CTNF 18/486,444 CTNF 82150 DETAILED ACTION A. This action is in response to the following communications: Request for Continued Examination filed 05/01/2026. B. Claims 1-20 remains pending. Continued Examination Under 37 CFR 1.114 07-42-04 C. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/01/2026 has been entered. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pasour, Ernest et al. (US Pub. 2022/0138606 A1), herein referred to as “Pasour” in view of ELBSAT, Mohammad N. et al. (US Pub. 2020/0356087 A1), herein referred to as “ELBSAT” . As for claims 1, 8 and 15, Pasour teaches . A method and corresponding system of claim 8 and computer-readable storage medium of claim 15 comprising: Specific to claim 8 a processor; and memory or storage comprising an algorithm or computer instructions, which when executed by the processor, performs an operation comprising (par. 47 hardware environment; processors): Specific to claim 15 having a computer-readable program code embodied therewith, the computer-readable program code executable by one or more computer processors to perform an operation comprising (par. 47 hardware environment; machine-readable storage medium): determining, via a process model, a variable state of the process model, wherein the variable state includes a first input state variable, a first output state variable, and a first control parameter (fig. 16 user interface for selecting a model to use on a data set wherein the data can be variable and upon running the model on the data set updating data based upon found predictions; first, second to the nth amount of predictions can be made on the dataset; par. 178); generating, via a short-term prediction module, a first prediction of a first update of the variable state (par. 178 predictor to the model outcome); generating, via a terminal value prediction module, a second prediction of a second update to the variable state (par. 179 other predictors based upon more than one update to variable state (e.g. between 0 and 1); generating, via a control optimization module, a second control parameter based on the first prediction and the second prediction (par. 181 allowing the user to select a validation method for validating the model which is based upon first and second prediction value updates from the dataset); and controlling, via a processor, a production process of the process model based on the second control parameter (par. 181-182 user interface give controlling function over the model to the user to select a production process ‘validation AIC, BIC etc..’ which will process the model based upon second control parameters as shown in figure 16). Pasour teaches instructions to cause a computing system to generate an updated model for a set of regression models; but does not discuss using a mixed-integer linear program (MILP); also Pasour does not teach wherein the first prediction represents an optimized prediction of multiple updates on a short-term future time horizon; however in the same field endeavor ELBSAT teaches generating, via a short-term prediction module, a first prediction of a first update of the variable state, wherein the first prediction represents an optimized prediction of multiple updates on a short-term future time horizon (par. 206 Objective function generator 935 can be configured to generate the objective function J by summing the operational cost term, the maintenance cost term, and the capital cost term formulated by cost predictors 910 , 920 , and 930 . Par., 208 Objective function generator 935 can be configured to impose constraints on one or more variables or parameters in the objective function J; par. 212 Objective function optimizer 940 can be configured to optimize the objective function J periodically (e.g., once per day, once per week, once per month, etc.) to dynamically update the predicted cost and/or the net present value NPV.sub.cost based on the closed-loop feedback from connected equipment 610 ); generating, via a terminal value prediction module, a second prediction of a second update to the variable state, wherein the second prediction represents optimized predictions of variable states on a long-term future time horizon at a time after the short-term future time horizon (par. 212 years are implied but none the less monthly is long term in view of daily as mentioned the optimizer can predict futures across multiple times); generating, via a control optimization module, a second control parameter based on the first prediction and the second prediction, wherein the control optimization module is a mixed- integer linear programming (MILP) model that combines the first prediction and the second prediction into a single objective function and resolves the single objective function to generate the second control parameter (par. 209 Objective function optimizer 940 can use any of a variety of optimization techniques to formulate and optimize the objective function J. For example, objective function optimizer 940 can use integer programming, mixed integer linear programming, stochastic optimization, convex programming, dynamic programming, or any other optimization technique to formulate the objective function J, define the constraints, and perform the optimization; par. 206 the predictors are combined to determine future predictions in the calculations); and controlling, via a processor, a production process of the process model based on the second control parameter (par. 206-213 objective function optimizer 940 generates optimization results. The optimization results may include the optimal values of the decision variables in the objective function J for each time step i in the optimization period. The optimization results include operating decisions, equipment maintenance decisions, and/or equipment purchase decisions for each device of connected equipment 610 ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine ELBSAT into Pasour because ELBSAT suggests the use of a model predictive maintenance system for building equipment for performing maintenance too frequently may result in a low operating cost