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 .
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.
The claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea.
Step 1:
Claim 1 is drawn to a method.
Step 2A Prong 1:
Claim 1 recites “a computer-implemented method…; receiving, by one or more processors,…; extracting, by one or more processor,….; generating, by the one or more processor,….; initiating, by the one mor more processors,….”; As such these recitations are directed to the mathematical concepts grouping within abstract ideas. Accordingly, these claims recite an abstract idea.
Step 2A Prong 2:
The claims recite the additional elements “one or more processors, pressure sensors”. The additional elements appears to be merely data collection required to perform the abstract idea recited and as such is insignificant extra solution. Accordingly, this additional limitation does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Furthermore, the additional element do not improve the functioning of a computer or to any other technology or technical field. The additional element does not implement the mathematical concept with a particular machine or effect a transformation.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element considered individually or in ordered combination is well-understood, routine and conventional activities.
Therefore claim 1 is not patent eligible.
As per claims 2-20, similar analysis is done as in claim 1 and are not patent eligible for the same reasons above. It is noted that the dependent claims do not include additional limitations that are significantly more than the abstract idea, the dependent claims recite limitations that fall within the mathematical concepts as discussed with the independent claims.
Therefore, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory 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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chopra et al., US Pg. Pub. No. (2019/0057560) referred to hereinafter as Chopra.
As per claim 1, Chopra teaches a computer-implemented method for early anti-ice valve fault detection (see at least abstract, Para 01, 4, 6, 13, 14, figs, 1-4), the computer-implemented method comprising: receiving, by one or more processors, engine data associated with a flight operation of an aircraft, wherein the engine data comprises timeseries data for one or more monitored engine parameters, and wherein the aircraft is associated with an anti-ice system comprising a thermal anti-ice valve and a pressure sensor (see at least abstract, Para 6, 14, 22, , figs, 1-4); extracting, by the one or more processors, from the engine data and using one or more feature extraction models, one or more feature datasets, wherein each feature dataset represents a data slice from the engine data that satisfies thermal anti-ice valve feature extraction criteria (see at least abstract, Para 13, 14, 22, , 32-33, figs, 1-4); generating, by the one or more processors, an anti-ice valve fault prediction by applying the feature dataset to one or more fault prediction models; and initiating, by the one or more processors, the performance of one or more prediction-based actions based on the anti-ice valve fault prediction (see at least abstract, Para 6, 13, 14, 22, , figs, 1-4).
As per claim 2, Chopra teaches a computer-implemented method of claim 1, wherein the anti-ice valve fault prediction indicates a stuck-open thermal anti-ice valve condition associated with the thermal anti-ice valve (see at least abstract, Para 01, 4, 6, 13, 14, 22, 28, 32-33, figs, 1-4).
As per claim 3, Chopra teaches a computer-implemented method of claim 1, wherein the one or more feature extraction models comprise a machine learning model (see at least abstract, Para 01, 4, 6, 13, 14, 22, 28, 32-33, figs, 1-4).
As per claim 4, Chopra teaches a computer-implemented method of claim 1, wherein each feature dataset comprises an anti-ice command data and anti-ice pressure data associated with the corresponding data slice, wherein the anti-ice command data is configured to facilitate selective supply of hot engine bleed air flow to one or more components of the anti-ice system and the anti-ice pressure data indicates the occurrence of the hot engine bleed air flow (see at least abstract, Para 01, 4, 6, 13, 14, 22, 28, 32-33, figs, 1-4).
As per claim 5, Chopra teaches a computer-implemented method of claim 4, wherein generating the thermal anti-ice fault prediction comprises: for each feature dataset: comparing the anti-ice command data to the anti-ice pressure data (see at least abstract, Para 01, 4, 6, 13, 14, 22, 28, 32-33, figs, 1-4); determining whether the anti-ice command data and the anti-ice pressure data match (see at least abstract, Para 01, 4, 6, 13, 14, 22, 28, 32-33, figs, 1-4); and in response to determining that the anti-ice command data and the anti-ice pressure data do not match, increasing an abnormal anti-ice valve condition count for the flight operation (see at least abstract, Para 01, 4, 6, 13, 14, 22, 28, 32-33, figs, 1-4).
As per claim 6, Chopra teaches a computer-implemented method of claim 5, wherein generating the thermal anti-ice fault prediction further comprises: determining whether the abnormal anti-ice valve condition count satisfies a fault prediction threshold (see at least abstract, Para 01, 4, 6, 13, 14, 22, 28, 32-33, figs, 1-4); and in response to determining that the abnormal anti-ice valve condition count satisfies the fault prediction threshold, generating a positive anti-ice valve fault prediction indicative of a stuck-open thermal anti-ice valve (see at least abstract, Para 01, 4, 6, 13, 14, 22, 28, 32-33, figs, 1-4).
As per claim 7, Chopra teaches a computer-implemented method of claim 6, wherein the one or more prediction-based actions comprise: generating one or more recommendations, in response to a positive anti-ice valve fault prediction; and causing rendering of a user interface comprising the one or more recommendations on a user device (see at least abstract, Para 01, 4, 6, 13, 14, 22, 28, 32-33, figs, 1-4).
As per claims 8-20, the limitations of claims 8-20 are similar to the limitations of claims 1-7, therefore they are rejected based on the same rationale.
Conclusion
Please refer to from 892 for cited references.
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/MUSSA A SHAAWAT/Primary Examiner, Art Unit 3669