DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claim set received 19 February 2026 has been entered into the application.
Claims 1, 5, and 9 are amended.
Claims 3-4, 7-8, and 11-12 are previously cancelled.
Claims 13-15 are new.
Claims 1, 5, and 9 are objected to.
Claim(s) 1-2, 5-6, 9-10, and 13-15 are pending.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent India Application No. IN202021016253, filed on 15 April 2020.
Claim Objections
Claims 1, 5, and 9 are objected to because of the following informalities: “dividing a dataset Dps into a train set, validation set and test set; training the predictive model over the train set and tuning train set parameters using the validation set to obtain a tuned predictive model”. The claim should be amended to recite “dividing a dataset Dps into a training set, a validation set, and a test set; training the predictive model over the training set and tuning the training set parameters using the validation set to obtain a tuned predictive model” Appropriate correction is required to address the grammatical correctness of the claim.
Claims 1, 5, and 9 are objected to because of the following informalities: “…the trained DNN to learn probable effect of one or more treatments for a subject to further plan treat to the subject, and the counter factual treatment is defined as, a probable effect of one or more different treatments for the subject…”. The claim should be amended to recite “…the trained DNN to learn probable effects of one or more treatments for a subject to further plan treat to the subject, and wherein the counter factual treatment is defined as a probable effect of one or more different treatments for the subject…” Appropriate correction is required to address the grammatical correctness of the claim.
Claim Rejections - 35 USC § 112
35 USC § 112(b)
The instant rejection is maintained for reason for record in the Office Action mailed 19 November 2025 and modified in view of the amendments filed 19 February 2026. It is noted the amendments received 19 February 2026 are necessitated by new ground(s) of rejection.
The rejection of claims 1-2, 5-6, and 9-10 because treatments under 35 U.S.C §112(b) in the Office Action mailed 19 November 2025 is withdrawn of the amendments filed 19 February 2026.
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 13-15 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.
Claims 1, 5, and 9 recite “wherein the CI data comprises a factual treatment data, a factual response data, and a plurality of attributes associated with each of the plurality of subjects under test, wherein the plurality of attributes comprising at least one of height, weight, and blood pressure”. Claims 13-15 recite that the CI further includes height, weight, blood pressure associated with each of the plurality of subjects under test. However, claims 13-15 are not clear because claims 1, 5, and 9 contain limitations that already limit the CI data to height, weight, and blood pressure (i.e., attribute data). Therefore, it is not clear how claims 13-15 further limit CI data as claims 1, 5, and 9 already utilize CI data comprising height, weight, and blood pressure of the subject under test.
Claim Rejections - 35 USC § 101
The instant rejection is maintained for reason for record in the Office Action mailed 19 November 2025 and modified in view of the amendments filed 19 February 2026. It is noted the amendments received 19 February 2026 are necessitated by new ground(s) of rejection.
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 1-2, 5-6, 9-10, and 13-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Following the flowchart of the MPEP 2106
Step I - Process, Machine, Manufacture or Composition
Claims 1-2 and 13 are drawn to a processor implemented method, so a process.
Claims 5-6 and 14 are drawn to a system comprising processors, so a machine.
Claims 9-10 and 15 are drawn to instructions stored on machine readable information storage, so a manufacture.
2A Prong I - Identification of an Abstract Idea
Claims 1 is drawn to a processor implemented method. Claim 5 is drawn to a system. Claim 9 is drawn to computer readable storage medium (CRM). Claims 1, 5, and 9 encompass similar limitations. Thus, claims 1, 5, and 9 are examined similarly.
