DETAILED ACTION
This is responsive to amendments filed on 07/15/2025 in which claims 1-20 are presented for examination; Claims 1, 3, 11,17,19 and 20 have been amended.
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 17, and 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claims 1, 17, and 20, the amended claim recites “and in response to determining that the present phase of degradation of the physical component satisfies the target phase of degradation of the physical component, determine a degradation model from the measurement data, and produce simulated measurement data using the degradation model”, however the specification is devoid of such disclosure. The cited paragraph for the claim limitation support recites:
“[0032] Referring now to FIG. 6, in block 380, the system 100 may determine to perform further degradation in response to a determination that the degradation acceleration parameter data indicates a target phase of degradation to reach and that the present phase of degradation does not satisfy the target phase. If the system 100 determines to perform further degradation, the method 300 loops back to block 326 of FIG. 4, to continue to apply the accelerated degradation process to the physical component(s). Otherwise, the method 300, in the illustrative embodiment, advances to block 382 in which the system 100 produces simulated measurement data indicative of characteristics of the physical component(s) 140 at multiple phases of the degradation process. That is, the system 100 (e.g., the degradation analysis compute device 160) produces data indicative of measurements (e.g., images, etc.) that were not physically performed (e.g., using the degradation chamber 110 and the robot 130), to augment the measurement data from the degradation chamber 110 and the robot 130. In doing so, and as indicated in block 384, the system 100 (e.g., the degradation analysis compute device 160) may determine a degradation model from the measurement data (e.g., obtained from the robot 130 and/or degradation chamber 110) that produces data indicative of characteristics of the physical component(s) 140 for different degradation phases. In doing so, and as indicated in block 386, the system 100 (e.g., the degradation analysis compute device 160) may perform feature extraction (e.g., edge detection, object recognition, etc.) to identify characteristics of corresponding phases of degradation. For example, the system 100 (e.g., the degradation analysis compute device 160) may identify distinctive characteristics such as size, color, albedo, a bidirectional reflectance distribution function (BDRF), border structure, orientation, and/or spacing or correlations between features of the physical component(s) 140 for each degradation phase, based on an analysis of the measurement data. The system 100 may associate the extracted features with testing conditions (e.g., the measurement parameter data from block 308 defining angles to measure from, lighting conditions, etc.) under which the measurements (e.g., the measurement data) were obtained.”
As can be seen, the disclosure teaches that determination is made to see if degrade further, otherwise “the system 100 produces simulated measurement data indicative of characteristics of the physical component(s) 140 at multiple phases of the degradation process; whereas the claim states “and in response to determining that the present phase of degradation of the physical component satisfies the target phase of degradation of the physical component, determine a degradation model from the measurement data, and produce simulated measurement data using the degradation model.” The specification does not teach “determine a degradation model from the measurement data, and produce simulated measurement data using the degradation model” as a result of determination as to whether degrade further or not, rather it teaches “producing simulated measurement data indicative of characteristics of the physical component(s) 140 at multiple phases.” By doing this, “the system 100 (e.g., the degradation analysis compute device 160) may determine a degradation model from the measurement data (e.g., obtained from the robot 130 and/or degradation chamber 110) that produces data indicative of characteristics of the physical component(s) 140 for different degradation phases.”
Claim Rejections - 35 USC § 103
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.
Claims 1-13, 15, and 17- 20 are rejected under 35 U.S.C. 103 as being unpatentable over Gross et al. ( US 20200310396 A1) in view of BOUCHER et al. (US 20220355839 A1) and further in view of Gebraeel et al. (“A Neural Network Degradation Model for Computing and Updating Residual Life Distributions”, January, 2008)
Regarding claim 1, Gross teaches a system comprising:
circuitry configured to (Gross, paragraph, 0019):
apply an accelerated degradation process to a physical component of an industrial plant [to satisfy a target phase of degradation of the physical component], (Gross, paragraph “[0025] The disclosed embodiments operate by first gathering time-series sensor signals from a “golden system,” which is configured to match a configuration of an asset to be monitored. (This “golden system” is an asset that contains new and/or thoroughly tested components with no degradation modes, and which is operating as well as can be expected for that asset.) More specifically, we gather environment-specific time-series signals from the golden system while the golden system operates in an environmental-testing chamber, which cycles through all possible combinations of various environmental parameters, such as ambient temperature, relative humidity and altitude, while time-series signals are collected from sensors in the golden system. For example, the ambient temperature can be varied between −20° C. and 80° C. in 5-degree increments, the relative humidity can be varied between 5% and 95% in 5% increments, and the altitude can be varied between sea level and 10,000 feet in 2,000 foot increments. (Note that, in addition to ambient temperature, relative humidity and altitude, the disclosed embodiments can also vary other environmental parameters, such as ambient vibrations and ambient radiation.” Note: here, golden system contains all tested components(asset) on which degradation is applied i.e. temperature, humidity etc. (an accelerated degradation process);
Also, para 0033, “[0033] During a training phase, the system obtains time-series sensor signals 115 from a golden system 132, which is operating inside an environmental testing chamber 134, wherein the configuration of this golden system 132 matches a configuration of test system 102. Moreover, golden system 132 is ensured to be operating correctly and contains new and/or thoroughly tested components with no degradation. During the training phase, environmental-testing chamber 134 cycles through different combinations of environmental parameters, which enables training module 116 to train environment-specific inferential models 138 for each of the different combinations of the environmental parameters. These environment-specific inferential models 138 are then stored in a model database 120.” ),
wherein the accelerated degradation process irreversibly degrades one or more surfaces of the physical component (“[0022] Although ambient relative humidity can directly affect long-term aging phenomena for mechanical assets that do not contain embedded control elements (for example, through accelerated surface corrosion of metallic components and accelerated oxidation degradation of elastomeric elements), for the majority of electromechanical components and subsystems, variations in relative humidity can have a more pronounced impact on aging and degradation mechanisms….”; also see para 0021); Note: oxidation, and corrosion etc.. are irreversible degradations.)
obtain measurement data indicative of visual characteristics of the physical component at each of multiple phases of degradation [during the accelerated degradation process to satisfy the target phase of degradation of the physical component ](Gross, paragraph “[0030] We then perform a pairwise-differencing operation between the projected-ahead estimated values and the actual values for the received time-series signals to produce residuals. Finally, we perform a SPRT on the residuals to detect possible degradation. If any degradation is detected, we can set data disturbance flags for any affected signals, and can prepare for a scheduled shutdown if an imminently dangerous system state is predicted with a high confidence factor. Or, we can notify maintenance personnel to proactively schedule service actions at the earliest convenient time. We then continue sampling the signals by sliding the time window forward in units of signal sampling rate. This process continuously iterates to provide real-time assurance that assets in the field are operating within all reliability, availability, and serviceability (RAS) functional specifications, and to facilitate real-time detection of the onset of incipient degradation to facilitate avoiding catastrophic failures of assets in the field.” Note: here data is collected and severity (phases) of degradation is determined; for example it is checked if the system is performing within functional specification (phase of degradation which is acceptable), and detection of the onset of incipient degradation (phase, where if the issue is not addressed, it could result in catastrophic failure)),
Wherein the measurement data indicative of visual characteristics of the physical component comprises data indicative of degradation on the one or more surfaces of the physical component, and the measurement data is usable to train a neural network to identify a phase of degradation of another physical component (Gross, paragraph “[0033] During a training phase, the system obtains time-series sensor signals 115 from a golden system 132, which is operating inside an environmental testing chamber 134, wherein the configuration of this golden system 132 matches a configuration of test system 102. Moreover, golden system 132 is ensured to be operating correctly and contains new and/or thoroughly tested components with no degradation. During the training phase, environmental-testing chamber 134 cycles through different combinations of environmental parameters, which enables training module 116 to train environment-specific inferential models 138 for each of the different combinations of the environmental parameters. These environment-specific inferential models 138 are then stored in a model database 120.”
[0022] Although ambient relative humidity can directly affect long-term aging phenomena for mechanical assets that do not contain embedded control elements (for example, through accelerated surface corrosion of metallic components and accelerated oxidation degradation of elastomeric elements), for the majority of electromechanical components and subsystems, variations in relative humidity can have a more pronounced impact on aging and degradation mechanisms. This is because high relative humidity in conjunction with thermal swings leads to micro-condensation, which can cause shorting of interconnects and accelerated electromigration failures in PCB elements, and accelerated surface corrosion that leads to resistive failures in interconnects, as well as increased friction in all types of shaft-sleeve and rotating machinery components” Note: here, we can see that degradation problem detected in one part can impact the other parts; also see paragraph 0021. Also note that “one or more surfaces” under BRI will cover any surface of the component.).
in response to determining that the present phase of degradation of the physical component satisfies the target phase of degradation of the physical component, determine a degradation model from the measurement data, and produce simulated measurement data using the degradation model ( para “[0033] During a training phase, the system obtains time-series sensor signals 115 from a golden system 132, which is operating inside an environmental testing chamber 134, wherein the configuration of this golden system 132 matches a configuration of test system 102. Moreover, golden system 132 is ensured to be operating correctly and contains new and/or thoroughly tested components with no degradation. During the training phase, environmental-testing chamber 134 cycles through different combinations of environmental parameters, which enables training module 116 to train environment-specific inferential models 138 for each of the different combinations of the environmental parameters. These environment-specific inferential models 138 are then stored in a model database 120.” Note: here model for each combination is trained and stored (different phases of degradation).
