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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2023-148400, filed on 09/13/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/08/2024 and 09/05/2025 were considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6 are rejected under 35 U.S.C. 101 because the claimed invention in each of these claims is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
“A failure probability evaluation apparatus that targets on a component used in a plurality of machines to evaluate a failure probability of the component for each of the machines, the failure probability evaluation apparatus comprising:
a maintenance history database configured to store maintenance history data of the component in the plurality of machines;
an operation database configured to store operation data acquired in time series by a sensor of each of the plurality of machines; and
a calculation apparatus configured to identify, using the maintenance history database and the operation database, a failure probability function of the component, and to calculate, using the identified failure probability function, the failure probability of the component for each of the machines, wherein
the calculation apparatus is configured to calculate, using a damage model with the operation data as a parameter, a progression of damage to the component after installation of the sensor, learn the progression of the damage to the component after the installation of the sensor and estimate a progression of damage to the component before the installation of the sensor,
calculate accumulated damage to the component based on at least one of the progression of the damage to the component after the installation of the sensor and the progression of the damage to the component before the installation of the sensor, and
identify, using the accumulated damage, the failure probability function.
The claim limitations considered to fall within in the abstract idea are highlighted in bold font above; the remaining features are “additional elements.”
Step 1 of the subject matter eligibility analysis entails determining whether the claimed subject matter falls within one of the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. Claim 1 recites a process and is therefore falls within a statutory category.
Step 2A, Prong One of the analysis entails determining whether the claim recites a judicial exception such as an abstract idea. Under a broadest reasonable interpretation, the highlighted portion of claim 1 comprises process steps that fall within the abstract idea judicial exception. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, the highlighted subject matter falls within the mental processes category.
Individually and collectively, the steps:
“targets on a component used in a plurality of machines” to “evaluate a failure probability of the component for each of the machines”;
“a maintenance history database configured to store maintenance history data of the component in the plurality of machines”;
“an operation database configured to store operation data acquired in time series by a sensor of each of the plurality of machines”; and
“identify, using the maintenance history database and the operation database, a failure probability function of the component”;
“calculate, using the identified failure probability function, the failure probability of the component for each of the machines”;
“calculate, using a damage model with the operation data as a parameter, a progression of damage to the component after installation of the sensor, learn the progression of the damage to the component after the installation of the sensor and estimate a progression of damage to the component before the installation of the sensor”;
“calculate accumulated damage to the component based on at least one of the progression of the damage to the component after the installation of the sensor and the progression of the damage to the component before the installation of the sensor”; and
“identify, using the accumulated damage, the failure probability function”
may be performed as mental processes. Targeting a component to evaluate is an analysis, which may be performed as mental processes. The inclusion of databases configured to store data is the collection of information, which may be performed as mental processes. Identifying a failure probability function is an analysis, which may be performed as mental processes. Calculating the failure probability and a progression of damage, are analysis, which may be performed as mental processes. Learning the progression of the damage is an analysis, which may be performed as mental processes. Estimating a progression of damage to the component before the installation of the sensor is an analysis, which may be performed as mental processes. Calculating accumulated damage is an analysis, which may be performed as mental processes. Identifying the failure probability function is an analysis, which may be performed as mental processes. The type of high-level information collecting and analyzing data recited in these elements has been found by the Federal Circuit to constitute patent ineligible matter (see Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind).
Step 2A, Prong Two of the analysis entails determining whether a claim includes additional elements that integrate the recited judicial exception (e.g., abstract idea) into a practical application. In view of the various considerations encompassed by the Step 2A, Prong Two analysis, claim 1 does not include additional elements that integrate the recited abstract idea into a practical application. Based on the individual and collective limitations of claim 1, applying a broadest reasonable interpretation, the most significant of such considerations appear to include: improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)); applying the judicial exception with, or by use of, a particular machine (MPEP 2106.05(b)); and effecting a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)).
Regarding improvements to the functioning of a computer or other technology, none of the “additional elements” in any combination appear to integrate the abstract idea to technologically improve any aspect of a system that may be used to implement the highlighted steps such a generic computer. Instead, the claim recites additional elements, such as “a failure probability evaluation apparatus” and “calculation apparatus” is recited generically.
Regarding application of the judicial exception with, or by use of, a particular machine, the additional elements such as “a failure probability evaluation apparatus”, “a component used in a plurality of machines” and “calculation apparatus” are not utilized as a particularized manner of implementing the abstract idea process steps.
Regarding effectuation of a transformation or reduction of a particular article to a different state or thing, the claim includes no such transformation or reduction. Instead, the claim as a whole entails collecting and analyzing information of components.
