DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 XXXXXXXXXXXXXX has been entered.
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 .
Status of Claims
Claims X are canceled.
Claims X are amended.
Claims 1-20 are pending and have been examined.
This action is in reply to the papers filed on 10/02/2025 (effective filing date 01/20/2023).
Information Disclosure Statement
No Information Disclosure Statement has been filed.
The information disclosure statement(s) submitted: xxxxxxxx, has/have been considered by the Examiner and made of record in the application file.
Amendment
The present Office Action is based upon the original patent application filed on xxx as modified by the amendment filed on xxx.
Reasons For Allowance
Prior-Art Rejection withdrawn
Claims xxx are allowed. Independent claims X, Y, and Z all contain the same inventive scope. The closest prior art (See PTO-892, Notice of References Cited) does not teach the claimed:
The invention teaches… and the prior-art teaches…, however, the prior-art does not teach…
The closest prior-art (xxx) teach the features as disclosed in Non-final Rejection (xxxx), however, these cited references do not teach and the prior-art does not teach at least the following combination of features and/or elements:
determining, at a second time after associating the information corresponding to the first loyalty card with the logged location, that a second user computing device is located within a specified distance of the logged location using a second positioning system of the second user computing device; in response to determining that the second user computing device is located within the specified distance of the logged location of the first user computing device at the first time of detecting: retrieving information corresponding to a second loyalty card, the second loyalty card being associated with the merchant and the second user computing device; and displaying, by the second user computing device, data describing the second loyalty card.
Claim Rejections - 35 USC §101 - Withdrawn
Per Applicant’s amendments and arguments and considering new guidance in the MPEP, the rejections are withdrawn. Specifically, in Applicant’s Remarks (dated 03/14/2017, pgs. 8-11), Applicant traverses the 35 USC §101 rejections arguing that the amended claims recite new limitations that are not abstract, amount to significantly more, are directed to a practical application, etc… For example, Applicant argues….
In support of their arguments, Applicant cites to the following recent Fed. Cir. court cases (i.e., Alice Corp. v. CLS Bank Int’l, SRI Int’l, Inc. v. Cisco Systems, Inc., Ultramercial, Inc. v. Hulu, LLC, Berkheimer, Core Wireless, McRO, Enfish, Bascom, DDR, etc…).
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 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without significantly more. These claims recite a method, system, and computer readable medium / storage medium for determining maintenance intervals using a combination of models.
Claim 1 recites [a] method performed by a computing system for determining a maintenance interval for a subject aircraft configuration, the method comprising: obtaining sensor data reported by an electronic system location on-board each aircraft of a population of multiple aircraft of the subject aircraft configuration; obtaining a maintenance task definition that identifies an initial maintenance task for the subject aircraft configuration having a plurality of maintenance subtasks, an initial maintenance interval for the maintenance task, and one or more components of the subject aircraft configuration for each maintenance subtask; obtaining a failure mode definition that identifies a set of failure modes involving one or more components of the subject aircraft configuration for each of the plurality of maintenance subtasks; for the set of failure modes of a maintenance subtask of the plurality of maintenance subtasks, determining a maintenance interval for the maintenance subtask across the set of failure modes by performing a subtask process that includes: for each failure mode of the set of failure modes of the maintenance subtask, implementing one of a plurality of predictive models at the computing system to determine a life-time probability distribution of the failure mode based, at least in part, on the sensor data, and determining a maintenance interval for the one or more components of the maintenance subtask based, at least in part, on the life-time probability distribution determined for each failure mode of the set of failure modes; and outputting the maintenance interval for the maintenance subtask.
The claims are being rejected according to the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 5, p. 50-57 (Jan. 7, 2019)).
Step 1: Does the Claim Fall within a Statutory Category?
Yes. Claims 1-9 recite a method and, therefore, are directed to the statutory class of a process. Claims 10-18 recite a system/apparatus and, therefore, are directed to the statutory class of machine. Claims 19-20 recite a computer readable medium/storage machine and, therefore, are directed to the statutory class of a manufacture.
Step 2A, Prong One: Is a Judicial Exception Recited?
Yes. The following tables identify the specific limitations that recite an abstract idea. The column that identifies the additional elements will be relevant to the analysis in step 2A, prong two, and step 2B.
Claim 1: Identification of Abstract Idea and Additional Elements, using Broadest Reasonable Interpretation
Claim Limitation
Abstract Idea
Additional Element
1. A method performed by a computing system for determining a maintenance interval for a subject aircraft configuration, the method comprising:
No additional elements are positively claimed.
The preamble is given little patentable weight.
obtaining sensor data reported by an electronic system location on-board each aircraft of a population of multiple aircraft of the subject aircraft configuration;
This limitation includes the step(s) of: obtaining sensor data reported by an electronic system location on-board each aircraft of a population of multiple aircraft of the subject aircraft configuration.
But for the electronic system, this limitation is directed to processing and/or communicating known information to facilitate determining maintenance intervals using a combination of models which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
obtaining sensor data reported by an electronic system location on-board each aircraft of a population of multiple aircraft of the subject aircraft configuration…
The ‘sensor data’ and aircraft are NOT considered Additional Elements. Sensor data is interpreted as purely software or code.
Note that Applicant has not positively claimed the sensor(s), just the data collected. Similarly, Applicant has not positively claimed the aircraft.
obtaining a maintenance task definition that identifies an initial maintenance task for the subject aircraft configuration having a plurality of maintenance subtasks, an initial maintenance interval for the maintenance task, and one or more components of the subject aircraft configuration for each maintenance subtask;
This limitation includes the step(s) of: obtaining a maintenance task definition that identifies an initial maintenance task for the subject aircraft configuration having a plurality of maintenance subtasks, an initial maintenance interval for the maintenance task, and one or more components of the subject aircraft configuration for each maintenance subtask.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to facilitate determining maintenance intervals using a combination of models which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
obtaining a failure mode definition that identifies a set of failure modes involving one or more components of the subject aircraft configuration for each of the plurality of maintenance subtasks;
This limitation includes the step(s) of: obtaining a failure mode definition that identifies a set of failure modes involving one or more components of the subject aircraft configuration for each of the plurality of maintenance subtasks.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to facilitate determining maintenance intervals using a combination of models which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
for the set of failure modes of a maintenance subtask of the plurality of maintenance subtasks, determining a maintenance interval for the maintenance subtask across the set of failure modes by performing a subtask process that includes: for each failure mode of the set of failure modes of the maintenance subtask, implementing one of a plurality of predictive models at the computing system to determine a life-time probability distribution of the failure mode based, at least in part, on the sensor data, and determining a maintenance interval for the one or more components of the maintenance subtask based, at least in part, on the life-time probability distribution determined for each failure mode of the set of failure modes; and
This limitation includes the step(s) of: for the set of failure modes of a maintenance subtask of the plurality of maintenance subtasks, determining a maintenance interval for the maintenance subtask across the set of failure modes by performing a subtask process that includes: for each failure mode of the set of failure modes of the maintenance subtask, implementing one of a plurality of predictive models at the computing system to determine a life-time probability distribution of the failure mode based, at least in part, on the sensor data, and determining a maintenance interval for the one or more components of the maintenance subtask based, at least in part, on the life-time probability distribution determined for each failure mode of the set of failure modes.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to facilitate determining maintenance intervals using a combination of models which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
outputting the maintenance interval for the maintenance subtask.
This limitation includes the step(s) of: outputting the maintenance interval for the maintenance subtask.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to facilitate determining maintenance intervals using a combination of models which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk), and/or
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
No additional elements are positively claimed.
As shown above, under Step 2A, Prong One, the claims recite a judicial exception (an abstract idea). The claims are directed to the abstract idea of determining maintenance intervals using a combination of models, which, pursuant to MPEP 2106.04, is aptly categorized as a is aptly categorized as a mathematical concept, mental process, and/or a method of organizing human activity. Therefore, under Step 2A, Prong One, the claims recite a judicial exception.
