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 Amendment
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.
Claims 1, 5-11, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Weidl et al. (hereinafter Weidl), US 2005/0015217 A1, in view of Sharma et al. (hereinafter Sharma), “DoWhy: An End-to-End Library for Causal Inference,” (2020).
Weidl was disclosed in an IDS dated 4/20/2022.
Regarding independent claim 1, Weidl discloses a decision support system for an industrial plant ([0041], [0043], [0049], [0123-0125], [0134] root cause analyser entity 3 used for decision support on control and/or maintenance activities for a facility such as a pulp and paper mill 2), the decision support system including a controller, the controller being configured and operating to ([0043] analyser entity 3 comprise appropriate data processor as a means adapted to processing data based on object oriented data processing techniques): obtain a causal graph modeling causal assumptions relating to conditional dependence between variables in the industrial plant ([0041], [0054-0055] inference engine perform simultaneous processing of the hypotheses (modeling causal assumptions) can be facilitated by use of causally oriented graphical models (obtain a causal graph) wherein the inference engine accesses evidence including computed physical variables of the facility control system (relating to conditional dependence between variables in the industrial plant)); obtain observational data relating to operation of the industrial plant ([0061], [0088-0090] using hierarchically organised data models comprise collecting selections of user of observed failure chosen from a number of options in the failure tree checklist relating to operation of the facility); and perform causal inference ([0132] the inference engine 21 calculate probable root cause hypothesis) using the causal graph ([0135] the analysis above describes automated creating of causally oriented acyclic BN graphs) and the observational data to estimate at least one causal effect relevant for making decisions when operating the industrial plant ([0090], [0127-0130] disclose user manually select thereafter check all symptoms for the selected hypothesis (and the observational data) which provides at least part of the symptoms used as additional evidences to make the reasoning procedure more accurate (to estimate) in the root cause analysis as an indication whether the facility is operating as its optimal efficiency (at least one causal effect relevant for making decisions when operating the industrial plan)); wherein estimating at least one causal effect ([0129] an indication obtained whether the facility is operating as its optimal efficiency (wherein estimating at least one causal effect) comprises using conditional probabilistic formulae to estimate a strength of a causal relationship between two of the variables ([0065]-[0067], [0072] describes estimating relationship strengths using conditional probability tables and probabilistic methods to determine the probability that one node “causes” another); and presenting at least one causal effect ([0129], [0134] display rankings of possible root cause to provide improved operator guidance on control and/or maintenance activities, necessarily improving towards optimal efficiency of facility operation).
Weidl does not expressly teach wherein performing the causal inference comprises: identifying the at least one causal effect relevant for making the decisions when operating the industrial plant based on an input query;
estimating the at least one causal effect;
validating the at least one causal effect, wherein validating the at least one causal effect comprises performing data subset validation and/or performing placebo treatment by re-estimating the strength of the causal relationship using observational data in which data including a variable is replaced with data in which the same variable is not present.
However, Sharma teaches wherein performing causal inference comprises: identifying at least one causal effect relevant based on an input query (Abstract, Section 1, Section 2 describes DoWhy library for causal inference comprising step 2) identifying the target estimand (wherein performing causal inference comprises: identifying at least one causal effect) based on a causal question or input query); estimating the at least one causal effect (Abstract, Section 1 describe step 3) estimating the effect using statistical estimators for estimating the identified estimand), validating the at least one causal effect (Abstract, page 4 describe robustness checks of the estimand in its “Refute” step 4), wherein validating the at least one causal effect comprises performing data subset validation and/or performing placebo treatment by re-estimating strength of causal relationship using observational data in which data including a variable is replaced with data in which the same variable is not present (pages 4-5 describes “Placebo Treatment” wherein treatment variable is replaced with an independent random variable to see if the effect disappears and “Data Subset Validation” which tests if the effect changes when using a randomly selected subset of data).
