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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 X are new.
Claims 1-20 are pending and have been examined.
This action is in reply to the papers filed on 08/30/2024 (effective filing date 02/29, 2024).
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Prior-Art Rejection withdrawn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. 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 machine readable medium for scheduling vehicle maintenance.
Claim 1 recites [a] computer-implemented method for scheduling vehicle maintenance, comprising: receiving a fault code history and a service event history for a set of vehicles, the fault code history including fault code events for each of a set of fault codes, the service event history including service events for each of a set of service event types; correlating subsets of the set of fault codes with subsets of the service events at least partially based on the fault code history and the service event history; assigning a service priority rating to each fault code of the set of fault codes at least partially based on the correlated subset or subsets of the service events, the fault code history, and the service event history; and scheduling a vehicle maintenance event for a vehicle based on the service priority rating for each fault code event for the vehicle.
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 10-19 recite a system/apparatus and, therefore, are directed to the statutory class of machine. Claim 20 recites a non-transitory machine-readable medium 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 computer-implemented method for scheduling vehicle maintenance, comprising:
No additional elements are positively claimed.
receiving a fault code history and a service event history for a set of vehicles, the fault code history including fault code events for each of a set of fault codes, the service event history including service events for each of a set of service event types;
This limitation includes the step(s) of: receiving a fault code history and a service event history for a set of vehicles, the fault code history including fault code events for each of a set of fault codes, the service event history including service events for each of a set of service event types.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information (e.g., receiving, correlating, assigning, scheduling, etc…) to facilitate scheduling vehicle maintenance which may be categorized as any of the following:
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.
correlating subsets of the set of fault codes with subsets of the service events at least partially based on the fault code history and the service event history;
This limitation includes the step(s) of: correlating subsets of the set of fault codes with subsets of the service events at least partially based on the fault code history and the service event history.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information (e.g., receiving, correlating, assigning, scheduling, etc…) to facilitate scheduling vehicle maintenance which may be categorized as any of the following:
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.
assigning a service priority rating to each fault code of the set of fault codes at least partially based on the correlated subset or subsets of the service events, the fault code history, and the service event history; and
This limitation includes the step(s) of: assigning a service priority rating to each fault code of the set of fault codes at least partially based on the correlated subset or subsets of the service events, the fault code history, and the service event history.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information (e.g., receiving, correlating, assigning, scheduling, etc…) to facilitate scheduling vehicle maintenance which may be categorized as any of the following:
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.
scheduling a vehicle maintenance event for a vehicle based on the service priority rating for each fault code event for the vehicle.
This limitation includes the step(s) of: scheduling a vehicle maintenance event for a vehicle based on the service priority rating for each fault code event for the vehicle.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information (e.g., receiving, correlating, assigning, scheduling, etc…) to facilitate scheduling vehicle maintenance which may be categorized as any of the following:
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 scheduling vehicle maintenance, which, pursuant to MPEP 2106.04, is aptly categorized as a mental process and/or a method of organizing human activity. Therefore, under Step 2A, Prong One, the claims recite a judicial exception.
The method claims do NOT recite any additional elements. Consequently, at least the method claims must be construed as abstract and capable of being performed mentally and/or manually with just pen and paper. The Office encourages Applicant to positively claim the structural features necessary to perform each individual method step and feature.
Next, the aforementioned claims recite additional functional elements that are associated with the judicial exception, including: a processor and memory for implementing the system claims. 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: a processor and memory to execute the system and a “non-transitory machine-readable storage device” for storing executable instructions. 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 CRM claim 20 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 and 11-19 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 receiving data (e.g., fault code history and service event history), correlating subsets of data, assigning a service priority, and scheduling a vehicle maintenance event. 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 scheduling vehicle maintenance. 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 § 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, 2, 10, 11, 20 are rejected under 35 U.S.C. 103 as being unpatentable over: Swearingen et al. 2022/0398871; in view of Lake et al. 2020/0380794; in further view of Bhattacharyya et al. 2020/0273268.