Also, para “[0039] FIG. 2 presents a flow chart illustrating the process of performing prognostic-surveillance operations based on an environment-specific inferential model in accordance with the disclosed embodiments. During operation, the system receives time-series signals from sensors in the asset while the asset is operating (step 202). Next, the system obtains real-time environmental parameters for an environment in which the asset is operating (step 204). (Note that these real-time environmental parameters can either be directly measured or inferred from other parameters.) The system then selects an environment-specific inferential model for the asset based on the real-time environmental parameters, wherein the environment-specific inferential model was trained on a golden system while the golden system was operating in an environment that matches the real-time environmental parameters, wherein a configuration of the golden system matches a configuration of the asset, and wherein the golden system was certified to be operating correctly (step 206). The system then uses the environment-specific inferential model to generate estimated values for a sliding time window of values for the received time-series signals based on correlations among the received time-series signals (step 208). Next, the system uses the TPSS technique to project the estimated values for the received time-series signals into the future based on the time window (step 210), and then performs a pairwise-differencing operation between the actual values and the estimated values for the received time-series signals to produce residuals (step 212). Next, the system performs a SPRT on the residuals to produce SPRT alarms (step 214). Finally, the system determines from the SPRT alarms whether the asset is operating correctly (step 216).” Note: here inferential model based on configuration match (matching target degradation) is selected, and use to projected the estimated value for future time window (producing simulated measurements; also note that this is contingent language and system only have to be capable of performing this step, and it is very clear that we have inferential model for each configuration, and that can be used to estimate future data (simulate).).
Examiner Note: every combination is a target phase, and the measurement data is collected at each phase (target matching present); however Gross does not teach determining if target meets, the present, and if not then continuing. Gebraeel reference is used to teach that aspect. Simply put, in the chamber degradation at each combination is present phase of degradation of the physical component that satisfies the target phase of degradation.
Gross doesn’t explicitly teach:
[apply an accelerated degradation process to a physical component of an industrial plant] to satisfy a target phase of degradation of the physical component.
determine whether a present phase of degradation of the physical component satisfies the target phase of degradation of the physical component, in response to determining that the present phase of degradation of the physical component does not satisfy the target phase of degradation of the physical component , continue to apply the accelerated degradation process;
[and obtain measurement data indicative of visual characteristics of the physical component at each of multiple phases of degradation] during the accelerated degradation process to satisfy the target phase of degradation of the physical component.
Boucher teaches:
[apply an accelerated degradation process to a physical component of an industrial plant] to satisfy a target phase of degradation of the physical component (Para, “[0078] The component condition monitoring step can comprise estimating for at least one of the at least one component at least one of a degradation, a type of the degradation, a severity of the degradation, a location of a damage, a location of a degraded portion of the at least one component, a type of a failure, a presence of an anomaly, a damage and a probability of a failure. The component condition monitoring step can thus comprise generating degradation information. The at least one estimated measure can accordingly be referenced within this disclosure by “degradation information”. It is to be noted that estimating a degradation can also yield that a certain degradation has not (yet) happened or that a component is not subject to the certain degradation. Also, estimating the degradation information for the at least one component can comprise estimating the degradation information for a part or a portion of the component. For example, generating degradation information for a component comprising a bearing, as example for a part with a significant degradation, can be generating the degradation information for this bearing, or if there is a plurality of bearings, generating degradation information for some or all bearings and then agglomerating this information to degradation information regarding the component.” Note: here one can monitor the degradation severity and further also determine if the certain (target phase of degradation) has happened or not.)
[and obtain measurement data indicative of visual characteristics of the physical component at each of multiple phases of degradation] during the accelerated degradation process to satisfy the target phase of degradation of the physical component (Para 0078 teaches monitoring a severity of degradation; Also para, “[0029] The term “condition” is intended to refer to a state of an element. The state can be a degradation, a type of the degradation, a severity of the degradation, a damage, a wear, a state of maintenance such as a sufficient presence of a lubricant or a type of a lubricant or a failure. It can also be a type of a failure, a presence of an anomaly, a remaining useful lifetime of the element, a performance of the element or a probability of a failure. A condition can comprise at least one or a plurality of other conditions. A condition can comprise conditions of portion(s) of the element. A condition can comprise conditions that refer to different aspects of the state of the element, such as a condition referring to a mechanical wear of a portion of the element and to a corrosion of the same or another portion of the element.” Note: this reference also address explicitly “one or more surfaces” (para 0058), though under BRI it can be interpreted to be any part of equipment or all the equipment.)
In view of the teaching of BOUCHER, it would have been obvious for a person of ordinary skill in the art to apply teaching of Gross at the time the application was filed in order to determine the probability of failure of the component/element. (BOUCHER, paragraph, “[0029] The term “condition” is intended to refer to a state of an element. The state can be a degradation, a type of the degradation, a severity of the degradation, a damage, a wear, a state of maintenance such as a sufficient presence of a lubricant or a type of a lubricant or a failure. It can also be a type of a failure, a presence of an anomaly, a remaining useful lifetime of the element, a performance of the element or a probability of a failure. A condition can comprise at least one or a plurality of other conditions. A condition can comprise conditions of portion(s) of the element. A condition can comprise conditions that refer to different aspects of the state of the element, such as a condition referring to a mechanical wear of a portion of the element and to a corrosion of the same or another portion of the element.”)
Gross as modified by Boucher does not explicitly teaches:
determine whether a present phase of degradation of the physical component satisfies the target phase of degradation of the physical component, in response to determining that the present phase of degradation of the physical component does not satisfy the target phase of degradation of the physical component , continue to apply the accelerated degradation process;
Gebraeel teaches:
determine whether a present phase of degradation of the physical component satisfies the target phase of degradation of the physical component, in response to determining that the present phase of degradation of the physical component does not satisfy the target phase of degradation of the physical component , continue to apply the accelerated degradation process(Pg. 157, “according to industrial standards for machine vibration, ISO 2372 [26], an overall root mean square (rms) vibration acceleration level ranging between 2.0 and 2.2 Gs is considered a “danger level” for applications involving “medium-sized general-purpose” machinery. Each degradation test is terminated once the root mean square of the overall vibration acceleration reaches a threshold of 2.2 Gs. The corresponding amplitude of the degradation signal was found to range between 0.03 and 0.035 . We define the degradation signal amplitude of 0.03 as our failure threshold.” Note: here test is only terminated when threshold (target) degradation is achieved; also note, that threshold acceleration indicates target level of degradation (danger level).);
In view of the teaching of Gebraeel, it would have been obvious for a person of ordinary skill in the art to apply teaching of Gross as modified by Boucher at the time the application was filed in order to determine the residual life of degraded components. (Abstract, “The ability to accurately estimate the residual life of partially degraded components is arguably the most challenging problem in prognostic condition monitoring. This paper focuses on the development of a neural network-based degradation model that utilizes condition-based sensory signals to compute and continuously update residual life distributions of partially degraded components.”)
Regarding claim 2, Gross as modified by Boucher and Gebraeel teaches the system of claim 1.
Gross further teaches wherein to apply the accelerated degradation process comprises to apply the accelerated degradation process to the physical component in a degradation chamber configured to produce a target environment within the degradation chamber(Gross, paragraph “[0025] The disclosed embodiments operate by first gathering time-series sensor signals from a “golden system,” which is configured to match a configuration of an asset to be monitored. (This “golden system” is an asset that contains new and/or thoroughly tested components with no degradation modes, and which is operating as well as can be expected for that asset.) More specifically, we gather environment-specific time-series signals from the golden system while the golden system operates in an environmental-testing chamber, which cycles through all possible combinations of various environmental parameters, such as ambient temperature, relative humidity and altitude, while time-series signals are collected from sensors in the golden system. For example, the ambient temperature can be varied between −20° C. and 80° C. in 5-degree increments, the relative humidity can be varied between 5% and 95% in 5% increments, and the altitude can be varied between sea level and 10,000 feet in 2,000 foot increments. (Note that, in addition to ambient temperature, relative humidity and altitude, the disclosed embodiments can also vary other environmental parameters, such as ambient vibrations and ambient radiation.”)
Regarding claim 3, Gross as modified by Boucher and Gebraeel teaches the system of claim 1.
BOUCHER further teach :
wherein the simulated measurement data is indicative of characteristics of the physical component at multiple phases of degradation and is usable as training data for the neural network (BOUCHER, paragraph, “[0078] The component condition monitoring step can comprise estimating for at least one of the at least one component at least one of a degradation, a type of the degradation, a severity of the degradation, a location of a damage, a location of a degraded portion of the at least one component, a type of a failure, a presence of an anomaly, a damage and a probability of a failure. The component condition monitoring step can thus comprise generating degradation information. The at least one estimated measure can accordingly be referenced within this disclosure by “degradation information”. It is to be noted that estimating a degradation can also yield that a certain degradation has not (yet) happened or that a component is not subject to the certain degradation. Also, estimating the degradation information for the at least one component can comprise estimating the degradation information for a part or a portion of the component. For example, generating degradation information for a component comprising a bearing, as example for a part with a significant degradation, can be generating the degradation information for this bearing, or if there is a plurality of bearings, generating degradation information for some or all bearings and then agglomerating this information to degradation information regarding the component.”)