Claims 1, 5, and 9 recite:
computing, by the one or more hardware processors, a Propensity Score (PS) for each of the plurality of subjects under test for a treatment based on the CI data by using a predictive model, wherein the treatment is related to a disease,
This step can be performed in the human mind by performing computations to compute a Propensity Score (PS) for the test subjects and is therefore an abstract idea. This step can be performed in the human mind by selecting and characterizing the PS as a conditional probability of the subject under test and is therefore an abstract idea. This step encompasses performing mathematical/statistical computations to compute a PS and is therefore an abstract idea.
wherein the treatment is related to a disease
This step mere describes the treatment as related to a disease.
wherein the PS is a conditional probability of each of the subject under test, for responding to the treatment
This step describes the PS as a conditional probability of the subject responding to treatment.
wherein training the predictive model comprises, dividing a dataset Dps into a dataset into a training set, validation set and test set
This step can be performed in the human mind by organizing information into sets of information (i.e., training set, validation set, and test set) and is therefore an abstract idea.
training the predictive model over the training sets and tuning training set parameters using the validation set to obtain a tuned predictive model
This step is broadly recited and reads on performing mathematical computations. For example, the step encompasses taking information/data (i.e., training set/tuning training set parameters, validation set), manipulating the data via mathematical correlations/functions (i.e., predictive model), and organizing the data into a new form (i.e., tuned predictive model). See MPEP 2106.04(a)(2)(I)(A)(iv).
using the tuned predictive model to make predictions over the test set of CI data
This step can be performed in the human mind by organizing information (i.e., CI data) for making predictions and is therefore an abstract idea. This step is broadly recited and reads on performing mathematical computations (i.e., making predictions) which reads on abstract ideas.
computing, by the one or more hardware processors, a Generalized Propensity Score (GPS) for each of the plurality of subjects under test for a plurality of treatments based on the corresponding Propensity Score (PS)
This step can be performed in the human mind by organizing data (i.e., PS) for computing a Generalized Propensity Score (GPS) and is therefore an abstract idea. This step encompasses mathematical concepts of performing mathematical/statistical computations to compute the GPS and is therefore an abstract idea.
augmenting, by the one or more hardware processors, a plurality of task batches using the GPS, wherein each of the plurality of task batches comprises a plurality of sample subjects from the plurality of subjects under test, wherein augmenting each of the plurality of task batches comprising,
This step can be performed in the human mind by organizing data for augmenting the observed treatments based on a corresponding GPS and therefore is abstract idea. This step also describes the task batches as comprising subjects under test.
wherein augmenting each of the plurality of task batches comprising sample subject xi, nearest neighbor sample xj, observed treatment tj corresponding GPS
This step encompasses mathematical concepts such as the mathematical variables for augmenting the task batches and is therefore an abstract idea.
wherein the GPS is a vector describing a conditional probability of each sample subject xi and receiving a treatment tk.
This step can be performed in the human mind by characterizing the GPS as a vector describing a conditional probability of each sample subject xi and received a treatment tk and is therefore an abstract idea. This step encompasses mathematical concept of vector mathematics and is therefore an abstract idea.
wherein the GPS is defined
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This step can be performed in the human mind by characterizing and defining the GPS and is therefore an abstract idea. This step encompasses the mathematical concepts of using mathematical formulas to define the GPS and is therefore an abstract idea.
wherein the GPS vector is applied for augmenting task batches
This step can be performed in the human mind by following instructions to apply the GPS to task batches and is therefore an abstract idea. This step encompasses the mathematical concept of using vector mathematical and is therefore an abstract idea.
a GPS based matching is applied to select the plurality of nearest neighbor sample subjects xi with the observed treatments tj such that tj is not equal to ti and dGPS (i, j) is minimum
This step can be performed in the human mind by following instruction for applying a GPS to select nearest neighbor sample with the observed treatments and dGPS (i, j) to observe a minimum and is therefore an abstract idea. The step can be performed in the human mind by observing and evaluating if the observed treatment and dGPS are minimum and is therefore an abstract idea. This step encompasses mathematical concepts of using applying GPS based matching to select the nearest neighbor subjects xi with the observed treatments tj such that tj is not equal to ti and dGPS (i, j) and mathematical variables for applying the GPS based matching.
