DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/07/2026 has been entered.
Status of the Claims
Claims 1-19 and 21 are pending in the instant patent application. Claims 1-2, 4, 6, 11 and 15 are amended. Claim 20 is canceled and Claim 21 is new. This Final Office Action is in response to the claims filed.
Response to Claim Amendments
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §101 rejections. The rejections remain pending and are updated and addressed below in light of the arguments and per guidelines for 101 analysis (PEG 2019).
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §103 rejections. The rejections remain pending.
Response to 35 U.S.C. §101 Arguments
Applicant’s arguments regarding 35 U.S.C. §101 rejection of the claims have been fully considered, but are not persuasive.
Regarding Applicant’s arguments that the claim does not recite abstract ideas, Examiner respectfully disagrees. Examiner respectfully reminds Applicant, general purpose computer elements/structure, similar to the claimed invention, used to apply a judicial exception, by use of instruction implemented on a computer, has not been found by the courts to integrate the abstract idea into a practical application; see MPEP 2106.05(f). Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind; see MPEP 2106.04(a)(2)(III)(C).
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.
Regarding Claims 1-10 and 21, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 1-10 and 21 are directed to the abstract idea of scheduling maintenance for a fleet of vehicles.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 1, claim 1 recites establishing a maintenance schedule for the fleet of vehicles based on a baseline distribution of expected operations of the fleet of vehicles; after establishing the maintenance schedule, receiving operations data observed from the fleet of vehicles wherein the operations data are expected to be represented by the baseline distribution; comparing, based upon the operations data, the baseline distribution with each of a plurality of distributions within a set of candidate distributions, wherein the candidate distributions are predicted to characterize one or more aspects of operation of the fleet of vehicles determining that one of the candidate distributions more accurately represents the operations data than the baseline distribution; changing the maintenance schedule based on the candidate distribution that more accurately represents the operations data.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind and/or with pen/paper (including an observation, evaluation, judgment, opinion). In addition, dependent claims 8-9 fall within the Mathematical Processes grouping of abstract ideas due to the mathematical relationships/calculations taking place.
Accordingly, the claim recites an abstract idea and dependent claims 2-10 and 21 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim does not recite any elements that would integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 1 includes various elements that are not directed to the abstract idea under 2A. These elements include the generic computing elements described in the Applicant's specification in at least Para 0091. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claim 1 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Regarding Claims 11-19, they are directed to a device, however the claims are directed to a judicial exception without significantly more. Claims 11-19 are directed to the abstract idea of monitoring a collection of machines.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 11, claim 11 recites establish a maintenance schedule for the fleet of vehicles based on a baseline distribution of expected operations; receive operations data observed from the fleet of vehicles wherein the operations data are expected to be represented by the baseline distribution; compare, based upon the operations data, the baseline distribution with each of a plurality of distributions within a set of candidate distributions, wherein the candidate distributions are predicted to characterize one or more aspects of operation of the fleet of vehicles; determine that one of the candidate distributions more accurately represents the operations data than the baseline distribution; and responsive to the determining, assign the one candidate distribution as the baseline distribution and change the maintenance schedule for the fleet of vehicles.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind and/or with pen/paper (including an observation, evaluation, judgment, opinion). In addition, dependent claims 17-18 fall within the Mathematical Processes grouping of abstract ideas due to the mathematical relationships/calculations taking place.
Accordingly, the claim recites an abstract idea and dependent claims 12-19 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of processing circuitry, memory and a computing device. The processing circuitry, memory and a computing device are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 11-14 includes various elements that are not directed to the abstract idea under 2A. These elements include processing circuitry, memory, a computing device the generic computing elements described in the Applicant's specification in at least Para 0091. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claims 11-14, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Response to 35 U.S.C. §103 Arguments
Applicant’s arguments regarding 35 U.S.C. §103 rejection of the claims have been fully considered, but are not persuasive. Furthermore, Applicant’s arguments are moot in light of newly amended language.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4, 6-7, 10-13, 15-16 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clark et al. (US 2018/0047224 A1) in view of Harutyunyan et al. (US 2023/0229548 A1) further in view of Guenther et al. (US 7,945,427 B2).