In view of the teaching of BOUCHER, it would have been obvious for a person of ordinary skill in the art to apply teaching of Gross as modified by Boucher and Gebraeel at the time the application was filed in order to determine the probability of failure of the component/element. (BOUCHER, paragraph, “[0029] The term “condition” is intended to refer to a state of an element. The state can be a degradation, a type of the degradation, a severity of the degradation, a damage, a wear, a state of maintenance such as a sufficient presence of a lubricant or a type of a lubricant or a failure. It can also be a type of a failure, a presence of an anomaly, a remaining useful lifetime of the element, a performance of the element or a probability of a failure. A condition can comprise at least one or a plurality of other conditions. A condition can comprise conditions of portion(s) of the element. A condition can comprise conditions that refer to different aspects of the state of the element, such as a condition referring to a mechanical wear of a portion of the element and to a corrosion of the same or another portion of the element.”)
Regarding claim 4, Gross as modified by Boucher and Gebraeel teaches the system of claim 1.
BOUCHER further teaches wherein the circuitry is further configured to obtain the measurement data that is additionally indicative of a performance characteristic of the physical component at each of the multiple phases of degradation (BOUCHER, paragraph, “[0087] Furthermore, supervised machine learning models can be advantageous for estimating a condition of an element, for example a severity of a degradation. Said estimating can comprise using (a) feature(s) extracted from sensed data or data that were processed in the data processing step, such as an RMS-value of a current signal, an acceleration signal and/or features extracted from the spectral analysis of the acceleration signal.” Note, here a severity of degradation tells which phase of degradation the component is; also se paragraphs 0079 for remaining life.)
In view of the teaching of BOUCHER, it would have been obvious for a person of
ordinary skill in the art to apply teaching of Gross as modified by Boucher and Gebraeel at the time the application was filed in order to determine the probability of failure of the component/element. (BOUCHER, paragraph, “[0029] The term “condition” is intended to refer to a state of an element. The state can be a degradation, a type of the degradation, a severity of the degradation, a damage, a wear, a state of maintenance such as a sufficient presence of a lubricant or a type of a lubricant or a failure. It can also be a type of a failure, a presence of an anomaly, a remaining useful lifetime of the element, a performance of the element or a probability of a failure. A condition can comprise at least one or a plurality of other conditions. A condition can comprise conditions of portion(s) of the element. A condition can comprise conditions that refer to different aspects of the state of the element, such as a condition referring to a mechanical wear of a portion of the element and to a corrosion of the same or another portion of the element.”)
Regarding claim 5, Gross as modified by Boucher and Gebraeel teaches the system of claim 1.
Gross further teaches wherein to obtain the measurement data comprises to obtain the measurement data with a robot having a sensor configured to produce the measurement data (Gross, paragraph “[0032] FIG. 1 illustrates an exemplary prognostic-surveillance system 100, which uses an environment-specific inferential model 108 to monitor time-series sensor signals 104 from a test system 102. Test system 102 can generally include any type of machinery or facility, which includes sensors and generates time-series signals. Moreover, time-series signals 104 can originate from any type of sensor located in test system 102, including, but not limited to: a voltage sensor; a current sensor; a pressure sensor; a rotational-speed sensor; and a vibration sensor.” Note, robot is nothing more than a machine that can perform the function.)
Regarding claim 6, Gross as modified by Boucher and Gebraeel teaches the system of claim 1.
Gross further teaches wherein to apply the accelerated degradation process comprises to subject the physical component to vibration, gas, vapor, abrasive conditions, impacts, thermal cycling, thermal shock, liquid spray, liquid soak, humidity, light, radiation, mold, fungus, or an electric arc in a degradation chamber (Gross, paragraph, “0021] Environmental parameters, such as ambient temperature, relative humidity and altitude, can greatly affect the operation of an asset in the field. For example, as temperatures of internal components and subsystems increase, thermal cycling can greatly affect the reliability of system internals. Many degradation mechanisms are accelerated by thermal cycling at elevated temperatures, including: accelerated solder fatigue; interconnect fretting; differential thermal expansion between bonded materials (which can lead to delamination failures); thermal mismatches between mating surfaces; differentials in the coefficients of thermal expansion between materials used in embedded-control packages; wire bond shear and flexure fatigue; passivation cracking; electromigration failures; electrolytic corrosion; thermomigration failures; crack initiation and propagation; delamination between chip dies and molding compounds (as well as between molding compounds and lead frames); die deadhesion fatigue; repeated stress reversals in brackets leading to dislocations, cracks, and eventual mechanical failures; deterioration of MPI connectors through elastomeric stress relaxation in polymers; and others.” Note, also see Fig. 2, step 206 that teaches golden system operating on selected environmental parameters)
Regarding claim 7, Gross as modified by Boucher and Gebraeel teaches the system of claim 1.
Gross further teaches wherein the accelerated degradation process irreversibly degrades the one or more surfaces of the physical component via at least one of rust, corrosion, discoloration, decomposition, wear, weathering, leaching, crazing, pitting, and cracking (Gross, paragraph, “0021] Environmental parameters, such as ambient temperature, relative humidity and altitude, can greatly affect the operation of an asset in the field. For example, as temperatures of internal components and subsystems increase, thermal cycling can greatly affect the reliability of system internals. Many degradation mechanisms are accelerated by thermal cycling at elevated temperatures, including: accelerated solder fatigue; interconnect fretting; differential thermal expansion between bonded materials (which can lead to delamination failures); thermal mismatches between mating surfaces; differentials in the coefficients of thermal expansion between materials used in embedded-control packages; wire bond shear and flexure fatigue; passivation cracking; electromigration failures; electrolytic corrosion; thermomigration failures; crack initiation and propagation; delamination between chip dies and molding compounds (as well as between molding compounds and lead frames); die deadhesion fatigue; repeated stress reversals in brackets leading to dislocations, cracks, and eventual mechanical failures; deterioration of MPI connectors through elastomeric stress relaxation in polymers; and others.” Note, also see paragraph 0022 for corrosion and oxidation)
Regarding claim 8, Gross as modified by Boucher and Gebraeel teaches the system of claim 1.
Gross further teaches wherein the circuitry is further configured to obtain the measurement data that is additionally indicative of a performance characteristic of the physical component at each of the multiple phases of degradation by performing at least one of strength testing, cycle fatigue resistance testing, vibration resistance testing, modulus testing, and softening point testing (Gross, paragraph, “[0025] The disclosed embodiments operate by first gathering time-series sensor signals from a “golden system,” which is configured to match a configuration of an asset to be monitored. (This “golden system” is an asset that contains new and/or thoroughly tested components with no degradation modes, and which is operating as well as can be expected for that asset.) More specifically, we gather environment-specific time-series signals from the golden system while the golden system operates in an environmental-testing chamber, which cycles through all possible combinations of various environmental parameters, such as ambient temperature, relative humidity and altitude, while time-series signals are collected from sensors in the golden system. For example, the ambient temperature can be varied between −20° C. and 80° C. in 5-degree increments, the relative humidity can be varied between 5% and 95% in 5% increments, and the altitude can be varied between sea level and 10,000 feet in 2,000 foot increments. (Note that, in addition to ambient temperature, relative humidity and altitude, the disclosed embodiments can also vary other environmental parameters, such as ambient vibrations and ambient radiation.”)
Regarding claim 9, Gross as modified by Boucher and Gebraeel teaches the system of claim 1.
Gebraeel further teaches wherein to obtain the measurement data comprises to perform destructive measurements on multiple samples of the physical component (Gebraeel, Pg. 155, “The ability to accurately predict a component’s remaining life is arguably the most challenging component of degradation modeling. This is primarily due to the randomness inherent in most degradation processes. To account for this dispersion, we study the degradation process of a sample of identical components, specifically, thrust ball bearings. Bearings can be monitored using vibration signals and are a good source for real-world degradation-based sensory information. In addition, the low-cost of test bearings enables high-volume destructive testing, thus, ensuring fidelity of our validation experiments.”)
In view of the teaching of Gebraeel, it would have been obvious for a person of
ordinary skill in the art to apply teaching of Gross as modified by Boucher and Gebraeel at the time the application was filed in order to ensure fidelity of the experiment. ((Gebraeel, Pg. 155, “…..To account for this dispersion, we study the degradation process of a sample of identical components, specifically, thrust ball bearings. Bearings can be monitored using vibration signals and are a good source for real-world degradation-based sensory information. In addition, the low-cost of test bearings enables high-volume destructive testing, thus, ensuring fidelity of our validation experiments.”)
Regarding claim 10, Gross as modified by Boucher and Gebraeel teaches the system of claim 1.
Gross as modified by Boucher does not explicitly teach wherein to apply the accelerated degradation process to the physical component of the industrial plant comprises to apply the accelerated degradation process to a representative subsection of the physical component.