18/821,227 – Claim 1. Swearingen et al. 2022/0398871 teaches A computer-implemented method for scheduling vehicle maintenance (Swearingen et al. 2022/0398871 [0028] As explained above, example implementations of the present disclosure relate generally to vehicular engineering and, in particular, to one or more of the design, construction, operation or use of vehicles such as aircraft 100. Thus, referring now to FIG. 2, example implementations may be used in the context of an aircraft manufacturing and service method 200. During pre-production, the example method may include specification and design 202 of the aircraft, manufacturing sequence and processing planning 204 and material procurement 206. During production, component and subassembly manufacturing 208 and system integration 210 of the aircraft takes place. Thereafter, the aircraft may go through certification and delivery 212 in order to be placed in service 214. While in service by an operator, the aircraft may be scheduled for service and maintenance (which may also include modification, reconfiguration, refurbishment or the like).), comprising: receiving a fault code history and a service event history for a set of vehicles, the fault code history including fault code events for each of a set of fault codes, the service event history including service events for each of a set of service event types (Swearingen et al. 2022/0398871 [0008 - maintenance of a vehicle that includes vehicle systems configured to report faults … receive a fault report indicating failed tests reported by a vehicle system, diagnose a failure mode onboard the vehicle … determine a service recommendation of a service action to address the failure mode … responsive to diagnosis of the alternate failure mode, determine a maintenance recommendation of a maintenance action to address the alternate failure mode; and generate a maintenance message including the maintenance recommendation] Some example implementations provide a system for use in maintenance of a vehicle that includes vehicle systems configured to report faults to an onboard reasoner, the system comprising an onboard computer including the onboard reasoner that is configured to receive a fault report indicating failed tests reported by a vehicle system, diagnose a failure mode onboard the vehicle, from the failed tests, and using an onboard diagnostic model that represents or describes known relationships between possible failed tests reported by the vehicle systems, and possible failure modes onboard the vehicle systems, the onboard reasoner further configured to determine a service recommendation of a service action to address the failure mode; and an off-board computer including an off-board copy of the onboard reasoner configured to receive the fault report indicating the failed tests, and further address the fault report based on a measure of fix effectivity of the service action, including the off-board copy of the onboard reasoner configured to at least: receive the measure of fix effectivity of the service action as performed to address the failure mode; diagnose the failure mode or an alternate failure mode onboard the vehicle, from the failed tests and the measure of fix effectivity of the service action, and using an off-board copy of the onboard diagnostic model; and responsive to diagnosis of the alternate failure mode, determine a maintenance recommendation of a maintenance action to address the alternate failure mode; and generate a maintenance message including the maintenance recommendation. [0045 - diagnose the failure mode or an alternate failure mode based on similar data … may include current and past vehicle parametric and BIT data from the vehicle, as well as … Vehicle maintenance history and fix] As shown at 406, the off-board copy 312 of the onboard reasoner 304 at the off-board computer 310 may diagnose the failure mode or an alternate failure mode based on similar data as the onboard reasoner, across a fleet of vehicles 316. This may include current and past vehicle parametric and BIT data from the vehicle, as well as similar parametric and BIT data from similar vehicles in the fleet. Vehicle maintenance history and fix effectivity may also be factored in by the off-board copy of the onboard reasoner. The fix effectivity here may be determined at 408, by the off-board computer or in some examples the second off-board computer 318, from evaluation of maintenance actions performed across the fleet of vehicles. Effective maintenance may be indicated by one to one correspondence between a single maintenance action and a single failure mode. If additional troubleshooting tasks, additional replaced parts, additional or secondary maintenance actions are needed, fix effectivity may be reduced. [0046 - related maintenance history…] On the second off-board computer 318, yet another diagnosis of the failure mode or an alternate failure mode may be made based on historical parametric and BIT data generated by vehicles over one or more fleets of vehicles, as shown at 410. Similar lab and simulation data, with related maintenance history and fix effectivity, from a plurality of operators may also be factored in by the second off-board computer. The second off-board computer may determine fix effectivity from evaluation of all related maintenance performed on vehicles across the one or more fleets of vehicles. [0060 - historical service actions] The system 302, which further includes an off-board computer including an off-board copy 312 of the onboard reasoner 304, is configured to receive the fault report indicating the failed tests from the specific vehicle 302 and record the performance of the service action on the vehicle 302, (such as a service action of an antiskid wheel-speed transducer repair, and is further configured to record performance of related service actions on other vehicles that encountered the same fault report (including historical service actions), where such related service actions on other vehicles included performance of additional services (such as servicing the brake controller 506, the electrically-controlled hydraulic valve 508, etc.), as shown at blocks 708, 710 and 712.); correlating subsets of the set of fault codes with subsets of the service events at least partially based on the fault code history and the service event history (Swearingen et al. 2022/0398871 [0034] As explained above, a vehicle 302 such as an aircraft 100 includes a plurality of high-level systems 112. These high-level systems may include a plurality of sensors and subsystems providing fault and sensor data that is communicated via an aircraft data communications bus network and/or an onboard network system (ONS) to an aircraft condition monitoring system (ACMS). The ACMS may collect, monitor, record and report real-time aircraft system data, which may include error messages from a flight deck effects (FDE) system, system test reports, fault reports and other information. The data collected by the ACMS is used, for example, to perform cabin pressure and temperature monitoring, hard landing detection, flight crew monitoring, and engine monitoring in addition to many other aircraft performance functions. The received data is then utilized to analyze aircraft performance, record significant flight events, report aircraft system test reports and fault reports, and troubleshoot faults.); assigning a service priority