but a high maintenance cost, whereas performing maintenance too infrequently may result in a low maintenance cost but a higher operating cost. It can be difficult to determine an appropriate maintenance strategy for building equipment in the interest of reducing total life cycle cost (par.3-4). The rationale to modify or combine the prior art does not have to be expressly stated in the prior art; the rationale may be expressly or impliedly contained in the prior art or it may be reasoned from knowledge generally available to one of ordinary skill in the art, established scientific principles, or legal precedent established by prior case law. In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988); In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992); see also In re Kotzab, 217 F.3d 1365, 1370, 55 USPQ2d 1313, 1317 (Fed. Cir. 2000) (setting forth test for implicit teachings); In re Eli Lilly & Co ., 902 F.2d 943, 14 USPQ2d 1741 (Fed. Cir. 1990) (discussion of reliance on legal precedent); In re Nilssen, 851 F.2d 1401, 1403, 7 USPQ2d 1500, 1502 (Fed. Cir. 1988) (references do not have to explicitly suggest combining teachings); Ex parte Clapp, 227 USPQ 972 (Bd. Pat. App. & Inter. 1985) (examiner must present convincing line of reasoning supporting rejection); and Ex parte Levengood, 28 USPQ2d 1300 (Bd. Pat. App. & Inter. 1993) (reliance on logic and sound scientific reasoning). As for claims 2, 9 and 16, Pasour teaches . The method of claim 1, wherein the process model is a machine learning model trained to learn underlying relationships and dynamics of inputs and outputs of the production process; wherein the first input state variable represents a controllable input of the production process; and wherein the first output state variable represents a determined output or a measured output of the production process (par. 170-171 example of finding relationship between one or more target values and determining output based upon predictions identified in the dataset of information). As for claims 3, 10 and 17, Pasour teaches . The method of claim 1, wherein the first input state variable further represents an input of the process model; wherein the first output state variable further represents an output of one or more processing stages of the process model; and wherein the first control parameter represents an adjustment to an input state variable (par. 190-191 term settings presented in the user interface fig. 16 allows the user to adjust the model for desired variable state information related to machine log dataset). As for claims 4, 11 and 18, Pasour teaches . The method of claim 1, wherein the short-term prediction module is a machine learning model or a statistical regression model configured to: receive the variable state; generate the first update of the variable state based on the variable state; and generate the first prediction based on the first update (par. 193 example of first predictions based upon first update is generating candidate predictors which allows for updating model; fig. 17 par. 197-198 goes into more detail upon fine tuning the term predictors and how they updated and affect the model chosen in fig. 16). As for claims 5, 12 and 19, Pasour teaches . The method of claim 4, wherein the first prediction represents a minimum or maximum of updated variable states across a first time period, and wherein the first time period ranges from a present time to a first future time period (par. 66 The data received and collected by computing environment 214, no matter what the source or method or timing of receipt, may be processed over a period of time for a client to determine results data based on the client's needs and rules). As for claims 6, 13 and 20, Pasour teaches . The method of claim 1, wherein the terminal value prediction module is a machine learning model or a statistical regression model configured to: receive the variable state; generate the second update of the variable state based on historical data of input state variables and output state variables of the production process to determine future input state variables and future output state variables; and generate the second prediction based on the second update (par. 241-243 user interface displays histogram for user interface this histogram is built upon model running and making predictions on datasets trained on the model). As for claims 7, 14 and 20, Pasour teaches . The method of claim 6, wherein the second prediction represents a minimum or a maximum of updated variable states across a second time period, wherein the second time period ranges from a first future time period to a second future time period (par. 241-243 user interface displays histogram for user interface this histogram is built upon model running and making predictions on datasets trained on the model; the histogram shows more than one time entry for user selection thereby allowing the user to view predictions related to documents across different selected times). (Note :) It is noted that any citation to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275, 277 (CCPA 1968)) . Response to Arguments Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Inquires 07-100 AIA Any inquiry concerning this communication should be directed to NICHOLAS AUGUSTINE at telephone number (571)270-1056 . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. PNG media_image1.png 208 559 media_image1.png Greyscale /NICHOLAS AUGUSTINE/Primary Examiner, Art Unit 2178 May 27, 2026 Application/Control Number: 18/486,444 Page 2 Art Unit: 2178 Application/Control Number: 18/486,444 Page 3 Art Unit: 2178 Application/Control Number: 18/486,444 Page 4 Art Unit: 2178 Application/Control Number: 18/486,444 Page 5 Art Unit: 2178 Application/Control Number: 18/486,444 Page 6 Art Unit: 2178 Application/Control Number: 18/486,444 Page 7 Art Unit: 2178 Application/Control Number: 18/486,444 Page 8 Art Unit: 2178 Application/Control Number: 18/486,444 Page 9 Art Unit: 2178 Application/Control Number: 18/486,444 Page 10 Art Unit: 2178
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