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 .
Acknowledgements
This communication is in response to Application No. 19/242,683 filed on 6/18/2025.
Claims 1-20 are currently pending.
Claims 1-20 have been rejected as follows.
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) without significantly more.
Claim 1, 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a non-transitory machine-readable medium and method for optimizing clinical trial configuration.
The limitations of deriving a plurality of item scores for each of a plurality of subjects, wherein the plurality of item scores for a given subject of the plurality of subjects:
is based, at least in part, on a first set of subject data corresponding to a randomized control trial; and
answers a set of items from at least one medical evaluation;
identifying, from the plurality of item scores, at least one parameter, wherein the at least one parameter comprises an expected value for each of the set of items across the plurality of subjects;
optimizing, from the at least one parameter, at least one vector of item weights, wherein:
the at least one vector of item weights is derived using a mean-variance analysis; and
each item weight of the at least one vector of item weights comprises a non-negative number;
determining, for each of the plurality of subjects, an initial composite score, wherein:
the initial composite score is determined from:
one of the at least one vector of item weights; and the plurality of item scores;
the mean-variance analysis optimizes the at least one vector of item weights by deriving the item weights that minimize a variance for a resulting collection of composite scores; and
the resulting collection of composite scores comprises the initial composite score determined for each of the plurality of subjects; and
applying the resulting collection of composite scores as a second set of subject data used in implementing a clinical trial, wherein applying the resulting collection of composite scores comprises:
determining, based on the resulting collection of composite scores, at least one decision rule for the clinical trial; and
deriving, based on the at least one decision rule, one or more of:
a desired type-I error rate for the clinical trial; or a desired type-Il error rate for the clinical trial
as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting a processor (computer), the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the database and automation, this claim encompasses a person deriving scores, identifying parameters, optimizing weights, determining an initial composite score, optimizing weights using mean-variance, and determining a decision rule in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of (claim 11) a processor, that implements the identified abstract idea. The processor is not described by the applicant and is recited at a high-level of generality (i.e., a generic computer performing a generic computer functions of processing) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
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 additional element of a processor coupled to memory to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Dependent Claims
Claims 2-10, 12-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim 2, 12 merely describes the score is determined non-linearly. Claim 3, 13 merely describes parameter comprising covariance measurements corresponding to the vector of item weights. Claim 4, 14 merely describes covariance measurements comprise a covariance matrix corresponding to expected value for each set of items. Claim 5, 15 merely describes the vector of weights. Claim 6, 16 merely describes mean variance is performed and weights. Claim 7, 17 merely describes minimizing variance or maximizing ratio of target mean to standard deviation. Claim 10, 20 merely describes additional composite score based on subsequent data and adding additional scores to a collection.
Claim 8, 18 further recites the additional element of using the trained generative model to generate digital subject data. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element was found to represent mere instructions to implement the abstract idea on a generic computer. This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible. Claim 8, 19 merely describes scores based on digital subject data generated by models and the models training data. Claim 9, 19 merely describes the clinical trial is based on data from the generative models.
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, 5-9, 11, 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Fisher (US 20210057108) in view of Jatkoe (US 20030194734) in view of Zien (US 20150370982)
CLAIM 1, 11
Kannan teaches A method for optimizing a clinical trial configuration, the method comprising: (Fisher para 34 teaches systems and methods to determine treatment effects for a randomized control trial (RCT) using data sampled from a generative model, design RCTs, and/or determine decision rules for treatments)
deriving a plurality of item scores for each of a plurality of subjects, wherein the plurality of item scores for a given subject of the plurality of subjects: (Fisher para 38 teaches each subject can be described by a vector made up of variables of data from a subject taken at certain times where a panel of data from the subject at that time)
is based, at least in part, on subject data corresponding to a randomized control trial; and (Fisher para 38 teaches subject in an RCT (randomized control trial))
answers a set of items from at least one medical evaluation; (Fisher para 38 teaches subject in an RCT (randomized control trial). Para 47 teaches determining treatment effects from panel data collected from subject of RCT (randomized control trial), i.e., medical evaluation answers)
identifying, from the plurality of item scores, at least one parameter, wherein the at least one parameter comprises an […] value for each of the set of items across the plurality of subjects; (Fisher para 38 teaches subject in an RCT (randomized control trial). Para 47 teaches determining treatment effects from panel data collected from subject of RCT (randomized control trial), i.e., medical evaluation answers. para 38 teaches variables include composite scores constructed from the characteristics of a patient. Examiner notes a composite score requires item scores and weights and summary measure to result in a composite score which is the single resulting measure.)