Wherein the dGPS (i, j) is defined by an equation
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This step can be performed in the human mind by characterizing the dGPS as defined by the equation
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and is therefore an abstract idea. This step encompasses the mathematical concepts of using equation defining the
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computing a balanced representation by training the balancing network until a difference between distribution of balancing network outputs from each of the plurality of balancing layers for a plurality of distinct treatments is minimum
This step can be performed in the human mind by observing and evaluating differences between distribution of balancing network outputs from each of the balancing layers of the distinct treatments are minimum by training a balanced network to compute balanced representations and is therefore an abstract idea. The step encompasses the mathematical concepts of finding differences between layers and is therefore an abstract idea. This step encompasses the mathematical concepts of training a balanced network which encompasses the mathematical/statistical computations and is therefore an abstract idea.
computing a factual response for each of the plurality of treatments from the corresponding branch of the hypothesis network based on the balancing representation
This step can be performed in the human mind by computing a factual response for each treatment and is therefore an abstract idea. This step encompasses performing mathematical computations related to factual response of treatments from corresponding branch of the hypothesis network based on the balancing representation which encompasses performing mathematical computations to compute a factual response based on balancing representation and is therefore an abstract idea.
computing a factual error by computing absolute difference between the factual response and an actual response balancing representation
This step can be performed in the human mind by following instructions to compute a factual error and is therefore an abstract idea. This step recites computing a factual error by computing differences which encompasses the mathematical concepts of using different between variables and is therefore an abstract idea.
optimizing the DNN using a loss function based on the factual error and a balancing error
This step can be performed in the human mind by following instructions to use a loss function based on the factual error and a balancing error for optimizing the DNN and is therefore an abstract idea. This step encompasses mathematical concepts of using mathematical functions (i.e., loss function) and matrix mathematics for optimizing the DNN and is therefore an abstract idea.
wherein the balancing error being a minimum mean discrepancy (MMD) measure between pairwise distributions of two different treatments,
This step can be performed in the human mind by following instructions to measure pairwise distribution of two different treatments to observe the balancing error being a minimum mean discrepancy (MMD) and is therefore an abstract idea. This step encompasses the mathematical concept of measuring pairwise distributions of different treatments to measure the MMD which encompasses using mathematical/statistical computations for the mean measure of the MMD and is therefore an abstract idea.
wherein the loss function is defined by an equation
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This step can be performed in the human mind by characterizing the loss function as defined by the equation above and is therefore an abstract idea. This step encompasses using mathematical concepts of using loss function equations and is therefore an abstract idea.
Where α,γ > 0 are hyperparameters controlling a strength of imbalance penalties, R(h) is a model complexity, p^m (.) amd p^q(.) represent distribution corresponding to m-th treatment and a q-th treatment and disc(.,.) is the MMD measure.
This step encompasses the mathematical concepts of using mathematical variables for the loss function equation and is therefore an abstract idea.
wherein a balancing representation ϕ(.) and a hypothesis h(.) are learnt jointly by training the DNN using the loss function that incorporates the factual error and the balancing error
This step can be performed in the human mind by following instructions to use the loss function that incorporates the factual error and the balancing error for training the DNN to learn the balancing representation and a hypothesis which encompasses using mathematical/statistical computations of using functions and using matrix mathematics of the DNN and is therefore an abstract idea.
wherein the loss function controls the factual error less than a predetermined factual threshold and the balancing error less than a predetermined balancing threshold, wherein the values of the pre-determined factual threshold and the pre-determined balancing threshold varies based on the data set DPS used and wherein the balancing error is the MMD between pairwise distributions of two different treatments
This step can be performed in the human by observing and evaluating the factual and balancing errors to be less than a threshold and is therefore an abstract idea. This step encompasses mathematically comparing using quantitative thresholds to which the factual and balancing errors are compared which reads on abstract ideas. This step encompasses mathematical/statistical concepts of using loss function for controlling the factual error and the balancing errors to be less than a predetermined threshold which reads on abstract ideas.