rating to each fault code of the set of fault codes at least partially based on the correlated subset or subsets of the service events, the fault code history, and the service event history (Swearingen et al. 2022/0398871 [0045] As shown at 406, the off-board copy 312 of the onboard reasoner 304 at the off-board computer 310 may diagnose the failure mode or an alternate failure mode based on similar data as the onboard reasoner, across a fleet of vehicles 316. This may include current and past vehicle parametric and BIT data from the vehicle, as well as similar parametric and BIT data from similar vehicles in the fleet. Vehicle maintenance history and fix effectivity may also be factored in by the off-board copy of the onboard reasoner. The fix effectivity here may be determined at 408, by the off-board computer or in some examples the second off-board computer 318, from evaluation of maintenance actions performed across the fleet of vehicles. Effective maintenance may be indicated by one to one correspondence between a single maintenance action and a single failure mode. If additional troubleshooting tasks, additional replaced parts, additional or secondary maintenance actions are needed, fix effectivity may be reduced. [0046] On the second off-board computer 318, yet another diagnosis of the failure mode or an alternate failure mode may be made based on historical parametric and BIT data generated by vehicles over one or more fleets of vehicles, as shown at 410. Similar lab and simulation data, with related maintenance history and fix effectivity, from a plurality of operators may also be factored in by the second off-board computer. The second off-board computer may determine fix effectivity from evaluation of all related maintenance performed on vehicles across the one or more fleets of vehicles.); and scheduling a vehicle maintenance event for a vehicle based on the service priority rating for each fault code event for the vehicle (Swearingen et al. 2022/0398871 [0028 - may be scheduled for service and maintenance] As explained above, example implementations of the present disclosure relate generally to vehicular engineering and, in particular, to one or more of the design, construction, operation or use of vehicles such as aircraft 100. Thus, referring now to FIG. 2, example implementations may be used in the context of an aircraft manufacturing and service method 200. During pre-production, the example method may include specification and design 202 of the aircraft, manufacturing sequence and processing planning 204 and material procurement 206. During production, component and subassembly manufacturing 208 and system integration 210 of the aircraft takes place. Thereafter, the aircraft may go through certification and delivery 212 in order to be placed in service 214. While in service by an operator, the aircraft may be scheduled for service and maintenance (which may also include modification, reconfiguration, refurbishment or the like).).
Swearingen et al. 2022/0398871 may not expressly disclose the “correlating” features, however, Lake et al. 2020/0380794 teaches (Lake et al. 2020/0380794 [0033 - one or more maintenance actions may be recommended for addressing the fault based on the correlations between fault records and maintenance records] Correlation scores are then used to correlate fault records with maintenance records. As such, when a fault is encountered (e.g., when a fault is detected by the monitoring component of the vehicle and a fault message is provided to the maintenance system containing a fault code of the fault that was detected), one or more maintenance actions may be recommended for addressing the fault based on the correlations between fault records and maintenance records. In certain examples, all maintenance records that correlate to a fault record corresponding to the fault (e.g., a fault record that has the same fault code as the fault) with a correlation score above a threshold are provided as recommended maintenance actions.). 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 Swearingen et al. 2022/0398871 to include the features as taught by Lake et al. 2020/0380794. One of ordinary skill in the art would have been motivated to do so in order to implement well known tools and features useful for scheduling vehicle maintenance which should prove to improve user experience, maximize profits, and optimize revenue.
Swearingen et al. 2022/0398871 may not expressly disclose the “service priority rating” features, however, Bhattacharyya et al. 2020/0273268 teaches (Bhattacharyya et al. 2020/0273268 [0029 - vehicle analysis module can determine whether the failure would strand a vehicle or driver, and if so, recommend urgent repair or replacement, while recommending less urgent repair or replacement for less catastrophic failures – interpreted as “a service priority rating”] The onboard vehicle analysis module and the vehicle management system can provide or analyze additional data that can be used for diagnostic analysis. For example, such data can include data provided by the manufacturer regarding diagnostic conditions, data obtained by crowd sourcing or otherwise analyzing data provided by a community of fleet vehicles (including, for example, predictive diagnoses based on community analysis of diagnostic trends), or the like. The vehicle diagnostic functionality performed in the vehicle by the onboard vehicle analysis module or at the vehicle management system by the offboard vehicle analysis module can include, among other things, comparing collected vehicle data to a set of conditions in order to perform preventative diagnostic analysis of the vehicle before a failure occurs. For example, the vehicle analysis module can analyze one or more fault codes in combination with data from the engine, such as mileage, engine hours, number of starter cycles, manufacturer data, or other data to determine whether an engine component should be replaced. The vehicle analysis module can further assess the severity or level of a predicted failure, such as whether it may be a catastrophic failure, a moderate failure, or other less serious failure. The vehicle analysis module can also take into account the predictive cost of the effects of the failure in determining whether to recommend repair or replacement. For example, the vehicle analysis module can determine whether the failure would strand a vehicle or driver, and if so, recommend urgent repair or replacement, while recommending less urgent repair or replacement for less catastrophic failures. Thus, the vehicle analysis module can categorize diagnoses by the severity of predicted events, such as catastrophic events requiring immediate attention, major events requiring attention within specified number of days, minor events that can be evaluated at a next maintenance interval, and/or other categorizations. In one example, the vehicle analysis module may determine that a starter engine should be replaced at the next scheduled service interval.). 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 Swearingen et al. 2022/0398871 to include the features as taught by Bhattacharyya et al. 2020/0273268. One of ordinary skill in the art would have been motivated to do so in order to implement well known tools and features useful for scheduling vehicle maintenance which should prove to improve user experience, maximize profits, and optimize revenue.
