant argues that:
(1) the Examiner agreed during the interview that the amendments to claim 1 should overcome the Section 101 rejection. Thus, Applicant requests that the Examiner withdraw the Section 101 rejection of independent claim 1 and its dependent claims 2-11. Applicant amends independent claims 12 and 18 to include similar amendments as claim 1. Thus, Applicant requests that the Examiner withdraw the Section 101 rejection of independent claims 12 and 18, as well as their dependent claims 13-17, 19, and 20;
(2) Examiner Erickson agreed that the above amendments to claim 1 appear to overcome the obviousness rejection. Thus, Applicant requests the withdrawal of the Section 103 rejection of independent claim 1, and its dependent claims 2-11, and the allowance of these claims. Applicant amends independent claims 12 and 18 to include similar amendments as claim 1. Thus, Applicant requests that the Examiner withdraw the obviousness rejection of independent claims 12 and 18, as well as the obviousness rejection of dependent claims 13-17, 19, and 20, and allow these claims.
In response to argument (1), the Examiner does not find the Applicant’s argument(s) persuasive. The Examiner maintains that the claimed limitations recite an abstract idea that is not integrated into a practical application. For example, the final step in independent claims 1, 12, and 18 recites “conducting the remote diagnostic audiometric testing on the second subject but not the first subject”, the Examiner maintains that this step requires a person to follow the instructions to conduct the remote diagnostic audiometric testing. Additionally, the claim recites “training a machine learning framework based on a first subset of the external data to generate a trained machine learning framework;” at a high degree of generality, amount no more than generally linking the abstract idea to a particular technical environment. The recitation is also similar to adding the words “apply it” to the abstract idea. As set forth in MPEP 2106.05(f), merely reciting the words “apply it” or an equivalent, is an example of when an abstract idea has not been integrated into a practical application. The 35 U.S.C. 101 rejection(s) stand.
In response to argument (2), the Examiner does not find the Applicant’s argument(s) persuasive. After further search and additional consideration, the Examiner maintains the prior art rejections under 35 U.S.C. 103. The Examiner maintains that the Applicant’s claims, as amended, remain encompassed by the combination of Ennist/Will. The 35 U.S.C. 103 rejection(s) stand.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bennett S Erickson whose telephone number is (571)270-3690. The examiner can normally be reached Monday - Friday: 9:00am - 5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached at (571) 272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Bennett Stephen Erickson/Primary Examiner, Art Unit 3683
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