DETAILED ACTION
This action is responsive to claims filed 02/27/2026. Claims 1, 4–5, 8, 10, 13–14, 17, and 19 are amended. Claims 3, 7, 12, 16, and 20 are cancelled. Claims 21–25 are newly added.
Claims 1–2, 4–6, 8–11, 13–15, 17–19, and 21–25 are pending for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
In reference to specification objections
Applicant’s amended specification and remarks, filed on 02/27/2026, with respect to the objections to the specification have been fully considered and are persuasive. Thus, the objections to the specification are withdrawn.
In reference to §112 rejections
Applicant’s amended claims along and remarks, filed on 02/27/2026, with respect to the §112 rejections have been fully considered and are persuasive. Thus, the §112 rejections are withdrawn.
In reference to 35 USC § 101
Applicant’s arguments, filed on 02/27/2026, with respect to the § 101 rejections have been fully considered and are persuasive.
Examiner notes that while the claims recite several limitations that are abstract ideas (mental processes), the claims as a whole are not directed to an abstract idea. Applicant amended the claims, which collectively now recite a detailed method directed toward a data driven optimization for industrial process refinement. The newly amended independent claims now include " wherein the new neighborhood is selected as a region around the optimization solution that encompasses a number of overlapped historical data points in the historical data, and wherein the optimization solution is a center of the new neighborhood, and wherein the number of overlapped historical data points are present in both the new neighborhood and the current neighborhood;" and "recreating, by the number of processor units, the regression model using the number of overlapped historical data points" These additional limitations are not abstract ideas (see MPEP 2106.04(a)). Thus, these limitations must be considered additional elements to the abstract idea. Examiner notes that these additional elements integrate the abstract idea into a practical application because the entire claim amounts to a detailed method that requires implementing a specific combination of hardware with the methods of creatin regression models over time, determining solutions, and selection new optimizations within newly generated neighborhoods (as opposed to a broad recitation at a high level of generality), and the specific combination of hardware and instructions recited in the additional element amounts to an improvement to the functioning of a computer/field, as set forth by MPEP 2106.05(a)), which states “the claim must include the components or steps of the invention that provide the improvement described in the specification.” Pursuant to this requirement set forth by the MPEP, examiner points out that the Specification states in at least [0050–55, 0085–86]: Therefore, as pointed out by applicant in the Remarks on pg. 12, “These claims do not merely recite abstract mathematical formulas or mental reasoning. Rather, they recite a specific computational framework in which regression modeling, neighborhood selection, and optimization are performed in a distributed computing environment to control and improve operation of a real-world or virtual system.” Therefore, the additional elements reflect the improvement set forth and explains what the resulting improvement is.
Thus, the additional limitations do amount to significantly more, and the § 101 rejections are withdrawn.
In reference to 35 USC § 103
Applicant’s arguments filed on 02/27/2026, with respect to the newly amended limitations have been fully considered, but the arguments 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 teach or matter specifically challenged in the argument.
Applicant argues, beginning on Pg. 14 in the Remarks, that the Office has failed at explaining how a person of ordinary skill in the art could have arrived at the claimed invention based on information available at the time of the effective filing date of the present application. Specifically, applicant argues “that not all the claimed elements were known in the prior art." Examiner respectfully disagrees. Examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See 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), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). (Emphasis added). In this case, examiner points to the motivation specifically cited for each reference cited within mailed actions and within this action. Without any explicit challenge to the contrary based on currently cited art, examiner submits that the provided passage from the respective citations for the combination has more than met the burden of producing some teaching, suggestion, or motivation to combine. See § 103 below for a detailed analysis.
With respect to the remaining arguments including the dependent claims, without specific arguments detailing how the cited art does not teach each limitation, examiner maintains the rejections.
Thus, examiner maintains the § 103 rejections.