BOUCHER further teaches wherein to apply the accelerated degradation process to the physical component of the industrial plant comprises to apply the accelerated degradation process to a representative subsection of the physical component (BOUCHER, paragraph, “[0032] The term “components” is intended to refer to elements of railway infrastructure that are configure to be parts of assets. A component of a switch can for example be a frog, a point blade, a guard rail or a switch motor. A component can comprise at least one or a plurality of parts. A component, its part(s) or portion(s) can be subject to one or a plurality of degradations.”)
In view of the teaching of BOUCHER, it would have been obvious for a person of
ordinary skill in the art to apply teaching of Gross as modified by Boucher and Gebraeel at the time the application was filed in order to determine the probability of failure of the component/element. (BOUCHER, paragraph, “[0029] The term “condition” is intended to refer to a state of an element. The state can be a degradation, a type of the degradation, a severity of the degradation, a damage, a wear, a state of maintenance such as a sufficient presence of a lubricant or a type of a lubricant or a failure. It can also be a type of a failure, a presence of an anomaly, a remaining useful lifetime of the element, a performance of the element or a probability of a failure. A condition can comprise at least one or a plurality of other conditions. A condition can comprise conditions of portion(s) of the element. A condition can comprise conditions that refer to different aspects of the state of the element, such as a condition referring to a mechanical wear of a portion of the element and to a corrosion of the same or another portion of the element.”)
Regarding claim 11, Gross as modified by Boucher and Gebraeel teaches the system of claim 1.
Gross further teaches:
wherein determining the degradation model from the measurement data (Gross, paragraph “[0033] During a training phase, the system obtains time-series sensor signals 115 from a golden system 132, which is operating inside an environmental testing chamber 134, wherein the configuration of this golden system 132 matches a configuration of test system 102. Moreover, golden system 132 is ensured to be operating correctly and contains new and/or thoroughly tested components with no degradation. During the training phase, environmental-testing chamber 134 cycles through different combinations of environmental parameters, which enables training module 116 to train environment-specific inferential models 138 for each of the different combinations of the environmental parameters. These environment-specific inferential models 138 are then stored in a model database 120.”),
BOUCHER further teaches:
includes performing feature extraction to identify characteristics of corresponding phases of degradation and utilizing a symbolic regression engine to identify correlations in feature development as a function of time (BOUCHER, paragraph, “[0087] Furthermore, supervised machine learning models can be advantageous for estimating a condition of an element, for example a severity of a degradation. Said estimating can comprise using (a) feature(s) extracted from sensed data or data that were processed in the data processing step, such as an RMS-value of a current signal, an acceleration signal and/or features extracted from the spectral analysis of the acceleration signal.”
“[0088] A supervised machine learning model can be a regression model, that is, it outputs a continuous variable such as a speed of a train or a health indicator relating to a particular component. A supervised machine learning model can be a classification model, that is, it outputs discrete values, such as classes, types and/or categories, for input values.” Note: also see paragraph 0096 for estimation over the time(function of time))
and wherein the simulated measurement data is indicative of characteristics of the physical component at multiple phases of degradation and is usable as training data for the neural network (BOUCHER, paragraph, “[0078] The component condition monitoring step can comprise estimating for at least one of the at least one component at least one of a degradation, a type of the degradation, a severity of the degradation, a location of a damage, a location of a degraded portion of the at least one component, a type of a failure, a presence of an anomaly, a damage and a probability of a failure. The component condition monitoring step can thus comprise generating degradation information. The at least one estimated measure can accordingly be referenced within this disclosure by “degradation information”. It is to be noted that estimating a degradation can also yield that a certain degradation has not (yet) happened or that a component is not subject to the certain degradation. Also, estimating the degradation information for the at least one component can comprise estimating the degradation information for a part or a portion of the component. For example, generating degradation information for a component comprising a bearing, as example for a part with a significant degradation, can be generating the degradation information for this bearing, or if there is a plurality of bearings, generating degradation information for some or all bearings and then agglomerating this information to degradation information regarding the component.”)
In view of the teaching of BOUCHER, it would have been obvious for a person of ordinary skill in the art to apply teaching of Gross as modified by Boucher and Gebraeel at the time the application was filed in order to determine the probability of failure of the component/element. (BOUCHER, paragraph, “[0029] The term “condition” is intended to refer to a state of an element. The state can be a degradation, a type of the degradation, a severity of the degradation, a damage, a wear, a state of maintenance such as a sufficient presence of a lubricant or a type of a lubricant or a failure. It can also be a type of a failure, a presence of an anomaly, a remaining useful lifetime of the element, a performance of the element or a probability of a failure. A condition can comprise at least one or a plurality of other conditions. A condition can comprise conditions of portion(s) of the element. A condition can comprise conditions that refer to different aspects of the state of the element, such as a condition referring to a mechanical wear of a portion of the element and to a corrosion of the same or another portion of the element.”)
Regarding claim 12 Gross as modified by Boucher and Gebraeel teaches the system of claim 11.
Gross further teaches wherein the circuitry is further configured to incorporate, with the symbolic regression engine, a known equation that describes a degradation process of the physical component (Gross, “[0026] Next, we train a different environment-specific inferential model based on time-series signals for each of the different combinations of environmental parameters. In some embodiments, this inferential model is an MSET model. However, other types of nonlinear, nonparametric regression-based machine-learning models can also be used, such as neural nets and support vector machines.” Note: here regression is known technique that measures a relationship between a dependent variable and one or more independent variables; here model is used to for detection of performance related, and age related degradation; for explicit teaching also see BOUCHER reference as cited with regard to claim 11.)
Regarding claim 13, Gross as modified by Boucher and Gebraeel teaches the system of claim 11.
BOUCHER further teaches wherein the circuitry is further configured to determine the degradation model for one or more local geometries of the physical component (BOUCHER, paragraph, “[0066] Furthermore, the data storing step can comprise storing specification data that relate to at least one property of at least one element of the represented railway infrastructure system. Specification data specify a property of the element. Examples for specification data can be an element's geometry, an element's material composition, an element's manufacturer, an element's dimensional tolerance, or the like. Specification data can also relate to a function or a functional property of an element, such as operating rules of a network. Specification data can also comprise information regarding connections, interactions and/or interdependencies of an element to at least one or a plurality of other elements and/or to external/boundary conditions, in particular geological conditions at the location of the asset.”)
In view of the teaching of BOUCHER, it would have been obvious for a person of ordinary skill in the art to apply teaching of Gross as modified by Boucher and Gebraeel at the time the application was filed in order to determine the probability of failure of the component/element. (BOUCHER, paragraph, “[0029] The term “condition” is intended to refer to a state of an element. The state can be a degradation, a type of the degradation, a severity of the degradation, a damage, a wear, a state of maintenance such as a sufficient presence of a lubricant or a type of a lubricant or a failure. It can also be a type of a failure, a presence of an anomaly, a remaining useful lifetime of the element, a performance of the element or a probability of a failure. A condition can comprise at least one or a plurality of other conditions. A condition can comprise conditions of portion(s) of the element. A condition can comprise conditions that refer to different aspects of the state of the element, such as a condition referring to a mechanical wear of a portion of the element and to a corrosion of the same or another portion of the element.”)
Regarding claim 15, Gross as modified by Boucher and Gebraeel teaches the system of claim 1.
Gross further teaches wherein the circuitry is further configured to produce simulated measurement data with a neural network that has been trained with the measurement data (Gross, paragraph, “[0026] Next, we train a different environment-specific inferential model based on time-series signals for each of the different combinations of environmental parameters. In some embodiments, this inferential model is an MSET model. However, other types of nonlinear, nonparametric regression-based machine-learning models can also be used, such as neural nets and support vector machines.” Note: also see, forecasting using the model (paragraph 0029), thus simulating measurement data with model.)