Claim 10, has similar limitations as of Claim(s) 1, therefore it is REJECTED under the same rationale as Claim(s) 1.
18/821,227 – Claim 20. Swearingen et al. 2022/0398871 further teaches A non-transitory machine-readable medium having tangibly stored thereon executable instructions for execution by one or more processors, wherein the executable instructions, in response to execution by the one or more processors, cause the one or more processors to perform the method of claim 1 (Swearingen et al. 2022/0398871 [0066; 0069; 0071]).
18/821,227 – Claim 2. Swearingen et al. 2022/0398871 further teaches The computer-implemented method of claim 1, wherein the subsets of the set of fault codes include two or more fault codes of the set of fault codes (Swearingen et al. 2022/0398871 [0008] Some example implementations provide a system for use in maintenance of a vehicle that includes vehicle systems configured to report faults to an onboard reasoner, the system comprising an onboard computer including the onboard reasoner that is configured to receive a fault report indicating failed tests reported by a vehicle system, diagnose a failure mode onboard the vehicle, from the failed tests, and using an onboard diagnostic model that represents or describes known relationships between possible failed tests reported by the vehicle systems, and possible failure modes onboard the vehicle systems, the onboard reasoner further configured to determine a service recommendation of a service action to address the failure mode; and an off-board computer including an off-board copy of the onboard reasoner configured to receive the fault report indicating the failed tests, and further address the fault report based on a measure of fix effectivity of the service action, including the off-board copy of the onboard reasoner configured to at least: receive the measure of fix effectivity of the service action as performed to address the failure mode; diagnose the failure mode or an alternate failure mode onboard the vehicle, from the failed tests and the measure of fix effectivity of the service action, and using an off-board copy of the onboard diagnostic model; and responsive to diagnosis of the alternate failure mode, determine a maintenance recommendation of a maintenance action to address the alternate failure mode; and generate a maintenance message including the maintenance recommendation.).
Swearingen et al. 2022/0398871 may not expressly disclose the “two or more fault codes” features, however, Lake et al. 2020/0380794 teaches (Lake et al. 2020/0380794 [0004 - receiving data related to a plurality of fault codes, wherein the data comprises parameters for each given fault code of the plurality of fault codes] Certain embodiments provide a computer-implemented method, comprising: receiving data related to a plurality of fault codes, wherein the data comprises parameters for each given fault code of the plurality of fault codes; determining, for each respective fault code of the plurality of fault codes, a unique subset of the parameters for the respective fault code based on frequencies of the parameters within the plurality of fault codes; receiving a plurality of fault records; receiving a plurality of maintenance records; for each respective maintenance record of the plurality of maintenance records: identifying one or more candidate fault records of the plurality of fault records for correlation with the respective maintenance record based on a time window; and correlating a subset of the one or more candidate fault records with the respective maintenance record based on the unique subset of the parameters for the respective fault code corresponding to each respective candidate fault record of the one or more candidate fault records; and providing output to a client system based on the correlating. [0033 - one or more maintenance actions may be recommended for addressing the fault based on the correlations between fault records and maintenance records] Correlation scores are then used to correlate fault records with maintenance records. As such, when a fault is encountered (e.g., when a fault is detected by the monitoring component of the vehicle and a fault message is provided to the maintenance system containing a fault code of the fault that was detected), one or more maintenance actions may be recommended for addressing the fault based on the correlations between fault records and maintenance records. In certain examples, all maintenance records that correlate to a fault record corresponding to the fault (e.g., a fault record that has the same fault code as the fault) with a correlation score above a threshold are provided as recommended maintenance actions.). 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 Swearingen et al. 2022/0398871 to include the features as taught by Lake et al. 2020/0380794. One of ordinary skill in the art would have been motivated to do so in order to implement well known tools and features useful for scheduling vehicle maintenance which should prove to improve user experience, maximize profits, and optimize revenue.
Claim 11, has similar limitations as of Claim(s) 2, therefore it is REJECTED under the same rationale as Claim(s) 2.
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over: Swearingen et al. 2022/0398871; in view of Lake et al. 2020/0380794; in further view of Bhattacharyya et al. 2020/0273268; in view of Bellini, III et al. 2015/0039380.
18/821,227 – Claim 3. Swearingen et al. 2022/0398871 further teaches The computer-implemented method of claim 1, further comprising: estimating at least one of service availability and part availability for at least one of the set of the service event types, and wherein the assigning of the service priority rating is at least partially based on the at least one of the estimated service availability and the estimated part availability for the at least one of the set for the service type events (Swearingen et al. 2022/0398871 [0003] Vehicles such as aircraft, ground vehicles, watercraft, amphibious vehicles, spacecraft and the like, typically include some type of performance monitoring system that records data regarding the vehicle performance, which includes the performance of the various systems (and subsystems) of the vehicle. The data include a record of certain performance events that occur during the operation of the vehicle. The performance monitoring system typically conducts data collection and reports all of the data collected to the user. The user then may utilize the data in determining the type of maintenance or repair, if any, that the vehicle may need. For example, if the data indicates that a particular mechanical or electromechanical system of the vehicle is malfunctioning or that the performance of one or more mechanical or electromechanical systems may contribute to a future vehicle failure, then the user can perform the appropriate repair on the vehicle at the next opportunity.).