Claim Rejections - 35 USC § 103
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 1–2, 4–6, 8–11, 13–15, 17–19, and 24–25 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al., (US 20220011760 A1), hereinafter “Zhou”, in view of Todoriki et al., (WO 2022044335 A1), hereinafter “Todoriki”, further in view of Willemain et al., (US 20230267007 A1), hereinafter “Willemain”, and further in view Xu et al., ("An Iterative Neighborhood Local Search Algorithm for Capacitated Centered Clustering Problem," in IEEE Access, vol. 10, pp. 34497-34510, 2022, doi: 10.1109/ACCESS.2022.3162692, https://ieeexplore.ieee.org/document/9743384, (Year: 2022)), hereinafter “Xu”.
Regarding claim 1, Zhou teaches:
creating, by a number of processor units, a regression model using historical data in a current neighborhood, wherein the historical data is for a system over time (Zhou Fig. 6, ¶¶0042–0043: “Further, the embodiments described herein with respect to system 500 can be implemented with any appropriate logic, wherein the logic, as referred to herein, can include any suitable hardware (e.g., a processor, an embedded controller, or an application specific integrated circuit, among others), software (e.g., an application, among others), firmware, or any suitable combination of hardware, software, and firmware, in various embodiments … In order to generate regression models for use in real-time decision support, choices are made regarding how the regression models should be built and how the corresponding optimization models should be formulated … In block 602, a time series representation of the determined sensor dataset from block 601 is extracted, and autoencoder and neighborhood embedding of the extracted time series representation is determined”—[wherein the regression model is built in real-time by the computer system including processors (i.e., by a number of processing units) using sensor based historical data (i.e., system over time), and the neighborhood embedding of the extracted time series representation is determined (i.e., using historical data in a current neighborhood)]);
generating, by the number of processor units, an optimization solution using the regression model created from the current neighborhood [and an objective function] (Zhou ¶0028: “Therefore, the results of an optimization solution performed using the regression model can be less reliable over a relatively long decision support time (e.g., 24 or 48 hours). In embodiments of the invention, any assumptions that were used in the creation of the model can be identified, and how the assumptions affect the continuing operation of the model can be determined. These include using temporary or ad-hoc regression models that agree with the current plant state, such as plant operating-mode or operating-neighborhood based modeling practices, as well as the use of statistical hypothesis testing to limit the length of the lookahead time horizon for decision support”—[(emphasis added)]);
determining, by the number of processor units, whether the optimization solution is within the current neighborhood (Zhou Fig. 7, ¶¶0009–0010, 0052: “Another non-limiting example computer-implemented method includes identifying a neighborhood of a current output of a process step regression model, the process step regression model corresponding to a single stage of a manufacturing process. Based on identifying the neighborhood, opportunity modeling of the single stage of the manufacturing process is performed based on the process step regression model. Based on being unable to identify the neighborhood, the process step regression model is regenerated” and “In block 702, the neighborhood can be identified by determining whether a current state of the manufacturing process as given by the regression models is located in a proper range for optimization using the regression model”—[wherein the current output is the optimizations solution]);
selecting, by the number of processor units, a new neighborhood containing the historical data in response to the optimization solution not being within the current neighborhood, wherein the new neighborhood is based on the optimization solution and becomes the current neighborhood (Zhou Figs. 1, 5, ¶¶0009–0010, 0041, 0052–0053: “Another non-limiting example computer-implemented method includes identifying a neighborhood of a current output of a process step regression model, the process step regression model corresponding to a single stage of a manufacturing process. Based on identifying the neighborhood, opportunity modeling of the single stage of the manufacturing process is performed based on the process step regression model. Based on being unable to identify the neighborhood, the process step regression model is regenerated” and “If the new model feasibility module 507 determines that the models 503 are not feasible, new model building module 502 can construct new models based on historical data 501” and “Neighborhood size analysis is performed by checking the size of a neighborhood to find out a similar scenario in the historical data based on the reduced time series data … n block 703, it is determined whether the neighborhood of the current state was successfully identified in block 702. If it is determined that the neighborhood was not identified in block 702, flow proceeds to block 704, in which it is determined that the regression models have deteriorated. Opportunity modeling of the manufacturing process using the regression models is stopped in block 704, regeneration of the models is recommended (e.g., according to system 500 of FIG. 5), and method 700 ends”—[wherein regeneration includes the new neighborhood which becomes the current neighborhood based on the optimization solution]); and