Next, the aforementioned claims recite additional functional elements that are associated with the judicial exception, including: logic and storage machines for storing and processing information. Examiner understands these limitations to be insignificant extrasolution activity. (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Cf. Diamond v. Diehr, 450 U.S. 175, 191-192 (1981) ("[I]nsignificant post-solution activity will not transform an unpatentable principle in to a patentable process.”).
The aforementioned claims also recite additional technical elements including: logic and storage machines for storing and processing information.. These limitations are recited at a high level of generality and appear to be nothing more than generic computer components. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 134 S. Ct. at 2358, 110 USPQ2d at 1983. See also 134 S. Ct. at 2389, 110 USPQ2d at 1984.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application?
No. The judicial exception is not integrated into a practical application. The additional elements listed above that relate to computing components are recited at a high level of generality (i.e., as generic components performing generic computer functions such as communicating, receiving, processing, analyzing, and outputting/displaying data) such that they amount to no more than mere instructions to apply the exception using generic computing components. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, the claims do not purport to improve the functioning of the computer itself. There is no technological problem that the claimed invention solves. Rather, the computer system is invoked merely as a tool. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, these claims are directed to an abstract idea.
Furthermore, looking at the elements individually and in combination, under Step 2A, Prong Two, the claims as a whole do not integrate the judicial exception into a practical application because they fail to: improve the functioning of a computer or a technical field, apply the judicial exception in the treatment or prophylaxis of a disease, apply the judicial exception with a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or apply the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. Rather, the claims merely use a computer as a tool to perform the abstract idea(s), and/or add insignificant extra-solution activity to the judicial exception, and/or generally link the use of the judicial exception to a particular technological environment.
Step 2B: Does the Claim Provide an Inventive Concept?
Next, under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Simply put, as noted above, there is no indication that the combination of elements improves the functioning of a computer (or any other technology), and their collective functions merely provide conventional computer implementation. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements relating to computing components amount to no more than applying the exception using a generic computing components. Mere instructions to apply an exception using a generic computing component cannot provide an inventive concept. Furthermore, the broadest reasonable interpretation of the claimed computer components (i.e., additional elements) includes any generic computing components that are capable of being programmed to communicate, receive, send, process, analyze, output, or display data.
Additionally, pursuant to the requirement under Berkheimer, the following citations are provided to demonstrate that the additional elements, identified as extra-solution activity, amount to activities that are well-understood, routine, and conventional. See MPEP 2106.05(d).
Capturing an image (code) with an RFID reader. Ritter, US Patent No. 7734507 (Col. 3, Lines 56-67); “RFID: Riding on the Chip” by Pat Russo. Frozen Food Age. New York: Dec. 2003, vol. 52, Issue 5; page S22.
Receiving or transmitting data over a network. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
Storing and retrieving information in memory. Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Outputting/Presenting data to a user. Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015); MPEP 2106.05(g)(3).
Using a machine learning model to determine user segment characteristics for an ad campaign. https://whites.agency/blog/how-to-use-machine-learning-for-customer-segmentation/.
Thus, taken alone and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea), and are ineligible under 35 USC 101.
Independent system claim 10 and storage machine claim 19 also contains the identified abstract ideas, with the additional elements of a processor and storage medium, which are a generic computer components, and thus not significantly more for the same reasons and rationale above.
Dependent claims 2-9, 11-18, and 20 further describe the abstract idea. The additional elements of the dependent claims fail to integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea. Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
As such, the claims are not patent eligible.
Invention Could be Performed Manually
It is conceivable that the invention could be performed manually without the aid of machine and/or computer. For example, Applicant claims obtaining data, obtaining a definition, determining a maintenance interval, etc... Each of these features could be performed manually and/or with the aid of a simple generic computer to facilitate the transmission of data.
See also Leapfrog Enterprises, Inc. v. Fisher-Price, Inc., and In re Venner, which stand for the concept that automating manual activity and/or applying modern electronics to older mechanical devices to accomplish the same result is not sufficient to distinguish over the prior art. Here, applicant is merely claiming computers to facilitate and/or automate functions which used to be commonly performed by a human.
Leapfrog Enterprises, Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 82 USPQ2d 1687 (Fed. Cir. 2007) "[a]pplying modern electronics to older mechanical devices has been commonplace in recent years…"). The combination is thus the adaptation of an old idea or invention using newer technology that is commonly available and understood in the art.
In In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958), the court held that broadly providing an automatic or mechanical means to replace manual activity which accomplished the same result is not sufficient to distinguish over the prior art. MPEP 2144.04, III Automating a Manual Activity.
MPEP 2144.04 III - Automating a Manual Activity and In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958) further stand for and provide motivation for using technology, hardware, computer, or server to automate a manual activity.
Therefore, the Office finds no improvements to another technology or field, no improvements to the function of the computer itself, and no meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Therefore, based on the two-part Alice Corp. analysis, there are no limitations in any of the claims that transform the exception (i.e., the abstract idea) into a patent eligible application.
Claim Rejections - Not an Ordered Combination
None of the limitations, considered as an ordered combination provide eligibility, because taken as a whole, the claims simply instruct the practitioner to implement the abstract idea with routine, conventional activity.
Claim Rejections - Preemption
Allowing the claims, as presently claimed, would preempt others from determining maintenance intervals using a combination of models. Furthermore, the claim language only recites the abstract idea of performing this method, there are no concrete steps articulating a particular way in which this idea is being implemented or describing how it is being performed.
Claim Rejections - 35 USC § 101 – electromagnetic signals per se
Claims 19 and 20 are also rejected under 35 USC §101 as being directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to electromagnetic signals per se. Claim 19 is directed to a “storage machine for a computing system, the storage machine comprising: one or more storage devices having instructions stored thereon executable by a logic machine of the computing system to…”. Paragraph [0086] of Applicant’s specification (US PGPub. 2026/0030598) states “aspects of the instructions described herein alternatively may be propagated by a communication medium (e.g., an electromagnetic signal, an optical signal, etc.) that is not held by a physical device for a finite duration”. Thus, it is clear from the Specification that in at least one embodiment the claimed ‘storage machine’ could be an electromagnetic signal being communicated through a hardwired or a wireless communication connection. Therefore, since such signals do not fall within any of the four recognized categories of patent eligible subject matter, Claims 19 and 20 are rejected as being directed to non-statutory subject matter, i.e. electromagnetic signals per se. See the David Kappos memo titled “Subject Matter Eligibility of Computer Readable Media” dated 1/26/2010 and available at: http://www.uspto.gov/patents/law/notices/101_crm_20100127.pdf. Adding “non-transitory” should remedy this 101 issue. For example, amending to state a “non-transitory storage machine for a computing system, the non-transitory storage machine comprising: one or more non-transitory storage devices having instructions stored thereon executable by a logic machine of the computing system to …” should fix this 101 issue.
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 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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, 10, 19 are rejected under 35 U.S.C. 103 as being unpatentable over: Bailey et al. 2019/0304212; in view of Hipp et al. 2019/0108084; in further view of Rodrigues 2015/0254601.
