DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Office Action is responsive to communication filed on 9/1/2023.
Claims 1-20 are presented for examination.
Information Disclosure Statement
Examiner notes that no information disclosure statement has been filed as of the date of this office action.
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 1-16 are rejected under 35 U.S.C. 103 as being unpatentable over MIKLOSOVIC (US20210341896A1) in view of LAKOMIAK (US20100082158A1) (hereinafter – “MIKLOSOVIC-LAKOMIAK”).
Regarding claim 1, MIKLOSOVIC teaches a system comprising:
one or more computer-readable storage media (Fig. 10, [0009], [0071]: storage system 1003);
one or more processors coupled to the one or more computer-readable storage media (Fig. 10, [0009], [0072]: processing system 1002); and
program instructions stored on the one or more computer-readable storage media that, based on being read and executed by the one or more processors, direct the system to (Fig. 10, [0009], [0071]: software 1005);
obtain a plurality of performance metrics, each performance metric associated with a device in an industrial automation environment ([0030]: condition monitoring includes data acquisition; [0032]: “Variable frequency drive 110 supplies power to motors 124 of industrial operation 120 and receives signal data from industrial operation 120. Analytic engine 111 runs fault detection process 112 to detect faults within industrial operation 120 based on the signal data. Analytic engine 111 is a configurable analytics processor that provides flexibility for condition monitoring and includes an application layer that hides complexity and simplifies the user experience for individual applications and fault detection. Analytic engine may be configured to monitor various applications and detect degradation or other faults of a motor and a connected mechanical load from industrial operation 120”; [0034]: “analytic engine 111 may collect data from devices of industrial operation […] system analytics 131 may aggregate and contextualize information”);
contextualize each of the performance metrics based on contextualization information specific to each of the performance metrics to produce device health metrics, each device health metric corresponding to a performance metric of the performance metrics ([0030]: condition monitoring includes feature extraction; [0034]: “system analytics 131 may aggregate and contextualize information”; [0035]: “Environment 200 includes drive 210 representative of a variable frequency comprising an analytics engine such as drive 110 […] Metrics module 213 processes the data to generate metrics data that can be utilized for fault detection. The data collected by select and capture module 212 and the processing performed by metrics module 213 may, in some embodiments, depend on settings specific to one or more fault conditions being monitored. For example, for a given fault condition, settings may change which drive signal is selected to capture in select and capture module 212 as well as the manner in which metrics module 213 processes the data by changing signal paths to implement various filters and algorithms, performing measurements, utilizing specific parameters, or other settings that may affect processing to produce metrics specific to a fault condition. Metrics are calculated independently for baseline and runtime captures and then differences are calculated between them. Metrics may then be output by metrics module 213 and provided to one or more systems and modules for condition monitoring”);
classify each of the device health metrics into device health metric categories based on applying a rule set to each of the device health metrics, wherein the rule set is selectively applied to a respective device health metrics based on a type of the respective device health metric ([0030]: condition monitoring includes detection which includes categorizing conditions; [0034]: “system analytics 131 may […] detect system level fault conditions”; [0036]: “The output of metrics module 213 is provided to stand alone detection module 214 which may then use the metrics produced by metrics module 213 to perform fault detection within drive 210. Standalone detection, in some examples, comprises determining if one or more fault conditions is present based on the settings specific to at least one fault being monitored. Detection methods include thresholding or machine learning. In addition to supplying the metrics to stand alone detection module 214, metrics may be provided to additional systems for condition monitoring or other purposes. In the present example, metrics are provided to system detection module 240 for system-level fault detection”; [0041]: “Thresholds 351 may perform a variety of different condition monitoring functions including determining whether any measurements or metrics exceeded thresholds indicating an unhealthy state”);
provide a device health metric and a device health metric category associated with a selected device to a user ([0034]: “system analytics 131 may […] provide insights related to preventative maintenance, energy diagnostics, system modeling, performance optimization, and similar insights. At the enterprise level, enterprise analytics, cloud analytics, or a combination thereof may present information to users on devices and systems including mobile devices and desktop computers”; [0041]: “Detection section 350, in some examples, may display measurements, differences, or percent degradation. Differences and percentages may be displayed to give users a real-time or near real-time indication of the amount of mechanical and electrical degradation over time”; [0052]: “Detection 540 may then provide the information as output to indicate a status of the device or system with status 550. Detection 540 may further provide the information to additional systems for external condition monitoring”).