repeating, by the number of processor units, the creating the regression model, generating the optimization solution, determining whether the optimization solution is within the current neighborhood until the optimization solution is within the current neighborhood (Zhou Figs. 1, 6, ¶0049: “Method 600 can be repeated at any appropriate interval throughout operation of embodiments of a manufacturing process modeling system (e.g., system 100 of FIG. 1) in order to ensure fidelity of a regression model that is being used to monitor and optimize a manufacturing process such as manufacturing process 101 of FIG. 1”); and
selecting the new neighborhood in response to the optimization solution not being within the current neighborhood (Zhou Figs. 1, 5, 6, ¶0049, 0041, 0052–0053: “Method 600 can be repeated at any appropriate interval throughout operation of embodiments of a manufacturing process modeling system (e.g., system 100 of FIG. 1) in order to ensure fidelity of a regression model that is being used to monitor and optimize a manufacturing process such as manufacturing process 101 of FIG. 1” and ““If the new model feasibility module 507 determines that the models 503 are not feasible, new model building module 502 can construct new models based on historical data 501” and “Neighborhood size analysis is performed by checking the size of a neighborhood to find out a similar scenario in the historical data based on the reduced time series data … n block 703, it is determined whether the neighborhood of the current state was successfully identified in block 702. If it is determined that the neighborhood was not identified in block 702, flow proceeds to block 704, in which it is determined that the regression models have deteriorated. Opportunity modeling of the manufacturing process using the regression models is stopped in block 704, regeneration of the models is recommended (e.g., according to system 500 of FIG. 5), and method 700 ends”),
wherein the new neighborhood is selected as a region around the optimization solution that encompasses a number of overlapped historical data points in the historical data [wherein the optimization solution is a center of the new neighborhood] (Zhou Fig. 7, ¶0052: “In block 702, a neighborhood of the current state of the manufacturing process that is given by the regression models is identified based on the extracted time series data of block 701. In block 702, the neighborhood can be identified by determining whether a current state of the manufacturing process as given by the regression models is located in a proper range for optimization using the regression model. The current operational state of as represented by embedding should not be an outlier in the embedded space based on all available sensor data. If the current state is determined to be an outlier, the regression models corresponding to the manufacturing process may not be reliable. In some embodiments of block 702, dimension reduction of the extracted time series data is performed using t-SNE. Neighborhood size analysis is performed by checking the size of a neighborhood to find out a similar scenario in the historical data based on the reduced time series data. A center (y.sub.1, y.sub.2).sub.0 can be defined as the center of the independent variable domain, i.e. the average of (y.sub.1, y.sub.2) of the mapped domain. An average distance of values of (y.sub.1, y.sub.2) from the center can be defined as r.sub.0. It can be determined whether an embedding of the current operational state is located at a central region of the domain. A single instance of a runtime state located at the edge of the domain indicates a risk of applying the regression model to this state. If it is assumed that the map of an input is (y.sub.1, y.sub.2), the distance ratio
[00001].Math.(y1,y2)-(y1,y2)0.Math.r0
can be defined as the criteria to determine whether the current state is covered by the model in block 702, and the neighborhood is identified based on the current state being determined to be covered by the regression models”—[wherein the analysis determines the optimization i.e., the solution by defining and finding neighborhoods, making sure they are covered by calculating the distances of the neighborhood boundaries and identifying outliers]), and