19/348,336 – Claim 1. Bailey et al. 2019/0304212 teaches A method performed by a computing system for determining a maintenance interval for a subject aircraft configuration (Bailey et al. 2019/0304212 [0023 - the historical maintenance database determines that a particular type of aircraft part is replaced once in a time interval (e.g., a year) on average on various aircraft…]), the method comprising: obtaining sensor data reported by an electronic system location on-board each aircraft of a population of multiple aircraft of the subject aircraft configuration (Bailey et al. 2019/0304212 [0020 - health data analyzer monitors health data received from an aircraft. The health data indicates at least a location of the aircraft, sensor data from sensors on the aircraft, or on-board analysis data from analysis performed on the aircraft][0021 - wiring that connects the temperature sensor to the data collection device of the aircraft, the data collection device…][0031 - A data collection device aboard the aircraft 104 receives the measurements from the sensors and stores the measurements in a memory with timestamps, aircraft operation modes, an identifier of the corresponding sensors…]); obtaining a maintenance task definition that identifies an initial maintenance task for the subject aircraft configuration having a plurality of maintenance subtasks (Bailey et al. 2019/0304212 [0037]), an initial maintenance interval for the maintenance task (Bailey et al. 2019/0304212 [0023-0024; 0044-0045]), and one or more components of the subject aircraft configuration for each maintenance subtask (Bailey et al. 2019/0304212 [0003 - a list of parts associated with the maintenance condition; 0021-0026]); obtaining a failure mode definition that identifies a set of failure modes involving one or more components of the subject aircraft configuration for each of the plurality of maintenance subtasks (Bailey et al. 2019/0304212 [0030 - aircraft health data analyzer 120 is configured to query the maintenance database 106 for a parts list 155 indicating parts that could potentially be associated with the maintenance condition][0036 - the maintenance database 106 includes an aircraft parts table, a parts table, and a condition table. The aircraft parts table includes an aircraft ID column and a part ID column that indicate identifiers of parts included in various aircraft. The parts table includes a part ID column and a part type column that indicate part types of various aircraft parts. The condition table includes a part type column and a condition ID column that indicates types of aircraft parts associated with various maintenance conditions]); for the set of failure modes of a maintenance subtask of the plurality of maintenance subtasks, determining a maintenance interval for the maintenance subtask across the set of failure modes by performing a subtask process that includes (Bailey et al. 2019/0304212 [0022; 0047; 0092; 0133]): for each failure mode of the set of failure modes of the maintenance subtask (Bailey et al. 2019/0304212 [0019; 0022; 0025; 0030; 0067]), implementing one of a plurality of predictive models at the computing system to determine a life-time probability distribution of the failure mode based, at least in part, on the sensor data (Bailey et al. 2019/0304212 [0022; 0023 - health data analyzer determines a predicted replacement schedule of the parts indicated by the list. For example, the historical maintenance database determines that a particular type of aircraft part is replaced once in a time interval (e.g., a year) on average on various aircraft][0030; 0044-0049]), and determining a maintenance interval for the one or more components of the maintenance subtask based, at least in part, on the life-time probability distribution determined for each failure mode of the set of failure modes (Bailey et al. 2019/0304212 [0022 - failure rates correspond to replacement rates, predicted replacement dates, remaining proportions of predicted replacement intervals][0022-0023; 0030; 0044-0049]); and outputting the maintenance interval for the maintenance subtask (Bailey et al. 2019/0304212 [0003; 0025; 0029-0030; 0049; Fig. 7]).
Bailey et al. 2019/0304212 may not expressly disclose the “failure mode definition” features, however, Hipp et al. 2019/0108084 teaches (Hipp et al. 2019/0108084 [0011 - component failure modes descriptions] In a further possible embodiment the analytical artifacts are provided by transforming at least one system evaluation criterion into one or more corresponding relevant state patterns at ports at the system boundary and/or inside of the system of interest and by generating the analytical artifact on the basis of the relevant state patterns and on the basis of the component failure modes descriptions of the components of the system of interest.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bailey et al. 2019/0304212 to include the features as taught by Hipp et al. 2019/0108084. One of ordinary skill in the art would have been motivated to do so to implement determining maintenance intervals using a combination of models which should prove to improve user experience, maximize profits, and optimize revenue.
Bailey et al. 2019/0304212 may not expressly disclose the “lifetime-probability distribution” features, however, Rodrigues 2015/0254601 teaches (Rodrigues 2015/0254601 [0013 - degradation index of a component monitored by a PHM system, the failure threshold and the estimated Remaining Useful Life probability distribution] FIG. 1 shows the example evolution of the degradation index of a component monitored by a PHM system, the failure threshold and the estimated Remaining Useful Life probability distribution; [0003 - technology herein relates to data processing systems that automatically determine when aircraft components should be replaced to avoid failures] The technology herein relates to data processing systems that automatically determine when aircraft components should be replaced to avoid failures.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bailey et al. 2019/0304212 to include the features as taught by Rodrigues 2015/0254601. One of ordinary skill in the art would have been motivated to do so to implement determining maintenance intervals using a combination of models which should prove to improve user experience, maximize profits, and optimize revenue.
19/348,336 – Claim 10. Bailey et al. 2019/0304212 further teaches A computing system of one or more computing devices, comprising: a logic machine; and a storage machine having instructions stored thereon executable by the logic machine to (Bailey et al. 2019/0304212 [0005; 0018; 0027; 0069; 0153-0155; Figs. 4, 12, 27]): … obtain sensor data reported by an electronic system location on-board each aircraft of a population of multiple aircraft of the subject aircraft configuration; obtain a maintenance task definition that identifies an initial maintenance task for the subject aircraft configuration having a plurality of maintenance subtasks, an initial maintenance interval for the maintenance task, and one or more components of the subject aircraft configuration for each maintenance subtask; obtain a failure mode definition that identifies a set of failure modes involving one or more components of the subject aircraft configuration for each of the plurality of maintenance subtasks; for the set of failure modes of a maintenance subtask of the plurality of maintenance subtasks, determine a maintenance interval for the maintenance subtask across the set of failure modes by performing a subtask process that includes: for each failure mode of the set of failure modes of the maintenance subtask, implement one of a plurality of predictive models at the computing system to determine a life-time probability distribution of the failure mode based, at least in part, on the sensor data, and determine a maintenance interval for the one or more components of the maintenance subtask based, at least in part, on the life-time probability distribution determined for each failure mode of the set of failure modes; and output the maintenance interval for the maintenance subtask.
19/348,336 – Claim 19. Bailey et al. 2019/0304212 further teaches A storage machine for a computing system, the storage machine comprising: one or more storage devices having instructions stored thereon executable by a logic machine of the computing system to (Bailey et al. 2019/0304212 [0005; 0018; 0027; 0069; 0153-0155; Figs. 4, 12, 27]): … obtain sensor data reported by an electronic system location on-board each aircraft of a population of multiple aircraft of the subject aircraft configuration; obtain a maintenance task definition that identifies an initial maintenance task for the subject aircraft configuration having a plurality of maintenance subtasks, an initial maintenance interval for the maintenance task, and one or more components of the subject aircraft configuration for each maintenance subtask; obtain a failure mode definition that identifies a set of failure modes involving one or more components of the subject aircraft configuration for each of the plurality of maintenance subtasks; for the set of failure modes of a maintenance subtask of the plurality of maintenance subtasks, determine a maintenance interval for the maintenance subtask across the set of failure modes by performing a subtask process that includes: for each failure mode of the set of failure modes of the maintenance subtask, implement one of a plurality of predictive models at the computing system to determine a life-time probability distribution of the failure mode based, at least in part, on the sensor data, and determine a maintenance interval for the one or more components of the maintenance subtask based, at least in part, on the life-time probability distribution determined for each failure mode of the set of failure modes; and output the maintenance interval for the maintenance subtask.
Claims 10 and 19, have similar limitations as of Claim 1, therefore they are REJECTED under the same rationale as Claim 1.
Claims 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over: Bailey et al. 2019/0304212; in view of Hipp et al. 2019/0108084; in further view of Rodrigues 2015/0254601; in view of Doulatshahi et al. 2008/0147264.
19/348,336 – Claim 2. Bailey et al. 2019/0304212 further teaches The method of claim 1, further comprising: determining an adjusted maintenance interval for the maintenance task that differs from the initial maintenance interval, wherein the adjusted maintenance interval is based, at least in part, on the maintenance interval of the maintenance subtask (Bailey et al. 2019/0304212 [0022 - failure rates correspond to replacement rates, predicted replacement dates, remaining proportions of predicted replacement intervals][0022-0023; 0030; 0044-0049]); and outputting the adjusted maintenance interval for the maintenance task (Bailey et al. 2019/0304212 [0003; 0025; 0029-0030; 0049; Fig. 7]).