Because Weidl and Sharma address performing causal inference, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings wherein performing causal inference comprises: identifying at least one causal effect relevant based on an input query; estimating the at least one causal effect; validating the at least one causal effect, wherein validating the at least one causal effect comprises performing data subset validation and/or performing placebo treatment by re-estimating strength of causal relationship using observational data in which data including a variable is replaced with data in which the same variable is not present as suggested by Sharma into Weidl’s decision support system for the industrial plant, with a reasonable expectation of success, to teach wherein performing the causal inference comprises: identifying the at least one causal effect relevant for making the decisions when operating the industrial plant based on an input query; estimating the at least one causal effect, wherein estimating the at least one causal effect comprises using conditional probabilistic formulae to estimate a strength of a causal relationship between two of the variables; validating the at least one causal effect, wherein validating the at least one causal effect comprises performing data subset validation and/or performing placebo treatment by re-estimating the strength of the causal relationship using observational data in which data including a variable is replaced with data in which the same variable is not present; and presenting at least one causal effect. This modification would have been motivated by the desire to making sensitivity tests critical to gaining confidence in provided results (Sharma Section 1) wherein adding placebo tests and subset validation would enhance the reliability and objectivity of root cause findings.
Regarding dependent claim 5, Weidl, in view of Sharma, teach the decision support system of claim 1, wherein presenting the at least one causal effect comprises outputting the at least one causal effect to a human plant operator and/or outputting the at least one causal effect to an automated plant operation system (see Weidl [0129], [0134] display rankings of possible root cause to provide improved operator guidance on control and/or maintenance activities, necessarily improving towards optimal efficiency of facility operation).
Regarding dependent claim 6, Weidl, in view of Sharma, teaches the decision support system of claim 1, further configured to perform root-cause analysis in relation to a given variable in the industrial plant (see Weidl [0129] performing a root cause analysis in relation to an indication of optimal efficiency of the facility) to identify which of other variables in the causal graph are most likely to influence the given variable (see Sharma pages 1-2 describes the framework for identifying influences through is “Identify” step and teaches “II. Identify the causal estimand. Based on the casual graph, DoWhy finds all possible ways of identifying a desired causal effect based on the graphical model”. This step uses graph-based criteria and do-calculus to find potential ways find expressions that can identify the causal effect and specifically includes direct and indirect effect identification to determine how variables influence one another).
Regarding dependent claim 7, Weidl, in view of Sharma, teach the decision support system of claim 6, wherein performing the root-cause analysis for the given variable (see Weidl [0048]-[0053] teaches an inference engine that can "calculate the probable root causes (hypotheses) starting from the observed failure" and produce a "ranking of most probable root causes") comprises: performing the causal inference to estimate the strength of the causal relationship between the given variable and each of the other variables (see Sharma page 2, 4 teaches a formalized "Estimate" step using an API: "III. Estimate the causal effect. DoWhy supports methods based on both back-door criterion and instrumental variables... to estimate the target estimand).
Regarding dependent claim 8, Weidl, in view of Sharma, teach the decision support system of claim 1, further configured to find corrective actions capable of effecting changes in a given variable (see Weidl [0133] In addition, the causality structure of the network allows examination of the impact of intended interventions (further configured to find corrective actions) in order to predict what will happen if (effecting changes in) an action is taken (a given variable)).
Regarding dependent claim 9, Weidl, in view of Sharma, teach the decision support system of claim 8, wherein finding the corrective actions (see Weidl [0133] In addition, the causality structure of the network allows examination of the impact of intended interventions in order to predict what will happen if (effecting changes in) an action is taken) comprises using the causal graph and the observational data to identify controlled variables in the industrial plant that exhibit a causal relationship with the given variable (see Sharma pages 2-3 teaches that the teaches that the "Identify" step is used to find "expressions that can identify the causal effect" based on the causal graph, explicitly identifying the relationship between an Action (controlled variable) and the Outcome shown in Figure 1).
Regarding dependent claim 10, Weidl, in view of Sharma, teach the decision support system of claim 8, wherein finding the corrective actions (see Weidl [0133] examination involving predicting what will happen if actions taken) comprises: performing the causal inference (see Weidl [0132-0133] the examination of predicting what will happen is allowed by the causality structure of the network in addition to the inference engine 21 calculating the probable root cause (comprises performing the causal inference)) to estimate the strength of the causal relationship between the given variable and each of other variables in the causal graph that is a controlled variable (see Sharma page 1, 4 teaches using statistical methods for "estimating the identified estimand" to determine the size of the effect an action has on an outcome to determine action in the optimal sequence has the highest impact on the target variable).
