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 .
Claims 1-20 are pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4 and 13 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 4 and 13 each recites limitation “perform one or more operations the performance metrics” which is indefinite. It is unclear regarding the relationship between “perform one or more operations” and “the performance metrics”. Appropriated correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reichard et al. (hereinafter “Reichard”) (US 20160299496 A1) in view of Miklosovic et al. (hereinafter “Miklosovic”) (US 20210341901 A1).
As to claim 1, Reichard teaches a system comprises:
a controller coupled to an industrial device in an industrial automation environment [Figs. 1, 7] [0039-0042, 0053-0058] and comprising:
one or more controller processors; a controller memory coupled to the one or more controller processors and having controller program instructions stored thereon that, based on being read and executed by the one or more controller processors [Figs. 1, 7] [0039-0042, 0053-0058], direct the one or more controller processors to:
obtain device health metrics associated with the industrial device, wherein the device health metrics comprise contextualized performance metrics associated with the industrial device [0015, 0022-0023, 0031, 0033-0035, 00421]; and
a user interface device coupled to the controller [Figs. 1, 4-6] [0015-0017, 0020, 0053-0055, 0063] and comprising:
a user interface; one or more device processors; and a device memory coupled to the one or more device processors and having device program instructions stored thereon that, based on being read and executed by the one or more device processors, direct the one or more device processors to [Figs. 1, 4-6] [0015-0017, 0020, 0053-0055, 0063]:
receive the device health metrics associated with the industrial device from the controller; and display, via the user interface, first indications corresponding to the device health metrics [0004, 00152, 0022-0023, 00313, 0033-0035, 0042].
Reichard teaches a system and method for facilitating management of industrial operations by obtaining and displaying industrial status to the user, such as operational status metrics of machines, key performance indicators (KPIs), control instructions for directing the operation of machines. In some examples, the operational data for some KPIs may comprise dynamic charts or trends, real-time video, or some other graphical content [0015, 0020-0022]. Reichard does not explicitly teaches obtaining and displaying device health metrics categories which comprise categorizations of a health of the industrial device based on one or more of the device health metrics.
However, Miklosovic teaches a system and method for providing condition monitoring in industrial environments, especially, Miklosovic teaches obtaining and displaying device health metrics and device health metrics categories associated with the industrial device, wherein the device health metrics comprise contextualized performance metrics associated with the industrial device, and wherein the device health metrics categories comprise categorizations of a health of the industrial device based on one or more of the device health metrics ([0047] 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; [0048] Sensors 230 may include vibration, temperature, acoustic, or other external sensors that collect data related to operation of machine 220 and provide the data to select and capture module 212. Select and capture module 212 collects data from a drive signal or sensors 230, depending on what signal is selected, and provides the captured data to metrics module 213. Select and capture module 212 is used during baseline and runtime captures. Metrics module 213 processes the data to generate metrics data that can be utilized for fault detection; [0056] 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….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. ….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; [0067] thresholds 541 compares metrics and differences against predetermined thresholds to identify if any metrics exceed thresholds for healthy conditions. In other examples, detection 540 may implement machine learning techniques to recognize healthy or unhealthy conditions based on the metrics [0081] Machine learning module 1020 may use the fault signature information provided by drive 1010 as input to produce output 1030, in which a status is identified. The status may include a number of broken rotor bars (i.e., 1 BRB, 2 BRB, 3 BRB, 4 BRB, or healthy)).
Reichard and Miklosovic are analogous art because they are from the same field of endeavor of industrial operations management. At the time before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to obtain and display different type of industrial status data to operator for performing industrial processing control. The suggestion for doing so would have been obvious to prepare a detail operation report includes different machine operation metrics and associated healthy status conditions to provide a better industrial operations management. Therefore, it would have been obvious to an ordinary person skilled in the art before the effective filing date of the invention to incorporate the teachings of Miklosovic with the teachings of Reichard for the purpose of providing industrial report includes different machine operation metrics and associated healthy status conditions as specified in the claim 1.