Regarding claim 17, Gross teaches a method comprising:
applying, by a system for producing training data, an accelerated degradation process to a physical component of an industrial plant [to satisfy a target phase of degradation of the physical component], (Gross, paragraph “[0025] The disclosed embodiments operate by first gathering time-series sensor signals from a “golden system,” which is configured to match a configuration of an asset to be monitored. (This “golden system” is an asset that contains new and/or thoroughly tested components with no degradation modes, and which is operating as well as can be expected for that asset.) More specifically, we gather environment-specific time-series signals from the golden system while the golden system operates in an environmental-testing chamber, which cycles through all possible combinations of various environmental parameters, such as ambient temperature, relative humidity and altitude, while time-series signals are collected from sensors in the golden system. For example, the ambient temperature can be varied between −20° C. and 80° C. in 5-degree increments, the relative humidity can be varied between 5% and 95% in 5% increments, and the altitude can be varied between sea level and 10,000 feet in 2,000 foot increments. (Note that, in addition to ambient temperature, relative humidity and altitude, the disclosed embodiments can also vary other environmental parameters, such as ambient vibrations and ambient radiation.” Note: here, golden system contains all tested components(asset) on which degradation is applied i.e. temperature, humidity etc. (an accelerated degradation process
Also, para, “[0033] During a training phase, the system obtains time-series sensor signals 115 from a golden system 132, which is operating inside an environmental testing chamber 134, wherein the configuration of this golden system 132 matches a configuration of test system 102. Moreover, golden system 132 is ensured to be operating correctly and contains new and/or thoroughly tested components with no degradation. During the training phase, environmental-testing chamber 134 cycles through different combinations of environmental parameters, which enables training module 116 to train environment-specific inferential models 138 for each of the different combinations of the environmental parameters. These environment-specific inferential models 138 are then stored in a model database 120.”));
wherein the accelerated degradation process irreversibly degrades one or more surfaces of the physical component (“[0022] Although ambient relative humidity can directly affect long-term aging phenomena for mechanical assets that do not contain embedded control elements (for example, through accelerated surface corrosion of metallic components and accelerated oxidation degradation of elastomeric elements), for the majority of electromechanical components and subsystems, variations in relative humidity can have a more pronounced impact on aging and degradation mechanisms….”; also see para 0021); Note: oxidation, and corrosion etc.. are irreversible degradations.);
obtaining, by the system, measurement data indicative of visual characteristics of the physical component at each of multiple phases of degradation [during the accelerated degradation process to satisfy the target phase of degradation of the physical component], (Gross, paragraph “[0030] We then perform a pairwise-differencing operation between the projected-ahead estimated values and the actual values for the received time-series signals to produce residuals. Finally, we perform a SPRT on the residuals to detect possible degradation. If any degradation is detected, we can set data disturbance flags for any affected signals, and can prepare for a scheduled shutdown if an imminently dangerous system state is predicted with a high confidence factor. Or, we can notify maintenance personnel to proactively schedule service actions at the earliest convenient time. We then continue sampling the signals by sliding the time window forward in units of signal sampling rate. This process continuously iterates to provide real-time assurance that assets in the field are operating within all reliability, availability, and serviceability (RAS) functional specifications, and to facilitate real-time detection of the onset of incipient degradation to facilitate avoiding catastrophic failures of assets in the field.” Note: here data is collected and severity (phases) of degradation is determined; for example it is checked if the system is performing within functional specification (phase of degradation which is acceptable), and detection of the onset of incipient degradation (phase, where if the issue is not addressed, it could result in catastrophic failure)),
wherein the measurement data indicative of visual characteristics of the physical component comprises data indicative of degradation on the one or more surfaces of the physical component, and the measurement data is usable to train a neural network to identify a phase of degradation of another physical component(Gross, paragraph “[0033] During a training phase, the system obtains time-series sensor signals 115 from a golden system 132, which is operating inside an environmental testing chamber 134, wherein the configuration of this golden system 132 matches a configuration of test system 102. Moreover, golden system 132 is ensured to be operating correctly and contains new and/or thoroughly tested components with no degradation. During the training phase, environmental-testing chamber 134 cycles through different combinations of environmental parameters, which enables training module 116 to train environment-specific inferential models 138 for each of the different combinations of the environmental parameters. These environment-specific inferential models 138 are then stored in a model database 120.”
[0022] Although ambient relative humidity can directly affect long-term aging phenomena for mechanical assets that do not contain embedded control elements (for example, through accelerated surface corrosion of metallic components and accelerated oxidation degradation of elastomeric elements), for the majority of electromechanical components and subsystems, variations in relative humidity can have a more pronounced impact on aging and degradation mechanisms. This is because high relative humidity in conjunction with thermal swings leads to micro-condensation, which can cause shorting of interconnects and accelerated electromigration failures in PCB elements, and accelerated surface corrosion that leads to resistive failures in interconnects, as well as increased friction in all types of shaft-sleeve and rotating machinery components” Note: here, we can see that degradation problem detected in one part can impact the other parts; also see paragraph 0021. Also note that “one or more surfaces” under BRI will cover any surface of the component)
in response to determining that the present phase of degradation of the physical component satisfies the target phase of degradation of the physical component, determine a degradation model from the measurement data, and produce simulated measurement data using the degradation model ( para “[0033] During a training phase, the system obtains time-series sensor signals 115 from a golden system 132, which is operating inside an environmental testing chamber 134, wherein the configuration of this golden system 132 matches a configuration of test system 102. Moreover, golden system 132 is ensured to be operating correctly and contains new and/or thoroughly tested components with no degradation. During the training phase, environmental-testing chamber 134 cycles through different combinations of environmental parameters, which enables training module 116 to train environment-specific inferential models 138 for each of the different combinations of the environmental parameters. These environment-specific inferential models 138 are then stored in a model database 120.” Note: here model for each combination is trained and stored (different phases of degradation).
Also, para “[0039] FIG. 2 presents a flow chart illustrating the process of performing prognostic-surveillance operations based on an environment-specific inferential model in accordance with the disclosed embodiments. During operation, the system receives time-series signals from sensors in the asset while the asset is operating (step 202). Next, the system obtains real-time environmental parameters for an environment in which the asset is operating (step 204). (Note that these real-time environmental parameters can either be directly measured or inferred from other parameters.) The system then selects an environment-specific inferential model for the asset based on the real-time environmental parameters, wherein the environment-specific inferential model was trained on a golden system while the golden system was operating in an environment that matches the real-time environmental parameters, wherein a configuration of the golden system matches a configuration of the asset, and wherein the golden system was certified to be operating correctly (step 206). The system then uses the environment-specific inferential model to generate estimated values for a sliding time window of values for the received time-series signals based on correlations among the received time-series signals (step 208). Next, the system uses the TPSS technique to project the estimated values for the received time-series signals into the future based on the time window (step 210), and then performs a pairwise-differencing operation between the actual values and the estimated values for the received time-series signals to produce residuals (step 212). Next, the system performs a SPRT on the residuals to produce SPRT alarms (step 214). Finally, the system determines from the SPRT alarms whether the asset is operating correctly (step 216).” Note: here inferential model based on configuration match (matching target degradation) is selected, and use to projected the estimated value for future time window (producing simulated measurements; also note that this is contingent language and method claim does not require this step to be performed if other condition is meet, thus it has no patentable weight.).
Gross doesn’t explicitly teach:
[applying, by a system for producing training data, an accelerated degradation process to a physical component of an industrial plant ]to satisfy a target phase of degradation of the physical component,
[and obtaining, by the system, measurement data indicative of visual characteristics of the physical component at each of multiple phases of degradation] during the accelerated degradation process to satisfy the target phase of degradation of the physical component,
determining, by the system whether a present phase of degradation of the physical component satisfies the target phase of degradation of the physical component, in response to determining that the present phase of degradation of the physical component does not satisfy the target phase of degradation of the physical component, continuing by the system , to apply the accelerated degradation process;
Boucher teaches:
[applying, by a system for producing training data, an accelerated degradation process to a physical component of an industrial plant ]to satisfy a target phase of degradation of the physical component(Para, “[0078] The component condition monitoring step can comprise estimating for at least one of the at least one component at least one of a degradation, a type of the degradation, a severity of the degradation, a location of a damage, a location of a degraded portion of the at least one component, a type of a failure, a presence of an anomaly, a damage and a probability of a failure. The component condition monitoring step can thus comprise generating degradation information. The at least one estimated measure can accordingly be referenced within this disclosure by “degradation information”. It is to be noted that estimating a degradation can also yield that a certain degradation has not (yet) happened or that a component is not subject to the certain degradation. Also, estimating the degradation information for the at least one component can comprise estimating the degradation information for a part or a portion of the component. For example, generating degradation information for a component comprising a bearing, as example for a part with a significant degradation, can be generating the degradation information for this bearing, or if there is a plurality of bearings, generating degradation information for some or all bearings and then agglomerating this information to degradation information regarding the component.” Note: here one can monitor the degradation severity and further also determine if the certain (target phase of degradation) has happened or not.)
[and obtaining, by the system, measurement data indicative of visual characteristics of the physical component at each of multiple phases of degradation] during the accelerated degradation process to satisfy the target phase of degradation of the physical component(Para 0078 teaches monitoring a severity of degradation; Also para, “[0029] The term “condition” is intended to refer to a state of an element. The state can be a degradation, a type of the degradation, a severity of the degradation, a damage, a wear, a state of maintenance such as a sufficient presence of a lubricant or a type of a lubricant or a failure. It can also be a type of a failure, a presence of an anomaly, a remaining useful lifetime of the element, a performance of the element or a probability of a failure. A condition can comprise at least one or a plurality of other conditions. A condition can comprise conditions of portion(s) of the element. A condition can comprise conditions that refer to different aspects of the state of the element, such as a condition referring to a mechanical wear of a portion of the element and to a corrosion of the same or another portion of the element.” Note: this reference also address explicitly “one or more surfaces” (para 0058), though under BRI it can be interpreted to be any part of equipment or all the equipment.)
In view of the teaching of BOUCHER, it would have been obvious for a person of ordinary skill in the art to apply teaching of Gross at the time the application was filed in order to determine the probability of failure of the component/element. (BOUCHER, paragraph, “[0029] The term “condition” is intended to refer to a state of an element. The state can be a degradation, a type of the degradation, a severity of the degradation, a damage, a wear, a state of maintenance such as a sufficient presence of a lubricant or a type of a lubricant or a failure. It can also be a type of a failure, a presence of an anomaly, a remaining useful lifetime of the element, a performance of the element or a probability of a failure. A condition can comprise at least one or a plurality of other conditions. A condition can comprise conditions of portion(s) of the element. A condition can comprise conditions that refer to different aspects of the state of the element, such as a condition referring to a mechanical wear of a portion of the element and to a corrosion of the same or another portion of the element.”)