Regarding Claim 1, Clark teaches the limitation of Claim 1 which states
establishing a maintenance schedule for the fleet of vehicles based on a baseline distribution of expected operations of the fleet of vehicles (Clark: Para 0036, 0076-0082 via fleet data which includes maintenance data, for example, component removal records, mean time between removal metrics, accumulated component usage/time, system/component fault data, maintenance schedules, inspection results, reliability metrics, component life-limits, component installation history, etc. The fleet data 302 may include operations data, for example, product usage history/schedule, product availability/readiness history/status, usage schedules, product locations, product missions, product operators, target performance parameters, repair data, for example, repair costs, scrap rate, repair actions/work-in-progress, repair-turnaround-times (RTAT), repair schedules… The maintenance optimization module may provide for a formulation of recommendations for maintenance actions (e.g., inspections, replacements, maintenance consolidation, etc.). The recommendations may be based on product-health condition-indicators and opportunistic factors associated with scheduled maintenance, planned usage, and cost factors… The maintenance optimization module 332 may consider numerous inputs or conditions in generating one or more outputs. Such inputs may include health and usage monitoring systems (HUMS) condition indications, usage history, usage projections, cost, resource availability and maintenance schedules).
after establishing the maintenance schedule, receiving operations data observed from the fleet of vehicles, wherein the operations data are expected to be represented by the baseline distribution (Clark: Para 0030 via Memory 117 may store executable instructions. The executable instructions may be stored or organized in any manner and at any level of abstraction, such as in connection with one or more applications, processes, routines, procedures, methods, etc. The instructions stored in the memory 117 may be executed by one or more processors, such as a processor 116. Memory 117 also stores data in the form of received data 118 and reference data 119. Received data 118 includes fleet data received from a fleet 120 of vehicles (such data may include electronic data extracted directly from vehicle systems and/or electronic data extracted from maintenance, operations and supply/logistics data systems associated with fleet support). Reference data 119 includes data to which the received fleet data may be compared for analysis, as described in further detail herein. Memory 117 may be co-located with processor 116, or remotely located from processor 116 and accessed by processor 116 via a network);
However, Clark does not explicitly disclose the limitation of Claim 1 which states comparing based upon the operations data the baseline distribution with each of a plurality of distributions within a set of candidate distributions, wherein the candidate distributions are predicted to characterize one or more aspects of operation of the fleet of vehicles.
Harutyunyan though, with the teachings of Clark, teaches of
comparing based upon the operations data the baseline distribution with each of a plurality of distributions within a set of candidate distributions, wherein the candidate distributions are predicted to characterize one or more aspects of operation of the fleet of vehicles (Harutyunyan: Para 0109-0111 via The LOF's determined for the event distributions are rank ordered and an event distribution with the smallest corresponding LOF is the baseline distribution. In other words, the baseline distribution P.sup.b satisfies the condition LOF(P.sup.b)≤LOF(P.sup.n) for n=1, ..., Nand b≠n. Ideally, the smallest LOF is unique and the corresponding event distribution is the baseline distribution as represented by Equation (19). In the case where them are two or more equal value LOF minima, the corresponding two or more event-type distributions are candidate baseline distributions. Entropies are computed for the two or more candidate baseline distributions. The candidate baseline distribution with the largest corresponding entropy is identified at the baseline distribution. For example, suppose there are two candidate baseline distributions... If H(P.sup.b.sup.1)>H(P.sup.b.sup.2), then the candidate baseline distribution P.sup.b.sup.1 is the baseline distribution. If H(P.sup.b.sup.2)>H(P.sup.b.sup.1), then the candidate baseline distribution P.sup.b.sup.2 is the baseline distribution).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Clark with the teachings of Harutyunyan in order to have comparing based upon the operations data the baseline distribution with each of a plurality of distributions within a set of candidate distributions, wherein the candidate distributions are predicted to characterize one or more aspects of operation of the fleet of vehicles. The motivations behind this being to incorporate the teachings of baseline distributions. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention and simple substitution would yield predictable results.
In addition, Clark does not explicitly disclose the limitation of Claim 1 which states determining that one of the candidate distributions more accurately represents the operations data than the baseline distribution.