Regarding dependent claim 11, Weidl, in view of Sharma, teach the decision support system of claim 1, further configured to perform what-if analysis (see Weidl [0133] In addition, the causality structure of the network allows examination of the impact of intended interventions) to identify one or more possible side effects of changing a controlled variable in the industrial plant (see Weidl [0041], [0133] very useful for control of complex processes of the facility (in the industrial plant) in order to predict what will happen including serious unwanted or dangerous consequences (to identify one or more possible side effects) if an action is taken (of changing a controlled variable)).
Regarding independent claim 13, this is a method claim that is substantially the same as the system of claim 1. Thus, claim 13 is rejected for the same reasons as claim 1.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Weidl, in view of Sharma, as applied in the rejection of claim 11 above, and further in view of Robins et al. (hereinafter Robins), ”Marginal structural models and causal inference in epidemiology” (2000).
Regarding dependent claim 12, Weidl, in view of Sharma, teach all the elements of claim 11.
Weidl and Sharma do not expressly teach wherein performing the what-if analysis comprises: identifying first and second variables in the causal graph which each exhibit a causal relationship with a third variable but not with each other, and performing the causal inference to estimate a degree to which the causal effect of the first variable on the third variable is modified by the second variable.
However, Robins teaches wherein performing what-if analysis (Section 2 Counterfactuals in Point-Treatment Studies) comprises: identifying first and second variables in a causal graph which each exhibit a causal relationship with a third variable but not with each other (page 551 right column and FIGURE 2, c, showing the true causal graph identifying treatment A0 variable and measure covariate L0 variable that each exhibit a causal relationship with outcome Y variable but not with each other), and performing a causal inference to estimate degree to which a causal effect of the first variable on the third variable is modified by the second variable (page 556 right column Section 9 Effect Modification by Pretreatment Covariates suggest using the marginal structural models MSM to generalize (and performing a causal inference) to obtain an unbiased estimates (to estimate a degree to which a causal effect) of the treatment A variable has on outcome Y (of the first variable on the third variable) variable is modified by a vector V of the covariate L0 variable (is modified by the second variable) as in model 19).
Because Robins and Weidl, in view of Sharma, address the issue of what-if analysis with variables with in a causal graph, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings wherein performing what-if analysis comprises: identifying first and second variables in a causal graph which each exhibit a causal relationship with a third variable but not with each other, and performing a causal inference to estimate a degree to which a causal effect of the first variable on the third variable is modified by the second variable as suggested by Robins into Weidl and Sharma’s decision support system, with a reasonable expectation of success, to teach wherein performing the what-if analysis comprises: identifying first and second variables in the causal graph which each exhibit a causal relationship with a third variable but not with each other, and performing the causal inference to estimate a degree to which a causal effect of the first variable on the third variable is modified by the second variable. This modification would have been motivated by the desire to introduce a new class of causal models that allow for improved adjustment of confounding situations (Robins page 550 Abstract).
Response to Arguments
Applicant’s claim amendments and Remarks filed 12/1/2025 regarding the 35 U.S.C. 112(b) rejections set forth in Office Action dated 9/18/2025 are persuasive, consequently, said 35 U.S.C. 112(b) rejections are hereby withdrawn.
Applicant’s claim amendments and Remarks filed 12/1/2025 regarding the 35 U.S.C. 101 rejections set forth in Office Action dated 9/18/2025 when considered in light of Ex parte Desjardins made the claims patent eligible. While they utilize mathematical concepts, these concepts are integrated into a specific, practical application: an industrial decision support system with a validation mechanism that improves the system's ability to identify root causes and corrective actions. The "placebo treatment" limitation transforms the abstract concept of "inference" into a specific, concrete data-processing protocol that improves the technical functioning of the plant monitoring system, consequently, said 35 U.S.C. 101 rejections are hereby withdrawn.
Applicant’s Remarks filed 12/1/2025 traversing the 35 U.S.C. 102/103 rejections of the pending claims as well as the requested rejoinder of any withdrawn claim have been fully considered but are moot in light of the new grounds of rejection set forth above under 35 U.S.C. 103 over Weidl, in view of Sharma.
Conclusion
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 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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/KC CHEN/Primary Patent Examiner, Art Unit 2143
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