DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after February 13, 2024, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
The Amendment filed on 09/18/2025 has been entered. Claims 1-20 are pending in the instant patent application. Claims 1 and 20 are amended. 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, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 1-10 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 1, claim 1 recites receiving operations data observed from the collection of machines wherein the operations data are expected to be represented by a baseline distribution characterizing one or more aspects of operation of at least one machine of the collection of machines; comparing based upon 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 at least one machine of the collection of machines; discovering a new trend in the operations data if it is determined that: one of the candidate distributions more accurately represents the operations data than the baseline distribution; or the baseline distribution is improbable; responsive to the determining, assigning the one candidate distribution as the baseline distribution; and triggering an alert to indicate the discovered new trend in 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 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 receive operations data observed from the collection of machines wherein the operations data are expected to be represented by a baseline distribution characterizing one or more aspects of operation of at least one machine of the collection of machines; 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 at least one machine of the collection of machines; discover a new trend in the operations data if it is determined that: one of the candidate distributions more accurately represents the operations data than the baseline distribution; or the baseline distribution is improbable; responsive to the determining, assigning the one candidate distribution as the baseline distribution; and trigger an alert to indicate the discovered new trend in 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 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.
Regarding Claim 20, it is directed to a non-transitory computer readable medium, however the claim is directed to a judicial exception without significantly more. Claim 20 is directed to the abstract idea of monitoring a collection of machines.
Performing the Step 2A Prong 1 analysis while referring specifically to least independent Claim 20, claim 20 recites receive operations data observed from a collection of machines wherein the operations data are expected to be represented by a baseline distribution characterizing one or more aspects of operation of at one machine of the collection of machines; comparing based upon 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 at least one machine of the collection of machines; discover a new trend in the operations data if it is determined that: one of the candidate distributions more accurately represents the operations data than the baseline distribution; or the baseline distribution is improbable; responsive to the determining, assigning the one candidate distribution as the baseline distribution; and trigger an alert to indicate the discovered new trend in 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 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.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a computing device. The computing device is merely generic computing devices and does 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. Claim 20 includes various elements that are not directed to the abstract idea under 2A. These elements include a computing device and 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 20 is not drawn to eligible subject matter as it is 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.
Regarding Applicant’s arguments that the motivation reasoning was improper, Examiner respectfully disagrees. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, when read in its entirety, Examiner reiterates the claim language, that the reference teaches said claim language, along with stating the motivation as well as indicating that they’re in the same CPC class. Thus, sufficient and proper.
Regarding Applicant’s arguments that the cited art does not teach the limitation of discovering a new trend in the operations data if one of the candidate distributions more accurately represents the operations data than the baseline distribution, Examiner respectfully disagrees. Taking the claim language under its broadest reasonable interpretation, Examiner maintains that the cited art teaches the limitation.
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 19-20 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 limitations of Claim 1 which state
receiving operations data observed from the collection of machines wherein the operations data are expected to be represented by a baseline distribution characterizing one or more aspects of operation of at least one machine of the Application/Control Number: 18/440,339 Art Unit: 3623 Page 10 collection of machines (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 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 at least one machine of the collection of machines.
Harutyunyan though, with the teachings of Clark, teaches of
comparing based upon 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 at least one machine of the collection of machines (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 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 at least one machine of the collection of machines. 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 discovering a new trend in the operations data if it is determined that: one of the candidate distributions more accurately represents the operations data than the baseline distribution; or the baseline distribution is improbable.
Guenther though, with the teachings of Clark/Harutyunyan, teaches of
discovering a new trend in the operations data if it is determined that: one of the candidate distributions more accurately represents the operations data than baseline distribution; or the baseline distribution is improbable (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 discovering a new trend in the operations data if it is determined that: one of the candidate distributions more accurately represents the operations data than the baseline distribution; or the baseline distribution is improbable. 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, teaches the limitations of Claim 1 which state
responsive to the determining, assigning the one candidate distribution as the baseline distribution (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,... , N and 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); and
triggering an alert to indicate the discovered new trend in the operations data (Clark: Para 0037, 0050 via The data quality assurance module 304 receives the fleet data 302 and generates corrected data. The data quality assurance module 304 may provide a notification of any data continuity/integrity issues that need to be addressed regarding, for example, incoming operational and maintenance data from users or customers. The data quality assurance module 304 may provide visibility into received data relative to required data elements or values. Parameters may include whether or not data or information has been received or transmitted and whether the data is appropriately formatted. Inconsistencies or discrepancies may be flagged or highlighted. The data quality assurance module 304 detects a receipt or absence of expected data associated with the first set of data, changes in the dataset since prior analysis and data anomalies (e.g., values outside of expected or possible ranges, such as a product accumulating more than 24 hours of usage within a single day)... The maintenance/operations monitoring/alerting Page 15 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.)).
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 or that the baseline distribution is improbable (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 distribution 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 the collection of machines is a fleet of vehicles 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.
Regarding Claim 20, it is analogous to Claim 1 and is rejected for the same reasons (Harutyunyan: Para 0162).
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.
Conclusion
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/T.E.S./ Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623