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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 6 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The present claim recites “wherein the technical specification and the physical simulation model are trained with the infrastructure data.” It is unclear how a technical specification is trained. A specification is understood in the art as a document or similar data that describes technical details of a system or device. It is not a model of system that can be trained. For the purposes of examination under prior art, the examiner will disregard the claim to training the technical specification, and will interpret the present claim as though it recited “wherein is trained with the infrastructure data.”
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites “processing the scenario specific data to determine a vulnerability metric for a probability of failure for a component of the infrastructural system.” Processing data and determining a metric are mental processes that evaluate information and judge a value for a metric. The claim further recites “determining, based on the vulnerability metric, an in-situ failure risk of the infrastructural system incorporating a complexity of environmental conditions.” Determining a failure risk is also a mental process of judgement or evaluation that considers information (the vulnerability metric and environmental conditions) and estimates a failure risk. The claim further recites “determining, based on the vulnerability metric, a recommended maintenance task for the component of the infrastructural system.” This too is a mental process of judgement or evaluation that considers information and decides on a recommended task.
This judicial exception is not integrated into a practical application because the only result of the claim is “providing the in-situ failure risk of the infrastructural system and the recommended maintenance task for the component to the interface.” Providing information as an output is mere data gathering, which is insignificant extra-solution activity and therefore not significantly more than an abstract idea. The claims does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are “A computer program product stored on non-transitory machine-readable media and comprising machine executable instructions for implementing a method for assessing the resilience of an infrastructural system” and “receiving, from an interface, scenario specific data regarding conditions for the infrastructural system.” The recited computer program product is a generic computing component, so they do not render the claim more than an abstract idea. Receiving data is mere data gathering, which is insignificant extra-solution activity.
Claim 2 recites “wherein the probability of failure for the component of the infrastructural system is determined by processing the scenario specific data through an infrastructural system model, and wherein the environmental conditions are determined according to the infrastructural system model and the scenario specific data.” A model is a generic computing component, regardless of what the model is called, so it is not significantly more than an abstract idea; it is merely a method for processing or considering data. Processing data through a model is therefore also a mental process of judgement or evaluation. The claim does not recite a practical application and does not include any additional elements.
Claim 3 recites “wherein the infrastructural system model is trained with historical data that includes infrastructure data, spatial data, fragility features data, hazard features data, or historical risk feature data, regarding the infrastructural system.” The training is recited at a high degree of generality, so it is generic and therefore not significantly more than an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 4 recites additional details about data, which is an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 5 recites additional details about data (the fragility features data), which is an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 6 recites training at a high degree of generality, so it is generic and therefore not significantly more than an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 7 recites “wherein the hazard features data is determined based on a simulation model trained with forecast data or observations of natural hazards.” A model is a generic computing component, regardless of what the model is called, so it is not significantly more than an abstract idea; it is merely a method for processing or considering data. Processing data through a model is therefore also a mental process of judgement or evaluation. The claim does not recite a practical application and does not include any additional elements.
Claim 8 recites “wherein the simulation model comprises a weather prediction model, a numerical weather prediction model, or a global circulation model.” There models are all recited at a high degree of generality, so they are generic computing components that do not render the claim more than an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 9 recites “wherein the infrastructural system model is retrained with the determined probability of failure for the component of the infrastructural system.” The training is recited at a high degree of generality, so it is a generic computing component that does not render the claim more than an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 10 recites “wherein the infrastructural system model incorporates the complexity of environmental conditions by calculating failure risk scores based on various local or contemporaneous environmental factors, and wherein the contemporaneous environmental factors include soil properties, type and prevalence of local vegetation, drought conditions, or wind speeds.” The calculating step is recited at a high degree of generality, without detailing any specific calculations or equations. It is therefore a mental process of evaluation that is part of the determination of failure risk scores made by considering the recited types of data. The claim does not recite a practical application and does not include any additional elements.