predicting, by the one or more hardware processors, a plurality of counter factual treatment response corresponding to the factual treatment data for each of the plurality of subjects under test using the trained DNN to learn probable effect of one or more treatments for a subject to further plan treatments to the subject, and the counter factual treatment is defined as, a probable effect of one or more different treatments for the subject,
This step recites using deep neural network which encompasses mathematical concepts of matrix mathematics and is therefore a abstract idea. Here, this step encompasses taking information (i.e., factual treatment data), manipulating the data via mathematical correlation (i.e., DNN), and organizing the data into a different form (i.e., predicting counter factual treatment responses). See MPEP 2106.04(a)(2)(I)(A)(iv).
wherein the trained DNN learns the balanced representations in multiple treatment causal inference scenario using the loss function
This step encompasses a trained DNN using a loss function for learning the balanced representations in multiple treatment casual interference scenarios which encompasses performing mathematical/statistical operations/computations (i.e., loss functions) for learning the balanced representations in multiple treatment scenarios which reads on abstract ideas. This step encompasses using a DNN which encompasses performing vector and matrix mathematic which reads on abstract ideas.
predicts the plurality of counter factual treatment response accurately without confounding and selection bias
This step encompasses taking information (i.e., balanced representations in multiple treat casual interference scenarios which are statistical relationships for determining if a cause-and-effect relationship exists), manipulating the information using mathematical/statistical models (i.e., DNN, loss function), and organizing the data into a different form (i.e., prediction of counter factual treatment responses with confounding and selection bias) which reads on abstract ideas. See MPEP 2106.04(a)(2)(I)(A)(iv).
wherein to avoid overfitting, nearest neighbor’s samples are selected and one out of the selected nearest neighbor samples is picked at random for each counterfactual treatment of Xi
This can be performed in the human mind by selecting information/data (i.e., nearest neighbor’s samples) and selected nearest neighbor samples at random to avoid overfitting and is therefore an abstract idea.
Claims 2, 6, 10, and 13-15 are further drawn to limitations that describe the abstract ideas of claims 1, 5, and 9.
2A Prong II - Consideration of Practical Application
Here, claims 1, 5, and 9 set forth a method of data analysis to predict counter factual treatment responses corresponding to counter treatment data for the test subjects. As such, practicing the claims merely results in the prediction of counter factual treatment responses without confounding and selection bias corresponding to counter treatment data for the test subjects. Such a result only produces information and does not provide for a practical application in the physical-realm of physical things and acts, i.e., the claims do not utilize the data generated by the judicial exception to affect any type of change. See MPEP 2106.04(a)(2)(A)(iv). Therefore, the claims do not utilize the received Casual Inference data and the results of the abstract ideas to construct a practical application such as treating a subject, transformation of matter, or improving upon an existing technology.
For sake of compact prosecution, even if the predictive model is also considered an additional element the predictive model is used to generally apply the abstract idea without limiting how the training the predictive model functions to obtain a tuned prediction model. The predictive model is described at a high level such that it amounts to using a computer with a predictive model to apply the abstract idea. These limitations only recite the outcomes for “training the predictive model over training and tuning training set parameters to obtain a tuned predictive model” without any details about how the predictive model is trained to obtain a tuned predictive model which does not integrate the judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See 2024 Subject Matter Eligibility Update (AI) [Example 47 Claim 2] and MPEP 2106.05(f).
For further sake of compact prosecution, even if the deep neural network (DNN) is also considered an additional element, the DNN is used to generally apply the abstract idea without limiting how the trained (DNN) functions. The DNN is described at a high level such that it amounts to using a computer with a generic DNN to apply the abstract idea. These limitations only recite the outcomes for “predicting counter factual treatment corresponding to the factual treatment data” without any details about how the counter factual treatment responses corresponding to the factual treatment data are determined by using DNN which does not integrate the judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See 2024 Subject Matter Eligibility Update (AI) [Example 47 Claim 2] and MPEP 2106.05(f). Moreover, the “deep neural network” amounts to using computers and/or computer processes (i.e., DNN) that are tangential to performing the claimed steps. Here, the deep neural network is used to apply the abstract idea (i.e., perform the mathematical calculations). As such, using/training the DNN amounts to mere instructions to implement an abstract idea on a computer, and/or merely use computer processes as a tool to perform an abstract idea. See MPEP 2106.04(a)(2)(III)(C) and 2106.05(f).
