DETAILED ACTION
A. This action is in response to the following communications: Amendment filed: 01/20/2026. This action is made Final.
B. Claims 1-20 remain pending.
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.
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 Phan, Dung Tien, et al. (US Pub. 2022/0057786 A1), herein referred to as “Phan”.
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); however in the same field endeavor Phan teaches 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. 26 regressions based on piece-wise linear approach such decision trees, multivariate adaptive regression splines (MARS), and decision lists, nonconvex programs formulate (for example, (1) and (3)) are formulated as a respective mixed-integer linear program (MILP); par. 27 A tree ensemble model combines predictions from multiple decision trees ƒt(x). A decision tree uses a tree-like structure to predict the outcome for an input feature vector x. In practice, individual decision trees often suffer from high variance predictions and can overfit the training data, which lead to a poor out-of-sample predictive accuracy, if there is no restriction in the size of tree. By using the bagging techniques, the tree ensemble regression function outputs predictions by taking the weighted sum of multiple decision trees.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Phan into Pasour because Phan suggests alternative ways for working with regression models, sensor data and is directed to a site-wide operations management optimization for manufacturing and process control. A non-limiting example computer-implemented method includes selecting an optimization algorithm for the control system of a processing plant based on whether the control system is guided by a linear-based predictive model or a non-linear-based predictive model, in which a gradient is available. Calculating a set variable using the optimization algorithm. Predicting an output based on the calculated set variable. Comparing an actual output at the processing plant to the predicted output. Suspending a physical process at the processing plant in response to the actual output being a threshold value apart from the predicted output. Pasour is also concerned with working with regression models and system that may include various sensors that collect data to be processed and/or transmitted to a computing environment according to certain embodiments includes a power or energy grid. A variety of different network devices may be included in an energy grid, such as various devices within one or more power plants, energy farms (e.g., wind farm, solar farm, among others) energy storage facilities, factories, homes and businesses of consumers, among others.
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
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.
Inquires
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.
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/NICHOLAS AUGUSTINE/Primary Examiner, Art Unit 2178 February 19, 2026