wherein the number of overlapped historical data points are present in both the new neighborhood and the current neighborhood (Zhou Figs. 1, 5, 7, ¶0052–0053: “An average distance of values of (y.sub.1, y.sub.2) from the center can be defined as r.sub.0. It can be determined whether an embedding of the current operational state is located at a central region of the domain. A single instance of a runtime state located at the edge of the domain indicates a risk of applying the regression model to this state. If it is assumed that the map of an input is (y.sub.1, y.sub.2), the distance ratio
[00001].Math.(y1,y2)-(y1,y2)0.Math.r0
can be defined as the criteria to determine whether the current state is covered by the model in block 702, and the neighborhood is identified based on the current state being determined to be covered by the regression models” and “In block 703, it is determined whether the neighborhood of the current state was successfully identified in block 702. If it is determined that the neighborhood was not identified in block 702, flow proceeds to block 704, in which it is determined that the regression models have deteriorated. Opportunity modeling of the manufacturing process using the regression models is stopped in block 704, regeneration of the models is recommended (e.g., according to system 500 of FIG. 5), and method 700 ends. If it is determined in block 703 that the neighborhood of the current state was identified, flow proceeds from block 703 to block 705. In block 705, opportunity modeling of the manufacturing process is performed using the regression models based on the identified neighborhood. Method 700 can be repeated at any appropriate interval throughout operation of embodiments of a manufacturing process modeling system (e.g., system 100 of FIG. 1) in order to ensure fidelity of any regression models that are being used to monitor and optimize a manufacturing process such as manufacturing process 101 of FIG. 1”).
Zhou does not appear to explicitly teach:
[generating, by the number of processor units, an optimization solution using the regression model created from the current neighborhood] and an objective function;
[wherein the new neighborhood is selected as a region around the optimization solution that encompasses a number of overlapped historical data points in the historical data] wherein the optimization solution is a center of the new neighborhood; and
recreating, by the number of processor units, the regression model using the number of overlapped historical data points.
However, Todoriki teaches:
[generating, by the number of processor units, an optimization solution using the regression model created from the current neighborhood] and an objective function (Todoriki Eq. 2, Fig. 7, Pg. 3, 5th ¶: “Then, the linear regression model g is generated by approximating with the linear regression model using the feature amount of the neighborhood data as the explanatory variable and the output of the neighborhood data as the objective variable (step S5). For example, in Ridge regression, the objective function ξ (x) exemplified by the following equation (2) is used”).
The methods of Zhou, the teachings of Todoriki, and the instant application are analogous art because they pertain to optimizing machine learning models using regression techniques and neighborhoods.
It would be obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the methods of Zhou with the teachings of Todoriki to provide for an objective function to help compute the optimization solution. One would be motivated to do so to analyze the solution to prevent poor machine learning models from being used (Todoriki Pg. 3, 5th ¶ – 6th ¶: “This can prevent poor machine learning models from being used in mission-critical areas”).
Zhou in view of Todoriki does not appear to explicitly teach:
[wherein the new neighborhood is selected as a region around the optimization solution that encompasses a number of overlapped historical data points in the historical data] wherein the optimization solution is a center of the new neighborhood.
However, Willemain teaches:
wherein the optimization solution is a center of the new neighborhood (Willemain Figs. 2, 9, ¶0064: “At S7, if the solution provided by the initially selected CP value set at the center of the neighborhood 72 has the best performance metric (e.g., lowest cost), then the initially selected CP value set is utilized at S8 for the resource and the process ends”).
The methods of Zhou, the teachings of Willemain, and the instant application are analogous art because they pertain to optimizing machine learning models using regression techniques and neighborhoods.
It would be obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the methods of Zhou with the teachings of Willemain to provide to regenerate the models based on the overlapping data. One would be motivated to do so to provide the system with the best metrics for optimizations (Willemain ¶0064:).
Examiner notes Zhou in paragraph [0053] that “regeneration of the models is recommended (e.g., according to system 500 of FIG. 5).” Zhou does not explicitly teach recreating, by the number of processor units, the regression model using the number of overlapped historical data points.
Zhou in view of Todoriki and Willemain does not appear to explicitly teach:
recreating, by the number of processor units, the regression model using the number of overlapped historical data points.
