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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 8-9, 11-12, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bertola, Numa, et al. (“Methodology for selecting measurement points that optimize information gain for model updating,” Journal of Civil Structural Health Monitoring 13.6 (2023): 1351-1367; hereinafter “Bertola”).
Regarding Claim 1, Bertola teaches a system for intelligent selection of inputs for inference and quantification of uncertainty for active learning in a model (abstract and section 3.1—a system for selecting only informative sensor data for training is an active learning system), the system comprising:
a processor; a memory including instructions (section 1—an IoT system clearly comprises a processor and a memory including instructions) which, when executed by the processor, cause the system at least to perform:
computing an information gain by combining one or more metrics of interest and uncertainties in the inputs using information compression based on a first set of data as one or more pairs of values of inputs and outputs (sections 3.4 and 3.4.1—information gain is computed. Section 3.3.2 describes combining the information gain computation with uncertainty evaluation that removes non-informative measurement points. The information entropy is a metric of interest, as are the ranking, joint entropy, and threshold bounds discussed in section 3.4.2, and the error domain model falsification {EDMF} in section 3.5);
selecting one or more of the inputs, with a potential information gain higher than other inputs, to query the model and record values of one or more of the outputs (sections 3.4 and 3.4.1—inputs are selected that maximize the information gain);
computing, by the model, a measurement of performance outputs based upon the first set of data (section 3.5—updating the model comprises computing outputs and measuring performance so that the model can be updated to improve the outputs);
estimating the metrics of interest performance and quantifying the uncertainty associated with the estimation of the metrics of interest performance (section 3.5—EDMF is a metric of interest that is estimated);
updating the model based upon the estimated metrics of interest performance and quantification of the associated uncertainty (section 3.5); and
outputting the estimated metrics of interest performance and quantification of the associated uncertainty for a change to a process based upon the estimated metrics of interest performance and quantification of the associated uncertainty (section 3.6—the validation process can be seen as outputting the estimated metrics of interest and quantification of the associated uncertainty for a change to a process {i.e. a validation process}. The case study in sections 4-4.3 provide an example of outputting metrics of interest and uncertainty to the hierarchical algorithm for an excavation process).
Regarding Claim 12, Bertola teaches a processor-implemented method for intelligent selection of inputs for inference and quantification of uncertainty for active learning in a model (abstract and section 3.1—a method for selecting only informative sensor data for training is an active learning system). Bertola teaches the method comprising the steps of the present claim in the same manner as for claim 1, above.
Regarding Claims 8 and 19, Bertola teaches wherein the first set of data includes tabular data (section 3.1 and fig. 1—the data may be multiple sensor measurements over time, which can be represented as two-dimensional tabular data).
Regarding Claims 9 and 20, Bertola teaches wherein the first set of data includes design of experiment (DOE) points (section 3.3.2—the first set of data includes measurement points, which are filtered to remove non-informative points; the remaining points can be considered design of experiment points).
Regarding Claim 11, Bertola teaches wherein the one or more metrics of interest include one or more of the outputs (section 3.6—the validation process can be seen as outputting the estimated metrics of interest and quantification of the associated uncertainty for a change to a process {i.e. a validation process}. The case study in sections 4-4.3 provide an example of outputting metrics of interest and uncertainty to the hierarchical algorithm for an excavation process).
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 2-7, 10, and 13-18 are rejected under 35 U.S.C. 103 as being unpatentable over Bertola, as applied to claims 1 and 12, above, in view of Wang et al. (U.S. 2018/0246504, hereinafter “Wang”).
Regarding Claims 2 and 13, Bertola does not specifically teach wherein the first set of data includes manufacturing tolerances. However, Wang teaches a set of data that includes manufacturing tolerances (¶ [0002] and [0006]—the sensors may measure properties relative to manufacturing tolerances).
All of the claimed elements were known in Bertola and Wang and could have been combined by known methods with no change in their respective functions. It therefore would have been obvious to a person of ordinary skill in the art at the time of filing of the applicant’s invention to combine the manufacturing tolerances of Wang with the first set of data of Bertola to yield the predictable result of wherein the first set of data includes manufacturing tolerances. One would be motivated to make this combination for the purpose of ensuring the desired operation of a turbine or jet engine while reducing testing resources (Wang, ¶ [0002] – [0003]).
Regarding Claims 3 and 14, Bertola/Wang teaches wherein the manufacturing tolerances are uncertainties associated with one or more input values (this is the definition of a manufacturing tolerance, so it is inherently taught by Wang).
Regarding Claims 4 and 15, Bertola/Wang teaches wherein the one or more outputs are results of a simulation (Wang, fig. 3; ¶ [0054]).
Regarding Claims 5 and 16, Bertola/Wang teaches wherein the metrics of interest performance includes an engine performance output (Wang, ¶ [0053]).
Regarding Claims 6 and 17, Bertola/Wang teaches wherein the engine performance output includes a mechanical power of the engine (Wang, fig. 2; ¶ [0031] – [0032] and [0039]—sensors measure many operational characteristics of a turbine. The examiner takes official notice that mechanical power output is a well-known characteristic of a turbine engine and therefore an obvious quantity to measure and test).
Regarding Claims 7 and 18, Bertola/Wang teaches wherein the engine performance output includes a maximum efficiency of the engine (Wang, fig. 2; ¶ [0031] – [0032] and [0039]—sensors measure many operational characteristics of a turbine. The examiner takes official notice that efficiency is a well-known characteristic of a turbine engine output and therefore an obvious quantity to measure and test).
Regarding Claim 10, Bertola/Wang teaches wherein the first set of data includes measurements of engine performance output (Wang, fig. 2; ¶ [0031] – [0032] and [0039]—sensors measure many operational characteristics of a turbine. The examiner takes official notice that engine performance output is a well-known characteristic of a turbine engine and therefore an obvious quantity to measure and test).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. This art includes:
Branchaud-Charron et al. (U.S. 2021/0241135) teaches determining information gain of items in a labeled dataset and uncertainty of a machine learning model trained using the labeled dataset
Chopra et al. (U.S. 2020/0134367) teaches a method of interactive feature selection using information gain to build a machine learning model
Mermoud et al. (U.S. 2024/0305542) teaches active learning using information gain to select tests to run on a machine learning model with an uncertainty estimate
Omuya, Erick Odhiambo, George Onyango Okeyo, and Michael Waema Kimwele (“Feature selection for classification using principal component analysis and information gain,” Expert Systems with Applications 174 (2021): 114765) teaches selecting features for a classifier using principal component analysis and information gain
A. Preston, Y. Li, F. Sauer and K. -L. Ma, (“Visual Analysis of Simulation Uncertainty Using Cost-Effective Sampling,” 2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV), Berlin, Germany, 2018, pp. 1-11, doi: 10.1109/LDAV.2018.8739182) teaches a visualization tool for uncertainty in a simulation
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAL W SCHNEE whose telephone number is (571) 270-1918. The examiner can normally be reached M-F 7:30 a.m. - 6:00 p.m.
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/HAL SCHNEE/Primary Examiner, Art Unit 2129
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