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
2B Analysis - Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea.
The recited additional element of data gathering of claims 1, 5, and 9 does not add significantly more than the recited judicial exception because receiving data, such as Casual Inference (CI) data is deemed a well-understood and conventional insignificant extra-solution activity for receiving data that is subsequently analyzed by the abstract idea. See MPEP 2106.05(d)(II).
The recited additional element of using computer process, components, and equipment of claims 1, 5, and 9 does not add significantly more than the recited judicial exception because using computers to perform and analyze abstract ideas are deemed well-understood, routine, and conventional. See MPEP 2106.05(b).
The recited additional element of using computer processes (i.e., deep neural network and predictive models) of claims 1, 5, and 9 does not add significantly more than the recited judicial exception because using computers processes (i.e., deep neural networks/predictive models) for processing and analyzing abstract ideas is deemed well-understood, routine, and conventional. See MPEP 2106.05(d)(II) and 2106.05(g).
In conclusion, and when viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant’s arguments, filed 19 February 2026, have been fully considered and the rejection is maintained. However, upon further consideration, a new ground(s) of rejection is made in view of amendments received 19 February 2026.
The Applicant states the claims are not drawn to abstract ideas. The Applicant states the claims are drawn to an improvement in the function of a computer. The Applicant points to the MPEP 2106.04(d)(I) and 2106.05 for guidance. The Applicant states the claims are drawn to an improvement in accurately predicting counter factual treatment response. The Applicant states the improvement is also observed in the DNN by optimization. The Applicant states the claims are drawn to an improvement in terms of improving training models that cannot be considered to be brought about by the mind or by using pen and paper [remarks, 13-14].
In response, the argument is not persuasive because improvements are evaluated under Step 2A Prong II of the 101 analyses. With respect to improvement of a computer, this is evaluated under Step 2A Prong II. With respect to improving the DNN by optimizing the DNN using loss function using factual error and balancing error, these limitations read on mathematical concepts as optimizing an DNN using a loss function is a mathematical process for refining data which can be performed on a computer or by pen and paper. Furthermore, as noted in Step 2A Prong II of the 101 analyses above, the DNN does not provide an improvement because it is drawn to applying the abstract idea (i.e., perform the mathematical calculations).
The Applicant states the claims implement the judicial exception in conjunction with a particular machine. The Applicant points to MPEP 2106.05(b) in terms of using trained predictive model and trained DNN for guidance. The Applicant states, similarly, the Applicants claimed technique discloses a trained DNN that is used to predict data [remarks, pages 14]. Therefore, it can be seen that there is an improvement in decision making and functioning of a DNN by way of optimizing the functioning of the DNN which can be considered an abstract idea. The Applicant points to Desjardins for guidance [remarks, page 17-18].