Bailey et al. 2019/0304212 may not expressly disclose the “adjusted maintenance interval for the maintenance task” features, however, Doulatshahi et al. 2008/0147264 teaches (Doulatshahi et al. 2008/0147264 [0005 - a method of managing maintenance of a fleet of aircraft by a business entity for a plurality of customers includes collecting data from at least one aircraft in each of the fleets related to the operation of the aircraft, determining a range of acceptable values of performance parameters associated with the collected data and based on the collected data, analyzing the collected data having values outside the range of acceptable values, and modifying at least one of a maintenance requirement and an interval between maintenance actions to facilitate reducing the number of performance parameters values that are outside the range of acceptable values during future operation of the aircraft][0007 - analyzing the stored data for values and trends that exceed the determined acceptable ranges of values and acceptable trends of the performance data and maintenance information, and modifying an interval between maintenance actions to facilitate reducing the number of performance data and maintenance information that are outside the range of acceptable values during future operation of the aircraft.]). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bailey et al. 2019/0304212 to include the features as taught by Doulatshahi et al. 2008/0147264. One of ordinary skill in the art would have been motivated to do so to implement determining maintenance intervals using a combination of models which should prove to improve user experience, maximize profits, and optimize revenue.
19/348,336 – Claim 11. The computing system of claim 10, wherein the instructions are further executable by the logic machine to: determine an adjusted maintenance interval for the maintenance task that differs from the initial maintenance interval, wherein the adjusted maintenance interval is based, at least in part, on the maintenance interval of the maintenance subtask; and output the adjusted maintenance interval for the maintenance task.
Claim 11, has similar limitations as of Claim 2, therefore it is REJECTED under the same rationale as Claim 2.
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over: Bailey et al. 2019/0304212; in view of Hipp et al. 2019/0108084; in further view of Rodrigues 2015/0254601.
19/348,336 – Claim 3. Bailey et al. 2019/0304212 further teaches The method of claim 1, further comprising: performing the subtask process for each other maintenance subtask of the plurality of maintenance subtasks to determine the maintenance interval for the one or more components of that other maintenance subtask (Bailey et al. 2019/0304212 [0022-0023; 0030; 0044-0049]); and outputting the maintenance interval for each other maintenance subtask of the plurality of maintenance subtasks (Bailey et al. 2019/0304212 [0003; 0025; 0029-0030; 0049; Fig. 7]).
19/348,336 – Claim 12. The computing system of claim 10, wherein the instructions are further executable by the logic machine to: perform the subtask process for each other maintenance subtask of the plurality of maintenance subtasks to determine the maintenance interval for the one or more components of that other maintenance subtask; and output the maintenance interval for each other maintenance subtask of the plurality of maintenance subtasks.
Claim 12, has similar limitations as of Claim 3, therefore it is REJECTED under the same rationale as Claim 3.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over: Bailey et al. 2019/0304212; in view of Hipp et al. 2019/0108084; in further view of Rodrigues 2015/0254601; in view of Doulatshahi et al. 2008/0147264.
19/348,336 – Claim 4. Bailey et al. 2019/0304212 further teaches The method claim 3, further comprising: determining an adjusted maintenance interval for the maintenance task that differs from the initial maintenance interval, wherein the adjusted maintenance interval is based, at least in part, on the maintenance interval of each maintenance subtask of the plurality of maintenance subtasks (Bailey et al. 2019/0304212 [0022-0023; 0030; 0044-0049]); and outputting the adjusted maintenance interval for the maintenance task (Bailey et al. 2019/0304212 [0003; 0025; 0029-0030; 0049; Fig. 7]).
Bailey et al. 2019/0304212 may not expressly disclose the “adjusted maintenance interval for the maintenance task” features, however, Doulatshahi et al. 2008/0147264 teaches (Doulatshahi et al. 2008/0147264 [0005 - a method of managing maintenance of a fleet of aircraft by a business entity for a plurality of customers includes collecting data from at least one aircraft in each of the fleets related to the operation of the aircraft, determining a range of acceptable values of performance parameters associated with the collected data and based on the collected data, analyzing the collected data having values outside the range of acceptable values, and modifying at least one of a maintenance requirement and an interval between maintenance actions to facilitate reducing the number of performance parameters values that are outside the range of acceptable values during future operation of the aircraft][0007 - analyzing the stored data for values and trends that exceed the determined acceptable ranges of values and acceptable trends of the performance data and maintenance information, and modifying an interval between maintenance actions to facilitate reducing the number of performance data and maintenance information that are outside the range of acceptable values during future operation of the aircraft.]). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bailey et al. 2019/0304212 to include the features as taught by Doulatshahi et al. 2008/0147264. One of ordinary skill in the art would have been motivated to do so to implement determining maintenance intervals using a combination of models which should prove to improve user experience, maximize profits, and optimize revenue.
19/348,336 – Claim 13. The computing system claim 12, wherein the instructions are further executable by the logic machine to: determine an adjusted maintenance interval for the maintenance task that differs from the initial maintenance interval, wherein the adjusted maintenance interval is based, at least in part, on the maintenance interval of each maintenance subtask of the plurality of maintenance subtasks; and output the adjusted maintenance interval for the maintenance task.
Claim 13, has similar limitations as of Claim 4, therefore it is REJECTED under the same rationale as Claim 4.
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over: Bailey et al. 2019/0304212; in view of Hipp et al. 2019/0108084; in further view of Rodrigues 2015/0254601; in view of Doulatshahi et al. 2008/0147264.
19/348,336 – Claim 5. Bailey et al. 2019/0304212 further teaches The method of claim 4, wherein the adjusted maintenance interval is based on the maintenance interval of a maintenance subtask of the plurality of maintenance subtasks having the shortest duration among the plurality of maintenance subtasks (Bailey et al. 2019/0304212 [0022-0023; 0030; 0044-0049]).
Bailey et al. 2019/0304212 may not expressly disclose the “adjusted maintenance interval” features, however, Doulatshahi et al. 2008/0147264 teaches (Doulatshahi et al. 2008/0147264 [0005 - a method of managing maintenance of a fleet of aircraft by a business entity for a plurality of customers includes collecting data from at least one aircraft in each of the fleets related to the operation of the aircraft, determining a range of acceptable values of performance parameters associated with the collected data and based on the collected data, analyzing the collected data having values outside the range of acceptable values, and modifying at least one of a maintenance requirement and an interval between maintenance actions to facilitate reducing the number of performance parameters values that are outside the range of acceptable values during future operation of the aircraft][0007 - analyzing the stored data for values and trends that exceed the determined acceptable ranges of values and acceptable trends of the performance data and maintenance information, and modifying an interval between maintenance actions to facilitate reducing the number of performance data and maintenance information that are outside the range of acceptable values during future operation of the aircraft.]). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bailey et al. 2019/0304212 to include the features as taught by Doulatshahi et al. 2008/0147264. One of ordinary skill in the art would have been motivated to do so to implement determining maintenance intervals using a combination of models which should prove to improve user experience, maximize profits, and optimize revenue.
19/348,336 – Claim 14. The computing system of claim 13, wherein the adjusted maintenance interval is based on the maintenance interval of a maintenance subtask of the plurality of maintenance subtasks having the shortest duration among the plurality of maintenance subtasks.
Claim 14, has similar limitations as of Claim 5, therefore it is REJECTED under the same rationale as Claim 5.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over: Bailey et al. 2019/0304212; in view of Hipp et al. 2019/0108084; in further view of Rodrigues 2015/0254601; in view of Aalund et al. US 10,124,893.