[…], from the at least one parameter, at least one vector of item weights, wherein: the at least one vector of item weights […] and (Fisher para 38 teaches subject in an RCT (randomized control trial). Para 47 teaches determining treatment effects from panel data collected from subject of RCT (randomized control trial), i.e., medical evaluation answers. para 38 teaches variables include composite scores constructed from the characteristics of a patient. Examiner notes a composite score requires item scores and weights and summary measure to result in a composite score which is the single resulting measure.)
each item weight of the at least one vector of item weights comprises a […] number; (Fisher para 38 teaches subject in an RCT (randomized control trial). Para 47 teaches determining treatment effects from panel data collected from subject of RCT (randomized control trial), i.e., medical evaluation answers. para 38 teaches each subject can be described by a vector made up of variables of data from a subject taken at certain times where a panel of data from the subject at that time and may be a value, i.e., number. para 38 teaches variables include composite scores constructed from the characteristics of a patient. Examiner notes a composite score requires item scores and weights and summary measure to result in a composite score which is the single resulting measure.)
determining, for each of the plurality of subjects, an initial composite score, wherein: (Fisher para 38 teaches variables include composite scores constructed from the characteristics of a patient.)
the initial composite score is determined from the at least one vector of item weights and the plurality of item scores; (Fisher para 38 teaches variables include composite scores constructed from the characteristics of a patient. Para 38 teaches each subject can be described by a vector made up of variables of data from a subject taken at certain times where a panel of data from the subject at that time. Examiner notes a composite score requires item scores and weights and summary measure to result in a composite score which is the single resulting measure. )
[…] the at least one vector of item weights by deriving the item weights […] for a resulting collection of composite scores; and (Fisher para 38 teaches variables include composite scores constructed from the characteristics of a patient. Examiner notes a composite score requires item scores and weights and summary measure to result in a composite score which is the single resulting measure.)
the resulting collection of composite scores comprises the initial composite score determined for each of the plurality of subjects; and (Fisher para 38 teaches variables include composite scores constructed from the characteristics of a patient.)
applying the resulting collection of composite scores as a second set of subject data used in implementing a clinical trial, wherein applying the resulting collection of composite scores comprises: (Fisher para 71teaches designing and incorporating data into clinical trials. para 38 teaches variables include composite scores constructed from the characteristics of a patient.)
determining, based on the resulting collection of composite scores, at least one decision rule for the clinical trial; and (Fisher para 39 teaches determining decision rules based on data. para 38 teaches variables include composite scores constructed from the characteristics of a patient.)
deriving, based on the at least one decision rule, one or more of: a desired type-I error rate for the clinical trial; or a desired type-Il error rate for the clinical trial. (Fisher para 16 teaches a desired type-I or desired type-II error rate. Para 80 teaches controlling the type-I error rate for the clinical trial based on the clinical trial design, i.e., decision rule)
Fisher does not teach however Jatkoe does teach
identifying, from the plurality of item scores, at least one parameter, wherein the at least one parameter comprises an expected value for each of the set of items across the plurality of subjects; (Jatkoe para 22 teaches expected return)
optimizing, from the at least one parameter, at least one vector of item weights, wherein: the at least one vector of item weights is derived using a mean-variance analysis; and (Jatkoe para 22 teaches optimization of weights minimize variance using mean-variance analysis)
the mean-variance analysis optimizes the at least one vector of item weights by deriving the item weights that minimize a variance for a resulting collection of composite scores; and (Jatkoe para 22 teaches optimization of weights minimize variance using mean-variance analysis)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the item score weights of the composite score as taught by Fisher with the optimizing weights using mean-variance analysis to minimize variance which requires expected values as taught by Jatkoe because it would be beneficial to weight items to improve identification of a disease state or condition of a subject as taught by Jatkoe para 17.
