DETAILED ACTION
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 § 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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more (See 2019 Update: Eligibility Guidance).
Independent Claim(s) 1 recites
flight-by-flight severity prediction for aircraft components,
accessing time series flight-by-flight data relating to a component of an aircraft, the time series flight-by-flight data comprising performance data;
determining an estimated degree of distress or a performance deterioration for the component by:
providing the time series flight-by-flight data as input to a prediction model comprising: a machine learning model;
and
a physics-based model,
wherein
the prediction model
outputs the estimated degree of distress;
determining a flight-by-flight severity prediction for the component based on the estimated degree of distress;
and
providing a preemptive recommendation for the component based on the determined flight-by-flight severity prediction
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)].
Independent Claim(s) 11 recites
flight-by-flight severity prediction for aircraft components,
accessing time series flight-by-flight data relating to a component of an aircraft,
the time series flight-by-flight data comprising
performance data;
determining an estimated degree of distress for the component
by:
providing the time series flight-by-flight data as input to a prediction model
comprising:
a sawtooth model
configured to
estimate at least one of a post-wash deterioration rate or an inter-wash deterioration rate of the component;
and
a machine learning model
is configured to
estimate at least one of an post-wash rate severity or a recoverable rate severity of the component,
wherein
the prediction model
outputs the estimated degree of distress;
predicting, by the prediction model a per-flight deterioration based on:
the estimated at least one of the post-wash deterioration rate or the inter-wash deterioration rate;
and
the estimated at least one of the post-wash rate severity or the recoverable rate severity;
and
determining a degree of performance deterioration for the component based on the per-flight deterioration of the component
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)].
Independent Claim(s) 17 recites
severity prediction for aircraft components,
accessing time series flight-by-flight data relating to a component of an aircraft,
the time series flight-by-flight data comprising
performance data;
determining, by a prediction model, an estimated degree of distress for the component based on the time series flight-by-flight data;
determining a flight-by-flight severity prediction for the component based on the estimated degree of distress;
and
providing a preemptive recommendation for the component based on the determined flight-by-flight severity prediction
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)].
In combination with Independent Claim(s) 1, 11, 17, Claim(s) 2-10, 12-16, 18-20 recite(s)
determining a scrap rate based on the flight-by-flight severity prediction for the component.
determining an engine removal probability prediction for the component based on the flight-by-flight severity prediction for the component.
performing a maximum likelihood estimation (MLE) for an engine removal probability distribution or minimizing a mean square error (MSE) between a predicted deterioration and an actual deterioration;
determining a tunable parameter by minimizing a loss function based on the MLE or the MSE;
and
determining a per-flight damage of the component based on the tunable parameter.
performing a maximum likelihood estimation (MLE) for at least one of a Gumbel or a Weibull distribution;
determining a tunable parameter by minimizing a loss function based on the MLE;
and
determining a per-flight damage of the component based on the tunable parameter.
the time series flight-by-flight data further comprises at least one of
statistical data, a Weibull distribution, data generated by probabilistic models, or data generated by trend analytics.
determining a Weibull severity prediction for the component based on the flight-by-flight severity prediction for the component.
the time series flight-by-flight data further comprises at least one of
an engine build configuration, a utilization history, or an allowable fallout risk.
the preemptive recommendation includes
indicating whether a group of engines may successfully complete a deployment of a specified duration or indicating an optimal wash interval.
the time series flight-by-flight data further includes
a duration of deployment.
the preemptive recommendation includes at least one of
a part demand for a fleet asset or an indication of which asset from the fleet asset is best suited for deployment
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)].
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f)) (i.e. A system for; the system comprising: a processor; a memory including instructions which, when executed by the processor, cause the system at least to perform:; A processor-implemented method for);
Adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)) (i.e. generic data acquisition/output); or
Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)) (i.e. for aircraft components; the component is an engine).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The additional elements simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)) (i.e. See Alice Corp. and cited references for evidence of additional elements (i.e., generic computer structure)).
Claim Rejections - 35 USC § 102
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 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) 1-3, 5-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by JOHNSON ET AL. (US 20170323274 A1) (hereinafter “JOHNSON”).
With respect to Claim(s) 1, JOHNSON teaches ‘A method of providing a recommendation for optimizing operations of a set of industrial assets’ and the BRI of:
A system (See, e.g., Fig(s). 3)
for
flight-by-flight severity prediction for aircraft components (See, e.g., ¶ ABSTRACT),
the system comprising:
a processor; a memory including instructions which, when executed by the processor, cause the system at least to perform (See, e.g., Fig(s). 3):
accessing time series flight-by-flight data relating to a component of an aircraft, the time series flight-by-flight data comprising performance data (See, e.g., ¶ 0025, 0182);
determining an estimated degree of distress or a performance deterioration for the component (See, e.g., ¶ 0035, 0187)
by:
providing the time series flight-by-flight data as input to a prediction model (See, e.g., ¶ 0187)
comprising:
a machine learning model (See, e.g., ¶ 0118);
and
a physics-based model (See, e.g., ¶ 0035),
wherein
the prediction model
outputs the estimated degree of distress (See, e.g., ¶ 0035);
determining a flight-by-flight severity prediction for the component based on the estimated degree of distress (See, e.g., ¶ 0189);
and
providing a preemptive recommendation for the component based on the determined flight-by-flight severity prediction (See, e.g., ¶ 0025, 0035).