Claim 11 recites further details about data, which is an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 12 recites further details about data, which is an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 13 recites “wherein the in-situ failure risk of the infrastructural system includes an aggregate risk of failure for a particular hazardous event.” An aggregate is simply combining two or more risk scores, performed using any method, so it is a mental process of judgement or evaluation. The claim does not recite a practical application and does not include any additional elements.
Claim 14 recites processing the scenario specific data to determine a plurality of analytical results related to the infrastructural system. Processing data is a mental process of judgement or evaluation, as is determining results. The claim does not recite a practical application and does not include any additional elements.
Claim 15 recites further details about data, which is an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 16 recites further details about data (the analytical results), which is an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 17 recites “wherein the infrastructural system comprises a collection of infrastructural components; and wherein the infrastructural components include electrical grid components, electrical generation components, natural gas distribution system components, water system components, telecommunication components, or transportation infrastructure.” The components are recited at a high degree of generality, so they are generic and therefore do not render the claim significantly more than an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 18 recites elements substantially similar to those of claim 1, so it recites an abstract idea without significantly more for the same reasons. The user device and electronic processor are generic computing components, so they also do not render the claim more than an abstract idea.
Claim 19 recites “wherein the electronic processor is configured to, before processing the scenario specific data, train an infrastructural system model with historical data that includes infrastructure data, spatial data, fragility features data, hazard features data, or historical risk feature data, regarding the infrastructural system.” The training is recited at a high degree of generality, so it is generic and therefore not significantly more than an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 20 recites elements substantially similar to those of claim 1, so it recites an abstract idea without significantly more for the same reasons.
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)(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.
Claims 1-8 and 12-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Boucher et al. (U.S. 2022/0355839, hereinafter “Boucher”).
Regarding Claim 1, Boucher teaches a computer program product stored on non-transitory machine-readable media and comprising machine executable instructions for implementing a method for assessing the resilience of an infrastructural system (¶ [0001], [0039], and [0163]), the method comprising:
receiving, from an interface, scenario specific data regarding conditions for the infrastructural system (¶ [0019], [0060] – [0061], and [0445]—scenario specific data regarding conditions for railway infrastructure are received from sensors);
processing the scenario specific data to determine a vulnerability metric for a probability of failure for a component of the infrastructural system (¶ [0078]—the scenario specific data are processed to determine degradation of at least one component);
determining, based on the vulnerability metric, an in-situ failure risk of the infrastructural system incorporating a complexity of environmental conditions (¶ [0078]—based on the degradation, a probability of failure is determined);
determining, based on the vulnerability metric, a recommended maintenance task for the component of the infrastructural system (¶ [0049] and [0136] – [0137]—recommended maintenance activities are recommended based on the vulnerability metric); and
providing the in-situ failure risk of the infrastructural system and the recommended maintenance task for the component to the interface (¶ [0140] – [0141] and [0478]—the failure risk and recommended maintenance are clearly output to a user interface to inform a user of the risk and recommendations).
Regarding Claim 18, Boucher teaches a system for assessing resilience of an infrastructural system (¶ [0001], [0020], and [0038] – [0039]), the system comprising:
a user device having a user interface, the user device including an electronic processor (¶ [0481] – [0488]) configured to:
receive, from the user interface, scenario specific data regarding conditions of the infrastructural system (¶ [0019], [0060] – [0061], and [0445]—scenario specific data regarding conditions for railway infrastructure are received from sensors);
process the scenario specific data to determine a vulnerability metric for a probability of failure for a component of the infrastructural system (¶ [0078]—the scenario specific data are processed to determine degradation of at least one component);
determine, based on the vulnerability metric, an in-situ failure risk of the infrastructural system incorporating a complexity of environmental conditions (¶ [0078]—based on the degradation, a probability of failure is determined);
determine, based on the vulnerability metric, a recommended maintenance task for the component of the infrastructural system (¶ [0049] and [0136] – [0137]—recommended maintenance activities are recommended based on the vulnerability metric); and
provide the in-situ failure risk of the infrastructural system and the recommended maintenance task for the component to the user interface (¶ [0140] – [0141] and [0478]—the failure risk and recommended maintenance are clearly output to a user interface to inform a user of the risk and recommendations).
