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 .
Response to Arguments
Claim Interpretation under 35 USC 112(f)
Applicant has amended the claims such that the claims no longer invoke 112(f) interpretation.
Claim Rejections – 35 USC 112
Applicant has amended the claims at issue and the previous rejections have therefore been withdrawn.
Claim Rejections – 35 USC 101
Applicant’s arguments, filed 5/1/2026, with respect to 35 U.S.C. 101 have been fully considered and are persuasive. The claims recite “using sensing data obtained from sensors of a production plant” and “adjusting an input to the production plant”, wherein “adjusting” is interpreted as “to bring the parts of to a true or more effective relative position” (Merriam-Webster definition C), such that the claims are integrated into a practical application. The 101 rejections of claims 1-6 have therefore been withdrawn.
Prior Art Rejections
Applicant’s arguments, filed 5/1/2026, with respect to the rejection(s) of claim(s) 1-6 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of new prior art.
Claim Objections
Claims 1, 3-6 are objected to because of the following informalities:
Claim 1 line 13, change “perfoming operation processing” to “performing operation processing”;
Claim 1 line 14, change “where a conditon is changed” to “where a condition is changed”;
Claim 1 line 16, change “sensing data obained” to “sensing data obtained”;
Claim 1 line 22, change “is futher configured” to “is further configured”; and
Claims 5 and 6 are similarly objected as claim 1 for typographical errors.
Claim 3 line 2, change “futher configured” to “further configured”.
Claim 4 line 2, change “futehr” to “further”.
Appropriate correction is required.
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.
Claims 1-3, 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Takano et al. (US 20180314964 A1, hereinafter “Takano”, provided in IDS filed 05/06/2024) in view of Tibshirani (Regression Shrinkage and Selection Via the Lasso, hereinafter “Tibshirani”, provided in IDS filed 10/21/2022) in further view of Phan et al. (US 20210026314 A1, hereinafter “Phan”).
As per claim 1, Takano teaches A regression analysis device comprising:
A storage device (Takano: Fig. 2 element 56);
and processing circuitry (Takano: Fig. 2 element 51) configured to: read out, from the storage device storing training data used as a target variable and an explanatory variable of a regression model (Takano: [0054]; record acquisition unit acquiring records shown in Table 1) and a constraint condition defining in advance whether the explanatory variable should be varied positively or negatively to vary the target variable in a positive direction or a negative direction, the training data and the constraint condition (Takano: [0013]; [0024]; [0053]);
and repeatedly update, using the training data, coefficients of the explanatory variable in the regression model to minimize a cost function (Takano: [0057], maximizing likelihood estimation corresponds to minimizing cost)
However, while Takano discloses any maximum likelihood algorithm may be used ([0060]), and that the method may use a penalty term ([0009]), Takano does not explicitly disclose the regularization, or penalty term, minimizing a cost function nor the processes is used in production plants. Thus, Takano does not teach including a regularization term that increases a cost in a case where the constraint condition is contravened; and perfoming operation processing using the regression model to predict a prediction value in a case where a conditon is changed, by calculating the prediction value of a production plant by using the regression model with the updated coefficients of the explanatory variable, wherein a formula for the regression model is construed using sensing data obained from sensors of a production plant to evaluate accuracy, wherein the sensing data includes values from sensors in the production plant, and wherein the updating of the coefficients of the explanatory variable in the regression model is performed under sign constraint, wherein the condition is a condition of the production plant, and the processing circuitry is futher configured to adjust an input to the production plant based on the calculated prediction value using the regression model and a desired value of the production plant.
Tibshirani teaches including a regularization term that increases a cost in a case where the constraint condition is contravened (Tibshirani: Section 2.1).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify, with a reasonable expectation of success, the estimation unit of Takano with the teachings of Tibshirani. One would have been motivated to combine these references because both references disclose optimizing regression models (Tibshirani: section 8 second paragraph), and Tabshirani’s teachings of absolute value constraints may be useful in statistical estimation problems (Tabshirani: pg. 286, last paragraph).
Takano/Tibshirani does not teach and perfoming operation processing using the regression model to predict a prediction value in a case where a conditon is changed, by calculating the prediction value of a production plant by using the regression model with the updated coefficients of the explanatory variable, wherein a formula for the regression model is construed using sensing data obained from sensors of a production plant to evaluate accuracy, wherein the sensing data includes values from sensors in the production plant, and wherein the updating of the coefficients of the explanatory variable in the regression model is performed under sign constraint, wherein the condition is a condition of the production plant, and the processing circuitry is futher configured to adjust an input to the production plant based on the calculated prediction value using the regression model and a desired value of the production plant.
Phan teaches and perfoming operation processing using the regression model to predict a prediction value in a case where a conditon is changed, by calculating the prediction value of a production plant by using the regression model with the updated coefficients of the explanatory variable (Phan: [0004]; [0082], wherein Phan teaches prediction results are used to generate regression models; [0085], wherein the system is adjusted when targets and/or constraints change),
wherein a formula for the regression model is construed using sensing data obained from sensors of a production plant to evaluate accuracy (Phan: [0083]),
wherein the sensing data includes values from sensors in the production plant (Phan: [0083]), and wherein the updating of the coefficients of the explanatory variable in the regression model is performed under sign constraint (Phan: [0085]),
wherein the condition is a condition of the production plant (Phan: [0087]),
and the processing circuitry is futher configured to adjust an input to the production plant based on the calculated prediction value using the regression model and a desired value of the production plant (Phan: [0085]).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify, with a reasonable expectation of success, the system of Takano with the production plant system of Phan. One would have been motivated to combine these references because both references disclose optimization using regression models, and applying the optimization method on to production plants allows devising optimal operational plans and identifying potential failure events (Phan: [0034]).
As per claim 2, Takano/Tibshirani/Phan further teaches The regression analysis device according to claim 1, wherein the regularization term increases the cost in accordance with a sum of absolute values of the coefficients in an interval where the coefficients are positive or negative depending on the constraint condition (Tibshirani: Section 2.1).
As per claim 3, Takano/Tibshirani/Phan further teaches The regression analysis device according to claim 1, wherein the processing circuitry is futher configured to make the coefficients zero in a case where the coefficients do not converge to a value satisfying the constraint condition (Takano: Table 3; [0063]-[0064]).
As per claim 5, the claim is directed to a method that implements the same or similar features as the device of claim 1, and is therefore rejected for at least the same reasons therein.
As per claim 6, the claim is directed to non-transitory computer readable medium that implements the same or similar features as the device of claim 1, and is therefore rejected for at least the same reasons therein.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Takano/Tibshirani/Phan in further view of Hunt et al. (US 20180336484 A1, hereinafter “Hunt”).
As per claim 4, Takano/Tibshirani/Phan teaches The regression analysis device according to claim 1,
However, while Takano suggests any maximum likelihood algorithm may be used ([0060]), Takano does not explicitly list a proximal gradient method. Thus, Takano does not teach wherein the processing circuitry is futehr configured to update the coefficients by a proximal gradient method.
Hunt teaches wherein the processing circuitry is futehr configured to update the coefficients by a proximal gradient method (Hunt: [0052]).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify, with a reasonable expectation of success, the estimation unit of Takano with the proximal gradient method of Hunt. One would have been motivated to combine these references because both references disclose finding maximum likelihood, and combining prior art elements according to known methods to yield predictable results (method of performing maximum likelihood estimation).
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.
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/P.N.L./
Phat LeExaminer, Art Unit 2182 (571) 272-0546
/ANDREW CALDWELL/Supervisory Patent Examiner, Art Unit 2182
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