With respect to improving the DNN by optimizing the DNN using loss function using factual error and balancing error and as noted in Step 2A Prong II of the 101 analyses, these limitations read on mathematical concepts as optimizing an DNN using a loss function is a mathematical process for refining data which can be performed on a computer or by pen and paper. With respect to Desjardins and in light of its specification, the claims are drawn to training a machine learning model that “allows the model to preserve performance on earlier tasks even as it learns new ones, directly addressing the technical problem of 'catastrophic forgetting' in continual learning systems." Furthermore, one improvement identified in the Specification of Desjardins is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. 21. The Specification of Desjardins also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Moreover, the court was persuaded because the improvement was to how the machine learning model itself operates, and not, for example, the identified mathematical calculation. Here, in the instant case, the claims are drawn to computing a Propensity Score, General Propensity Score (GPS), computing balanced representations, factual responses, and computing factual error for subsequently predicting counter treatment responses which reads on mathematical concepts. Additionally, in the instant case, the claims utilize a DNN for performing calculations for producing mathematical correlations between data which reads on taking information (i.e., balanced representations), manipulating the information using mathematical/statistical models (i.e., DNN, loss function), and organizing the data into a different form (i.e., prediction of counter factual treatment responses without confounding and selection bias, optimize DNN) which reads on abstract ideas. See MPEP 2106.04(a)(2)(I)(A)(iv). Thus, the claims do not provide an improvement. It is noted that improvements are evaluated under Step 2A Prong II of the 101 analyses.
The Applicant points to specification paragraphs [0061-0063] with respect to using MultiMBNN algorithm. The Applicant reiterates paragraphs [0061-0063] of the specification. The Applicant points to figures 4A-4C for further guidance. The Applicant states the claims subject matter has a practical application of the providing matching framework by learning the balances representation in multiple causal interference scenarios [remarks, pages 18-20].
In response and as noted in Step 2A Prong II above, the claims set forth a method of data analysis to predict counter factual treatment responses corresponding to counter treatment data for the test subjects using a predictive model and DNN for processing the CI data. Such a result only produces information and does not provide for a practical application in the physical-realm of physical things and acts, i.e., the claims do not utilize the data generated by the judicial exception to affect any type of change. See MPEP 2106.04(a)(2)(A)(iv). Additionally, and as noted in Step 2A Prong II, even if the deep neural network (DNN) is considered an additional element, the recitation of using the DNN is equivalent to the words "apply it". See 2024 Subject Matter Eligibility Update (AI) [Example 47 Claim 2] and MPEP 2106.05(f). Moreover, the “deep neural network” amounts to using computers and/or computer processes (i.e., DNN) that are tangential to performing the claimed steps. Here, the deep neural network is used to apply the abstract idea (i.e., perform the mathematical calculations). As such, using/training the DNN amounts to mere instructions to implement an abstract idea on a computer, and/or merely use computer processes as a tool to perform an abstract idea. See MPEP 2106.04(a)(2)(III)(C) and 2106.05(f). Therefore, the matching framework by learning balanced representation does not integrate the judicial exception into a practical application because the claimed method is drawn to abstract ideas and applying the exception.
The Applicant states the claims recite additional elements that amount to significantly more than the recited judicial exception [remarks, page 21]. The Applicant states the claimed subject matter comprises additional elements that amount to significantly more than the abstract idea in terms of addressing inadequacies of the matching framework by learning the balanced representations in multiple treatment causal inference scenario [remarks, page 21]. The Applicant points to specification paragraphs [0064-0065] and table 1 for guidance. The Applicant submits the following additional elements “optimizing the DNN…” and “predicting a plurality of counter factual treatment responses…” are additional elements that amount to significantly more [remarks, page 22-23].
In response and as noted in Step 2A Prong I of the 101 analyses above, optimizing data and predicting data are not additional elements, they are abstract ideas. Optimizing data reads on abstract ideas (i.e., using formulas and models to select the best input values to maximize efficiency). Also, predicting data using a DNN and/or a predictive model encompasses taking information (i.e., factual treatment data), manipulating the data via mathematical correlation (i.e., DNN, predictive models), and organizing the data into a different form (i.e., predicting counter factual treatment responses). See MPEP 2106.04(a)(2)(I)(A)(iv). Therefore, optimizing data and predicting values are not additional elements that are evaluated under Step 2B of the 101 analyses.
Conclusion
Claims 1-2, 5-6, 9-10, and 13-15 are rejected.
No claims are allowed.
Finality
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/J.C.P./ Examiner, Art Unit 1687
/Anna Skibinsky/
Primary Examiner, AU 1635
Prosecution Insights