Guenther though, with the teachings of Clark/Harutyunyan, teaches of
determining that one of the candidate distributions more accurately represents the operations data than the baseline distribution (Guenther: Col 7 lines 42-54 via In one application of part failure forecasting, lifetime maintenance and fleet statistic datasets were compared with the five different lifetime distribution models. Results over 0.05 when evaluating the P-value from each model were to be considered to be appropriate candidates. In this particular application, each model was manually tested and upon comparing the resulting values of the dataset for each of the five models, the exponential model resulted the best fit with a P-value of 0.190. The results eliminated the normal (P<0.005), lognormal (P=0.005), and gamma (P<0.001) distributions candidates while the Weibull (P=0.083) and exponential (P=0.190) distributions were identified as potential candidates).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Clark/Harutyunyan, with the teachings of Guenther in order to have determining that one of the candidate distributions more accurately represents the operations data than the baseline distribution. The motivations behind this being to incorporate the teachings of providing unanticipated demand predictions relating to the maintenance of platforms, such as a flight platform. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
The combination of Clark/Harutyunyan/Guenther further teaches the limitation of Claim 1 which states
changing the maintenance schedule based on the candidate distribution that more accurately represents the operations data (Clark: Para 0035, 0077-0082 via A conditioning process 202 includes conditioning the fleet data to facilitate analysis of the complete dataset. A monitoring process 204 includes monitoring of the fleet data for key attributes which may include health, cost, performance or profitability drivers and associated sub-elements. An alerting process 206 includes alerting of adverse conditions evident within the fleet data. Examples of adverse conditions include, but are not limited to: top cost, readiness and profitability drivers, adverse trend characteristics, outliers, threshold exceedances, statistical changes and variance from baselines or comparison populations. An investigation process 208 includes investigation of the adverse conditions, such as those mentioned above, in pursuit of root cause of the condition. A recommendation process 210 includes generating a recommendation for corrective action associated with the conditions and/or investigations. A reporting process 212 includes reporting of analytic outputs, recommendations, metrics and fleet information to process stakeholders… The maintenance optimization module 332 may reveal maintenance tasks that may be consolidated. The maintenance optimization module 332 may provide flexibility for maintenance actions to be planned or bundled with other maintenance tasks in order to reduce costs and to minimize unavailability. Additionally, the maintenance optimization module 332 may identify proactive maintenance activities to perform. The maintenance optimization module 332 may monitor the health of components or devices. The maintenance optimization module 332 may compute a projected degradation of one or more components or devices. Such a computation may be based on a current health status and forecasted usage. The maintenance optimization module 332 may employ multi-objective optimization schemes to provide potential courses-of-action. Associated cost/risk profiles may be generated for each potential course-of-action. A potential schedule of maintenance activities may be analyzed or adjusted based on each potential course-of-action. The maintenance optimization module 332 may consider numerous inputs or conditions in generating one or more outputs. Such inputs may include health and usage monitoring systems (HUMS) condition indications, usage history, usage projections, cost, resource availability and maintenance schedules. The maintenance optimization module 332 may accept or receive constraints. Such constraints may be manually entered or added to the maintenance optimization tool. The constraints may be based on maintenance/operational scheduling, resource availability, and/or any flexibility associated with the maintenance/operational scheduling. The maintenance optimization module 332 may provide opportunities to consolidate maintenance tasks, perform proactive maintenance or defer maintenance. Possible courses-of-action and associated cost/risk/schedule profiles for each may be provided. Such output may be based on multi-objective optimization schemes. The maintenance optimization module 332 may provide a correlation of maintenance and HUMS data).
Regarding Claim 2, the combination of Clark/Harutyunyan/Guenther teaches the limitations of Claim 2 which state
further comprising triggering an alert responsive to the determining that one of the candidate distributions more accurately represents the operations data than the baseline distribution . (Guenther: Col 7 lines 42-54 via In one application of part failure forecasting, lifetime maintenance and fleet statistic datasets were compared with the five different lifetime distribution models. Results over 0.05 when evaluating the P-value from each model were to be considered to be appropriate candidates. In this particular application, each model was manually tested and upon comparing the resulting Pvalues of the dataset for each of the five models, the exponential model resulted the best fit with a P-value of 0.190. The results eliminated the normal (P< 0.005), lognormal (P=0.005), and gamma (P<0.001) distributions candidates while the Weibull (P=0.083) and exponential (P=0.190) distributions were identified as potential candidates).