The above additional elements, considered individually and in combination with the claim elements reciting an abstract idea do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under Step 2B.
Regarding Step 2B, independent claim 1, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are generically recited and are well-understood/conventional in the relevant art as evidenced by the prior art of record as indicated in the rejections under 35 U.S.C. §103.
Independent claim 1 is therefore not patent eligible.
Dependent claims 2-6 provide additional features/steps which are part of an expanded algorithm that includes the abstract idea of the independent claims (Step 2A, Prong One). None of dependent claims 2-6 recite additional elements that integrate the abstract idea into practical application (Step 2A, Prong Two). Claims 2 further details identifying the damage model, which may be performed as mental processes. Claim 4 recites a user interface, which is recited generically and is not utilized in a particularized manner. Claim 5 further details calculating accumulated damage, predicting accumulated damage, which may be performed as mental processes. Claim 6 recites changing the prediction of accumulated damage, which may be performed as mental processes, and does not amount to a transformation of an article. Claims 2-6 all fail the “significantly more” test under the step 2B for the same reasons as discussed with regards to the independent claims. The dependent claims 2-6 therefore are also ineligible subject matter.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, and 4-6 is/are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Fenstermacher et al. (US 20220187817 A1, provided by applicant)
Regarding claim 1, Fenstermacher teaches A failure probability evaluation apparatus (Fig. 3) that targets on a component used in a plurality of machines ([0037] lines 1-10, “Substations generally include many different pieces of equipment arranged in a way that allows the substation to carry out the function or functions desired. These pieces of equipment within a substation may generally be referred to as assets in the power industry, with some representative types of assets including, for instance, transformers, circuit breakers, switches, relays, capacitor banks, lightning arrestors, and feeder units, among many other different types of equipment.”) to evaluate a failure probability of the component for each of the machines (Fig. 7, step 702e), the failure probability evaluation apparatus comprising:
a maintenance history database configured to store maintenance history data of the component in the plurality of machines ([0053] lines 1-6, “Yet another type of data source 304 may take the form of an asset maintenance data source 304C, which may comprise a computing system that is configured to generate and/or receive data related to the maintenance of a plurality of assets—referred to herein as “maintenance data”—and then send this maintenance data to asset data platform 302”);
an operation database configured to store operation data acquired in time series by a sensor of each of the plurality of machines ([0047] lines 1-7, “The operating data that is captured and sent by asset 304A may take various forms. As one possibility, an asset's operating data may include sensor data that comprises time-series measurements for certain operating parameters of the asset, examples of which may include temperature, pressure, vibration, fluid level, voltage, current, magnetic field, electric field, among many others.”); and
a calculation apparatus ([0039] lines 8-12, “computing system 302 may generally serve as an “asset data platform” that is configured to perform functions to facilitate the monitoring, analysis, and/or management of various types of assets, which, as mentioned, may take various forms.”) configured to identify, using the maintenance history database and the operation database, a failure probability function of the component, and to calculate, using the identified failure probability function, the failure probability of the component for each of the machines ([0064] lines 1-12, “The asset-related data that is output for receipt by client station 306A may take various forms. As one example, this asset-related data may include a restructured version of asset-related data that was received by asset data platform 302 from one or more data sources 304 (e.g., operating data, maintenance data, etc.). As another example, this asset-related data may include data that is generated by asset data platform 302 based on the asset-related data received from data sources 304, such as data resulting from the data analytics operations performed by asset data platform 302 (e.g., derived failure probabilities, recommendations, alerts, etc.).”), wherein