Fisher in view of Jatkoe does not teach, however, Zien does teach
each item weight of the at least one vector of item weights comprises a non-negative number; (Zien para 95 teaches non-negative weights )
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the weights as taught by Fisher with non-negative weights as taught by Zien because it would be beneficial to have a non-negative measure for interpretability as taught by Zien para 95.
CLAIM 5, 15
Fisher teaches wherein each vector of the at least one vector of item weights is uniquely generated for the given subject of the plurality of subjects. (Fisher para 38 teaches each subject can be described by a vector made up of variables of data from a subject taken at certain times where a panel of data from the subject at that time.)
CLAIM 6, 16
Fisher teaches wherein: the […] composite score; and summing every item weight of the at least one vector of item weights produces a singular numerical constant. (Fisher para 38 teaches subject in an RCT (randomized control trial). Para 47 teaches determining treatment effects from panel data collected from subject of RCT (randomized control trial), i.e., medical evaluation answers. para 38 teaches variables include composite scores constructed from the characteristics of a patient. Examiner notes a composite score requires item scores and weights and summary measure to result in a composite score which is the single resulting measure. Examiner notes summing of weights is not recited as being performed in the method and only that the summing produces a numerical constant though summing is not recited as being performed. Examiner uses the broadest reasonable interpretation of a numerical constant which would necessarily be the sum of weights because the weights being fixed numbers would necessarily result in a summation which is also fixed and thus necessarily a numerical constant. )
Fisher does not teach, however Jatkoe does teach wherein: the mean-variance analysis is performed at least in part based on a pre- determined target mean composite score; (Jatkoe para 22 teaches mean-variance method of minimizing risk (variance) for given level of expected return and maximizing expected return for a given level of risk (variance). Para 27 teaches an acceptable input of sensitivity or specificity or variance.)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the composite score as taught by Fisher with the mean-variance analysis to optimize weights based upon a pre-determined target mean as taught by Jatkoe because it would be beneficial to weight items to improve identification of a disease state or condition of a subject as taught by Jatkoe para 17.
CLAIM 7, 17
Fisher teaches wherein the […] comprises performing one or more of: […] across initial composite scores predicted for the plurality of subjects; or […] across initial composite scores predicted for the plurality of subjects. (Fisher para 38 teaches subject in an RCT (randomized control trial). Para 47 teaches determining treatment effects from panel data collected from subject of RCT (randomized control trial), i.e., medical evaluation answers. para 38 teaches variables include composite scores constructed from the characteristics of a patient. Examiner notes a composite score requires item scores and weights and summary measure to result in a composite score which is the single resulting measure.)
Fisher does not teach, however Jatkoe does teach The method of claim 6, wherein the mean-variance analysis comprises performing one or more of: minimizing a variance across initial composite scores predicted for the plurality of subjects; or maximizing a ratio of a target mean to a standard deviation across initial composite scores predicted for the plurality of subjects. (Jatkoe para 22 teaches mean-variance method of minimizing risk (variance) for given level of expected return and maximizing expected return for a given level of risk (variance). Para 27 teaches an acceptable input of sensitivity or specificity or variance.)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the composite score as taught by Fisher with the optimizing weights using mean-variance analysis to minimize variance or maximize a ration of target mean to a standard deviation which requires expected values as taught by Jatkoe because it would be beneficial to weight items to improve identification of a disease state or condition of a subject as taught by Jatkoe para 17.