19/348,336 – Claim 6. Bailey et al. 2019/0304212 further teaches The method of claim 1, wherein the plurality of predictive models includes at least two or more of: a minor-evident model that considers a magnitude of a failure of the component, a condition-based model that considers whether a condition has been met on a per-aircraft basis based on sensor data obtained from the aircraft, a risk-equivalent model that considers in-service risk (Bailey et al. 2019/0304212 [0020-0021; 0031; 0102 - sensors][0022 – The failure rates correspond to replacement rates, predicted replacement dates, remaining proportions of predicted replacement intervals, or any combination thereof]).
Bailey et al. 2019/0304212 may not expressly disclose the “predictive model” features, however, Aalund et al. US 10,124,893 discloses different predictive models and failure types ("The collected sensor data may be used as input for any suitable combination of the predictive model(s) associated with the system/subsystem to produce an output corresponding to a likelihood and/or time by which (or a time period within which) the system/subsystem is likely to fail" col. 3:24-29; "the outputs (e.g., failure predictions) of multiple predictive models may be combined (e.g., averaged, weighted and combined, etc.) to produce a single output (e.g., a single failure prediction)" col. 3:30-33; "the corrective actions may be identified based on the failure prediction (indicating a type of failure" col. 3:56-58). Aalund et al. US 10,124,893 further discloses that different tasks may be performed based on the output of the predictive models ("Based on the output of the predictive model(s), one or more corrective actions may be identified and executed. For example, execution of a corrective action may cause the UAV to perform an immediate landing, return to a takeoff location, send a notification to a remote system, request instructions, request remote control from a ground management system, transfer operations to a redundant system" col. 3:41-47). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bailey et al. 2019/0304212 to include the features as taught by Aalund et al. US 10,124,893. One of ordinary skill in the art would have been motivated to do so to implement determining maintenance intervals using a combination of models which should prove to improve user experience, maximize profits, and optimize revenue.
19/348,336 – Claim 15. The computing system of claim 10, wherein the plurality of predictive models includes at least two or more of: a minor-evident model that considers a magnitude of a failure of the component, a condition-based model that considers whether a condition has been met on a per-aircraft basis based on sensor data obtained from the aircraft, a risk-equivalent model that considers in-service risk.
Claim 15, has similar limitations as of Claim 6, therefore it is REJECTED under the same rationale as Claim 6.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over: Bailey et al. 2019/0304212; in view of Hipp et al. 2019/0108084; in further view of Rodrigues 2015/0254601; in view of Durant et al. 2022/0028287.
19/348,336 – Claim 6. Bailey et al. 2019/0304212 further teaches The method of claim 1, wherein the plurality of predictive models includes at least two or more of: a minor-evident model that considers a magnitude of a failure of the component, a condition-based model that considers whether a condition has been met on a per-aircraft basis based on sensor data obtained from the aircraft, a risk-equivalent model that considers in-service risk (Bailey et al. 2019/0304212 [0020-0021; 0031; 0102 - sensors][0022 – The failure rates correspond to replacement rates, predicted replacement dates, remaining proportions of predicted replacement intervals, or any combination thereof]).
Bailey et al. 2019/0304212 may not expressly disclose the “predictive model” features, however, Durant et al. 2022/0028287 teaches (Durant et al. 2022/0028287 [0261 - prediction model… of the present disclosure may assist to recommend flight maintenance interventions … when they are most needed … rather than when the results of damage of the aircraft engine are observed by sensors…] Embodiments of the present disclosure may automatically estimate an exposure of ice water content of the at least one aircraft flight along any given flight trajectory in real time using the weather prediction model. Embodiments of the present disclosure may assist, when in operation, for aircraft flight planning and adjusting the flight trajectory of the at least one aircraft flight according to the expected ice water content exposure to obtain a modified flight trajectory. Embodiments of the present disclosure may reduce or mitigate the risk of ice water content exposure to the at least one aircraft flight by adjusting the aircraft flight planning. Embodiments of the present disclosure may prevent damages to the aircraft engines or in-flight failures, and thereby increases the aircraft engine's lifetime. Embodiments of the present disclosure may assist to recommend flight maintenance interventions such as engine washes when they are most needed (e.g. immediately after the ice water content risk exposure) rather than when the results of damage of the aircraft engine are observed by sensors. Embodiments of the present disclosure may prioritise maintenance for the at least one aircraft flight according to the exposure rate of the at least one aircraft flight with the ice water contents.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bailey et al. 2019/0304212 to include the features as taught by Durant et al. 2022/0028287. One of ordinary skill in the art would have been motivated to do so to implement determining maintenance intervals using a combination of models which should prove to improve user experience, maximize profits, and optimize revenue.
19/348,336 – Claim 15. The computing system of claim 10, wherein the plurality of predictive models includes at least two or more of: a minor-evident model that considers a magnitude of a failure of the component, a condition-based model that considers whether a condition has been met on a per-aircraft basis based on sensor data obtained from the aircraft, a risk-equivalent model that considers in-service risk.
Claim 15, has similar limitations as of Claim 6, therefore it is REJECTED under the same rationale as Claim 6.
Claims 7and 16 are rejected under 35 U.S.C. 103 as being unpatentable over: Bailey et al. 2019/0304212; in view of Hipp et al. 2019/0108084; in further view of Rodrigues 2015/0254601; in view of Aalund et al. US 10,124,893.
19/348,336 – Claim 7. Bailey et al. 2019/0304212 further teaches The method of claim 1, wherein the predictive model implemented to determine the life-time probability distribution of the failure mode is a first predictive model; wherein the life-time probability distribution is a first life-time probability distribution (Bailey et al. 2019/0304212 [0022; 0023 - health data analyzer determines a predicted replacement schedule of the parts indicated by the list. For example, the historical maintenance database determines that a particular type of aircraft part is replaced once in a time interval (e.g., a year) on average on various aircraft][0030; 0044-0049]); and wherein the method further comprises, for each failure mode of the set of failure modes of the maintenance subtask (Bailey et al. 2019/0304212 [0019; 0022; 0025; 0030; 0067]), implementing a second predictive model of the plurality of predictive models at the computing system to determine a second life-time probability distribution of the failure mode based, at least in part, on the sensor data (Bailey et al. 2019/0304212 [0022 - failure rates correspond to replacement rates, predicted replacement dates, remaining proportions of predicted replacement intervals][0022-0023; 0030; 0044-0049]), and determining the maintenance interval for the one or more components of the maintenance subtask further based (Bailey et al. 2019/0304212 [0023-0024; 0044-0045]), at least in part, on the first life-time probability distribution and the second life-time probability distribution determined for each failure mode of the set of failure modes (Bailey et al. 2019/0304212 [0022 - failure rates correspond to replacement rates, predicted replacement dates, remaining proportions of predicted replacement intervals][0022-0023; 0030; 0044-0049]).
Bailey et al. 2019/0304212 may not expressly disclose the “failure mode definition” features, however, Hipp et al. 2019/0108084 teaches (Hipp et al. 2019/0108084 [0011 - component failure modes descriptions] In a further possible embodiment the analytical artifacts are provided by transforming at least one system evaluation criterion into one or more corresponding relevant state patterns at ports at the system boundary and/or inside of the system of interest and by generating the analytical artifact on the basis of the relevant state patterns and on the basis of the component failure modes descriptions of the components of the system of interest.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bailey et al. 2019/0304212 to include the features as taught by Hipp et al. 2019/0108084. One of ordinary skill in the art would have been motivated to do so to implement determining maintenance intervals using a combination of models which should prove to improve user experience, maximize profits, and optimize revenue.