As to claim 2, Miklosovic teaches obtain the device health metrics and the device health metrics categories, the controller program instructions further direct the one or more controller processors to receive the device health metrics and the device health metrics categories from a server ([0047] analytic engine 111 may collect data from devices of industrial operation 120 and other sources in various formats. Analytic engine may use collected data to perform condition monitoring, power and energy monitoring, predictive life analysis, load characterization, or similar analyses. At the system level, 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), and wherein the system further comprises the server, wherein the server comprises: one or more server processors; and a server memory coupled to the one or more server processors and having server program instructions stored thereon that, based on being read and executed by the one or more server processors, direct the one or more server processors to: obtain performance metrics associated with the industrial device ([0047] analytic engine 111 may collect data from devices of industrial operation 120 and other sources in various formats; [0048] Sensors 230 may include vibration, temperature, acoustic, or other external sensors that collect data related to operation of machine 220 and provide the data to select and capture module 212. Select and capture module 212 collects data from a drive signal or sensors 230, depending on what signal is selected, and provides the captured data to metrics module 213); contextualize the performance metrics based on contextualization information specific to the performance metrics to produce the device health metrics ([0047] 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; [0048] Sensors 230 may include vibration, temperature, acoustic, or other external sensors that collect data related to operation of machine 220 and provide the data to select and capture module 212. Select and capture module 212 collects data from a drive signal or sensors 230, depending on what signal is selected, and provides the captured data to metrics module 213. Select and capture module 212 is used during baseline and runtime captures. Metrics module 213 processes the data to generate metrics data that can be utilized for fault detection; [0081] FIG. 10 includes block diagram 1000 for rotor fault detection in accordance with certain embodiments of the present technology); classify the device health metrics into the device health metric categories based on applying rule sets to the device health metrics; and provide the device health metrics and the device health metrics categories to the controller ([0056] 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….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. ….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; [0063-0065] Detection 460 includes differences 461, thresholds 462, and metrics out 463. The modules of detection 460 may utilize the metrics provided to detection 460 to identify unhealthy operating conditions, degradation, differences, exceeded thresholds, or the like; [0081] Machine learning module 1020 may use the fault signature information provided by drive 1010 as input to produce output 1030, in which a status is identified. The status may include a number of broken rotor bars (i.e., 1 BRB, 2 BRB, 3 BRB, 4 BRB, or healthy)).
As to claim 3, Miklosovic teaches obtain the device health metrics and the device health metrics categories, the controller program instructions further direct the one or more controller processors to: obtain performance metrics associated with the industrial device ([0047] analytic engine 111 may collect data from devices of industrial operation 120 and other sources in various formats; [0048] Sensors 230 may include vibration, temperature, acoustic, or other external sensors that collect data related to operation of machine 220 and provide the data to select and capture module 212. Select and capture module 212 collects data from a drive signal or sensors 230, depending on what signal is selected, and provides the captured data to metrics module 213); contextualize the performance metrics based on contextualization information specific to the performance metrics to produce the device health metrics ([0047] 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; [0048] Sensors 230 may include vibration, temperature, acoustic, or other external sensors that collect data related to operation of machine 220 and provide the data to select and capture module 212. Select and capture module 212 collects data from a drive signal or sensors 230, depending on what signal is selected, and provides the captured data to metrics module 213. Select and capture module 212 is used during baseline and runtime captures. Metrics module 213 processes the data to generate metrics data that can be utilized for fault detection; [0081] FIG. 10 includes block diagram 1000 for rotor fault detection in accordance with certain embodiments of the present technology); classify the device health metrics into the device health metric categories based on applying rule sets to the device health metrics ([0056] 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….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. ….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; [0063-0065] Detection 460 includes differences 461, thresholds 462, and metrics out 463. The modules of detection 460 may utilize the metrics provided to detection 460 to identify unhealthy operating conditions, degradation, differences, exceeded thresholds, or the like; [0081] Machine learning module 1020 may use the fault signature information provided by drive 1010 as input to produce output 1030, in which a status is identified. The status may include a number of broken rotor bars (i.e., 1 BRB, 2 BRB, 3 BRB, 4 BRB, or healthy)).