However, Xu teaches:
recreating, by the number of processor units, the regression model using the number of overlapped historical data points (Xu Fig. 3, pg. 34502, B. INEXACT ITERATIVE NEIGHBORHOOD LOCAL SEARCH ALGORITHM (IINLS): “Applying clustering search to each generation of the population usually does not help get a better solution. On the contrary, it will shorten the number of generations and fail to get good performance. Therefore, we only perform the clustering search for every K generations … To reduce the search scope of swap neighborhoods, we only check points in the overlapping areas. Figure 3 describes an example of three clusters where clusters A and C are not overlapping and so swaps between the two clustersare not considered”).
The methods of Zhou, the teachings of Xu, and the instant application are analogous art because they pertain to optimizing machine learning models using regression techniques and neighborhoods.
It would be obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the methods of Zhou with the teachings of Xu to provide to regenerate the models based on the overlapping data. One would be motivated to do so to provide the system with a faster solutions to optimizations (Xu pg. 34502, B. INEXACT ITERATIVE NEIGHBORHOOD LOCAL SEARCH ALGORITHM (IINLS)).
Regarding claim 2, Zhou in view of Todoriki, Willemain, and Xu teaches all the limitations of claim 1.
Zhou teaches:
halting repeating, by the number of processor units, the creating, generating, determining, and selecting steps in response to a selected number of iterations occurring with the optimization solution not being within the current neighborhood (Zhou ¶0053: “In block 703, it is determined whether the neighborhood of the current state was successfully identified in block 702. If it is determined that the neighborhood was not identified in block 702, flow proceeds to block 704, in which it is determined that the regression models have deteriorated. Opportunity modeling of the manufacturing process using the regression models is stopped in block 704, regeneration of the models is recommended (e.g., according to system 500 of FIG. 5), and method 700 ends”).
Regarding claim 4, Zhou in view of Todoriki and Xu teaches all the limitations of claim 1.
Zhou teaches:
wherein the region is selected to encompass a number of the historical data points that results in the regression model generating predictions within a threshold value when making the predictions using test data (Zhou Fig. 6, ¶0045: “If it is determined in block 603 that the current state of the manufacturing process that is given by the regression model is not within the predefined range, flow proceeds from block 603 to block 605. In block 605, the accuracy of the regression model is detected based on sensor data from one or more time periods preceding the current time period. In block 605, an expected output from the regression model can be compared to an actual output of the manufacturing process based on sensor data from the manufacturing process for the one or more other time periods, and the determined difference can be compared to a predetermined threshold”).
Regarding claim 5, Zhou in view of Todoriki, Willemain, and Xu teaches all the limitations of claim 1.
Zhou teaches:
wherein selecting, by the number of processor units, the new neighborhood containing the historical data comprises:
[increasing, by the number of processor units, a size of a region for the new neighborhood] in response to a number of historical data points in the new neighborhood being less than a creation threshold (Zhou Figs. 6, 7, ¶¶0047, 0053: “If it is determined in block 606 that a mismatch was detected, and the degree of the detected mismatch is relatively small (e.g., less than a decision support threshold), it is determined that the regression model has deteriorated, and flow proceeds from block 606 to block 608. In block 608, model regeneration is performed by generating a temporary dynamic regression model based on process data from a shortened time window (e.g., weeks, days, and hours before the current time)” and “In block 703, it is determined whether the neighborhood of the current state was successfully identified in block 702. If it is determined that the neighborhood was not identified in block 702, flow proceeds to block 704, in which it is determined that the regression models have deteriorated. Opportunity modeling of the manufacturing process using the regression models is stopped in block 704, regeneration of the models is recommended (e.g., according to system 500 of FIG. 5), and method 700 ends”—[(emphasis added)]).