Bailey et al. 2019/0304212 may not expressly disclose the “lifetime-probability distribution” features, however, Rodrigues 2015/0254601 teaches (Rodrigues 2015/0254601 [0013 - degradation index of a component monitored by a PHM system, the failure threshold and the estimated Remaining Useful Life probability distribution] FIG. 1 shows the example evolution of the degradation index of a component monitored by a PHM system, the failure threshold and the estimated Remaining Useful Life probability distribution; [0003 - technology herein relates to data processing systems that automatically determine when aircraft components should be replaced to avoid failures] The technology herein relates to data processing systems that automatically determine when aircraft components should be replaced to avoid failures.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bailey et al. 2019/0304212 to include the features as taught by Rodrigues 2015/0254601. One of ordinary skill in the art would have been motivated to do so to implement determining maintenance intervals using a combination of models which should prove to improve user experience, maximize profits, and optimize revenue.
Bailey et al. 2019/0304212 may not expressly disclose the “predictive model” features, however, Aalund et al. US 10,124,893 discloses different predictive models and failure types ("The collected sensor data may be used as input for any suitable combination of the predictive model(s) associated with the system/subsystem to produce an output corresponding to a likelihood and/or time by which (or a time period within which) the system/subsystem is likely to fail" col. 3:24-29; "the outputs (e.g., failure predictions) of multiple predictive models may be combined (e.g., averaged, weighted and combined, etc.) to produce a single output (e.g., a single failure prediction)" col. 3:30-33; "the corrective actions may be identified based on the failure prediction (indicating a type of failure" col. 3:56-58). Aalund et al. US 10,124,893 further discloses that different tasks may be performed based on the output of the predictive models ("Based on the output of the predictive model(s), one or more corrective actions may be identified and executed. For example, execution of a corrective action may cause the UAV to perform an immediate landing, return to a takeoff location, send a notification to a remote system, request instructions, request remote control from a ground management system, transfer operations to a redundant system" col. 3:41-47). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bailey et al. 2019/0304212 to include the features as taught by Aalund et al. US 10,124,893. One of ordinary skill in the art would have been motivated to do so to implement determining maintenance intervals using a combination of models which should prove to improve user experience, maximize profits, and optimize revenue.
19/348,336 – Claim 16. The computing system of claim 10, wherein the predictive model implemented to determine the life-time probability distribution of the failure mode is a first predictive model; wherein the life-time probability distribution is a first life-time probability distribution; and wherein the instructions are further executable by the logic machine to: for each failure mode of the set of failure modes of the maintenance subtask, implement a second predictive model of the plurality of predictive models at the computing system to determine a second life-time probability distribution of the failure mode based, at least in part, on the sensor data, and determine the maintenance interval for the one or more components of the maintenance subtask further based, at least in part, on the first life-time probability distribution and the second life-time probability distribution determined for each failure mode of the set of failure modes.
Claim 16, has similar limitations as of Claim 7, therefore it is REJECTED under the same rationale as Claim 7.
Claims 7and 16 are rejected under 35 U.S.C. 103 as being unpatentable over: Bailey et al. 2019/0304212; in view of Hipp et al. 2019/0108084; in further view of Rodrigues 2015/0254601; in view of Durant et al. 2022/0028287.
19/348,336 – Claim 7. Bailey et al. 2019/0304212 further teaches The method of claim 1, wherein the predictive model implemented to determine the life-time probability distribution of the failure mode is a first predictive model; wherein the life-time probability distribution is a first life-time probability distribution (Bailey et al. 2019/0304212 [0022; 0023 - health data analyzer determines a predicted replacement schedule of the parts indicated by the list. For example, the historical maintenance database determines that a particular type of aircraft part is replaced once in a time interval (e.g., a year) on average on various aircraft][0030; 0044-0049]); and wherein the method further comprises, for each failure mode of the set of failure modes of the maintenance subtask (Bailey et al. 2019/0304212 [0019; 0022; 0025; 0030; 0067]), implementing a second predictive model of the plurality of predictive models at the computing system to determine a second life-time probability distribution of the failure mode based, at least in part, on the sensor data (Bailey et al. 2019/0304212 [0022 - failure rates correspond to replacement rates, predicted replacement dates, remaining proportions of predicted replacement intervals][0022-0023; 0030; 0044-0049]), and determining the maintenance interval for the one or more components of the maintenance subtask further based (Bailey et al. 2019/0304212 [0023-0024; 0044-0045]), at least in part, on the first life-time probability distribution and the second life-time probability distribution determined for each failure mode of the set of failure modes (Bailey et al. 2019/0304212 [0022 - failure rates correspond to replacement rates, predicted replacement dates, remaining proportions of predicted replacement intervals][0022-0023; 0030; 0044-0049]).
Bailey et al. 2019/0304212 may not expressly disclose the “failure mode definition” features, however, Hipp et al. 2019/0108084 teaches (Hipp et al. 2019/0108084 [0011 - component failure modes descriptions] In a further possible embodiment the analytical artifacts are provided by transforming at least one system evaluation criterion into one or more corresponding relevant state patterns at ports at the system boundary and/or inside of the system of interest and by generating the analytical artifact on the basis of the relevant state patterns and on the basis of the component failure modes descriptions of the components of the system of interest.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bailey et al. 2019/0304212 to include the features as taught by Hipp et al. 2019/0108084. One of ordinary skill in the art would have been motivated to do so to implement determining maintenance intervals using a combination of models which should prove to improve user experience, maximize profits, and optimize revenue.
Bailey et al. 2019/0304212 may not expressly disclose the “lifetime-probability distribution” features, however, Rodrigues 2015/0254601 teaches (Rodrigues 2015/0254601 [0013 - degradation index of a component monitored by a PHM system, the failure threshold and the estimated Remaining Useful Life probability distribution] FIG. 1 shows the example evolution of the degradation index of a component monitored by a PHM system, the failure threshold and the estimated Remaining Useful Life probability distribution; [0003 - technology herein relates to data processing systems that automatically determine when aircraft components should be replaced to avoid failures] The technology herein relates to data processing systems that automatically determine when aircraft components should be replaced to avoid failures.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bailey et al. 2019/0304212 to include the features as taught by Rodrigues 2015/0254601. One of ordinary skill in the art would have been motivated to do so to implement determining maintenance intervals using a combination of models which should prove to improve user experience, maximize profits, and optimize revenue.
Bailey et al. 2019/0304212 may not expressly disclose the “predictive model” features, however, Durant et al. 2022/0028287 teaches (Durant et al. 2022/0028287 [0261 - prediction model… of the present disclosure may assist to recommend flight maintenance interventions … when they are most needed … rather than when the results of damage of the aircraft engine are observed by sensors…] Embodiments of the present disclosure may automatically estimate an exposure of ice water content of the at least one aircraft flight along any given flight trajectory in real time using the weather prediction model. Embodiments of the present disclosure may assist, when in operation, for aircraft flight planning and adjusting the flight trajectory of the at least one aircraft flight according to the expected ice water content exposure to obtain a modified flight trajectory. Embodiments of the present disclosure may reduce or mitigate the risk of ice water content exposure to the at least one aircraft flight by adjusting the aircraft flight planning. Embodiments of the present disclosure may prevent damages to the aircraft engines or in-flight failures, and thereby increases the aircraft engine's lifetime. Embodiments of the present disclosure may assist to recommend flight maintenance interventions such as engine washes when they are most needed (e.g. immediately after the ice water content risk exposure) rather than when the results of damage of the aircraft engine are observed by sensors. Embodiments of the present disclosure may prioritise maintenance for the at least one aircraft flight according to the exposure rate of the at least one aircraft flight with the ice water contents.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bailey et al. 2019/0304212 to include the features as taught by Durant et al. 2022/0028287. One of ordinary skill in the art would have been motivated to do so to implement determining maintenance intervals using a combination of models which should prove to improve user experience, maximize profits, and optimize revenue.