Regarding Claim 2, Boucher teaches wherein the probability of failure for the component of the infrastructural system is determined by processing the scenario specific data through an infrastructural system model, and wherein the environmental conditions are determined according to the infrastructural system model and the scenario specific data (¶ [0041]).
Regarding Claims 3and 19, Boucher teaches wherein the infrastructural system model is trained with historical data that includes infrastructure data, spatial data, fragility features data, hazard features data, or historical risk feature data, regarding the infrastructural system (¶ [0041] and [0465]. ¶ [0456] describes historical risk feature data).
Regarding Claim 4, Boucher teaches wherein the scenario specific data is consistent with the historical data (¶ [0126] and [0464] – [0465]).
Regarding Claim 5, Boucher teaches wherein the fragility features data is determined based on a technical specification, a physical simulation model, or a plurality of fragility curves related to the infrastructural system, and wherein the fragility curves are generated based on observations, physical tests, or a computer simulation (¶ [0066] and [0110]).
Regarding Claim 6, Boucher teaches wherein the technical specification and the physical simulation model are trained with the infrastructure data (¶ [0126] and [0456]).
Regarding Claim 7, Boucher teaches wherein the hazard features data is determined based on a simulation model trained with forecast data or observations of natural hazards (¶ [0023] explains that the models are simulation models. ¶ [0063] describes meteorological data {i.e. forecast data}).
Regarding Claim 8, Boucher teaches wherein the simulation model comprises a weather prediction model, a numerical weather prediction model, or a global circulation model (¶ [0063], [0092], and [0110]—the model may operate on environmental data including weather data, so it comprises a weather prediction model).
Regarding Claim 12, Boucher teaches wherein the infrastructural system is defined according to a technical function, a geographical location, or political borders (¶ [0002] and [0030]—the infrastructural system is defined according to a technical function such as rail transport).
Regarding Claim 13, Boucher teaches wherein the in-situ failure risk of the infrastructural system includes an aggregate risk of failure for a particular hazardous event (¶ [0020] and [0096]).
Regarding Claim 14, Boucher teaches wherein the method further comprises: processing the scenario specific data to determine a plurality of analytical results related to the infrastructural system (¶ [0045]).
Regarding Claim 15, Boucher teaches wherein the analytical results relate to a subset of components of the infrastructural system (¶ [0045]).
Regarding Claim 16, Boucher teaches wherein the analytical results include a physical response of components and materials of the infrastructural system that are exposed to hazards, a response in the operational capacity of the components of an infrastructural system that are exposed to hazards, an overall reliability of the infrastructural system over periods of time; a scenario-based analysis evaluating the effects of upgrades to the infrastructural system or other hypothetical scenarios; a physical response of components and materials exposed to hazards; or component fragility for component designs and configurations developed from manufacturer technical specifications, laboratory tests, or computerized physical simulations (¶ [0045] and [0058] – [0059]—the analytical results include physical responses of components of the infrastructural system).
Regarding Claim 17, Boucher teaches wherein the infrastructural system comprises a collection of infrastructural components; and wherein the infrastructural components include electrical grid components, electrical generation components, natural gas distribution system components, water system components, telecommunication components, or transportation infrastructure (¶ [0002] and [0030]).
Regarding Claim 20, Boucher teaches a computer implemented method for assessing the resilience of an infrastructural system (¶ [0001] and [0038]). Boucher teaches the method comprising the steps of the present claim in the same manner as for claim 1, above.
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 9 is rejected under 35 U.S.C. 103 as being unpatentable over Boucher, as applied to claim 2, above, in view of Akbar, Adnan, et al. (“Predictive analytics for complex IoT data streams,” IEEE Internet of Things Journal 4.5 (2017): 1571-1582; hereinafter “Akbar”).