As to claim 4, Miklosovic teaches contextualize the performance metrics, the controller program instructions further direct the one or more controller processors to perform one or more operations the performance metrics ([0047] 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; [0048] Sensors 230 may include vibration, temperature, acoustic, or other external sensors that collect data related to operation of machine 220 and provide the data to select and capture module 212. Select and capture module 212 collects data from a drive signal or sensors 230, depending on what signal is selected, and provides the captured data to metrics module 213. Select and capture module 212 is used during baseline and runtime captures. Metrics module 213 processes the data to generate metrics data that can be utilized for fault detection; [0056] 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….).
As to claim 5, Miklosovic teaches the rule sets comprise one or more of threshold data, a time range, and a quantity ([0056] 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. ….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.….; [0067] thresholds 541 compares metrics and differences against predetermined thresholds to identify if any metrics exceed thresholds for healthy conditions. In other examples, detection 540 may implement machine learning techniques to recognize healthy or unhealthy conditions based on the metrics…).
As to claim 6, Miklosovic teaches the device health metrics categories comprise a healthy category, an unhealthy category, and an approaching unhealthy category ([0054-0056, 0063-0067, 0077-0081] detection 540 may implement machine learning techniques to recognize healthy or unhealthy conditions based on the metrics…Machine learning module 1020 may use the fault signature information provided by drive 1010 as input to produce output 1030, in which a status is identified. The status may include a number of broken rotor bars (i.e., 1 BRB, 2 BRB, 3 BRB, 4 BRB, or healthy).
As to claim 7, Miklosovic teaches the display of the user interface device comprises text and a background having a default contrast with respect to the text, and in response to one or more of the device health metrics categories indicating the health of the industrial device is unhealthy, the device program instructions direct the one or more device processors to invert a contrast of the background with respect to the default contrast [0047-0048, 0056, 0063-0067, 0077-0081].
As to claim 8, Miklosovic teaches the user interface device further comprises a light-emitting diode (LED) capable of emitting light of two or more colors, and wherein the device program instructions further direct the one or more device processors to emit, via the LED, a first color of the two or more colors based on the device health metrics categories indicating the health of the industrial device is healthy and emit, via the LED, a second color of the two or more colors based on the device health metrics categories indicating the health of the industrial device is approaching unhealthy [0047-0048, 0056, 0063-0067, 0077-0081].
As to claim 9, Reichard teaches the user interface device is coupled to one or more further controllers, each of the one or more further controllers coupled to one or more industrial devices in the industrial automation environment [Figs. 1, 4-6] [Figs. 1, 4-6] [0015-0017, 0022-0023, 0031, 0033-0035, 0042, 0044].
As to claim 10, Reichard teaches the user interface device further comprises a communication interface, and wherein the device program instructions further direct the one or more device processors to establish, via the communication interface, a connection with a user device [0025-0027, 0035-0038, 0058].
As to claim 11, Reichard teaches a system comprises:
a controller coupled to an industrial device in an industrial automation environment [Figs. 1, 7] [0039-0042, 0053-0058] and comprising:
one or more controller processors; a controller memory coupled to the one or more controller processors and having controller program instructions stored thereon that, based on being read and executed by the one or more controller processors[Figs. 1, 7] [0039-0042, 0053-0058], direct the one or more controller processors to:
obtain device health metrics associated with the industrial device, wherein the device health metrics comprise contextualized performance metrics associated with the industrial device [0015, 0022-0023, 0031, 0033-0035, 0042]; and
a user interface device coupled to the controller [Figs. 1, 4-6] [0015-0017, 0020, 0053-0055, 0063] and comprising:
a communication interface; one or more device processors; and a device memory coupled to the one or more device processors and having device program instructions stored thereon that, based on being read and executed by the one or more device processors, direct the one or more device processors to [Figs. 1, 4-6] [0015-0017, 0020-0023, 0025-0027, 0053-0055, 0063]:
receive a request to establish a wireless connection to a user device; establish, via the communication interface, the wireless connection with the user device based on the request [0021-0023, 0025-0027]; and
provide, via the wireless connection, first indications corresponding to the device health metrics to the user device [0004, 0015, 0022-0023, 0031, 0033-0035, 0042].