19/348,336 – Claim 16. The computing system of claim 10, wherein the predictive model implemented to determine the life-time probability distribution of the failure mode is a first predictive model; wherein the life-time probability distribution is a first life-time probability distribution; and wherein the instructions are further executable by the logic machine to: for each failure mode of the set of failure modes of the maintenance subtask, implement a second predictive model of the plurality of predictive models at the computing system to determine a second life-time probability distribution of the failure mode based, at least in part, on the sensor data, and determine the maintenance interval for the one or more components of the maintenance subtask further based, at least in part, on the first life-time probability distribution and the second life-time probability distribution determined for each failure mode of the set of failure modes.
Claim 16, has similar limitations as of Claim 7, therefore it is REJECTED under the same rationale as Claim 7.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over: Bailey et al. 2019/0304212; in view of Hipp et al. 2019/0108084; in further view of Rodrigues 2015/0254601; in view of Aalund et al. US 10,124,893.
19/348,336 – Claim 8. Bailey et al. 2019/0304212 further teaches The method of claim 7, wherein the plurality of predictive models includes at least two or more of: a minor-evident model that considers a magnitude of a failure of the component, a condition-based model that considers whether a condition has been met on a per-aircraft basis based on sensor data obtained from the aircraft, a risk-equivalent model that considers in-service risk (Bailey et al. 2019/0304212 [0031; 0102 - sensor][0038 – maintenance condition][0075 – components][0078 – algorithm (interpreted as predictive models)]).
Bailey et al. 2019/0304212 may not expressly disclose the “predictive model” features, however, Aalund et al. US 10,124,893 discloses different predictive models and failure types ("The collected sensor data may be used as input for any suitable combination of the predictive model(s) associated with the system/subsystem to produce an output corresponding to a likelihood and/or time by which (or a time period within which) the system/subsystem is likely to fail" col. 3:24-29; "the outputs (e.g., failure predictions) of multiple predictive models may be combined (e.g., averaged, weighted and combined, etc.) to produce a single output (e.g., a single failure prediction)" col. 3:30-33; "the corrective actions may be identified based on the failure prediction (indicating a type of failure" col. 3:56-58). Aalund et al. US 10,124,893 further discloses that different tasks may be performed based on the output of the predictive models ("Based on the output of the predictive model(s), one or more corrective actions may be identified and executed. For example, execution of a corrective action may cause the UAV to perform an immediate landing, return to a takeoff location, send a notification to a remote system, request instructions, request remote control from a ground management system, transfer operations to a redundant system" col. 3:41-47). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bailey et al. 2019/0304212 to include the features as taught by Aalund et al. US 10,124,893. One of ordinary skill in the art would have been motivated to do so to implement determining maintenance intervals using a combination of models which should prove to improve user experience, maximize profits, and optimize revenue.
19/348,336 – Claim 17. The computing system of claim 16, wherein the plurality of predictive models includes at least two or more of: a minor-evident model that considers a magnitude of a failure of the component, a condition-based model that considers whether a condition has been met on a per-aircraft basis based on sensor data obtained from the aircraft, a risk-equivalent model that considers in-service risk.
Claim 17, has similar limitations as of Claim 8, therefore it is REJECTED under the same rationale as Claim 8.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over: Bailey et al. 2019/0304212; in view of Hipp et al. 2019/0108084; in further view of Rodrigues 2015/0254601; in view of Durant et al. 2022/0028287.
19/348,336 – Claim 8. Bailey et al. 2019/0304212 further teaches The method of claim 7, wherein the plurality of predictive models includes at least two or more of: a minor-evident model that considers a magnitude of a failure of the component, a condition-based model that considers whether a condition has been met on a per-aircraft basis based on sensor data obtained from the aircraft, a risk-equivalent model that considers in-service risk (Bailey et al. 2019/0304212 [0031; 0102 - sensor][0038 – maintenance condition][0075 – components][0078 – algorithm (interpreted as predictive models)]).
Bailey et al. 2019/0304212 may not expressly disclose the “predictive model” features, however, Durant et al. 2022/0028287 teaches (Durant et al. 2022/0028287 [0261 - prediction model… of the present disclosure may assist to recommend flight maintenance interventions … when they are most needed … rather than when the results of damage of the aircraft engine are observed by sensors…] Embodiments of the present disclosure may automatically estimate an exposure of ice water content of the at least one aircraft flight along any given flight trajectory in real time using the weather prediction model. Embodiments of the present disclosure may assist, when in operation, for aircraft flight planning and adjusting the flight trajectory of the at least one aircraft flight according to the expected ice water content exposure to obtain a modified flight trajectory. Embodiments of the present disclosure may reduce or mitigate the risk of ice water content exposure to the at least one aircraft flight by adjusting the aircraft flight planning. Embodiments of the present disclosure may prevent damages to the aircraft engines or in-flight failures, and thereby increases the aircraft engine's lifetime. Embodiments of the present disclosure may assist to recommend flight maintenance interventions such as engine washes when they are most needed (e.g. immediately after the ice water content risk exposure) rather than when the results of damage of the aircraft engine are observed by sensors. Embodiments of the present disclosure may prioritise maintenance for the at least one aircraft flight according to the exposure rate of the at least one aircraft flight with the ice water contents.). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bailey et al. 2019/0304212 to include the features as taught by Durant et al. 2022/0028287. One of ordinary skill in the art would have been motivated to do so to implement determining maintenance intervals using a combination of models which should prove to improve user experience, maximize profits, and optimize revenue.
19/348,336 – Claim 17. The computing system of claim 16, wherein the plurality of predictive models includes at least two or more of: a minor-evident model that considers a magnitude of a failure of the component, a condition-based model that considers whether a condition has been met on a per-aircraft basis based on sensor data obtained from the aircraft, a risk-equivalent model that considers in-service risk.
Claim 17, has similar limitations as of Claim 8, therefore it is REJECTED under the same rationale as Claim 8.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over: Bailey et al. 2019/0304212; in view of Hipp et al. 2019/0108084; in further view of Rodrigues 2015/0254601,
19/348,336 – Claim 9. Bailey et al. 2019/0304212 further teaches The method of claim 1, wherein the sensor data is obtained via a set of sensors located on-board each aircraft of the population of multiple aircraft of the subject aircraft configuration (Bailey et al. 2019/0304212 [0020 - sensor data from sensors on the aircraft, or on-board analysis data from analysis performed on the aircraft][0021; 0031 - sensors][0032 - A data analyzer aboard the aircraft 104 is configured to generate on-board analysis data by analyzing the sensor data.]).
19/348,336 – Claim 18. The computing of claim 10, wherein the sensor data is obtained via a set of sensors located on-board each aircraft of the population of multiple aircraft of the subject aircraft configuration.
Claim 18, has similar limitations as of Claim 9, therefore it is REJECTED under the same rationale as Claim 9.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over: Bailey et al. 2019/0304212; in view of Hipp et al. 2019/0108084; in further view of Rodrigues 2015/0254601; in view of Doulatshahi et al. 2008/0147264.
19/348,336 – Claim 20. Bailey et al. 2019/0304212 further teaches The storage machine of claim 19, wherein the instructions are further executable by the logic machine to: perform the subtask process for each other maintenance subtask of the plurality of maintenance subtasks to determine the maintenance interval for the one or more components of that other maintenance subtask (Bailey et al. 2019/0304212 [0022-0023; 0030; 0044-0049]); output the maintenance interval for each other maintenance subtask of the plurality of maintenance subtasks (Bailey et al. 2019/0304212 [0003; 0025; 0029-0030; 0049; Fig. 7]); determine an adjusted maintenance interval for the maintenance task that differs from the initial maintenance interval, wherein the adjusted maintenance interval is based, at least in part, on the maintenance interval of each maintenance subtask of the plurality of maintenance subtasks (Bailey et al. 2019/0304212 [0022-0023; 0030; 0044-0049]); and output the adjusted maintenance interval for the maintenance task (Bailey et al. 2019/0304212 [0003; 0025; 0029-0030; 0049; Fig. 7]).