With respect to Claim(s) 11, JOHNSON teaches ‘A method of providing a recommendation for optimizing operations of a set of industrial assets’ and the BRI of:
A system (See, e.g., Fig(s). 3)
for
flight-by-flight severity prediction for aircraft components (See, e.g., ¶ ABSTRACT),
the system comprising:
a processor; a memory including instructions which, when executed by the processor, cause the system at least to perform (See, e.g., Fig(s). 3):
accessing time series flight-by-flight data relating to a component of an aircraft, the time series flight-by-flight data comprising performance data (See, e.g., ¶ 0025, 0182);
determining an estimated degree of distress for the component (See, e.g., ¶ 0035, 0187)
by:
providing the time series flight-by-flight data as input to a prediction model (See, e.g., ¶ 0187)
comprising:
a sawtooth model configured to estimate at least one of a post-wash deterioration rate or an inter-wash deterioration rate of the component (See, e.g., ¶ 0236);
and
a machine learning model (See, e.g., ¶ 0118),
wherein
the prediction model
outputs the estimated degree of distress (See, e.g., ¶ 0035);
predicting, by the prediction model a per-flight deterioration based on:
the estimated at least one of the post-wash deterioration rate or the inter-wash deterioration rate (See, e.g., ¶ 0236);
estimate at least one of an post-wash rate severity or a recoverable rate severity of the component; and the estimated at least one of the post-wash rate severity or the recoverable rate severity (See, e.g., ¶ 0236);
and
determining a degree of performance deterioration for the component based on the per-flight deterioration of the component (See, e.g., ¶ 0236).
With respect to Claim(s) 17, JOHNSON teaches ‘A method of providing a recommendation for optimizing operations of a set of industrial assets’ and the BRI of:
A processor-implemented method (See, e.g., Fig(s). 3)
for
severity prediction for aircraft components (See, e.g., ¶ ABSTRACT),
the method comprising:
accessing time series flight-by-flight data relating to a component of an aircraft, the time series flight-by-flight data comprising performance data (See, e.g., ¶ 0025, 0182);
determining, by a prediction model, an estimated degree of distress for the component based on the time series flight-by-flight data (See, e.g., ¶ 0035, 0187);
determining a flight-by-flight severity prediction for the component based on the estimated degree of distress (See, e.g., ¶ 0189);
and
providing a preemptive recommendation for the component based on the determined flight-by-flight severity prediction (See, e.g., ¶ 0025, 0035).
With respect to Claim(s) 2, 18, JOHNSON teaches the BRI of the parent claim(s).
JOHNSON further teaches the BRI of:
determining a scrap rate based on the flight-by-flight severity prediction for the component (See, e.g., ¶ 0140).
With respect to Claim(s) 3, JOHNSON teaches the BRI of the parent claim(s).
JOHNSON further teaches the BRI of:
determining an engine removal probability prediction for the component based on the flight-by-flight severity prediction for the component (See, e.g., ¶ 0140)).
With respect to Claim(s) 5, 12, JOHNSON teaches the BRI of the parent claim(s).
JOHNSON further teaches the BRI of:
the time series flight-by-flight data further comprises at least one of statistical data, a Weibull distribution, data generated by probabilistic models, or data generated by trend analytics (See, e.g., ¶ 0038).
With respect to Claim(s) 6, 13, JOHNSON teaches the BRI of the parent claim(s).
JOHNSON further teaches the BRI of:
the time series flight-by-flight data further comprises at least one of
an engine build configuration, a utilization history, or an allowable fallout risk (See, e.g., ¶ 0025).
With respect to Claim(s) 7, JOHNSON teaches the BRI of the parent claim(s).
JOHNSON further teaches the BRI of:
the component is
an engine (See, e.g., ¶ 0002).
With respect to Claim(s) 8, 14, JOHNSON teaches the BRI of the parent claim(s).
JOHNSON further teaches the BRI of:
the preemptive recommendation includes
indicating whether a group of engines may successfully complete a deployment of a specified duration or indicating an optimal wash interval (See, e.g., ¶ 0118, 0035).
With respect to Claim(s) 9, 15, JOHNSON teaches the BRI of the parent claim(s).
JOHNSON further teaches the BRI of:
the time series flight-by-flight data further includes
a duration of deployment (See, e.g., ¶ 0118, 0035).