MIKLOSOVIC is not relied on for based on a request from a user device, providing a device health metric and a device health metric category associated with a selected device to a user interface of the user device.
However, LAKOMIAK in an analogous art does teach this claim limitation.
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LAKOMIAK teaches methods and systems of using raw data originating from an industrial system or device to generate insight into the data based on end-user requirements at run-time of an industrial system (Fig. 3 above, [0001]: “invention relates to systems and methods for configuring, processing, and presenting machine condition monitoring information”), comprising:
provide, based on a request from a user device, a device health metric and a device health metric category associated with a selected device to a user interface of the user device ([0004]: “The method further includes the steps of normalizing each of the vibration parameters and presenting the normalized vibration parameters in an operator interface […] The system also includes a condition monitoring user interface configured to display normalized vibration parameters for the machine system”; [0016]: “operator interface 14 may allow a user […] to configure condition monitoring and control system 10, thereby enabling the system to monitor various machine condition parameters”; [0025]: operator may select the machine system type profile in the system being monitored; [0027]: “Machine selection interface 80 may allow an operator to choose the type of machine system that is connected to machine condition monitoring and control system […] system operator selects a system type of each machine system”; Fig. 6 below items 142 144 148 150 “NORMAL” maps to device health metric category).
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Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to apply the teachings of LAKOMIAK to the teachings of MIKLOSOVIC such that LAKOMIAK’s condition monitoring user interface could be used with MIKLOSOVIC’s condition monitoring system for the purposes of allowing a user to configure condition monitoring system. Based on the above, this is an example of “combining prior art elements according to known methods to yield predictable results.” MPEP 2143.
Regarding claim 2, MIKLOSOVIC-LAKOMIAK teaches the elements of claim 1 as outlined above. MIKLOSOVIC also teaches:
wherein to contextualize each of the performance metrics, the program instructions direct the system to perform one or more operations on each of the performance metrics ([0041]: “Metrics are then provided to detection section 350 comprising thresholds 351 and percent degradation 352 in addition to any other specified output locations such as additional condition monitoring modules within the drive or external systems. Differences may then be calculated between baseline metrics and the recent metric calculations. Percent degradation 352 may utilize a detection method that determines the percent of degradation between the baseline metrics and the recent metrics”).
Regarding claim 3, MIKLOSOVIC-LAKOMIAK teaches the elements of claim 1 as outlined above. MIKLOSOVIC also teaches:
wherein the rule set comprises one or more of threshold data, a time range, and a quantity ([0041]: “Metrics are then provided to detection section 350 comprising thresholds 351 […] Thresholds
351 may perform a variety of different condition monitoring functions including determining whether any measurements or metrics exceeded thresholds indicating an unhealthy state. Specific thresholds may be set to show when metrics exceed predetermined values […] thresholds are averaged over a specified number of detection cycles”).
Regarding claim 4, MIKLOSOVIC-LAKOMIAK teaches the elements of claim 3 as outlined above. LAKOMIAK also teaches:
wherein the rule set is user-selected via the user interface of the user device ([0025]: “complex calculations and algorithms performed on the raw measurement data 62 may then feed the results of such function blocks to an appropriate set of parameters, which are specific to the type of system and machine profile selected”; [0027]: “Machine selection interface 80 may allow an operator to choose the type of machine system that is connected to machine condition monitoring and control system […] operator selects a system type for each machine system, the machine system selection interface communicates the user selection, indicated by arrow 82, to a corresponding system type or profile 84”; [0028]: “corresponding system type or profile may include a table of information for each system, thereby enabling an automatic configuration of the selected system type based on information in the system type profile 84, indicated by element 86. System profile information 86 may include information such as machine name, alarm values, settings, band values, diagnostic status, diagnostic adviser, trend values, and so forth”).
Regarding claim 5, MIKLOSOVIC-LAKOMIAK teaches the elements of claim 1 as outlined above. MIKLOSOVIC also teaches:
wherein the device is a variable-speed drive ([0026]: “important instruments associated with motor control include the variable frequency drive”).