Regarding Claim 3, the combination of Clark/Harutyunyan/Guenther teaches the limitations of Claim 3 which state
wherein the triggering the alert further comprises providing a statistical distribution best describing the operations data to an outside recipient (Clark: Para 0050 via The maintenance/operations monitoring/alerting module 322 analyzes the conditioned data and provides data including: automatic alerts based on predefined and user defined/weighted criteria and statistical limits. Such criteria and limits may be assessed directly against the first set of data, or computations (e.g., rates, trends, statistical characteristics, etc.) made from the conditioned set. Top level key performance parameters (KPP) (e.g. fleet wide operational cost, readiness rates, reliability figures, program profit drivers, etc.) and sub-elements (e.g., individual product/component contributions to KPPs, logistics delay times, repair times, repair costs, replacement costs, scrap rates, etc.).
Regarding Claim 4, the combination of Clark/Harutyunyan/Guenther teaches the limitations of Claim 4 which state
further comprising updating the set of candidate distributions based on the operations data (Guenther: Col 5 lines 44-53 via If a confidence level that a part will fail at the depot maintenance level visit is above a threshold, procurement of the replacement part is commenced 220. Finally the part in question is checked 222 during a depot maintenance level visit for the platform on which the part is deployed. If it turns out that the part has not failed, and does not need to be replaced, the lifetime model for the part is updated 224, and fit optimization is reverified. Other upcoming depot maintenance level visits are re-evaluated 226 to determine the next best utilization for the unused, but now procured replacement part).
Regarding Claim 6, the combination of Clark/Harutyunyan/Guenther teaches the limitations of Claim 6 which state
wherein and the operations data comprises the distance traveled by the fleet of vehicles, hours in operation completed by the fleet of vehicles, amount of cargo carried by the fleet of vehicles, fuel usage by the fleet of vehicles, maintenance records by the fleet of vehicles, and weather conditions that occurred during operations of the fleet of vehicles (Clark: Para 0036 via FIG. 3 depicts a plurality of fleet analytic services modules 130 in an exemplary embodiment. A data quality assurance module 304 receives raw fleet data 302. The fleet data 302 may include maintenance data, for example, component removal records, mean time between removal metrics, accumulated component usage/time, system/component fault data, maintenance schedules, inspection results, reliability metrics, component life-limits, component installation history, etc. The fleet data 302 may include operations data, for example, product usage history/schedule, product availability/readiness history/status, usage Page 18 schedules, product locations, product missions, product operators, target performance parameters etc. The fleet data 302 may include supply-chain data, for example, vendor identifications, material costs/prices, shipping times, inventory levels/locations, new material orders, core returns, etc. The fleet data 302 may include health and/or usage data, for example, condition indicator data, health indicator data, parametric usage data, control inputs, temperatures, pressures, vibrations, regimes, system response data, speed, altitude, heading, environmental data, location, system faults/warnings, etc. The fleet data 302 may include ОEM enterprise data, for example, component/system design and configuration data, expected/required performance and reliability parameters, safety/reliability/engineering analyses, product-support history, product technical publications, financial data, new delivery schedules, business forecasts, etc. The fleet data 302 may include repair data, for example, repair costs, scrap rate, repair actions/work-in-progress, repair-turnaround-times (RTAT), repair schedules, etc. The fleet data 302 may include data manually entered by analysts, for example, comments, results of investigation steps, supporting data, cross-reference information, any data not received electronically, etc. This fleet data 302 may be received electronically, in real-time or substantially real-time, and from one or more sources associated with vehicles in the fleet 120).
Regarding Claim 7, the combination of Clark/Harutyunyan/Guenther teaches the limitations of Claim 7 which state
wherein the operations data are collected from a variety of different sources comprising sensors that are onboard a vehicle, flight crew input, remote nodes, airport authorities, airline personnel, and weather services (Clark: Para 0036 via FIG. 3 depicts a plurality of fleet analytic services modules 130 in an exemplary embodiment. A data quality assurance module 304 receives raw fleet data 302. The fleet data 302 may include maintenance data, for example, component removal records, mean time between removal metrics, accumulated component usage/time, system/component fault data, maintenance schedules, inspection results, reliability metrics, component life-limits, component installation history, etc. The fleet data 302 may include operations data, for example, product usage history/schedule, product availability/readiness history/status, usage schedules, product locations, product missions, product operators, target performance parameters etc. The fleet data 302 may include supply-chain data, for example, vendor identifications, material costs/prices, shipping times, inventory levels/locations, new material orders, core returns, etc. The fleet data 302 may include health and/or usage data, for example, condition indicator data, health indicator data, parametric usage data, control inputs, temperatures, pressures, vibrations, regimes, system response data, speed, altitude, heading, environmental data, location, system faults/warnings, etc. The fleet data 302 may include OEM enterprise data, for example, component/system design and configuration data, expected/required performance and reliability parameters, safety/reliability/engineering analyses, product-support history, product technical publications, financial data, new delivery schedules, business forecasts, etc. The fleet data 302 may include repair data, for example, repair costs, scrap rate, repair actions/work-in-progress, repair-turnaround-times (RTAT), repair schedules, etc. The fleet data 302 may include data manually Page 20 entered by analysts, for example, comments, results of investigation steps, supporting data, cross-reference information, any data not received electronically, etc. This fleet data 302 may be received electronically, in real-time or substantially real-time, and from one or more sources associated with vehicles in the fleet 120).
