Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This Office Action is sent in response to Applicant’s Communication received on 07/16/2026 for application number 18/288,217.
Response to Amendments
3. The Amendment filed 07/16/2026 has been entered. Claims 1, 7, and 8 have been amended. Claims 1-8 remain pending in the application.
4. Applicant’s amendments to claims 1, 7, and 8 have been fully considered and are persuasive. The amendments provided to overcome the §101 rejection set forth in the previous office action are sufficient. Accordingly, the 35 U.S.C § 101 rejection of claims 1-8 is respectfully withdrawn.
Response to Arguments
Applicant argues that the cited references do not disclose the amended features in independent claims 1, 7, and 8. However, the argument is moot since this is a newly presented limitation, thus changes the scope of the claims. However, newly found references, Yoo and Salsbury, are applied.
Claim Rejections – 35 USC § 103
5. 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 of this title, 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.
6. Claims 1, 2, 4, 5, 7, and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Achin et al. (U.S. Patent Application Pub. No. US 20180046926 A1) in view of Yoo et al. (U.S. Patent Application Pub. No. US 20200151613 A1), and further in view of Salsbury et al. (U.S. Patent Application Pub. No. US 20200149757 A1).
Claim 1: Achin teaches an estimation device (i.e. a predictive modeling apparatus; para. [0060]) comprising:
at least one memory configured to store instructions (i.e. a memory configured to store processor-executable instructions; para. [0060]); and
at least one processor configured to execute the instructions to (i.e. a processor configured to execute the processor-executable instructions; para. [0060]):
estimate the relationships among a plurality of items (i.e. determining that changes in the values of the first and second variables are correlated, with a temporal lag between the changes in the value of the first variable and the correlated changes in the value of the second variable; para. [0022, 0044]), on the basis of at least one of: the state of a prediction model that receives input of past values of the items or past values of some of the items (i.e. predicting the values of one or more output variables (“targets”) at one or more future times based on the values of one or more input variables (“features”) at one or more past times. Such predictions problems may be referred to as “time-series prediction problems,” and predictive models that model such problems may be referred to as “time-series predictive models”; para. [0013, 0015, 0042]) and then outputs a prediction value for at least one of the items (i.e. identifying one or more of the variables as targets; para. [0042], a predictive model that receive values of variables at past times and predicts values of target variables at future times); and differences in the prediction accuracy of the prediction model with respect to different inputs (i.e. determining a first respective accuracy score of each of the fitted predictive models; shuffling values of a feature across respective observations included in the initial dataset, thereby generating a modified dataset representing a modified prediction problem; (d) determining a second respective accuracy score of each of the fitted predictive models, wherein the second accuracy score of each fitted model represents an accuracy with which the fitted model predicts one or more outcomes of the modified prediction problem; and (e) determining a respective model-specific predictive value of the feature for each of the fitted models, wherein the model-specific predictive value of the feature for each fitted model is based on the first and second accuracy scores of the fitted model; para. [0045, 0048, 0060]).
Achin does not explicitly teach determine whether the prediction value of the prediction model is correct or not by comparing a magnitude of an error with a predetermined threshold, wherein the prediction accuracy is the magnitude of the error between the prediction value of the prediction model and a correct value in training data for the prediction model; and automatically control, based on the estimation result, an estimation target of the estimation device.
However, Yoo teaches determine whether the prediction value of the prediction model is correct or not (i.e. when the prediction of the target model matches the correct answer, “1” may be tagged, and when the prediction of the target model does not match the correct answer, “0” may be tagged; para. [0104]) by comparing a magnitude of an error with a predetermined threshold (i.e. the learning apparatus 100 tags the first value (e.g., 0) when the prediction error of the data sample is greater than or equal to a threshold, and when the prediction error is less than the threshold, the second value (e.g., 1) may also be tagged with the label information; para. [0107]), wherein the prediction accuracy is the magnitude of the error between the prediction value of the prediction model and a correct value (i.e. the prediction error means a difference between a prediction value (that is, confidence score) and an actual value (that is, correct answer information); para. [0106]) in training data for the prediction model (i.e. the learning apparatus 100 acquires label information on the selected data sample group, and trains the target model using the label information; para. [0101, 0102]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Achin to include the feature of Yoo. One would have been motivated to make this modification because it provides an objective and consistent criterion for determining prediction accuracy, thereby improving the reliability of the accuracy comparisons.