Todoriki teaches:
increasing, by the number of processor units, a size of a region for the new neighborhood [in response to a number of historical data points in the new neighborhood being less than a creation threshold] (Todoriki Pg. 3, 4th ¶: “More specifically, by varying a part of the feature amount of the data x which is the original input instance, the neighborhood data z is generated on a specific number of samples, for example, 100 to10000 (step S1)”; see also Pg. 8, 7th ¶ – 8th ¶: “Further, when the difference between the number of neighboring data of the positive example and the negative example is equal to or less than the threshold value (step S107Yes), it can be identified that the positive example and the negative example are equal. In this case, the first generation unit 15B determines whether or not the total number of neighboring data reaches a threshold value, for example, N.sub.max or more (step S108). Then, when the total number of neighborhood data does not reach the threshold value N.sub.max (step S108No), it can be identified that the total number of neighborhood data is insufficient for generating a linear regression model. In this case, the first generation unit 15B proceeds to step S104 and repeats the generation of neighborhood data”; see also Pg. 9, 2nd ¶: “If the chi-square statistic X .sup.2 is not below the significance level in the test in step S110, that is, if the null hypothesis H .sub.0 is rejected (step S111No), the distance uniformity test fails. Can be identified. In this case, when the cumulative number of times the distance uniformity test fails is less than a threshold value, for example, the upper limit of failure File.sub.max (step S115Yes), the distance function D (x, z) and the kernel width σ are set. Let me try again. At this time, a distance function different from the distance function used when the distance uniformity test fails is automatically set, or a recommendation of a different distance function is output to the client terminal 30”—[wherein when the data is found to be less than a creation threshold, the system recommends regeneration, which repeats the process of generating the neighborhood data by increasing the number (e.g., 100 to 20000)]).
The same motivation utilized in claim 1 to combine Zhou with Todoriki is equally applicable to claim 5.
Regarding claim 6, Zhou in view of Todoriki, Willemain, and Xu teaches all the limitations of claim 1.
Todoriki teaches:
wherein selecting, by the number of processor units, the new neighborhood containing the historical data comprises:
increasing, by the number of processor units, a size of a region for the new neighborhood in response to a selected number of iterations in generating the optimization solution occurring without the new neighborhood for the optimization solution being within the current neighborhood used to create the regression model (Todoriki Pg. 3, 4th ¶: “More specifically, by varying a part of the feature amount of the data x which is the original input instance, the neighborhood data z is generated on a specific number of samples, for example, 100 to10000 (step S1)”; see also Pg. 8, 7th ¶ – 8th ¶: “Further, when the difference between the number of neighboring data of the positive example and the negative example is equal to or less than the threshold value (step S107Yes), it can be identified that the positive example and the negative example are equal. In this case, the first generation unit 15B determines whether or not the total number of neighboring data reaches a threshold value, for example, N.sub.max or more (step S108). Then, when the total number of neighborhood data does not reach the threshold value N.sub.max (step S108No), it can be identified that the total number of neighborhood data is insufficient for generating a linear regression model. In this case, the first generation unit 15B proceeds to step S104 and repeats the generation of neighborhood data”; see also Pg. 9, 2nd ¶: “If the chi-square statistic X .sup.2 is not below the significance level in the test in step S110, that is, if the null hypothesis H .sub.0 is rejected (step S111No), the distance uniformity test fails. Can be identified. In this case, when the cumulative number of times the distance uniformity test fails is less than a threshold value, for example, the upper limit of failure File.sub.max (step S115Yes), the distance function D (x, z) and the kernel width σ are set. Let me try again. At this time, a distance function different from the distance function used when the distance uniformity test fails is automatically set, or a recommendation of a different distance function is output to the client terminal 30. You can also do it. On the other hand, when the cumulative number of times the distance uniformity test fails reaches the threshold value (step S115No), the retry of generation of neighboring data is terminated and the process proceeds to step S112”—[(emphasis added) wherein when the cumulative number of times is found to be less than a creation threshold, the system recommends regeneration, which repeats the process of generating the neighborhood data by increasing the number (e.g., 100 to 20000)]).
The same motivation utilized in claim 1 to combine Zhou with Todoriki is equally applicable to claim 6.
Regarding claim 8, Zhou in view of Todoriki, Willemain, and Xu teaches all the limitations of claim 1.