Gross as modified by Boucher does not explicitly teaches:
determining, by the system whether a present phase of degradation of the physical component satisfies the target phase of degradation of the physical component, in response to determining that the present phase of degradation of the physical component does not satisfy the target phase of degradation of the physical component, continuing by the system , to apply the accelerated degradation process;
Gebraeel teaches:
determining, by the system whether a present phase of degradation of the physical component satisfies the target phase of degradation of the physical component, in response to determining that the present phase of degradation of the physical component does not satisfy the target phase of degradation of the physical component, continuing by the system , to apply the accelerated degradation process (Pg. 157, “according to industrial standards for machine vibration, ISO 2372 [26], an overall root mean square (rms) vibration acceleration level ranging between 2.0 and 2.2 Gs is considered a “danger level” for applications involving “medium-sized general-purpose” machinery. Each degradation test is terminated once the root mean square of the overall vibration acceleration reaches a threshold of 2.2 Gs. The corresponding amplitude of the degradation signal was found to range between 0.03 and 0.035 . We define the degradation signal amplitude of 0.03 as our failure threshold.” Note: here test is only terminated when threshold (target) degradation is achieved; also note, that threshold acceleration indicates target level of degradation (danger level).);
In view of the teaching of Gebraeel, it would have been obvious for a person of ordinary skill in the art to apply teaching of Gross as modified by Boucher at the time the application was filed in order to determine the residual life of degraded components. (Abstract, “The ability to accurately estimate the residual life of partially degraded components is arguably the most challenging problem in prognostic condition monitoring. This paper focuses on the development of a neural network-based degradation model that utilizes condition-based sensory signals to compute and continuously update residual life distributions of partially degraded components.”)
Regarding claim 18, Gross as modified by Boucher and Gebraeel teaches the method of claim 17.
Gross further teaches wherein applying the accelerated degradation process comprises applying the accelerated degradation process to the physical component in a degradation chamber configured to produce a target environment within the degradation chamber(Gross, paragraph “[0025] The disclosed embodiments operate by first gathering time-series sensor signals from a “golden system,” which is configured to match a configuration of an asset to be monitored. (This “golden system” is an asset that contains new and/or thoroughly tested components with no degradation modes, and which is operating as well as can be expected for that asset.) More specifically, we gather environment-specific time-series signals from the golden system while the golden system operates in an environmental-testing chamber, which cycles through all possible combinations of various environmental parameters, such as ambient temperature, relative humidity and altitude, while time-series signals are collected from sensors in the golden system. For example, the ambient temperature can be varied between −20° C. and 80° C. in 5-degree increments, the relative humidity can be varied between 5% and 95% in 5% increments, and the altitude can be varied between sea level and 10,000 feet in 2,000 foot increments. (Note that, in addition to ambient temperature, relative humidity and altitude, the disclosed embodiments can also vary other environmental parameters, such as ambient vibrations and ambient radiation.”).
Regarding claim 19, Gross as modified by Boucher and Gebraeel teaches the method of claim 17.
BOUCHER further teach :
wherein the simulated measurement data is indicative of characteristics of the physical component at multiple phases of degradation and is usable as training data for the neural network (BOUCHER, paragraph, “[0078] The component condition monitoring step can comprise estimating for at least one of the at least one component at least one of a degradation, a type of the degradation, a severity of the degradation, a location of a damage, a location of a degraded portion of the at least one component, a type of a failure, a presence of an anomaly, a damage and a probability of a failure. The component condition monitoring step can thus comprise generating degradation information. The at least one estimated measure can accordingly be referenced within this disclosure by “degradation information”. It is to be noted that estimating a degradation can also yield that a certain degradation has not (yet) happened or that a component is not subject to the certain degradation. Also, estimating the degradation information for the at least one component can comprise estimating the degradation information for a part or a portion of the component. For example, generating degradation information for a component comprising a bearing, as example for a part with a significant degradation, can be generating the degradation information for this bearing, or if there is a plurality of bearings, generating degradation information for some or all bearings and then agglomerating this information to degradation information regarding the component.”)
In view of the teaching of BOUCHER, it would have been obvious for a person of ordinary skill in the art to apply teaching of Gross as modified by Boucher and Gebraeel at the time the application was filed in order to determine the probability of failure of the component/element. (BOUCHER, paragraph, “[0029] The term “condition” is intended to refer to a state of an element. The state can be a degradation, a type of the degradation, a severity of the degradation, a damage, a wear, a state of maintenance such as a sufficient presence of a lubricant or a type of a lubricant or a failure. It can also be a type of a failure, a presence of an anomaly, a remaining useful lifetime of the element, a performance of the element or a probability of a failure. A condition can comprise at least one or a plurality of other conditions. A condition can comprise conditions of portion(s) of the element. A condition can comprise conditions that refer to different aspects of the state of the element, such as a condition referring to a mechanical wear of a portion of the element and to a corrosion of the same or another portion of the element.”)
Regarding claim 20, Gross teaches One or more non-transitory machine-readable storage media comprising a plurality of instructions stored thereon that, in response to being executed, cause a system to (Gross, paragraph, 0018):
apply an accelerated degradation process to a physical component of an industrial plant [to satisfy a target phase of degradation of the physical component], (Gross, paragraph “[0025] The disclosed embodiments operate by first gathering time-series sensor signals from a “golden system,” which is configured to match a configuration of an asset to be monitored. (This “golden system” is an asset that contains new and/or thoroughly tested components with no degradation modes, and which is operating as well as can be expected for that asset.) More specifically, we gather environment-specific time-series signals from the golden system while the golden system operates in an environmental-testing chamber, which cycles through all possible combinations of various environmental parameters, such as ambient temperature, relative humidity and altitude, while time-series signals are collected from sensors in the golden system. For example, the ambient temperature can be varied between −20° C. and 80° C. in 5-degree increments, the relative humidity can be varied between 5% and 95% in 5% increments, and the altitude can be varied between sea level and 10,000 feet in 2,000 foot increments. (Note that, in addition to ambient temperature, relative humidity and altitude, the disclosed embodiments can also vary other environmental parameters, such as ambient vibrations and ambient radiation.” Note: here, golden system contains all tested components(asset) on which degradation is applied i.e. temperature, humidity etc. (an accelerated degradation process)
Also, para 0033, “[0033] During a training phase, the system obtains time-series sensor signals 115 from a golden system 132, which is operating inside an environmental testing chamber 134, wherein the configuration of this golden system 132 matches a configuration of test system 102. Moreover, golden system 132 is ensured to be operating correctly and contains new and/or thoroughly tested components with no degradation. During the training phase, environmental-testing chamber 134 cycles through different combinations of environmental parameters, which enables training module 116 to train environment-specific inferential models 138 for each of the different combinations of the environmental parameters. These environment-specific inferential models 138 are then stored in a model database 120.” ),
wherein the accelerated degradation process irreversibly degrades one or more surfaces of the physical component (“[0022] Although ambient relative humidity can directly affect long-term aging phenomena for mechanical assets that do not contain embedded control elements (for example, through accelerated surface corrosion of metallic components and accelerated oxidation degradation of elastomeric elements), for the majority of electromechanical components and subsystems, variations in relative humidity can have a more pronounced impact on aging and degradation mechanisms….”; also see para 0021); Note: oxidation, and corrosion etc.. are irreversible degradations.);
and obtain measurement data indicative of visual characteristics of the physical component at each of multiple phases of degradation [during the accelerated degradation process to satisfy the target phase of degradation of the physical component ](Gross, paragraph “[0030] We then perform a pairwise-differencing operation between the projected-ahead estimated values and the actual values for the received time-series signals to produce residuals. Finally, we perform a SPRT on the residuals to detect possible degradation. If any degradation is detected, we can set data disturbance flags for any affected signals, and can prepare for a scheduled shutdown if an imminently dangerous system state is predicted with a high confidence factor. Or, we can notify maintenance personnel to proactively schedule service actions at the earliest convenient time. We then continue sampling the signals by sliding the time window forward in units of signal sampling rate. This process continuously iterates to provide real-time assurance that assets in the field are operating within all reliability, availability, and serviceability (RAS) functional specifications, and to facilitate real-time detection of the onset of incipient degradation to facilitate avoiding catastrophic failures of assets in the field.” Note: here data is collected and severity (phases) of degradation is determined; for example it is checked if the system is performing within functional specification (phase of degradation which is acceptable), and detection of the onset of incipient degradation (phase, where if the issue is not addressed, it could result in catastrophic failure)),
wherein the measurement data indicative of visual characteristics of the physical component comprises data indicative of degradation on the one or more surfaces of the physical component, and measurement data is usable to train a neural network to identify a phase of degradation of another physical component Gross, paragraph “[0033] During a training phase, the system obtains time-series sensor signals 115 from a golden system 132, which is operating inside an environmental testing chamber 134, wherein the configuration of this golden system 132 matches a configuration of test system 102. Moreover, golden system 132 is ensured to be operating correctly and contains new and/or thoroughly tested components with no degradation. During the training phase, environmental-testing chamber 134 cycles through different combinations of environmental parameters, which enables training module 116 to train environment-specific inferential models 138 for each of the different combinations of the environmental parameters. These environment-specific inferential models 138 are then stored in a model database 120.”