Regarding claim 6, MIKLOSOVIC-LAKOMIAK teaches the elements of claim 5 as outlined above. MIKLOSOVIC also teaches:
wherein the device health metrics indicate health of one or more industrial devices coupled to the variable speed drive ([0027]: “Thus, a general-purpose, configurable analytic engine embedded in a motor drive (i.e., a VFD) is disclosed. An analytic engine in accordance with the present disclosure may include an application layer that hides configuration complexity and simplifies a user's experience. The analytic engine maybe configured to monitor various applications and detect degradation of a motor or its detected mechanical load early on”).
Regarding claim 7, MIKLOSOVIC-LAKOMIAK teaches the elements of claim 1 as outlined above. LAKOMIAK also teaches:
wherein the device health metric categories comprise a healthy category, an unhealthy category, and an approaching unhealthy category ([0035]: status indicator may display a status such as normal, danger, or warning (i.e., healthy, unhealthy, or approaching unhealthy, respectively).
Regarding claim 8, claim 8 recites a method comprising substantially the same limitations as claim 1 and is rejected as per claim 1.
Regarding claim 9, MIKLOSOVIC-LAKOMIAK teaches the elements of claim 8 as outlined above. MIKLOSOVIC also teaches:
wherein the performance metric is a first performance metric of a plurality of performance metrics ([0007]: condition monitoring module is configured to monitoring one or more fault conditions based on metrics),
wherein the device is a first device of a plurality of devices in the industrial automation environment ([0007]: “monitoring one or more fault conditions of the industrial operation comprises monitoring one or more fault conditions of the motor and/or the connected mechanical load”), and
wherein each performance metric of the plurality of the performance metrics is associated with a device of the plurality of the devices ([0007]: “monitor one or more fault conditions of the industrial operation based on metrics […] of the motor and/or the connected mechanical load”).
Regarding claim 10, MIKLOSOVIC-LAKOMIAK teaches the elements of claim 8 as outlined above. The remaining limitations of claim 10 are substantially the same as claim 2 and are rejected as per claim 2.
Regarding claim 11, MIKLOSOVIC-LAKOMIAK teaches the elements of claim 10 as outlined above. MIKLOSOVIC also teaches:
wherein the contextualization information comprises signals indicative of one or more of an electrical value, a mechanical value, and a thermal value associated with the device ([0030]: “input signal source, such as signals from vibration, temperature, or acoustic sensors or drive signals like phase current and voltage, torque reference, or velocity”).
Regarding claim 12, MIKLOSOVIC-LAKOMIAK teaches the elements of claim 8 as outlined above. The remaining limitations of claim 12 are substantially the same as claim 3 and are rejected as per claim 3.
Regarding claim 13, MIKLOSOVIC-LAKOMIAK teaches the elements of claim 12 as outlined above. The remaining limitations of claim 13 are substantially the same as claim 4 and are rejected as per claim 4.
Regarding claim 14, MIKLOSOVIC-LAKOMIAK teaches the elements of claim 8 as outlined above. The remaining limitations of claim 14 are substantially the same as claim 5 and are rejected as per claim 5.
Regarding claim 15, MIKLOSOVIC-LAKOMIAK teaches the elements of claim 14 as outlined above. The remaining limitations of claim 15 are substantially the same as claim 6 and are rejected as per claim 6.
Regarding claim 16, MIKLOSOVIC-LAKOMIAK teaches the elements of claim 8 as outlined above. The remaining limitations of claim 16 are substantially the same as claim 7 and are rejected as per claim 7.
Claims 17-20 are rejected under 35 U.S.C. as being unpatentable over LAKOMIAK (US20100082158A1) in view of MIKLOSOVIC (US20210341896A1) (hereinafter – “LAKOMIAK -MIKLOSOVIC”).