Swearingen et al. 2022/0398871 may not expressly disclose the “service priority” features, however, Bellini, III et al. 2015/0039380 teaches (Bellini, III et al. 2015/0039380 [0053 - assign a higher priority…] The service ticket translation module 901a may translate the data 808 into the service ticket 303 while maintaining the selected data 808 of the sales opportunity 302. The translation can comprise significant analysis of data. The service ticket translation module 901a may combine data from multiple sources; for example, the service ticket translation module 901a may derive the deadline for a service ticket by analyzing the Close date 701 to estimate the service start, analyzing the line items in the Quotes 701 to include any promises made to the customer, and analyzing the Inventory availability within Products within Forecast 701 to estimate the delay in procuring required parts. The service ticket translation module 901a may generate multiple service tickets 303 from a single sales opportunity 302; for example, if Products within Forecast 701 includes a reference to an email server, this can generate one service ticket 303 to install the hardware for the server, another service ticket 303 to install the operating system on the server, and still another service ticket 303 to install and configure the email server software. The service ticket translation module 901a may generate prioritization information within the service ticket 303 that may be helpful to a service manager for deciding how to schedule the service tickets 303; for example, the service ticket translation module 901a may assign a higher priority to a service ticket 303 translated from a sales opportunity 302 with a larger value of Revenue 701, and in another example, the service ticket translation module 901a may assign a higher priority to a service ticket 303 translated from a sales opportunity 302 for which the history of changes in Sales stage 701 indicate that the customer has expedited the purchase process. In the preceding description, references to items 701 will be described more fully with respect to FIGS. 7A-7C.). 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 Swearingen et al. 2022/0398871 to include the features as taught by Bellini, III et al. 2015/0039380. One of ordinary skill in the art would have been motivated to do so in order to implement well known tools and features useful for scheduling vehicle maintenance which should prove to improve user experience, maximize profits, and optimize revenue.
Claim 12, has similar limitations as of Claim(s) 3, therefore it is REJECTED under the same rationale as Claim(s) 3.
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over: Swearingen et al. 2022/0398871; in view of Lake et al. 2020/0380794; in further view of Bhattacharyya et al. 2020/0273268; in view of Everts et al. 2004/0138852.
18/821,227 – Claim 5. Swearingen et al. 2022/0398871 further teaches The computer-implemented method of claim 1, wherein the correlating includes determining a mean time to failure for each subset of fault codes (Swearingen et al. 2022/0398871 [0062] By utilizing identical diagnostic reasoners and the greater extent of data across a number of vehicles in the fleet, the off-board copy 312 of the onboard reasoner 304, is further configured to diagnose the failure mode or an alternated failure mode onboard the vehicle(s), and determine a maintenance recommendation of a maintenance action to address such a failure mode, where the maintenance recommendation of a maintenance action is determined based on the feedback of the fix effectivity and represents a more effective solution for application across all vehicles within the fleet, as shown at blocks 716 and 718. The off-board copy 312 of the onboard reasoner 304 is configured to generate a message of the maintenance recommendation, which may be provided to one or more vehicles in the fleet to hereby enable improvement to diagnostics for all vehicles in the fleet, for a fleet-wide improvement (e.g. operation of the vehicles across the fleet, the fault diagnostic models, etc.), as shown at block 720.).
Swearingen et al. 2022/0398871 may not expressly disclose the “mean time to failure” features, however, Everts et al. 2004/0138852 teaches (Everts et al. 2004/0138852 [0041 - determine MTBF (mean time between failures) for a specific fault…] The computer system and method described may be used by a wide variety of personnel. For example, a reliability engineer could use the system and method to determine MTBF (mean time between failures) for a specific fault in addition to making such calculations for board level and system level failures. Inventory control specialists may use the system and method for determining types of parts (e.g. field replaceable units) to stock for future needs due to failures. System hardware designers may use the system and method to partition hardware fault detection across multiple units in the system which they are designing, and may alter their system designs based on results from performing the method. Field engineers and technicians may use the system and method to aid troubleshooting and fault isolation in an operational system. Test and diagnostic development engineers may use the system and method to assess the fault coverage achieved by their test or diagnostic software or other assessment methodology.). 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 Swearingen et al. 2022/0398871 to include the features as taught by Everts et al. 2004/0138852. One of ordinary skill in the art would have been motivated to do so in order to implement well known tools and features useful for scheduling vehicle maintenance which should prove to improve user experience, maximize profits, and optimize revenue.
Claim 14, has similar limitations as of Claim(s) 5, therefore it is REJECTED under the same rationale as Claim(s) 5.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over: Swearingen et al. 2022/0398871; in view of Lake et al. 2020/0380794; in further view of Bhattacharyya et al. 2020/0273268; in view of Everts et al. 2004/0138852.