Regarding Claim 9, Boucher does not specifically teach wherein the infrastructural system model is retrained with the determined probability of failure for the component of the infrastructural system. However, Akbar teaches an infrastructural system model is retrained with a determined probability of failure for a component of the infrastructural system (section II. A—the model is retrained by calculating an error, which represents a difference between a predicted and actual probability of failure of an infrastructure system. See also section III. A).
All of the claimed elements were known in Boucher and Akbar 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 retraining of Akbar with the infrastructural system model of Boucher to yield the predictable result of wherein the infrastructural system model is retrained with the determined probability of failure for the component of the infrastructural system. One would be motivated to make this combination for the purpose of improving accuracy through training with an optimized moving window (Akbar, section I. C).
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Boucher, as applied to claim 2, above, in view of Ko, Eun Hui, Tatjana Dokic, and Mladen Kezunovic. (“Prediction model for the distribution transformer failure using correlation of weather data,” 5th International Colloquium on Transformer Research and Asset Management. Singapore: Springer Singapore, 2020; hereinafter “Ko”).
Regarding Claim 10, Boucher does not specifically teach wherein the infrastructural system model incorporates the complexity of environmental conditions by calculating failure risk scores based on various local or contemporaneous environmental factors, and wherein the contemporaneous environmental factors include soil properties, type and prevalence of local vegetation, drought conditions, or wind speeds. However, Ko teaches an infrastructural system model that incorporates complexity of environmental conditions by calculating failure risk scores based on various local or contemporaneous environmental factors, and wherein the contemporaneous environmental factors include soil properties, type and prevalence of local vegetation, drought conditions, or wind speeds (sections 2 and 4—failure risk scores are calculated based on environmental factors including many weather conditions, which include wind speed and wind gusts).
All of the claimed elements were known in Boucher and Ko 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 modeling based on wind and other weather conditions of Ko with the infrastructural system model of Boucher to yield the predictable result of wherein the infrastructural system model incorporates the complexity of environmental conditions by calculating failure risk scores based on various local or contemporaneous environmental factors, and wherein the contemporaneous environmental factors include soil properties, type and prevalence of local vegetation, drought conditions, or wind speeds. One would be motivated to make this combination for the purpose of improving the ability to mitigate failures caused by weather (Ko, section 1, first paragraph).
Regarding Claim 11, Boucher/Ko teaches wherein the scenario specific data includes forecasted hazardous events, synthetic scenarios, or hazardous events from the past, other regions, or infrastructural systems of the same type as the infrastructural system (Ko, sections 2 and 3—scenario specific data includes hazardous weather events from the past).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. This art includes:
Liu (U.S. 2023/0368096) teaches machine learning that predicts degradation and failure of railroad infrastructure and recommends maintenance/repair
Sun et al. (U.S. 2022/0236451) teaches machine learning that predicts power grid failure due to weather
Anderson et al. (U.S. 2013/0232094) teaches machine learning for proactive maintenance of a power grid
Wu, Haiyue, Aihua Huang, and John W. Sutherland (“Avoiding environmental consequences of equipment failure via an LSTM-based model for predictive maintenance,” Procedia Manufacturing 43 (2020): 666-673) teaches an LSTM/RNN model that determines machine/infrastructure condition from time series data collected from sensors
Ayvaz, Serkan, and Koray Alpay (“Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time,” Expert systems with applications 173 (2021): 114598) teaches machine learning that uses IoT data for predictive maintenance in a manufacturing facility
Serradilla, Oscar, et al. (“Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects,” Applied intelligence 52.10 (2022): 10934-10964) teaches deep learning techniques for predictive maintenance
Ren, Yali (“Optimizing predictive maintenance with machine learning for reliability improvement,” ASCE-asme journal of risk and uncertainty in engineering systems, part b: mechanical engineering 7.3 (2021): 030801) teaches using machine learning for predictive maintenance
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Huntley can be reached at 303-297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HAL SCHNEE/Primary Examiner, Art Unit 2129
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