Regarding claim 17, LAKOMIAK teaches:
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one or more computer-readable storage media (Fig. 2 memory 48);
one or more processors coupled to the one or more computer-readable storage media (Fig. 2 processor 50);
program instructions stored on the one or more computer-readable storage media (Fig. 2 monitoring software 52) that based on being read and executed by the one or more processors,
provide a user interface to a user device (Fig. 2 & [0023]: user device/display 56 “may enable an operator to assess a machine system condition”; [0031]: “FIGS. 6-8 illustrate embodiments of screens that display machine condition monitoring information to an operator” - Fig. 6 below shows user interface that displays machine condition information),
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wherein the user interface comprises:
a dashboard navigable by a user of the user device ([0034]: “menu bar 154, which includes buttons to navigate between screens”); and
indications corresponding to one or more devices of an industrial automation environment ([0002]: industrial equipment [0016]-[0017]: user may configure systems 16, 18, and 20 to be monitored by condition monitoring and may be systems susceptible to automation; Fig. 6 above shows a plurality of indications corresponding an industrial device), wherein the indications comprise:
device health indications indicative of a health of the one or more devices (Fig. 6, [0032]: normalized vibration parameters 124);
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behavioral indications indicative of a behavior of the one or more devices (Fig. 6, [0032]: status indicator 142, Fig. 7, [0035]: status indicator 166 may display status (e.g., behavioral indication) such as warning, danger, normal); and
maintenance indications indicative of maintenance tasks corresponding to the one or more devices based on the behavior indications and the device health indications (Fig. 7, [0036]: “a diagnostic message 186 may be communicated to help an operator address the problem indicated”, item 184 correct by balancing, check for looseness, add lubrication are indicative of maintenance tasks corresponding to one or more devices based determined from condition monitoring software, see Fig. 3 below).
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LAKOMIAK is not relied on for wherein the device health indications are based on contextualized metrics determined by performing operations on performance metrics associated with the one or more devices. LAKOMIAK is not relied on for wherein the behavioral indications are based on applying rule sets to the contextualized metrics.
However, MIKLOSOVIC in an analogous art does teach this claim limitation. MIKLOSOVIC teaches systems and methods for condition monitoring in an industrial environment ([0007]: “condition monitoring in industrial environments”) to determine:
device health indications indicative of a health of the one or more devices based on contextualized metrics determined by performing operations on performance metrics associated with the one or more devices ([0030]: condition monitoring includes data acquisition and feature extraction; [0034]: “system analytics 131 may aggregate and contextualize information to detect system level fault conditions and/or provide insights related to preventative maintenance, energy diagnostics, system modeling, performance optimization, and similar insights”; [0039]: “Runtime metrics section
330 and baseline metrics section 340 convert data heavy signals to data light metrics that may be used to detect fault conditions within the drive”; [0041]: “Differences may then be calculated between baseline metrics and the recent metric calculations. Percent degradation 352
may utilize a detection method that determines the percent of degradation between the baseline metrics and the recent metrics”); and
behavioral indications indicative of a behavior of the one or more devices based on applying rule sets to the contextualized metrics ([0041]: “Thresholds 351 may perform a variety of different condition monitoring functions including determining whether any measurements or metrics exceeded thresholds indicating an unhealthy state. Specific thresholds may be set to show when metrics exceed predetermined values”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to apply the teachings of MIKLOSOVIC to the teachings of LAKOMIAK such that MIKLOSOVIC’s contextualized metrics and threshold rule sets could be used with LAKOMIAK’s user interface for the purposes of identifying a suspect device prior to the device failing. Based on the above, this is an example of “combining prior art elements according to known methods to yield predictable results.” MPEP 2143.
Regarding claim 18, LAKOMIAK-MIKLOSOVIC teaches the elements of claim 17 as outlined above. MIKLOSOVIC also teaches a server configured to obtain the performance metrics associated with the one or more devices ([0031]: “External systems 130 serves to represent or include any layer of an industrial automation environment external to variable frequency drive 110, wherein the external analytics may collect and analyze data”; [0063]: system level detection may comprise servers).
The remaining limitations of claim 18 are substantially the same as claim 1 and are rejected as per claim 1.
Regarding claim 19, LAKOMIAK-MIKLOSOVIC teaches the elements of claim 18 as outlined above. The remaining limitations of claim 19 are substantially the same as claim 7 and are rejected as per claim 7.
Regarding claim 20, LAKOMIAK-MIKLOSOVIC teaches the elements of claim 17 as outlined above. The remaining limitations of the claim are substantially the same as claim 5 and are rejected due to the reasons outlined above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Azamfar et al. (US20220187798A1) teaches a system for estimating a health of a machine comprising a user dashboard.
Pamulaparthy et al. (US20180082568A1) teaches contextualizing raw data to determine a motor health index.
Kyes et al. (US20210287458A1) teaches a telemetry system to contextualize raw data to determine an indication of an operational state.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael V Farina whose telephone number is (571)272-4982. The examiner can normally be reached Mon-Thu 8:00-6:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Thomas Lee can be reached at (571) 272-3667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.V.F./Examiner, Art Unit 2115
/VINCENT H TRAN/Primary Examiner, Art Unit 2115