Zhou teaches:
determining, by the number of processor units, a number of historical data points present in an overlap between the new neighborhood for the optimization solution and the current neighborhood used to create the regression model in response to the new neighborhood not being within the current neighborhood and the overlap being present between the new neighborhood and the current neighborhood (Zhou Fig. 7, ¶0052: “In some embodiments of block 702, dimension reduction of the extracted time series data is performed using t-SNE. Neighborhood size analysis is performed by checking the size of a neighborhood to find out a similar scenario in the historical data based on the reduced time series data. A center (y.sub.1, y.sub.2).sub.0 can be defined as the center of the independent variable domain, i.e. the average of (y.sub.1, y.sub.2) of the mapped domain. An average distance of values of (y.sub.1, y.sub.2) from the center can be defined as r.sub.0. It can be determined whether an embedding of the current operational state is located at a central region of the domain. A single instance of a runtime state located at the edge of the domain indicates a risk of applying the regression model to this state. If it is assumed that the map of an input is (y.sub.1, y.sub.2), the distance ratio
[00001].Math.(y1,y2)-(y1,y2)0.Math.r0
can be defined as the criteria to determine whether the current state is covered by the model in block 702, and the neighborhood is identified based on the current state being determined to be covered by the regression models”—[wherein the neighborhood size analysis includes determining whether there is overlap to see if the current state is covered by the model]); and
using, by the number of processor units, the optimization solution in response to the number of the historical data points being greater than a threshold for historical data points needed for the regression model (Zhou Fig. 2, ¶0033: “Raw data 202 and monitored data 204 are compared by internal analysis module 205 determines an observed difference between raw data 202 and monitored data 204. Internal analysis module 205 can determine a fidelity of the regression model 201 to the manufacturing process based on the observed difference; based on the observed difference being above a threshold, trained model 201 can be regenerated. Internal analysis module 205 can also determine an appropriate lookahead time horizon for decision support for the manufacturing process by regression model 201”—[(emphasis added)]).
Regarding claim 9, Zhou in view of Todoriki, Willemain, and Xu teaches all the limitations of claim 1.
Zhou teaches:
wherein determining, by the number of processor units, whether the optimization solution is within the current neighborhood further comprises:
determining, by the number of processor units, whether a data point for the optimization solution is within the current neighborhood used to create the regression model (Zhou Fig. 7, ¶0052: “In some embodiments of block 702, dimension reduction of the extracted time series data is performed using t-SNE. Neighborhood size analysis is performed by checking the size of a neighborhood to find out a similar scenario in the historical data based on the reduced time series data. A center (y.sub.1, y.sub.2).sub.0 can be defined as the center of the independent variable domain, i.e. the average of (y.sub.1, y.sub.2) of the mapped domain. An average distance of values of (y.sub.1, y.sub.2) from the center can be defined as r.sub.0. It can be determined whether an embedding of the current operational state is located at a central region of the domain. A single instance of a runtime state located at the edge of the domain indicates a risk of applying the regression model to this state. If it is assumed that the map of an input is (y.sub.1, y.sub.2), the distance ratio
[00001].Math.(y1,y2)-(y1,y2)0.Math.r0
can be defined as the criteria to determine whether the current state is covered by the model in block 702, and the neighborhood is identified based on the current state being determined to be covered by the regression models”—[emphasis added]).
Regarding claim 10, Zhou teaches:
A computer system comprising: a number of processor units, wherein the number of processor units executes program instructions to: (Zhou ¶0072: “The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention”).
Regarding the remaining limitation of claim 10, although varying in scope, the remaining limitations of claim 10 are substantially the same as the limitations of claim 1. Thus, the remaining limitations of claim 10 are rejected using the same reasoning and analysis as claim 1 above, respectively.
Regarding claims 11–18, although varying in scope, the limitations of claims 11, 13–15, and 17–18 are substantially the same as the limitations of claims 2, 4–6, and 8–9, respectively. Thus, claims 11, 13–15, and 17–18 are rejected using the same reasoning and analysis as claims 2, 4–6, and 8–9 above, respectively.