Reichard teaches a system and method for facilitating management of industrial operations by obtaining and displaying industrial status to the user, such as operational status metrics of machines, key performance indicators (KPIs), control instructions for directing the operation of machines. In some examples, the operational data for some KPIs may comprise dynamic charts or trends, real-time video, or some other graphical content [0015, 0020-0022]. Reichard does not explicitly teaches obtaining and displaying device health metrics categories which comprise categorizations of a health of the industrial device based on one or more of the device health metrics.
However, Miklosovic teaches a system and method for providing condition monitoring in industrial environments, especially, Miklosovic teaches obtaining and displaying device health metrics and device health metrics categories associated with the industrial device, wherein the device health metrics comprise contextualized performance metrics associated with the industrial device, and wherein the device health metrics categories comprise categorizations of a health of the industrial device based on one or more of the device health metrics ([0047] 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; [0048] Sensors 230 may include vibration, temperature, acoustic, or other external sensors that collect data related to operation of machine 220 and provide the data to select and capture module 212. Select and capture module 212 collects data from a drive signal or sensors 230, depending on what signal is selected, and provides the captured data to metrics module 213. Select and capture module 212 is used during baseline and runtime captures. Metrics module 213 processes the data to generate metrics data that can be utilized for fault detection; [0056] 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….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. ….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; [0067] thresholds 541 compares metrics and differences against predetermined thresholds to identify if any metrics exceed thresholds for healthy conditions. In other examples, detection 540 may implement machine learning techniques to recognize healthy or unhealthy conditions based on the metrics [0081] Machine learning module 1020 may use the fault signature information provided by drive 1010 as input to produce output 1030, in which a status is identified. The status may include a number of broken rotor bars (i.e., 1 BRB, 2 BRB, 3 BRB, 4 BRB, or healthy).
Reichard and Miklosovic are analogous art because they are from the same field of endeavor of industrial operations management. At the time before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to obtain and display different type of industrial status data to operator for industrial processing control. The suggestion for doing so would have been obvious to prepare a detail operation report includes different machine operation metrics and associated healthy status conditions to provide a better industrial operations management. Therefore, it would have been obvious to an ordinary person skilled in the art before the effective filing date of the invention to incorporate the teachings of Miklosovic with the teachings of Reichard for the purpose of providing industrial report includes different machine operation metrics and associated healthy status conditions as specified in the claim 11.
As to claim 12, Miklosovic teaches obtain the device health metrics and the device health metrics categories, the controller program instructions further direct the one or more controller processors to receive the device health metrics and the device health metrics categories from a server ([0047] analytic engine 111 may collect data from devices of industrial operation 120 and other sources in various formats. Analytic engine may use collected data to perform condition monitoring, power and energy monitoring, predictive life analysis, load characterization, or similar analyses. At the system level, 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), and wherein the system further comprises the server, wherein the server comprises: one or more server processors; and a server memory coupled to the one or more server processors and having server program instructions stored thereon that, based on being read and executed by the one or more server processors, direct the one or more server processors to: obtain performance metrics associated with the industrial device ([0047] analytic engine 111 may collect data from devices of industrial operation 120 and other sources in various formats; [0048] Sensors 230 may include vibration, temperature, acoustic, or other external sensors that collect data related to operation of machine 220 and provide the data to select and capture module 212. Select and capture module 212 collects data from a drive signal or sensors 230, depending on what signal is selected, and provides the captured data to metrics module 213); contextualize the performance metrics based on contextualization information specific to the performance metrics to produce the device health metrics ([0047] 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; [0048] Sensors 230 may include vibration, temperature, acoustic, or other external sensors that collect data related to operation of machine 220 and provide the data to select and capture module 212. Select and capture module 212 collects data from a drive signal or sensors 230, depending on what signal is selected, and provides the captured data to metrics module 213. Select and capture module 212 is used during baseline and runtime captures. Metrics module 213 processes the data to generate metrics data that can be utilized for fault detection; [0081] FIG. 10 includes block diagram 1000 for rotor fault detection in accordance with certain embodiments of the present technology); classify the device health metrics into the device health metric categories based on applying rule sets to the device health metrics; and provide the device health metrics and the device health metrics categories to the controller ([0056] 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….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. ….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; [0063-0065] Detection 460 includes differences 461, thresholds 462, and metrics out 463. The modules of detection 460 may utilize the metrics provided to detection 460 to identify unhealthy operating conditions, degradation, differences, exceeded thresholds, or the like; [0081] Machine learning module 1020 may use the fault signature information provided by drive 1010 as input to produce output 1030, in which a status is identified. The status may include a number of broken rotor bars (i.e., 1 BRB, 2 BRB, 3 BRB, 4 BRB, or healthy)).