the calculation apparatus is configured to calculate, using a damage model with the operation data as a parameter, a progression of damage to the component after installation of the sensor ([0135] lines 1-8, “In other cases, the asset data platform 302 may first obtain data indicative of an initial “wearout” distribution for an asset or components of an asset and then use that initial wearout distribution or initial wearout distributions to derive an initial failure distribution. A wearout distribution generally refers to a data set that attempts to relate the useful life of an asset or a component of an asset as a function of the usage of the asset or asset component.”; lines 21-28, “In order to derive an initial failure distribution for an asset or a component of an asset, the asset data platform may refer to historical operating data for the asset as well as the historical and future maintenance schedule for the asset or the component thereof and attempt to predict where on the wearout distribution the asset or component is”) , learn the progression of the damage to the component after the installation of the sensor and estimate a progression of damage to the component before the installation of the sensor ([0151] “For instance, proceeding to block 702e, the asset data platform may evaluate any relationship between the asset-related data obtained for the given asset and asset-related data obtained for other assets in the same substation as the given asset and determine whether and to what extent to further adjust the derived failure probability for the given asset based on this relationship. In this way, the asset data platform 302 may consider an additional context in which the given asset has been operating in order to derive a more accurate measure of the probability of failure of the given asset.”), calculate accumulated damage to the component ([0144] “At block 702c, the asset data platform may evaluate the asset-related data obtained for the given asset in order to determine whether and to what extent to adjust the one or more initial failure distributions associated with the given asset, which results in one or more adjusted failure distributions. In embodiments in which the asset data platform obtains a single, asset-level initial failure distribution, the platform may evaluate the asset-related data obtained for the given asset and determine to adjust the single, asset-level initial failure distribution in some way based on the asset-related data. In embodiments in which the asset data platform obtains a plurality of component-level initial failure distributions that are each associated with a different possible failure mechanism associated with the given asset, the platform may evaluate the asset-related data obtained for the given asset and determine to adjust one or more of the plurality of component-level initial failure distributions, perhaps even adjusting these failure distributions in different ways, based on the asset-related data.”) based on at least one of the progression of the damage to the component after the installation of the sensor and the progression of the damage to the component before the installation of the sensor ([0154] “To help illustrate an example adjustment of an initial failure probability, FIG. 8 depicts a curve 802 and an adjusted curve 804. As mentioned, curve 802 may represent an initial failure distribution. Curve 804 may represent an adjusted failure distribution. For instance, at time t=0, the asset data platform may engage in the foregoing process and determine that the initial failure distribution (represented by curve 802) should be adjusted to reflect curve 804. Thus, where the failure probability of a given asset based on the initial failure distribution (represented by curve 802) may have yielded a failure probability of 40%, the failure probability of the given asset based on the adjusted failure distribution (represented by curve 804) may yield a failure probability of 70%. Other ways of adjusting the initial failure distribution for the given asset based on the evaluation of asset-related data may be possible as may other ways of deriving the respective failure probabilities for individual assets in a substation.”), and
identify, using the accumulated damage, the failure probability function (Fig. 7, step 702e; Fig. 8, adjusted failure distribution 804).
Regarding claim 2, Fenstermacher teaches The failure probability evaluation apparatus according to claim 1, wherein the calculation apparatus identifies the damage model ([0103] lines 1-5, “As one possibility, data analysis system 506 may create and/or execute predictive models related to asset operation based on asset-related data received from one or more data sources, such as predictive models that are configured to predict occurrences of failures at an asset.”).
Regarding claim 4, Fenstermacher teaches The failure probability evaluation apparatus according to claim 1, further comprising: a user interface ([0065] lines 1-6, “Along with the asset-related data that is output for receipt by client station 306A, asset data platform 302 may also output associated data and/or instructions that define the visual appearance of a front-end application (e.g., a graphical user interface (GUI)) through which the asset-related data is to be presented on client station 306A.”) configured to enable setting of a period from a present time ([0047] lines 14-22, “an asset's operating data may include data that has been derived from the asset's sensor data and/or abnormal-conditions data, examples of which may include “roll-up” data (e.g., an average, mean, median, etc. of the raw measurements for an operating parameter over a given time window) and “features” data (e.g., data values that are derived based on the raw measurements of two or more of the asset's operating parameters)”), wherein the calculation apparatus calculates, using the identified failure probability function, the failure probability of the component after the period elapses (Fig. 8), and displays the failure probability on the user interface (Fig. 7, step 710).