Regarding Claim 10, the combination of Clark/Harutyunyan/Guenther teaches the limitations of Claim 10 which state
wherein the candidate distributions are selected from a group of distributions comprising a chi distribution, chi-squared distribution, Erlang distribution, exponential distribution, gamma distribution, generalized-gamma distribution, a half-normal distribution, an inverse-gamma distribution, an inverse-Gaussian distribution, a lognormal distribution, a Nakagami distribution, a normal distribution, a Rayleigh distribution, and a reciprocal-inverse-Gaussian distribution (Guenther: Col 6 lines 28-51 via all updates to field and depot part maintenance history and fleet statistics part data are applied into a comparative engine that finds the best fit by contrasting the actual failure and lifetime data to several lifetime models. In one embodiment, the fit is evaluated using the Cramer-Von-Mises test statistic to find the most appropriate model. Cramer-Von-Mises testing produces a quantity called`P` value between 0 and 1 that describes how closely the resulting data emulates the distribution for each model. The value closest to 1 is the lifetime part model with the best fit according to the model analysis. The lifetime model producing the highest`P`value (which describes accuracy of fit for actual data to model) is selected as the lifetime model to predict future part failures. In one embodiment, analysis of the extracted data is conducted utilizing the five most prevalent lifetime distribution models to interpret the results. As illustrated in FIG. 5, the five distribution models include a normal distribution model 300, an exponential distribution model 302, a Weibull distribution model 304, a lognormal distribution model 306, and a gamma distribution model 308. The embodiments are not limited to these five distribution models and other distribution models can be added to these five or be substituted for one or more of the five distribution models that are illustrated in FIG. 5).
Regarding Claim 11, it is analogous to Claim 1 and is rejected for the same reasons (Clark: Para 0030).
Regarding Claim 12, the combination of Clark/Harutyunyan/Guenther teaches the limitations of Claim 12 which state
wherein the computing device is further configured to trigger an alert and provide a statistical distribution best describing the operations data amongst the candidate distributions to an outside recipient (Clark: Para 0050 via via The maintenance/operations monitoring/alerting module 322 analyzes the conditioned data and provides data including: automatic alerts based on pre-defined and user defined/weighted criteria and statistical limits. Such criteria and limits may be assessed directly against the first set of data, or computations (e.g., rates, trends, statistical characteristics, etc.) made from the conditioned set. Top-level key performance parameters (KPP) (e.g. fleet wide operational cost, readiness rates, reliability figures, program profit drivers, etc.) and sub-elements (e.g., individual product/component contributions to KPPs, logistics delay times, repair times, repair costs, replacement costs, scrap rates, etc.)) responsive to the determining that one of the candidate distributions more accurately represents the operations data than the baseline distribution (Guenther: Col 7 lines 42-54 via In one application part failure forecasting, lifetime maintenance and fleet statistic datasets were compared with the five different lifetime distribution models. Results over 0.05 when evaluating the P-value from each model were to be considered to be appropriate candidates. In this particular application, each model was manually tested and upon comparing the resulting P-values of the dataset for each of the five models, the exponential model resulted in the best fit with a P-value of 0.190. The results eliminated the normal (P< 0.005), lognormal (P=0.005), and gamma (P<0.001) distributions candidates while the Weibull (P=0.083) and exponential of (P=0.190) distributions were identified as potential candidates).
Regarding Claim 13, it is analogous to Claim 4 and is rejected for the same reasons.
Regarding Claim 15, it is analogous to Claim 6 and is rejected for the same reasons.
Regarding Claim 16, it is analogous to Claim 7 and is rejected for the same reasons.
Regarding Claim 19, it is analogous to Claim 10 and is rejected for the same reasons.