[0022] Although ambient relative humidity can directly affect long-term aging phenomena for mechanical assets that do not contain embedded control elements (for example, through accelerated surface corrosion of metallic components and accelerated oxidation degradation of elastomeric elements), for the majority of electromechanical components and subsystems, variations in relative humidity can have a more pronounced impact on aging and degradation mechanisms. This is because high relative humidity in conjunction with thermal swings leads to micro-condensation, which can cause shorting of interconnects and accelerated electromigration failures in PCB elements, and accelerated surface corrosion that leads to resistive failures in interconnects, as well as increased friction in all types of shaft-sleeve and rotating machinery components” Note: here, we can see that degradation problem detected in one part can impact the other parts; also see paragraph 0021. Also note that “one or more surfaces” under BRI will cover any surface of the component.)
in response to determining that the present phase of degradation of the physical component satisfies the target phase of degradation of the physical component, determine a degradation model from the measurement data, and produce simulated measurement data using the degradation model ( para “[0033] During a training phase, the system obtains time-series sensor signals 115 from a golden system 132, which is operating inside an environmental testing chamber 134, wherein the configuration of this golden system 132 matches a configuration of test system 102. Moreover, golden system 132 is ensured to be operating correctly and contains new and/or thoroughly tested components with no degradation. During the training phase, environmental-testing chamber 134 cycles through different combinations of environmental parameters, which enables training module 116 to train environment-specific inferential models 138 for each of the different combinations of the environmental parameters. These environment-specific inferential models 138 are then stored in a model database 120.” Note: here model for each combination is trained and stored (different phases of degradation).
Also, para “[0039] FIG. 2 presents a flow chart illustrating the process of performing prognostic-surveillance operations based on an environment-specific inferential model in accordance with the disclosed embodiments. During operation, the system receives time-series signals from sensors in the asset while the asset is operating (step 202). Next, the system obtains real-time environmental parameters for an environment in which the asset is operating (step 204). (Note that these real-time environmental parameters can either be directly measured or inferred from other parameters.) The system then selects an environment-specific inferential model for the asset based on the real-time environmental parameters, wherein the environment-specific inferential model was trained on a golden system while the golden system was operating in an environment that matches the real-time environmental parameters, wherein a configuration of the golden system matches a configuration of the asset, and wherein the golden system was certified to be operating correctly (step 206). The system then uses the environment-specific inferential model to generate estimated values for a sliding time window of values for the received time-series signals based on correlations among the received time-series signals (step 208). Next, the system uses the TPSS technique to project the estimated values for the received time-series signals into the future based on the time window (step 210), and then performs a pairwise-differencing operation between the actual values and the estimated values for the received time-series signals to produce residuals (step 212). Next, the system performs a SPRT on the residuals to produce SPRT alarms (step 214). Finally, the system determines from the SPRT alarms whether the asset is operating correctly (step 216).” Note: here inferential model based on configuration match (matching target degradation) is selected, and use to projected the estimated value for future time window (producing simulated measurements; also note that this is contingent language and stored instruction does not require this step to be performed if other condition is meet, thus it has no patentable weight.).
Gross doesn’t explicitly teach:
apply an accelerated degradation process to a physical component of an industrial plant [to satisfy a target phase of degradation of the physical component],
and obtain measurement data indicative of visual characteristics of the physical component at each of multiple phases of degradation [during the accelerated degradation process to satisfy the target phase of degradation of the physical component ],
determine whether a present phase of degradation of the physical component satisfies the target phase of degradation of the physical component, in response to determining that the present phase of degradation of the physical component does not satisfy the target phase of degradation of the physical component , continue to apply the accelerated degradation process;
Boucher teaches:
apply an accelerated degradation process to a physical component of an industrial plant [to satisfy a target phase of degradation of the physical component]( Para, “[0078] The component condition monitoring step can comprise estimating for at least one of the at least one component at least one of a degradation, a type of the degradation, a severity of the degradation, a location of a damage, a location of a degraded portion of the at least one component, a type of a failure, a presence of an anomaly, a damage and a probability of a failure. The component condition monitoring step can thus comprise generating degradation information. The at least one estimated measure can accordingly be referenced within this disclosure by “degradation information”. It is to be noted that estimating a degradation can also yield that a certain degradation has not (yet) happened or that a component is not subject to the certain degradation. Also, estimating the degradation information for the at least one component can comprise estimating the degradation information for a part or a portion of the component. For example, generating degradation information for a component comprising a bearing, as example for a part with a significant degradation, can be generating the degradation information for this bearing, or if there is a plurality of bearings, generating degradation information for some or all bearings and then agglomerating this information to degradation information regarding the component.” Note: here one can monitor the degradation severity and further also determine if the certain (target phase of degradation) has happened or not.)
and obtain measurement data indicative of visual characteristics of the physical component at each of multiple phases of degradation [during the accelerated degradation process to satisfy the target phase of degradation of the physical component ] (Para 0078 teaches monitoring a severity of degradation; Also para, “[0029] The term “condition” is intended to refer to a state of an element. The state can be a degradation, a type of the degradation, a severity of the degradation, a damage, a wear, a state of maintenance such as a sufficient presence of a lubricant or a type of a lubricant or a failure. It can also be a type of a failure, a presence of an anomaly, a remaining useful lifetime of the element, a performance of the element or a probability of a failure. A condition can comprise at least one or a plurality of other conditions. A condition can comprise conditions of portion(s) of the element. A condition can comprise conditions that refer to different aspects of the state of the element, such as a condition referring to a mechanical wear of a portion of the element and to a corrosion of the same or another portion of the element.” Note: this reference also address explicitly “one or more surfaces” (para 0058), though under BRI it can be interpreted to be any part of equipment or all the equipment.)
In view of the teaching of BOUCHER, it would have been obvious for a person of ordinary skill in the art to apply teaching of Gross at the time the application was filed in order to determine the probability of failure of the component/element. (BOUCHER, paragraph, “[0029] The term “condition” is intended to refer to a state of an element. The state can be a degradation, a type of the degradation, a severity of the degradation, a damage, a wear, a state of maintenance such as a sufficient presence of a lubricant or a type of a lubricant or a failure. It can also be a type of a failure, a presence of an anomaly, a remaining useful lifetime of the element, a performance of the element or a probability of a failure. A condition can comprise at least one or a plurality of other conditions. A condition can comprise conditions of portion(s) of the element. A condition can comprise conditions that refer to different aspects of the state of the element, such as a condition referring to a mechanical wear of a portion of the element and to a corrosion of the same or another portion of the element.”)
Gross as modified by Boucher does not explicitly teaches:
determine whether a present phase of degradation of the physical component satisfies the target phase of degradation of the physical component, in response to determining that the present phase of degradation of the physical component does not satisfy the target phase of degradation of the physical component , continue to apply the accelerated degradation process;
Gebraeel teaches:
determine whether a present phase of degradation of the physical component satisfies the target phase of degradation of the physical component, in response to determining that the present phase of degradation of the physical component does not satisfy the target phase of degradation of the physical component , continue to apply the accelerated degradation process(Pg. 157, “according to industrial standards for machine vibration, ISO 2372 [26], an overall root mean square (rms) vibration acceleration level ranging between 2.0 and 2.2 Gs is considered a “danger level” for applications involving “medium-sized general-purpose” machinery. Each degradation test is terminated once the root mean square of the overall vibration acceleration reaches a threshold of 2.2 Gs. The corresponding amplitude of the degradation signal was found to range between 0.03 and 0.035 . We define the degradation signal amplitude of 0.03 as our failure threshold.” Note: here test is only terminated when threshold (target) degradation is achieved; also note, that threshold acceleration indicates target level of degradation (danger level).);
in response to determining that the present phase of degradation of the physical component satisfies the target phase of degradation of the physical component , determine a degradation model from the measurement data, and produce simulated measurement data using the degradation model.
In view of the teaching of Gebraeel, it would have been obvious for a person of ordinary skill in the art to apply teaching of Gross as modified by Boucher at the time the application was filed in order to determine the residual life of degraded components. (Abstract, “The ability to accurately estimate the residual life of partially degraded components is arguably the most challenging problem in prognostic condition monitoring. This paper focuses on the development of a neural network-based degradation model that utilizes condition-based sensory signals to compute and continuously update residual life distributions of partially degraded components.”)
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Gross as modified by Boucher and Gebraeel in view of Soleimanmeigouni et al. (“Track geometry degradation and maintenance modelling: A review”, 2018)
Regarding claim 14, Gross as modified by Boucher and Gebraeel teaches the system of claim 13.
Gross as modified by Boucher and Gebraeel does not explicitly teach wherein to determine the degradation model for one or more local geometries comprises to determine the degradation model for raised features or inset features.