CLAIM 8, 18
Fisher teaches wherein: the plurality of item scores is derived based on digital subject data generated by a set of one or more generative models; and (Fisher para 34 teaches creating digital twins for each human subject in the study. Para 47 teaches generated subject data for digital twin data. Para 109 teaches generative models that can generate digital subject and/or digital twin data.)
at least one of the set of one or more generative models is a neural network trained, at least in part, based on a set of historical data, comprising one or more of: (Fisher para 109 teaches generative models that can generate digital subject and/or digital twin data and may include neural network.)
control arm data from historical control arms, patient registries, electronic health records, or real world data. (Fisher Para 48 teaches training on control arms from historical control arms, patient registries, electronic health records, and/or real world data. See also para 17 for models and Para 43, 49 for training of models. )
CLAIM 9, 19
Fisher teaches wherein the clinical trial is based, at least in part, on outcome data generated from the set of one or more generative models. (Fisher para 49 teaches generative models generating potential outcome data to increase statistical power of clinical trial)
Claims 2, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Fisher (US 20210057108) in view of Jatkoe (US 20030194734) in view of Zien (US 20150370982) in view of Solari (US 20180233223)
CLAIM 2, 12
Fisher teaches wherein determining, for each of the plurality of subjects, the initial composite score is performed […]. (Fisher para 38 teaches variables include composite scores constructed from the characteristics of a patient.)
Fisher does not teach, however Solari does teach the initial composite score is performed non-linearly (Solari para 303 teaches a piecewise score )
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the score as taught by Fisher with the score performed non-linearly as taught by Solari because it would be beneficial to increase interpretability and simplicity as taught by Solari para 303.
Claims 3, 4, 13, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Fisher (US 20210057108) in view of Jatkoe (US 20030194734) in view of Zien (US 20150370982) in view of Baines (Baines, Portfolio frontier analysis: Applying mean-variance analysis to health technology assessment for health systems under pressure, 2021 March 8)
CLAIM 3, 13
Fisher teaches wherein the at least one parameter further comprises […] to the at least one vector of item weights. (Fisher para 38 teaches subject in an RCT (randomized control trial). Para 47 teaches determining treatment effects from panel data collected from subject of RCT (randomized control trial), i.e., medical evaluation answers. para 38 teaches variables include composite scores constructed from the characteristics of a patient. Examiner notes a composite score requires item scores and weights and summary measure to result in a composite score which is the single resulting measure.)
Fisher does not teach, however Baines does teach wherein the at least one parameter further comprises a set of one or more covariance measurements corresponding to the at least one vector of item weights. (Baines section 3.1 para 3 explains mean-variance analysis including covariance between assets.)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the parameter as taught by Fisher with covariance measurements corresponding to the vector of weights as taught by Baines because mean-variance optimization as proposed by Markowitz necessarily requires covariance between the vector of weights to perform the optimization as taught by Baines section 3.1 para 3.
CLAIM 4, 14
Fisher teaches […] for each of the set of items across the plurality of subjects. (Fisher para 38 teaches each subject can be described by a vector made up of variables of data from a subject taken at certain times where a panel of data from the subject at that time)
Fisher does not teach, however Baines does teach wherein the set of one or more covariance measurements comprises a covariance matrix corresponding to the expected value for each of the set of items across the plurality of subjects. (Baines section 3.1 para 3 explains mean-variance analysis including covariance between ith and jth assets (matrix) corresponding to expected return )
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the parameter as taught by Fisher with covariance measurements corresponding to the expected value as taught by Baines because mean-variance optimization as proposed by Markowitz necessarily requires covariance matrix corresponding to expected return to perform the optimization as taught by Baines section 3.1 para 3.