Claim(s) 5 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clark et al. (US 2018/0047224 A1) in view of Harutyunyan et al. (US 2023/0229548 A1) in view of Guenther et al. (US 7,945,427 B2) further in view of Lin et al. (US 2011/0010215 A1).
Regarding Claim 5, while the combination of Clark/Harutyunyan/Guenther teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 5 which state further comprising initially determining the baseline distribution based on stored prior historical operations data, subject matter expertise, or outside analysis.
Lin though, with the teachings of Clark/Harutyunyan/Guenther, teaches of
further comprising initially determining the baseline distribution based on stored prior historical operations data, subject matter expertise, or outside analysis (Lin: Para 0006, 0041 via the disclosure provides a system for providing a bin ratio forecast prior to a mass production stage in a semiconductor manufacturing environment. The system includes a virtual fabrication system coupled to a network and a manufacturing execution system (MES) coupled to the network. The MES includes a bin-based control module configured to collect historical data from one or more processed wafer lots; collect measurement data from one or more skew wafer lots; generate an estimated baseline distribution based on the collected historical data and collected measurement data; generate an estimated performance distribution based on one or more specified parameters and the generated estimated baseline distribution;... At block 406, an estimated baseline distribution is then generated from the historical data and the measurement data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Clark/Harutyunyan/Guenther with the teachings of Lin in order to have further comprising initially determining the baseline distribution based on stored prior historical operations data, subject matter expertise, or outside analysis. The motivations behind this being to incorporate the teachings of generating baseline distributions using historical data. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention. As well as simple substitution of one known element for another to obtain predictable results.
Regarding Claim 14, it is analogous to Claim 5 and is rejected for the same reasons.
Claim(s) 8 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clark et al. (US 2018/0047224 A1) in view of Harutyunyan et al. (US 2023/0229548 A1) in view of Guenther et al. (US 7,945,427 B2) further in view of Lipowski et al. (US 2011/0153273 A1).
Regarding Claim 8, while the combination of Clark/Harutyunyan/Guenther teaches Claim 1, it does not explicitly disclose the limitations of Claim 1 which state wherein the comparing is based on a calculated Bayes Factor representing a ratio indicating a probability of one distribution from the set of candidate distributions being selected relative to the baseline distribution using Bayesian logic.
Lipowsky though, with the teachings of Clark/Harutyunyan/Guenther, teaches of
wherein the comparing is based on a calculated Bayes Factor representing a ratio indicating a probability of one distribution from the set of candidate distributions being selected relative to the baseline distribution using Bayesian logic (Lipowsky: Para 0051 via FIG. 5 shows the interrelationship between the probability distributions and the Bayes' factor. 501 designates the probability distribution of the current model, 503 that of the alternative model, 505 the offset h of the mean values of the two distributions and 507 the progression of the Bayes' factor. In the depicted example, it is h=1.645. What is crucial is that the Bayes' factor represents a monotonously increasing function, i.e., the greater the residuum, the greater the Bayes' factor. A threshold value 509 can be hereby defined, which, when exceeded, a measured value can be identified as a potential outlier or rapid change).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Clark/Harutyunyan/Guenther with the teachings of Lipowsky in order to have wherein the comparing is based on a calculated Bayes Factor representing a ratio indicating a probability of one distribution from the set of candidate distributions being selected relative to the baseline distribution using Bayesian logic. The motivations behind this being to incorporate the teachings of Bayes prediction. Furthermore, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention. As well as simple substitution of one known element for another to obtain predictable results.
Regarding Claim 17, it is analogous to Claim 8 and is rejected for the same reasons.
Claim(s) 9 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clark et al. (US 2018/0047224 A1) in view of Harutyunyan et al. (US2023/0229548 A1) in view of Guenther et al. (US 7,945,427 B2) further in view of Chang et al. (US 2021/0005278 A1).
Regarding Claim 9, while the combination of Clark/Harutyunyan/Guenther teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 9 which state wherein the comparing is based on a calculated sample Kullback-Liebler divergence which approximates an expected logarithmic deviation between the baseline distribution or the candidate distributions and a true, but unknown, distribution representing sampled process.