18/821,227 – Claim 6. Swearingen et al. 2022/0398871 further teaches The computer-implemented method of claim 1, wherein the correlating includes determining a mean time to failure for each fault code of the set of fault codes (Swearingen et al. 2022/0398871 [0062] By utilizing identical diagnostic reasoners and the greater extent of data across a number of vehicles in the fleet, the off-board copy 312 of the onboard reasoner 304, is further configured to diagnose the failure mode or an alternated failure mode onboard the vehicle(s), and determine a maintenance recommendation of a maintenance action to address such a failure mode, where the maintenance recommendation of a maintenance action is determined based on the feedback of the fix effectivity and represents a more effective solution for application across all vehicles within the fleet, as shown at blocks 716 and 718. The off-board copy 312 of the onboard reasoner 304 is configured to generate a message of the maintenance recommendation, which may be provided to one or more vehicles in the fleet to hereby enable improvement to diagnostics for all vehicles in the fleet, for a fleet-wide improvement (e.g. operation of the vehicles across the fleet, the fault diagnostic models, etc.), as shown at block 720.).
Swearingen et al. 2022/0398871 may not expressly disclose the “two or more fault codes” features, however, Everts et al. 2004/0138852 teaches (Everts et al. 2004/0138852 [0041 - determine MTBF (mean time between failures) for a specific fault…] The computer system and method described may be used by a wide variety of personnel. For example, a reliability engineer could use the system and method to determine MTBF (mean time between failures) for a specific fault in addition to making such calculations for board level and system level failures. Inventory control specialists may use the system and method for determining types of parts (e.g. field replaceable units) to stock for future needs due to failures. System hardware designers may use the system and method to partition hardware fault detection across multiple units in the system which they are designing, and may alter their system designs based on results from performing the method. Field engineers and technicians may use the system and method to aid troubleshooting and fault isolation in an operational system. Test and diagnostic development engineers may use the system and method to assess the fault coverage achieved by their test or diagnostic software or other assessment methodology.). 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 Swearingen et al. 2022/0398871 to include the features as taught by Everts et al. 2004/0138852. One of ordinary skill in the art would have been motivated to do so in order to implement well known tools and features useful for scheduling vehicle maintenance which should prove to improve user experience, maximize profits, and optimize revenue.
Claim 15, has similar limitations as of Claim(s) 6, therefore it is REJECTED under the same rationale as Claim(s) 6.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over: Swearingen et al. 2022/0398871; in view of Lake et al. 2020/0380794; in further view of Bhattacharyya et al. 2020/0273268; in view of Nagata et al. 2022/0392274.
18/821,227 – Claim 7. Swearingen et al. 2022/0398871 further teaches The computer-implemented method of claim 1, wherein the service priority rating is at least partially based on at least one of: downtime cost; and an estimated time to failure (Swearingen et al. 2022/0398871 [0008-0009, 0080]).
Swearingen et al. 2022/0398871 may not expressly disclose the “estimated time” features, however, Nagata et al. 2022/0392274 teaches (Nagata et al. 2022/0392274 [claim 6 - gives a higher priority to the autonomous vehicle for which the estimated time … required to repair the failure is shorter] 6. The information processing apparatus of claim 1, wherein the controller estimates, based on the vehicle information, a time required to repair a failure of the autonomous vehicle; and gives a higher priority to the autonomous vehicle for which the estimated time required to repair the failure is shorter. [0010 - order of priority] determine, in a case in which a suspension of operation occurs in a plurality of autonomous vehicles, an order of priority for a rescue team to go to the plurality of autonomous vehicles, based on the positional information, the vehicle information, the vehicle internal image, and the vehicle external image.). 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 Swearingen et al. 2022/0398871 to include the features as taught by Nagata et al. 2022/0392274. One of ordinary skill in the art would have been motivated to do so in order to implement well known tools and features useful for scheduling vehicle maintenance which should prove to improve user experience, maximize profits, and optimize revenue.
Claim 17, has similar limitations as of Claim(s) 7, therefore it is REJECTED under the same rationale as Claim(s) 7.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over: Swearingen et al. 2022/0398871; in view of Lake et al. 2020/0380794; in further view of Bhattacharyya et al. 2020/0273268; in view of Brown et al. 2020/0412624.
18/821,227 – Claim 16. Swearingen et al. 2022/0398871 further teaches The computing system of claim 10, wherein the machine-executable instructions, when executed by the one or more processors, cause the computing system to determine the service priority rating at least partially based on downtime cost (Swearingen et al. 2022/0398871 [0008-0009, 0080]).