Claims 10, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Fisher (US 20210057108) in view of Jatkoe (US 20030194734) in view of Zien (US 20150370982) in view of Ard (Ard, Optimal composite scores for longitudinal clinical trials under the linear mixed effects model, 2015 Jul 30)
CLAIM 10, 20
Fisher teaches wherein implementing the clinical trial comprises: […] (Fisher para 71teaches designing and incorporating data into clinical trials)
during at least one future time point in the clinical trial, obtaining subsequent data for the clinical trial, wherein: (Fisher para 50 teaches panel data as observed values of multiple characteristics at multiple discrete timepoints)
obtaining the subsequent data for the clinical trial comprises determining, for each of the plurality of subjects, an […] composite score; and (Fisher para 38 teaches variables include composite scores constructed from the characteristics of a patient. Para 38 teaches each subject can be described by a vector made up of variables of data from a subject taken at certain times where a panel of data from the subject at that time )
the […] composite score is determined from: […] of the at least one vector of item weights, […]; and a plurality of subsequent item scores; and (Fisher para 38 teaches variables include composite scores constructed from the characteristics of a patient. Para 38 teaches each subject can be described by a vector made up of variables of data from a subject taken at certain times where a panel of data from the subject at that time. Examiner notes a composite score is by definition a vector of weights and variables which create the composite score as a single measure representative of the variables and their corresponding weight.)
[…]
Fisher does not teach, however Ard does teach
using the second set of subject data as baseline data for the clinical trial; (Ard section 3. Example: LME Weight Calculation teaches analysis of change from baseline indicating composite score used as a baseline that is being used for analysis )
obtaining the subsequent data for the clinical trial comprises determining, for each of the plurality of subjects, an additional composite score; and (Ard section 3. Example: LME Weight Calculation teaches analysis of composite scores using changes from baseline indicating composite score used as a baseline that is being used for analysis and additional composite scores computed at future time points. See also Table 1 for composite score changes from baseline over time. )
the additional composite score is determined from: the same one, of the at least one vector of item weights, used to determine the initial composite score; and a plurality of subsequent item scores; and (Ard section 3. Example: LME Weight Calculation teaches analysis of composite scores using changes from baseline indicating composite score used as a baseline that is being used for analysis and additional composite scores computed at future time points. Examiner notes the composite score weights for each subsequent time are unchanged thus the same weights are used for the initial scoring as indicated in Section 2 Optimal weights. See also Table 1 for composite score changes from baseline over time. )
adding the additional composite score determined for each of the plurality of subjects to the resulting collection of composite scores. (Ard teaches Table 1 for composite score changes from baseline over time. )
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the clinical trial as taught by Fisher with using composite scores as a baseline, additional composite score based on same weight vector with subsequent data and adding the additional composite score for each subject in a collection of composite scores as taught by Ard because it would be beneficial to show change over time using composite scores in order to optimize statistical power to detect a treatment effect as taught by Ard section Abstract .
Prior Art Made of Record and Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20120221376 Austin
Para 13 teaches optimization of treatments and evaluating risk/benefit tradeoffs. Para 14 teaches clinical data. Para 15 teaches defining a optimization target. Para 78 teaches mean-variance optimization and non-negative weights.
US 11587172 Guzman
Column 27 lines 35-45 teach Natural Language Processing to extract a measure and a composite factor or indicator can be created from the data by assigning weights either arbitrarily or resulting from the optimization or signal extraction process; wherein such weights can range in value between −1 and 1, and collectively sum to a fixed number. A composite factor or indicator can be comprised of exclusively soft data, exclusively hard data, or a combination of hard and soft data, and the data can enter the expression current or lagged, linear or non-linear.
US 20110060603 Capelli
[0062] Physicians are rational and seek to improve chronic or complex diseases in the most effective manner (i.e., Physicians are rational mean-variance minimizers).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW KYLE TAPIA whose telephone number is (703)756-1662. The examiner can normally be reached 830 - 530.
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/A.K.T./Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
Prosecution Insights