Chang though, with the teachings of Clark/Harutyunyan/Guenther, teaches of
wherein the comparing is based on a calculated sample Kullback-Liebler divergence which approximates an expected logarithmic deviation between the baseline distribution or the candidate distributions and a true, but unknown, distribution representing sampled process (Chang: Para 0078 via Since the exact function mapping between real values and their belief probabilities is unknown, a non-parametric metric, i.e. Kullback-Liebler (KL) divergence is employed to compare the distribution of real observations to the distribution of predicted marginal probabilities. If the predicted and observed distribution of the child nodes match well, it can be concluded that the predictions based on G and well reflect the observed data D, which results in a smaller value of the KL-divergence. To force the KL divergence to behave as a true probability measure, symmetry and normalization modifications are made to this function defined on D and H such that k(D;H)=1-exp[-(KL(DI|H)+KL(HIID))/2]. The data likelihood function in Eq. 3 can be defined by any normalized monotonic decreasing function on the kernel. For model selection, where S=-log(K(D, H)) to represent the posterior score of the model, which is negatively correlated with the kernel value. To optimize the model, this score is maximized, which is equivalent to minimizing the KL-divergence).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Clark/Harutyunyan/Guenther with the teachings of Chang in order to have wherein the comparing is based on a calculated sample Kullback-Liebler divergence which approximates an expected logarithmic deviation between the baseline distribution or the candidate distributions and a true, but unknown, distribution representing sampled process. The teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention. As well as simple substitution of one known element for another to obtain predictable results.
Regarding Claim 18, it is analogous to Claim 9 and is rejected for the same reasons.
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clark et al. (US 2018/0047224 A1) in view of Harutyunyan et al. (US2023/0229548 A1) in view of Guenther et al. (US 7,945,427 B2) further in view of Hinduja et al. (US 2022/0129861 A1).
Regarding Claim 21, while the combination of Clark/Harutyunyan/Guenther teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 21 which states wherein changing the maintenance schedule comprises reducing a number of miles traveled by the vehicles prior to receiving service.
Hinduja though, with the teachings of Clark/Harutyunyan/Guenther, teaches of wherein changing the maintenance schedule comprises reducing a number of miles traveled by the vehicles prior to receiving service (Hinduja: Para 0014, 0098 via server is configured to receive first maintenance data, first vehicle data, first booking data, and a plurality of maintenance plans associated with a plurality of vehicles. Each of the plurality of maintenance plans is indicative of one or more historical scheduled maintenance sessions of a corresponding vehicle of the plurality of vehicles. The server is further configured to determine a plurality of features and a corresponding plurality of feature values for each of the plurality of vehicles based on the first maintenance data, the first vehicle data, the first booking data, and the plurality of maintenance plans. The server is further configured to train a prediction model based on the plurality of features and the corresponding plurality of feature values. The server is further configured to determine a maintenance criterion for a target vehicle based on the trained prediction model and a target dataset associated with the target vehicle. The target dataset includes second maintenance data, second vehicle data, and second booking data associated with the target vehicle. The maintenance criterion indicates an odometer reading range of the target vehicle during which a scheduled maintenance of the target vehicle is to be performed… the maintenance criterion, generated by the trained prediction model 210 may be dynamic in nature. The maintenance criterion of the target vehicle 216 may change with respect to physical and environmental conditions to which the target vehicle 216 is exposed. For example, a current odometer reading of the target vehicle 216 may be 20,000 km. The prediction model 210 may have determined that the target vehicle 216 may require maintenance during the odometer reading range 21,000 km to 23,000 km. For next three weeks, the target vehicle 216 may be driven with utmost care and within a geographical region having favorable environmental conditions. Therefore, based on the maintenance data, the vehicle data, and the booking data of the target vehicle 216 for the past three weeks, the prediction model 210 may determine a new maintenance criterion indicative of the odometer reading range “23,000 to 25,000”. A delay in the scheduled maintenance session exhibited by the maintenance criterion may be indicate that the target vehicle 216 has been well taken care of. Alternatively, an early scheduling of the scheduled maintenance session indicated by the maintenance criterion may be because the target vehicle 216 may have been exposed to uncertain and unfavorable conditions).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Clark/Harutyunyan/Guenther with the teachings of Hinduja in order to have wherein changing the maintenance schedule comprises reducing a number of miles traveled by the vehicles prior to receiving service. The motivations behind this being to incorporate the teachings of dynamic maintenance scheduling as taught by Hinduja. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure
Miller et al. (US 10,204,323 B1)
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/T.E.S./Examiner, Art Unit 3625
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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