Swearingen et al. 2022/0398871 may not expressly disclose the “downtime costs” features, however, Brown et al. 2020/0412624 teaches (Brown et al. 2020/0412624 [0032 – cost of downtime] At 218, method 200 determines an order of microservices to restore. Method 200 can utilize a microservice service restoration priority engine such as 110 of FIG. 1 in performing operation 218. The order of microservices to restore can be an ordered list of a first microservice to restore, followed by a second microservice to restore, etc. This ordered list can be generated using the information collected throughout performance of method 200 and/or method 300 including the information gathered on microservice interactions, the microservice restoration times, the expected total cost of downtime for each microservice failure or degradation, as well as other inputs to a microservice service restoration priority engine. In some embodiments, determination of the order of restoration can be a multivariate analysis and/or use non-monotonic logic. In some embodiments, the order of microservices to restore can be determined by prioritizing the microservice with the largest revenue impact (i.e., greatest total cost of down time) followed by the microservice with the next largest revenue impact, etc. In such embodiments, the microservices which will be restored first are those which would cost a company the most money if left unaddressed.). 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 Swearingen et al. 2022/0398871 to include the features as taught by Brown et al. 2020/0412624. One of ordinary skill in the art would have been motivated to do so in order to implement well known tools and features useful for scheduling vehicle maintenance which should prove to improve user experience, maximize profits, and optimize revenue.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over: Swearingen et al. 2022/0398871; in view of Lake et al. 2020/0380794; in further view of Bhattacharyya et al. 2020/0273268; in view of Herzog et al. 2019/0324430.
18/821,227 – Claim 8. Swearingen et al. 2022/0398871 further teaches The computer-implemented method of claim 1, wherein the assigning includes, for each of the set of fault codes, a total number of positive cases where fault code events led to service events and a total number of negative cases where an absence of fault code events led to an absence of service events (Swearingen et al. 2022/0398871 [0008-0009, 0080]).
Swearingen et al. 2022/0398871 may not expressly disclose the “positive and negative” features, however, Herzog et al. 2019/0324430 teaches (Herzog et al. 2019/0324430 [0207] While performing these functions, the data analytics platform 102 may assign false positives and false negatives equal priority, and may thus attempt to reduce the overall extent of false positives and false negatives. Alternatively, the data analytics platform 102 may assign one of false positives and false negatives a higher priority than the other. For example, reducing false negatives may be assigned a higher priority than reducing false negatives, so as to reduce the chances of a failure being missed. In such an example, the data analytics platform 102 may modify the final set of anomaly thresholds in an effort to minimize the extent of false negatives in the first instance, and may then to the extent possible, may attempt to reduce the extent of false positives while maintaining that minimized extent of false negatives. The data analytics platform 102 may modify the final set of anomaly thresholds in other manners as well.). 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 Swearingen et al. 2022/0398871 to include the features as taught by Herzog et al. 2019/0324430. One of ordinary skill in the art would have been motivated to do so in order to implement well known tools and features useful for scheduling vehicle maintenance which should prove to improve user experience, maximize profits, and optimize revenue.
Claim 18, has similar limitations as of Claim(s) 8, therefore it is REJECTED under the same rationale as Claim(s) 8.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over: Swearingen et al. 2022/0398871; in view of Lake et al. 2020/0380794; in further view of Bhattacharyya et al. 2020/0273268; in view of Zhao et al. 2025/0013236.
18/821,227 – Claim 9. Swearingen et al. 2022/0398871 further teaches The computer-implemented method of claim 1, wherein the correlating is performed using a machine learning model (Swearingen et al. 2022/0398871 [0008-0009, 0080]).
Swearingen et al. 2022/0398871 may not expressly disclose the “machine learning model” features, however, Zhao et al. 2025/0013236 teaches (Zhao et al. 2025/0013236 [0022 – machine learning models…] In some embodiments, the system determines a location for each of the service tasks. For example, a service task location may be a geolocation of the fleet vehicle that will be serviced. In other examples, the service task location may include one or more locations corresponding to a geolocation where a fleet vehicle is to be retrieved and a geolocation where the fleet vehicle is to be relocated or deployed. Machine learning models may be used to predict deployment locations where there will be a demand for fleet vehicles. In such cases, the machine learning model predictions may be employed in automatic generation of service tasks with specified deployment locations.). 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 Swearingen et al. 2022/0398871 to include the features as taught by Zhao et al. 2025/0013236. One of ordinary skill in the art would have been motivated to do so in order to implement well known tools and features useful for scheduling vehicle maintenance which should prove to improve user experience, maximize profits, and optimize revenue.
Claim 19, has similar limitations as of Claim(s) 9, therefore it is REJECTED under the same rationale as Claim(s) 9.
18/821,227 – Claim 10. A computing system for scheduling vehicle maintenance, comprising: one or more processors; and a memory storing machine-executable instructions that, when executed by the one or more processors, cause the computing system to: receive a fault code history and a service event history for a set of vehicles, the fault code history including fault code events for each of a set of fault codes, the service event history including service events for each of a set of service event types; correlate subsets of the set of fault codes with subsets of the service events at least partially based on the fault code history and the service event history; assign a service priority rating to each fault code of the set of fault codes at least partially based on the correlated subset or subsets of the service events, the fault code history, and the service event history; and schedule a vehicle maintenance event for a vehicle based on the service priority rating for each fault code event for the vehicle.