With respect to Claim(s) 10, 16, JOHNSON teaches the BRI of the parent claim(s).
JOHNSON further teaches the BRI of:
the preemptive recommendation includes at least one of
a part demand for a fleet asset or an indication of which asset from the fleet asset is best suited for deployment (See, e.g., ¶ 0118, 0035).
With respect to Claim(s) 19, JOHNSON teaches the BRI of the parent claim(s).
JOHNSON further teaches the BRI of:
determining a Weibull severity prediction for the component based on the flight-by-flight severity prediction for the component (See, e.g., ¶ 0025).
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.
Claim(s) 4, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over the cited reference(s) of the parent claim(s) in view of FREWEN ET AL. (US 20190086291 A1) (hereinafter “FREWEN”).
With respect to Claim(s) 4, JOHNSON teaches the BRI of the parent claim(s).
However, JOHNSON is lacking the explicit language of:
performing a maximum likelihood estimation (MLE) for an engine removal probability distribution or minimizing a mean square error (MSE) between a predicted deterioration and an actual deterioration; determining a tunable parameter by minimizing a loss function based on the MLE or the MSE; and determining a per-flight damage of the component based on the tunable parameter.
FREWEN teaches ‘A method and system of providing customizable service for an asset. The method including generating a predictive model for each asset of a fleet, each predictive model based on an operational profile for the asset and including a probability density function associated with the operational durability of the asset, establishing a maintenance strategy associated with the asset, and combining each of the predictive models to generate a compound fleet performance model, the fleet performance model including a combined probability density function. The method also includes collecting actual asset performance and maintenance data to generate actual asset metrics, determining a fleet performance profile based on the actual asset metrics indicative of a health assessment of the fleet, comparing the predicted fleet performance with the actual fleet performance, and ascertaining actionable choices for managing the assets based on a deviation of the predicted and actual fleet performance’ and the BRI of:
performing a maximum likelihood estimation (MLE) for an engine removal probability distribution or minimizing a mean square error (MSE) between a predicted deterioration and an actual deterioration; determining a tunable parameter by minimizing a loss function based on the MLE or the MSE; and determining a damage of the component based on the tunable parameter (See, e.g., ¶ 0052, 0064-0087).
It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify JOHNSON to include performing a maximum likelihood estimation (MLE) for an engine removal probability distribution or minimizing a mean square error (MSE) between a predicted deterioration and an actual deterioration; determining a tunable parameter by minimizing a loss function based on the MLE or the MSE; and determining a damage of the component based on the tunable parameter.
One of ordinary skill in the art would have been motivated to modify JOHNSON because it would be beneficial to improve managing an assets based on a deviation of the predicted and actual fleet performance. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results.
With respect to Claim(s) 20, JOHNSON teaches the BRI of the parent claim(s).
However, JOHNSON is lacking the explicit language of:
performing a maximum likelihood estimation (MLE) for at least one of a Gumbel or a Weibull distribution; determining a tunable parameter by minimizing a loss function based on the MLE; and determining a per-flight damage of the component based on the tunable parameter.
FREWEN teaches ‘A method and system of providing customizable service for an asset. The method including generating a predictive model for each asset of a fleet, each predictive model based on an operational profile for the asset and including a probability density function associated with the operational durability of the asset, establishing a maintenance strategy associated with the asset, and combining each of the predictive models to generate a compound fleet performance model, the fleet performance model including a combined probability density function. The method also includes collecting actual asset performance and maintenance data to generate actual asset metrics, determining a fleet performance profile based on the actual asset metrics indicative of a health assessment of the fleet, comparing the predicted fleet performance with the actual fleet performance, and ascertaining actionable choices for managing the assets based on a deviation of the predicted and actual fleet performance’ and the BRI of:
performing a maximum likelihood estimation (MLE) for at least one of a Gumbel or a Weibull distribution; determining a tunable parameter by minimizing a loss function based on the MLE; and determining a damage of the component based on the tunable parameter (See, e.g., ¶ 0052, 0064-0087).
It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify JOHNSON to include performing a maximum likelihood estimation (MLE) for at least one of a Gumbel or a Weibull distribution; determining a tunable parameter by minimizing a loss function based on the MLE; and determining a damage of the component based on the tunable parameter.
One of ordinary skill in the art would have been motivated to modify JOHNSON because it would be beneficial to improve managing an assets based on a deviation of the predicted and actual fleet performance. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAYMOND NIMOX whose telephone number is (469)295-9226. The examiner can normally be reached Mon-Thu 10am-8pm CT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ANDREW SCHECHTER can be reached at (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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RAYMOND NIMOX
Primary Examiner
Art Unit 2857
/RAYMOND L NIMOX/Primary Examiner, Art Unit
Prosecution Insights