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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 13 Feb 26 has been entered.
Response to Arguments
The applicant has argued that the currently amended independent claims overcome the previously rendered rejections under 35 U.S.C. 103. However, this argument is moot as a new grounds of rejection has been identified in a renewed search necessitated by said amendments.
Status of Claims
Claims 1-20 are pending and have been considered.
The priority date in this case is 06/28/2023 and no foreign priority has been claimed.
Claims 1-3, 6-12, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Xu (US 11,410,056) in view of Zhang (US 2021/0208545) in view of Ven Den Bergh (US 2017/0241798) in view of Jarka (US 2023/0159171).
Claims 4-5, and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Zhang in view of Ven Den Bergh in view of Jarka in view of Rajagopalan (US 2017/0274467).
Information Disclosure Statement
No Information Disclosure Statements have been filed in this application and therefore none have been considered.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 6-12, 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Xu (US 11,410,056) in view of Zhang (US 2021/0208545) in view of Ven Den Bergh (US 2017/0241798) in view of Jarka (US 2023/0159171) all of these references represent analogous art as they all relate to information systems of industrial machines (See BACKGROUND of Xu, Paragraph [0001] of Zhang, Paragraph [0001] of Van Den Bergh, and Paragraph [0001] of Jarka).
As per Claim 1:
Xu discloses the following limitations:
“A method, comprising: training, by a computing device, a machine learning (ML) model to predict condition-based maintenance for a set of aircraft, wherein operational data is input into the ML model, the operational data including one or more of: data from aircraft flight logs, sensor data related to aircraft, or data from aircraft maintenance logs;”
Xu discloses using a machine learning model to predict condition-based aircraft maintenance (See Column 5 Lines 20-50).
“determining that one or more alert criteria are satisfied based on the one or more predictions;”
Xu Figure 7 discloses generating an alert based on criteria being met by a predictive machine learning model concerning airplane maintenance.
“and outputting one or more alerts via one or more notification channels based on the alert criteria being satisfied.”
Xu Figure 7 discloses generating an alert based on criteria being met by a predictive machine learning model concerning airplane maintenance.
Xu does not disclose the following limitations that Zhang suggests:
“ generating, a set of configuration files for executing the ML model, wherein the set of configuration files is structured in a hierarchical form based at least in part on one or more execution environments for the ML model”
Zhang Paragraphs [0024]-[0025] disclose different machine learning modules utilizing configuration files that are tagged with hierarchical environmental tags being executed.
“deploying the ML model based on the set of configuration files”
Zhang Paragraphs [0024]-[0025] disclose machine learning modules being deployed based on configuration files.
“and upon determining that a runtime trigger specified in the set of configuration files is activated: accessing input data from a client system;”
Zhang Paragraphs [0024]-[0026] disclose accessing consumer data with models controlled by a trigger.
“generating one or more predictions by processing the input data using the deployed ML model”
Zhang Paragraph [0014] discloses making a prediction with a machine learning model.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Xu with the machine learning system disclosed by Zhang. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to make the system more effective by planning maintenance based on predictive indicators.
Xu in view of Zhang does not disclose the following limitations that Van Den Bergh does suggest:
“wherein the set of configuration files are associated with different operating systems of each aircraft of the set of aircraft”
Van Den Bergh Paragraph [0026] discloses configuration files associated with specific aircraft.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Xu in view of Zhang with the configuration files disclosed by Van Den Bergh. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to make the system more effective by making configuration files more targeted.
Xu in view of Zhang in view of Van Den Bergh does not disclose the following limitations that Jakara suggests:
“wherein common files within the set of configuration files are applied to each aircraft within the set of aircraft”
Jarka Paragraph [0047] suggests common features of configuration files shared between many aircraft models.
“wherein specific configuration files within the set of configuration files are applied to specific aircraft within the set of aircraft”
Jarka Paragraph [0019] suggests specific configuration files corresponding to specific aircraft models within a set of aircraft models.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Xu in view of Zhang in view of Van Den Bergh with the targeted nature of configuration files to common and specific targets suggested by Jarka. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to make the system more effective as files become more targeted towards specific requirements of aircraft models.
With regards to Claim 2, Xu in view of Zhang in view of Van Den Bergh discloses all of the limitations of Claim 1 and Xu further discloses the following limitations:
“wherein the set of configuration files specifies at least one of (i) the runtime trigger, (ii) one or more alert thresholds for the one or more alert criteria, (iii) the one or more notification channels and destination identifiers, and (iv) feature extraction information for one or more different aircraft types.”
Xu Column 4 Lines 36-68 discloses files that specify alert thresholds.
With regards to Claim 3, Xu in view of Zhang in view of Van Den Bergh discloses all of the limitations of Claim 1 and Xu further discloses the following limitations:
“wherein the one or more notification channels comprises at least one of (i) email, (ii) text message, (iii) push notification, or (iv) phone call.”
Xu Column 11 Lines 16-42 disclose push notifications being used for alerts.