Regarding claim 5, Fenstermacher teaches The failure probability evaluation apparatus according to claim 4, wherein the calculation apparatus calculates the accumulated damage to the component ([0144] “At block 702c, the asset data platform may evaluate the asset-related data obtained for the given asset in order to determine whether and to what extent to adjust the one or more initial failure distributions associated with the given asset, which results in one or more adjusted failure distributions. In embodiments in which the asset data platform obtains a single, asset-level initial failure distribution, the platform may evaluate the asset-related data obtained for the given asset and determine to adjust the single, asset-level initial failure distribution in some way based on the asset-related data. In embodiments in which the asset data platform obtains a plurality of component-level initial failure distributions that are each associated with a different possible failure mechanism associated with the given asset, the platform may evaluate the asset-related data obtained for the given asset and determine to adjust one or more of the plurality of component-level initial failure distributions, perhaps even adjusting these failure distributions in different ways, based on the asset-related data.”) from an initial stage of the machine or a maintenance time point of the component to the present time (Fig. 8), learns the operation data to predict the operation data in the period ([0151] “For instance, proceeding to block 702e, the asset data platform may evaluate any relationship between the asset-related data obtained for the given asset and asset-related data obtained for other assets in the same substation as the given asset and determine whether and to what extent to further adjust the derived failure probability for the given asset based on this relationship. In this way, the asset data platform 302 may consider an additional context in which the given asset has been operating in order to derive a more accurate measure of the probability of failure of the given asset.”), and predicts, using the damage model and the operation data in the period, the accumulated damage to the component in the period ([0103] lines 1-5, “As one possibility, data analysis system 506 may create and/or execute predictive models related to asset operation based on asset-related data received from one or more data sources, such as predictive models that are configured to predict occurrences of failures at an asset.”), and calculates, using the identified failure probability function, the accumulated damage to the component from the initial stage of the machine or the maintenance time point of the component to the present time, and the accumulated damage to the component in the period, the failure probability of the component after the period elapses ([0154] “To help illustrate an example adjustment of an initial failure probability, FIG. 8 depicts a curve 802 and an adjusted curve 804. As mentioned, curve 802 may represent an initial failure distribution. Curve 804 may represent an adjusted failure distribution. For instance, at time t=0, the asset data platform may engage in the foregoing process and determine that the initial failure distribution (represented by curve 802) should be adjusted to reflect curve 804. Thus, where the failure probability of a given asset based on the initial failure distribution (represented by curve 802) may have yielded a failure probability of 40%, the failure probability of the given asset based on the adjusted failure distribution (represented by curve 804) may yield a failure probability of 70%. Other ways of adjusting the initial failure distribution for the given asset based on the evaluation of asset-related data may be possible as may other ways of deriving the respective failure probabilities for individual assets in a substation.”).
Regarding claim 6, teaches The failure probability evaluation apparatus according to claim 5, wherein the calculation apparatus changes, based on information from an operation planning system that changes an operation plan of the machine ([0071] lines 1-11, “The asset-related data and/or instructions that are output for receipt by asset 306C may take various forms. As one example, asset data platform 302 may be configured to send asset 306C certain data that has been generated by asset data platform 302 based on the asset-related data received from data sources 304, such as data resulting from a data analytics operation performed by asset data platform 302 (e.g., derived failure probabilities, recommendations, alerts, etc.), in which case asset 306C may receive this data and then potentially adjust its operation in some way based on the received data.”; [0175] lines 11-15, “if the probability of failure of a substation is unacceptably high, the platform may suggest certain maintenance actions for an asset at that substation in order to bring the probability of failure of the substation down to an acceptable level.”), the prediction of the accumulated damage to the component in the period ([0103] lines 1-5, “As one possibility, data analysis system 506 may create and/or execute predictive models related to asset operation based on asset-related data received from one or more data sources, such as predictive models that are configured to predict occurrences of failures at an asset.”).
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.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fenstermacher as applied to claim 2 above, and further in view of Boucher et al. (US 20220355839 A1).
Regarding claim 3, Fenstermacher teaches The failure probability evaluation apparatus according to claim 2, wherein the calculation apparatus identifies the damage model such that a variation in a failure probability density function obtained by differentiating the failure probability (Fig. 8).
Fenstermacher does not teach the apparatus, wherein the calculation apparatus identifies the damage model such that a variation in a failure probability density function obtained by differentiating the failure probability function is minimized.
Boucher teaches an analogous apparatus (Abstract; Fig. 11), wherein the calculation apparatus identifies the damage model ([0101] “The predicting step comprises predicting for at least one element of the represented railway infrastructure system at least one of a degradation, a type of the degradation, a severity of the degradation, a type of a failure, a presence of an anomaly, a remaining useful lifetime, a performance and a probability of a failure, and thus generating prediction information for the at least one element.”; Fig. 9; [0112] “The prediction can also be made directly for a defined health indicator, such as by evaluating a model that is configured to estimate said defined health indicator directly based on input data.”) such that a variation in a failure probability density function obtained by differentiating the failure probability function is minimized ([0090] “The set of monitoring models can also comprise at least one reinforcement learning model. An reinforcement learning model is a model obtained from reinforcement machine learning. A reinforcement learning model can be optionally advantageous for optimisation purposes.”; [0022] The term “optimization” (or optimizing) is intended to comprise the (semi-) automated selection of a best available option (with regard to some criterion) or a set of best available options (with regard to some criterion or to multiple criteria) from some set of available options. It can be the best value(s) of some objective function given a defined domain (or input), including a variety of different types of objective functions and different types of domains.). The optiminisation is the minimizing of variation of the functions.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Fenstermacher to include the minimization of Boucher because it would yield predictable results, such as the model not deviating unrealistically from known information.
Conclusion
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/B.B.G./Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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