Regarding claim 19, Zhou teaches:
A computer program product for data driven optimization, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer system to cause the computer system to: (Zhou ¶0072: “The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention”).
Regarding the remaining limitation of claim 19, although varying in scope, the remaining limitations of claim 19 are substantially the same as the limitations of claim 1. Thus, the remaining limitations of claim 19 are rejected using the same reasoning and analysis as claim 1 above, respectively.
Regarding claim 24, Zhou in view of Todoriki and Xu teaches all the limitations of claim 1.
Zhou teaches:
wherein determination of whether the optimization solution is within the current neighborhood is based on whether control variable values and output values for the optimization solution is within the current neighborhood (Zhou Figs. 3–7, ¶0036, ¶0052–0053: “The generated variables are control variables of an optimization problem corresponding to the model 109 for the time period of [t.sub.1, t.sub.2, . . . , t.sub.m] with indices [1, 2, . . . , m] in which m is the time horizon. The remaining variables (i.e., non-control variables) can be initialized in the model 109 with respective values corresponding to t.sub.0, (e.g., before the time period [t.sub.1, t.sub.2, . . . , t.sub.m]). The model 109 can use periodic sensor data from manufacturing process 101 for the control variables to determine input and output relationships; however, model deterioration can result from using t.sub.0 values for the non-control variables. Determination of model deterioration based on identification of non-control variables is discussed in further detail below with respect to method 600 of FIG. 6”).
Regarding claim 25, although varying in scope, the limitations of claim 25 are substantially the same as the limitations of claim 24, above. Thus, claim 25 is rejected using the same reasoning and analysis as claim 24 above.
Claims 21–23 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou in view of Todoriki, Willemain, and Xu, and further in view of Thie et al., ("Introduction to Linear Programming and Game Theory," 2011, [Online], Third Edition, Available: http://openlibrary.org/books/OL38359821M/Introduction_to_Linear_Programming_and_Game_Theory, (Year: 2017)), hereinafter “Thie”.
Regarding claim 21, Zhou in view of Todoriki, Willemain, and Xu teaches all the limitations of claim 1.
Zhou in view of Todoriki, Willemain, and Xu does not appear to explicitly teach:
defining, by the number of processor units, an objective to maximize synthetic crude oil production in an oil sand plant while maintaining levels of intermediate products in storage tank using the objective function from the recreated regression model.
However, Thie teaches:
defining, by the number of processor units, an objective to maximize synthetic crude oil production in an oil sand plant while maintaining levels of intermediate products in storage tank using the objective function from the recreated regression model (Thie pgs. 38–39: “To formulate a mathematical model, we must first, as before, assign variables to represent the amounts of each activity that the dealer performs. Since, at the beginning of each month, the dealer must decide on how much oil to buy, distribute, and store during that month, three variables will be needed for each period. In particular, let Pi denote the number of gallons of oil purchased by the dealer during Month i,
where i— 1,2,3. Similarly, let D, represent the number of gallons of oil distributed during Month i and 5, the number of gallons in storage at the end of the month”—[wherein this limitation merely links the machine learning methods to the oil production space]).
The methods of Zhou, the teachings of Thie, and the instant application are analogous art because they pertain to using regression techniques with regard to oil.
It would be obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the methods of Zhou with the teachings of Thie to provide for an objective function to help compute the optimization solution specifically in the oil production space. One would be motivated to do so to analyze the solution to aid machine learning models being used in the oil space (Thie Pg. 38: “The operation of many systems or processes can be divided into distinct time periods that allow for the flexibility of activities during each period and are such that decisions for one period affect not only that period but also subsequent periods”).
Regarding claims 22–23, although varying in scope, the limitations of claims 22–23 are substantially the same as the limitations of claim 21, above. Thus, claims 22–23 are rejected using the same reasoning and analysis as claim 21 above, respectively.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/N.B.S./Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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