With regards to Claim 6, Xu in view of Zhang in view of Van Den Bergh discloses all of the limitations of Claim 1 and Xu further discloses the following limitations:
“wherein the set of configuration files comprises: a development configuration file for deploying the ML model in a development environment, wherein the ML model is manually triggered to generate alerts in the development environment, a test configuration file for deploying the ML model in a testing environment, wherein the ML model is tested to determine that the runtime trigger is activated automatically in the testing environment, and a production configuration file for deploying the ML model in a client-facing production environment, wherein the ML model is used for runtime inferencing in the production environment.”
Xu Column 8 Lines 18-45 disclose testing a machine learning model in a test environment.
With regards to Claim 7, Xu in view of Zhang in view of Van Den Bergh discloses all of the limitations of Claim 1 and Zhang further suggests the following limitations:
“updating the set of configuration files based on one or more client inputs; validating the updated set of configuration files across the one or more execution environments; and redeploying the ML model based on the updated set of configuration files.”
Zhang Paragraphs [0024]-[0025] disclose updating configuration files and redeploying machine learning models based on said updates.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Xu in view of Zhang further with the updating of configuration files disclosed by Zhang. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to make the system more accurate by ensuring up-to-date configuration files.
With regards to Claim 8, Xu in view of Zhang in view of Van Den Bergh discloses all of the limitations of Claim 1 and Xu further discloses the following limitations:
“upon determining that the runtime trigger specified in the set of configuration files is activated for a second time: accessing new input data from a client system; generating one or more new predictions by processing the new input data using the deployed ML model; determining that the one or more alert criteria are not satisfied based on the one or more new predictions; and refraining from generating new alerts based on the new input data.”
Xu Column 5 Lines 20-49 disclose re-running machine learning systems based on new input to identify false positives.
As per Claim 9: this claim is substantially similar to Claim 1 and is therefore rejected using the same references and rationale.
With regards to Claim 10, this claim is substantially similar to Claim 2 and is therefore rejected using the same references and rationale.
With regards to Claim 11, this claim is substantially similar to Claim 3 and is therefore rejected using the same references and rationale.
With regards to Claim 14, this claim is substantially similar to Claim 6 and is therefore rejected using the same references and rationale.
With regards to Claim 15, this claim is substantially similar to Claim 7 and is therefore rejected using the same references and rationale.
With regards to Claim 16, this claim is substantially similar to Claim 8 and is therefore rejected using the same references and rationale.
As per Claim 17: this claim is substantially similar to Claim 1 and is therefore rejected using the same references and rationale.
With regards to Claim 18, this claim is substantially similar to Claim 2 and is therefore rejected using the same references and rationale.
With regards to Claim 19, this claim is substantially similar to Claim 7 and is therefore rejected using the same references and rationale.
With regards to Claim 20, this claim is substantially similar to Claim 8 and is therefore rejected using the same references and rationale.
Claims 4-5, and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Xu (US 11,410,056) in view of Zhang (US 2021/0208545) in view of Van Den Bergh in view of Jarka in view of Rajagopalan (US 2017/0274467). Rajagopalan represents analogous art to the rest of the references as it also relates to information systems of industrial machines (See Paragraph [0050]).
With regards to Claim 4, Xu in view of Zhang in view of Van Den Bergh discloses all of the limitations of Claim 1 but does not disclose the following limitations that Rajagopalan teaches:
“further comprising storing the one or more alerts in a cloud-based database.”
Rajagopalan Paragraph [0086] discloses storing an alert on a cloud server.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Xu in view of Zhang in view of Van Den Bergh in view of Jarka with the cloud-based storage disclosed by Rajagopalan. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to make the system more efficient by utilizing cloud-based technology.
With regards to Claim 5, Xu in view of Zhang in view of Van Den Bergh in view of Rajagopalan discloses all of the limitations of Claim 4 and Rajagopalan further suggests the following limitations:
“wherein deploying the ML model based on the set of configuration files comprises deploying the ML model to a cloud-based computing system.”
Rajagopalan Paragraph [0086] discloses deploying a machine learning system via a cloud computing system.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Xu in view of Zhang in view of Ven Den Bergh in view of Rajagopalan with the cloud-based storage disclosed by Rajagopalan. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to make the system more efficient by utilizing cloud-based technology.
With regards to Claim 12, this claim is substantially similar to Claim 4 and is therefore rejected using the same references and rationale.
With regards to Claim 13, this claim is substantially similar to Claim 5 and is therefore rejected using the same references and rationale.
Further References Deemed Relevant
De Munck (US 2020/0115066)
Related to data analysis via machine learning in aircrafts.
Rice (US 2020/0369410)
Related to machine learning models used to predict aircraft engine life expectancy.
Bharadwaj (US 2019/0279443)
Related to aircraft health prediction.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Godfrey Maciorowski, whose telephone number is (571) 272-4652. The examiner can normally be reached on Monday-Friday from 7:30am to 5:00pm EST.
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If attempts to reach examiner by telephone are unsuccessful the examiner’s supervisor, Thomas Worden can be reached on (571) 272-4876. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/GODFREY ALEKSANDER MACIOROWSKI/Examiner, Art Unit 3658
/THOMAS E WORDEN/Supervisory Patent Examiner, Art Unit 3658