Bailey et al. 2019/0304212 may not expressly disclose the “adjusted maintenance interval for the maintenance task” features, however, Doulatshahi et al. 2008/0147264 teaches (Doulatshahi et al. 2008/0147264 [0005 - a method of managing maintenance of a fleet of aircraft by a business entity for a plurality of customers includes collecting data from at least one aircraft in each of the fleets related to the operation of the aircraft, determining a range of acceptable values of performance parameters associated with the collected data and based on the collected data, analyzing the collected data having values outside the range of acceptable values, and modifying at least one of a maintenance requirement and an interval between maintenance actions to facilitate reducing the number of performance parameters values that are outside the range of acceptable values during future operation of the aircraft][0007 - analyzing the stored data for values and trends that exceed the determined acceptable ranges of values and acceptable trends of the performance data and maintenance information, and modifying an interval between maintenance actions to facilitate reducing the number of performance data and maintenance information that are outside the range of acceptable values during future operation of the aircraft.]). Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bailey et al. 2019/0304212 to include the features as taught by Doulatshahi et al. 2008/0147264. One of ordinary skill in the art would have been motivated to do so to implement determining maintenance intervals using a combination of models which should prove to improve user experience, maximize profits, and optimize revenue.
Examiner’s Response to Arguments
Per Applicants’ amendments/arguments, the rejections are withdrawn.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Examiner’s Response: Claim Rejections – 35 USC §112
Per Applicants’ amendments/arguments, the rejections are withdrawn.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Examiner’s Response: Claim Rejections – 35 USC §101
Per Applicants’ amendments/arguments, the rejections are withdrawn. See notes above for additional reasoning and rationale for dropping 35 USC 101 rejection including Applicant’s amendments, arguments, lack of abstract idea, and practical integration.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Regarding Claims 1-15, on page(s) 6-12 of Applicant’s Remarks (dated 12/27/2016), Applicants traverse the 35 USC §101 rejections arguing the following:
Examiner’s Response: Claim Rejections – 35 USC § 102 / § 103
Per Applicants’ amendments/arguments, the rejections are withdrawn. See notes above for additional reasoning and rationale for dropping prior-art rejection including Applicant’s amendments and arguments and unique combination of features and elements not taught by the prior-art without hindsight reasoning.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Regarding Claim X, on page(s) 8-9 of Applicant’s Remarks / After Final Amendments (dated 07/15/2011), Applicant(s) argues that the cited reference(s) (Ellis and Vandermolen) fails to teach, describe, or suggest the amended features. Specifically, Applicant(s) argues that cited reference(s) do not teach, describe, or suggest the following: . With respect, Applicant’s arguments are deemed unpersuasive and the amended feature(s) remain rejected as follows.
With respect, Applicant’s arguments are deemed unpersuasive and the amended feature(s) remain rejected as follows.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
PERTINENT PRIOR ART – Patent Literature
The prior-art made of record and considered pertinent to applicant's disclosure.
Aalund et al. US 10,124,893 teaches A method performed by a computing system for determining a maintenance interval for a subject aircraft configuration ("determining whether one or more corrective actions are to be performed. In some cases, this determination may be based on an assessment of the real-time health of a UAV during execution of a mission plan, as well as a predicted a future state of one or more subsystems of the UAV" col. 2:11-16), the method comprising: obtaining sensor data reported by an electronic system of a population of the subject aircraft configuration ("the UAV may include sensors configured to collect data related to external forces, strain, structural integrity, vibration, temperature, humidity, electric current, voltage, rotations per minute (or other suitable rotation rate), imagery" col. 2:46-49; "The stored predictive model(s) may be trained utilizing historical data of the UAV and/or a group of UAVs to predict failure of the system and/or subsystem" col. 3:6-9); obtaining a failure mode definition that identifies a set of failure modes involving a component of the subject aircraft configuration ("the corrective actions may be identified based on the failure prediction (indicating a type of failure" col. 3:56-58 - it is considered that the definition of the failure modes are known since the failures are categorized in different types); implementing a first predictive model at the computing system to determine a first lifetime-probability distribution of a failure mode of the set of failure modes involving the component based, at least in part, on the sensor data ("Each predictive model may utilize the same, or a different, set of sensor data as input ... the UAV may utilize the first, less resource intensive, predictive model to determine a failure prediction" col. 4:36-40); implementing a second predictive model at the computing system that differs from the first predictive model to determine a second lifetime-probability distribution of a failure mode of the set of failure modes involving the component based, at least in part, on the sensor data ("Each predictive model may utilize the same, or a different, set of sensor data as input ... the second predictive model may be utilized by the UAV to determine a more accurate failure prediction" col. 4:36-43); determining a maintenance interval for the component based, at least in part, on the first lifetime-probability distribution and the second lifetime-probability distribution ("the outputs (e.g., failure predictions) of multiple predictive models may be combined (e.g., averaged, weighted and combined, etc.) to produce a single output (e.g., a single failure prediction)" col. 3:30-33; "the UAV may determine a failure prediction (e.g., a time at which a subsystem is likely to fail) utilizing the predictive models in a similar manner as described above" col. 4:47-50); and outputting the maintenance interval ("The collected sensor data may be used as input for any suitable combination of the predictive model(s) associated with the system/subsystem to produce an output corresponding to a likelihood and/or time by which (or a time period within which) the system/subsystem is likely to fail" col. 3:24-29).
Owens et al. 2017/0291722 [0018 - various sub-systems can each include one or more sensors to facilitate measurement and generation of data pertaining to operation of that sub-system of the aircraft 100 (and/or a component of that sub-system), to assist in performing diagnostics and health monitoring of one or more sub-systems]
Bushkov, JR. et al. 2023/0146900 [0050 - retrieving a plurality of data entries from an aircraft, the plurality of data entries comprising semantic information about a plurality of recurrent aircraft faults. The data entries may be from a pilot log. The entries may be from a maintenance log. The entries may be from aircraft fault sensors. The sensors may be pressure sensors, temperature sensors, force sensors, torque sensors, speed sensors, position and displacement sensors, level sensors, proximity sensors, multimeters, oscilloscopes, discharge probes, frequency generators, or other sensors]
LU et al. 2019/0092459 [0001 - failure prediction with an indicator as to why the failure prediction was made]
Mojtahedzadeh et al. 2020/0210968 [0001 - Examples of such vehicles include but are not limited to aircraft, air-cargo vehicles, automotive, unmanned aerial vehicles (UAV), maritime vehicles (ships, submarines, etc.), etc. In the case of an aircraft, maintenance planning data (MPD) is often provided to the operator of an aircraft by the manufacturer, and typically includes information related to recommended scheduled maintenance tasks, their intervals, required access, and other relevant information. Maintenance tasks often are grouped and performed in packages identified as “check packages”]
PERTINENT PRIOR ART – Non-Patent Literature (NPL)
The NPL prior-art made of record and considered pertinent to applicant's disclosure.
M. G. Walker, "Next generation prognostics and health management for unmanned aircraft," 2010 IEEE Aerospace Conference, Big Sky, MT, USA, 2010, pp. 1-14, doi: 10.1109/AERO.2010.5446842.
THIS ACTION IS MADE FINAL
Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
THIS ACTION IS MADE FINAL
Applicant’s amendment necessitated new grounds of rejection and FINAL Rejection.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW T. SITTNER whose telephone number is (571) 270-7137 and email: matthew.sittner@uspto.gov. The examiner can normally be reached on Monday-Friday, 8:00am - 5:00pm (Mountain Time Zone). Please schedule interview requests via email: matthew.sittner@uspto.gov
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah M. Monfeldt can be reached on (571) 270-1833.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MATTHEW T SITTNER/
Primary Examiner, Art Unit 3629b
Prosecution Insights