However, Salsbury teaches automatically control (i.e. Controller 502 a is shown receiving a performance variable y as feedback from plant 504 via input interface 526 and providing a control input u to plant 504 via output interface 524. Controller 502 a can adjust the control input u to drive the gradient of performance variable y to zero; para. [0085, 0091]), based on the estimation result (i.e. using the adjusted correlation coefficient to modulate the control input includes determining a new value of the control input based on a previous value of the control input, the adjusted correlation coefficient, and a controller gain value … the extremum-seeking controller estimates a normalized correlation coefficient ρ relating the performance variable y to the control input u; para. [0010, 0051, 0052]), an estimation target of the estimation device (i.e. ESC system 500 is shown to include a plant 504 and an extremum-seeking controller 502 a; para. [0085, 0087-0089]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Achin and Yoo to include the feature of Salsbury. One would have been motivated to make this modification because it allows the identified relationship to be acted upon automatically rather than merely analyzed, thereby improving operation of the system.
Claim 2: Achin, Yoo, and Salsbury teach the estimation device according to claim 1. Achin further teaches wherein the at least one processor is configured to execute the instructions to estimate the relationship between a first item and a second item (i.e. determining that changes in the values of the first and second variables are correlated, with a temporal lag between the changes in the value of the first variable and the correlated changes in the value of the second variable; para. [0022, 0044]) based on the prediction accuracy of the prediction value of the first item output by the prediction model (i.e. determining a first respective accuracy score of each of the fitted predictive models, wherein the first accuracy score of each fitted model represents an accuracy with which the fitted model predicts one or more outcomes of the initial prediction problem; para. [0045]) for the input of past values in a combination of items including the first item and the second item (i.e. predicting the values of one or more output variables (“targets”) at one or more future times based on the values of one or more input variables (“features”) at one or more past times. Such predictions problems may be referred to as “time-series prediction problems,” and predictive models that model such problems may be referred to as “time-series predictive models”; para. [0013, 0015]), and the prediction accuracy of the prediction value of the first item output by the prediction model for the input of past values of items excluding the second item from the item combination (i.e. shuffling values of a feature across respective observations included in the initial dataset, thereby generating a modified dataset representing a modified prediction problem … performing feature engineering includes removing a particular feature from the initial dataset based on the particular feature having a low model-specific predictive value; para. [0045, 0053]).
Claim 4: Achin, Yoo, and Salsbury teach the estimation device according to claim 1. Achin further teaches wherein the at least one processor is further configured to execute the instructions to perform multiple weightings for multiple items (i.e. calculating the weighted combination of the model-specific predictive values includes assigning respective weights to the model-specific predictive values; para. [0049, 0050, 0051]) and performs training to predict the value of the same item using the prediction model at each weighting (i.e. the actions of the method further include: prior to determining the second accuracy scores of the predictive models, refitting the predictive models to the modified dataset representing the modified prediction problem; para. [0045, 0048]), and estimate the relationship between the items based on the prediction accuracy of the value of the same item by the trained prediction model with each weighting (i.e. determining a first respective accuracy score of each of the fitted predictive models, wherein the first accuracy score of each fitted model represents an accuracy with which the fitted model predicts one or more outcomes of the initial prediction problem; para. [0045, 0048]).