18/821,227 – Claim 11. The computing system of claim 10, wherein the subsets of the set of fault codes include two or more fault codes of the set of fault codes.
18/821,227 – Claim 12. The computing system of claim 10, wherein the machine-executable instructions, when executed by the one or more processors, cause the computing system to: estimate at least one of service availability and part availability for at least some of the set of the service event types, and assign the service priority rating at least partially based on the at least one of the estimated service availability and the estimated part availability for the at least some of the set for the service type events.
18/821,227 – Claim 14. The computing system of claim 10, wherein the machine-executable instructions, when executed by the one or more processors, cause the computing system to determine a mean time to failure for each subset of fault codes.
18/821,227 – Claim 15. The computing system of claim 10, wherein the machine-executable instructions, when executed by the one or more processors, cause the computing system to determine a mean time to failure for each fault code of the set of fault codes.
18/821,227 – Claim 17. The computing system of claim 10, wherein the service priority rating is at least partially based on an estimated time to failure.
18/821,227 – Claim 18. The computing system of claim 10, wherein the machine-executable instructions, when executed by the one or more processors, cause the computing system to assign, for each of the set of fault codes, a total number of positive cases where fault code events led to service events and a total number of negative cases where an absence of fault code events led to an absence of service events.
18/821,227 – Claim 19. The computing system of claim 10, wherein the machine-executable instructions, when executed by the one or more processors, cause the computing system to correlate the subsets of the set of fault codes with subsets of the service events using a machine learning model.
No Prior-art Rejection / Potentially Allowable
Claims 4 and 13 cannot be rejected with prior-art. Individual claimed features are taught in the prior-art, however, the unique combination of features and elements are not taught by the prior-art without hindsight reasoning. These claims are further rejected to as being dependent upon a rejected base claim but might possibly be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
18/821,227 – Claim 4. The computer-implemented method of claim 1, wherein the fault code history is a first fault code history for a first set of vehicles of a first vehicle type, the service event history is a first service event history for the first set of vehicles, the service priority rating is a first service priority rating, the method further comprising: receiving a second fault code history and a second service event history for a second set of vehicles, the second fault code history including fault code events for each of the set of fault codes, the second service event history including service events for each of a set of service event types; correlating subsets of the set of fault codes with subsets of the service events at least partially based on the second fault code history and the second service event history; and assigning a second service priority rating to each fault code of the set of fault codes at least partially based on the correlated subset or subsets of the service events, the second fault code history, and the second service event history, wherein the revising includes revising the vehicle maintenance event schedule for the vehicle based on: if the vehicle is of the first vehicle type, the first service priority rating for each fault code event for the vehicle; or if the vehicle is of the second vehicle type, based on the second service priority rating for each fault code event for the vehicle.
18/821,227 – Claim 13. The computing system of claim 10, wherein the fault code history is a first fault code history for a first set of vehicles of a first vehicle type, the service event history is a first service event history for the first set of vehicles, the service priority rating is a first service priority rating, and wherein the machine-executable instructions, when executed by the one or more processors, cause the computing system to: receive a second fault code history and a second service event history for a second set of vehicles, the second fault code history including fault code events for each of the set of fault codes, the second service event history including service events for each of a set of service event types; correlate subsets of the set of fault codes with subsets of the service events at least partially based on the second fault code history and the second service event history; assign a second service priority rating to each fault code of the set of fault codes at least partially based on the correlated subset or subsets of the service events, the second fault code history, and the second service event history; and revise the vehicle maintenance event schedule for the vehicle based on the first service priority rating for each fault code event for the vehicle if the vehicle is of the first vehicle type, or based on the second service priority rating for each fault code event for the vehicle if the vehicle is of the second vehicle type.
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.
Wu 2010/0293030 [0044 - scheduled time of a reserved vehicle service task is received by the service center…][0005 - dispatching priority ranking table in the service center, selecting a vehicle from the database according to the dispatching priority ranking table when the service center receives a vehicle service request]
Zhao et al. 2025/0013236 [Abstract - generating a service task list based on the service locations, service tasks, and priority scores] [0080 - historical service task information][0087 - training datasets having historical information related to fleet vehicle]
Minster et al. 2017/0278312 [0048 - historical sensor data … may include detecting several low-tire-pressure events (each following a recent tire refill), which may in turn trigger detection that the tire probably has a leak. As another example, S210 may include correlating sensor data with maintenance events across a fleet for vehicles] [0059 - comparing the vehicle maintenance data (e.g., sensed data) to the severity database, the autonomous device is able to identify or select a severity level (or, in some cases, directly identify a priority score). The severity level data and/or the priority score data may be included in a maintenance request and transmitted to an external maintenance entity]
PERTINENT PRIOR ART – Non-Patent Literature (NPL)
The NPL prior-art made of record and considered pertinent to applicant's disclosure.
The BEST NPL prior-art reference is:
Other relevant NPL prior-art reference are:
Conclusion
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.
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/MATTHEW T SITTNER/
Primary Examiner, Art Unit 3629b