Soleimanmeigouni teaches wherein to determine the degradation model for one or more local geometries comprises to determine the degradation model for raised features or inset features (Soleimanmeigouni, Pg. 74, “Determining an indicator is an essential prerequisite to represent and evaluate the condition of railway track. The track condition can be represented in two different ways: track geometry condition or track structure condition. The defects and irregularities in track geometry are mostly used to represent the quality of the track and to plan track maintenance. Track geometry measures can be divided into five classes: (1) longitudinal level, (2) alignment, (3) gauge, (4) cant, and (5) twist. Longitudinal level is the track geometry of track centreline projected onto longitudinal vertical plane. Alignment is the track geometry of track centreline projected onto longitudinal horizontal plane. Gauge is the distance between the gauge faces of two adjacent rails at a given location below the running surface. Cant (cross-level) is the difference in height of the adjacent running tables computed from the angle between the running surface and a horizontal reference plane. Twist is the algebraic difference between two cross-levels taken at a defined distance apart, usually expressed as a gradient between the two points of measurement.5,6 Track inspection cars run over track with a specific speed, monitor track geometry, and record the mentioned track geometry measures for every assigned movement (usually 25 cm). Three indicators can be used to assess track geometry quality based on the recorded measurement data by inspection trains, i.e. mean value, standard deviation over a specific length, and extreme values of isolated defects.6” Note: here, the deviation over specific length can tell us whether it is raised feature or inset feature; also cant provides measure of elevation difference (raised or inset feature); also note BOUCHER already teaches that degradation can be performed on any part of element, that will include raised and inset feature depending, however for explicit teaching Soleimanmeigouni is used.)
In view of the teaching of Soleimanmeigouni, it would have been obvious for a person of ordinary skill in the art to apply teaching of Gross as modified by Boucher and Gebraeel at the time the application was filed in order to track the condition of element (Soleimanmeigouni, Pg. 74, “Determining an indicator is an essential prerequisite to represent and evaluate the condition of railway track. The track condition can be represented in two different ways: track geometry condition or track structure condition. The defects and irregularities in track geometry are mostly used to represent the quality of the track and to plan track maintenance. ……”)
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Gross as modified by Boucher and Gebraeel in view of Papadopoulos et al. (“Modelling of Material Ageing with Generative Adversarial Networks”, 2018)
Regarding claim 16, Gross as modified by Boucher and Gebraeel teaches the system of claim 15.
Gross as modified by Boucher and Gebraeel does not explicitly teach wherein to produce the simulated measurement data with the neural network comprises to produce the simulated measurement data with a generative adversarial network.
Papadopoulos teaches wherein to produce the simulated measurement data with a neural network comprises to produce the simulated measurement data with a generative adversarial network (Papadopoulos, section “III. METHOD DESCRIPTION”, “Utilizing appearance and surface measurements on artificially aged samples, it is possible to model ageing phenomena over time. To this end, architectures based on Generative Adversarial Networks (GAN) have been implemented in order to model degradation over time.” Note: also see Fig. 1 for such generative adversarial network.)
In view of the teaching of Papadopoulos, it would have been obvious for a person of ordinary skill in the art to apply teaching of Gross as modified by Boucher and Gebraeel at the time the application was filed in order to identify susceptible spots/area (Papadopoulos, Abstract, “Simulation of material appearance over time is crucial for Cultural Heritage (CH) investigation, since it would help in the identification of susceptible spots on artworks for corruption prevention…..”)
Response to Arguments
Applicant's arguments filed on 07/15/2025 have been fully considered but they are not persuasive.
Remarks - 35 USC § 103
In remarks, Pg. 12, applicant contends: “thus, in Gebraeel, once the root mean square of the overall vibration acceleration reaches a threshold of 2.2 Gs, the degradation test is terminated. However, nothing has been found in Gebraeel that teaches once the root mean square of the overall vibration acceleration reaches a threshold of 2.2 Gs (as the Office asserts, when threshold (target) degradation is achieved), the system will determine a degradation model from the measurement data and produce simulated measurement data using the degradation model, as is necessary for Gebraeel to meet the terms of Applicant's amended claim 1.”
As can be seen, the amended claim limitations are presented, claimed language is contingent language. Regarding contingent language, MPEP 2111.04 (II) recites:
“The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. For example, assume a method claim requires step A if a first condition happens and step B if a second condition happens. If the claimed invention may be practiced without either the first or second condition happening, then neither step A or B is required by the broadest reasonable interpretation of the claim. If the claimed invention requires the first condition to occur, then the broadest reasonable interpretation of the claim requires step A. If the claimed invention requires both the first and second conditions to occur, then the broadest reasonable interpretation of the claim requires both steps A and B.
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The broadest reasonable interpretation of a system (or apparatus or product) claim having structure that performs a function, which only needs to occur if a condition precedent is met, requires structure for performing the function should the condition occur. The system claim interpretation differs from a method claim interpretation because the claimed structure must be present in the system regardless of whether the condition is met and the function is actually performed.”
In view of the MPEP as cited above, the argument is moot for claims 17-20, as Gebraeel teaches determine whether a present phase of degradation of the physical component satisfies the target phase of degradation of the physical component, in response to determining that the present phase of degradation of the physical component does not satisfy the target phase of degradation of the physical component, continue to apply the accelerated degradation process(Pg. 157, “according to industrial standards for machine vibration, ISO 2372 [26], an overall root mean square (rms) vibration acceleration level ranging between 2.0 and 2.2 Gs is considered a “danger level” for applications involving “medium-sized general-purpose” machinery. Each degradation test is terminated once the root mean square of the overall vibration acceleration reaches a threshold of 2.2 Gs. The corresponding amplitude of the degradation signal was found to range between 0.03 and 0.035 . We define the degradation signal amplitude of 0.03 as our failure threshold.” Note: here test is only terminated when threshold (target) degradation is achieved; also note, that threshold acceleration indicates target level of degradation (danger level).)
Regarding claims 1-16, according to MPEP the requirement is “a system (or apparatus or product) claim having structure that performs a function, which only needs to occur if a condition precedent is met, requires structure for performing the function should the condition occur. The system claim interpretation differs from a method claim interpretation because the claimed structure must be present in the system regardless of whether the condition is met and the function is actually performed.
Gebraeel already teaches a structure that can perform the function of determining whether a present phase of degradation of the physical component satisfies the target phase of degradation of the physical component or not; further it has structure that in response to determining that the present phase of degradation of the physical component does not satisfy the target phase of degradation of the physical component, continue to apply the accelerated degradation process.
Furthermore, Gross teaches structure that can perform function of determining a degradation model from the measurement data, and produce simulated measurement data using the degradation model (see claim mapping above).
Also, see 35 U.S.C 112(a) rejection with regard to this limitation, as the specification teaches para, “[0032]….Otherwise, the method 300, in the illustrative embodiment, advances to block 382 in which the system 100 produces simulated measurement data indicative of characteristics of the physical component(s) 140 at multiple phases of the degradation process. That is, the system 100 (e.g., the degradation analysis compute device 160) produces data indicative of measurements (e.g., images, etc.) that were not physically performed (e.g., using the degradation chamber 110 and the robot 130), to augment the measurement data from the degradation chamber 110 and the robot 130. In doing so, and as indicated in block 384, the system 100 (e.g., the degradation analysis compute device 160) may determine a degradation model from the measurement data (e.g., obtained from the robot 130 and/or degradation chamber 110) that produces data indicative of characteristics of the physical component(s) 140 for different degradation phases.”
Gross reference teaches a structure that is capable of “gather environment-specific time-series signals from the golden system while the golden system operates in an environmental-testing chamber, which cycles through all possible combinations of various environmental parameters, such as ambient temperature, relative humidity and altitude, while time-series signals are collected from sensors in the golden system.” (para, 0025) Further, it teaches the system that is capable of “during the training phase, environmental-testing chamber 134 cycles through different combinations of environmental parameters, which enables training module 116 to train environment-specific inferential models 138 for each of the different combinations of the environmental parameters. These environment-specific inferential models 138 are then stored in a model database 120.” (See, para 0033)
Thus, it clearly teaches a system that is capable of producing simulated measurement data indicative of characteristics of the physical component(s) 140 at multiple phases of the degradation process.
Further the data can be simulated for future life cycle “the system then uses the environment-specific inferential model to generate estimated values for a sliding time window of values for the received time-series signals based on correlations among the received time-series signals (step 208).”
As can be seen this data is produced using the inferential model.
In addition, Boucher reference also teaches “[0078] The component condition monitoring step can comprise estimating for at least one of the at least one component at least one of a degradation, a type of the degradation, a severity of the degradation, a location of a damage, a location of a degraded portion of the at least one component, a type of a failure, a presence of an anomaly, a damage and a probability of a failure. The component condition monitoring step can thus comprise generating degradation information. The at least one estimated measure can accordingly be referenced within this disclosure by “degradation information”. It is to be noted that estimating a degradation can also yield that a certain degradation has not (yet) happened or that a component is not subject to the certain degradation. Also, estimating the degradation information for the at least one component can comprise estimating the degradation information for a part or a portion of the component. For example, generating degradation information for a component comprising a bearing, as example for a part with a significant degradation, can be generating the degradation information for this bearing, or if there is a plurality of bearings, generating degradation information for some or all bearings and then agglomerating this information to degradation information regarding the component.”
Thus at minimum, the systems as presented in Gross, and Boucher are capable of performing the claimed function.
Conclusion
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.
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/HUMA WASEEM/Examiner, Art Unit 3686
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686
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