Claim 5: Achin, Yoo, and Salsbury teach the estimation device according to claim 1. Achin further teaches wherein the at least one processor is further configured to execute the instructions to explain the validity of an operation with respect to the item (i.e. Even when accurate predictive models for a prediction problem are available, it can be difficult to understand (1) the prediction problem itself, and (2) how specific predictive models produce accurate prediction results. Metrics of “feature importance” can facilitate such understanding; para. [0044, 0053, 0054]) based on the estimation result (i.e. the actions of the method further include determining a model-independent predictive value of the feature based on the model-specific predictive values of the feature; para. [0045, 0049]).
Claims 7 and 8 are similar in scope to Claim 1 and are rejected under a similar rationale.
Claim Rejections – 35 USC § 103
7. 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 of this title, 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.
8. Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Achin, Yoo, Salsbury, and further in view of Isozaki et al. (U.S. Patent Application Pub. No. US 20240070486 A1).
Claim 3: Achin, Yoo, and Salsbury teach the estimation device according to claim 1. Achin does not explicitly teach wherein the prediction model includes a weighting node that performs weighting for past values with a weight of each item, and training of the prediction model includes training of the weight of each item, and wherein to estimate the relationship between the items based on the weights in the trained prediction model.
However, Isozaki teaches wherein the prediction model includes a weighting node that performs weighting for past values with a weight of each item, and training of the prediction model includes training of the weight of each item, and wherein to estimate the relationship between the items based on the weights in the trained prediction model (i.e. the causal model estimation unit 132 estimates a causal model for explaining a feature amount acquired by learning for each nearest node … The causal model estimation unit 132 calculates causal information that is an index of the causal relationship between the nearest node Lm and each input variable Xn, thereby detecting the presence or absence and strength of the causal relationship between the variables … wherein the control unit selects the first explanatory variable serving as the positive reason on a basis of the causal graph related to the nearest node having a positive weight among the nearest nodes, and selects the first explanatory variable serving as the negative reason on a basis of the causal graph related to the nearest node having a negative weight among the nearest nodes; para. [0059, 0064, 0257]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Achin, Yoo, and Salsbury to include the feature of Isozaki. One would have been motivated to make this modification because it improves interpretability of the predictive model.
9. Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Achin, Yoo, Salsbury, and further in view of Munawar et al. (U.S. Patent Application Pub. No. US 20190385091 A1).
Claim 6: Achin teaches the estimation device according to claim 1. Achin further does not explicitly teach adjust a priority of an agent's action in reinforcement learning with respect to the estimation target, which is the output source of the past values, based on the estimation result.
However, Munawar teaches wherein the at least one processor is further configured to execute the instructions (i.e. computer processing system further includes a processor, operatively coupled to the memory, for running the program code to obtain, from an environment; para. [0006]) to adjust a priority of an agent's action in reinforcement learning (i.e. during exploration, selecting an action to be taken to the environment from the event buffer with a predetermined probability; para. [0004, 0026]) with respect to the estimation target, which is the output source of the past values, based on the estimation result (i.e. obtaining, from an environment, a given experience that includes an action, a state and a reward. The method further includes storing the given experience in an experience buffer responsive to a value of the reward included in the given experience exceeding a first threshold. The method also includes responsive to obtaining another experience having another reward that less than or equal to the first threshold, searching the experience buffer for a candidate experience with a similar state to the other experience and copying the candidate experience into an event buffer. The method additionally includes during exploration, selecting an action to be taken to the environment from the event buffer with a predetermined probability; para. [0004, 0023, 0024]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Achin, Yoo, and Salsbury to include the feature of Munawar. One would have been motivated to make this modification because it improves RL exploration efficiency.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Hirai (Pub. No. US 20040019392 A1), FIG. 2 explains the operation; the correlation coefficient is computed based on the processing data table 21 which collects relevant data from the operation database. For example, the efficiency is obtained for a turbine and written in the correlation table 22. In other words, the correlation analyzing unit 12 analyzes the operation data of the marked element and adjacent elements to the marked element when the plant is in operation and a change occurs.
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 extension fee 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 date of this final action.
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 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/TAN H TRAN/Primary Examiner, Art Unit 2141
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