As to claim 13, Miklosovic teaches contextualize the performance metrics, the controller program instructions further direct the one or more controller processors to perform one or more operations the performance metrics ([0047] 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; [0048] Sensors 230 may include vibration, temperature, acoustic, or other external sensors that collect data related to operation of machine 220 and provide the data to select and capture module 212. Select and capture module 212 collects data from a drive signal or sensors 230, depending on what signal is selected, and provides the captured data to metrics module 213. Select and capture module 212 is used during baseline and runtime captures. Metrics module 213 processes the data to generate metrics data that can be utilized for fault detection; [0056] 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….).
As to claim 14, Miklosovic teaches the rule sets comprise one or more of threshold data, a time range, and a quantity ([0056] 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. ….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.….; [0067] thresholds 541 compares metrics and differences against predetermined thresholds to identify if any metrics exceed thresholds for healthy conditions. In other examples, detection 540 may implement machine learning techniques to recognize healthy or unhealthy conditions based on the metrics…).
As to claim 15, Miklosovic teaches the device health metrics categories comprise a healthy category, an unhealthy category, and an approaching unhealthy category ([0054-0056, 0063-0067, 0077-0081] detection 540 may implement machine learning techniques to recognize healthy or unhealthy conditions based on the metrics…Machine learning module 1020 may use the fault signature information provided by drive 1010 as input to produce output 1030, in which a status is identified. The status may include a number of broken rotor bars (i.e., 1 BRB, 2 BRB, 3 BRB, 4 BRB, or healthy).
As to claim 16, Reichard teaches the device program instructions direct the one or more device processors to establish the wireless connection using one of a near-field communication protocol, Bluetooth, and Wi-Fi [0043, 0050, 0052].
As to claim 17, Miklosovic teaches the display of the user interface device comprises text and a background having a default contrast with respect to the text, and in response to one or more of the device health metrics categories indicating the health of the industrial device is unhealthy, the device program instructions direct the one or more device processors to invert a contrast of the background with respect to the default contrast [0047-0048, 0054-0056, 0063-0067, 0071, 0077-0081].
As to claim 18, Miklosovic teaches the user interface device further comprises a light-emitting diode (LED) capable of emitting light of two or more colors, and wherein the device program instructions further direct the one or more device processors to emit, via the LED, a first color of the two or more colors based on the device health metrics categories indicating the health of the industrial device is healthy and emit, via the LED, a second color of the two or more colors based on the device health metrics categories indicating the health of the industrial device is approaching unhealthy [0047-0048, 0054-0056, 0063-0067, 0071, 0077-0081].
As to claim 19, Reichard teaches the user interface device is coupled to one or more further controllers, each of the one or more further controllers coupled to one or more industrial devices in the industrial automation environment [Figs. 1, 4-6] [Figs. 1, 4-6] [0015-0017, 0022-0023, 0031, 0033-0035, 0042, 0044].
As to claim 20, Reichard teaches a user interface device comprises:
a user interface [Figs. 1, 4-6] [0015-0017, 0020, 0053-0055, 0063];
one or more device processors; and a device memory coupled to the one or more device processors and having device program instructions stored thereon that, based on being read and executed by the one or more device processors, direct the one or more device processors to [Figs. 1, 4-6] [0015-0017, 0020, 0053-0055, 0063]:
obtain device health metrics associated with an industrial device from a controller, wherein the device health metrics comprise contextualized performance metrics associated with the industrial device [0015, 0022-0023, 0031, 0033-0035, 0042]; and
display, via the user interface, first indications corresponding to the device health metrics [0004, 0015, 0022-0023, 0031, 0033-0035, 0042].
Reichard teaches a system and method for facilitating management of industrial operations by obtaining and displaying industrial status to the user, such as operational status metrics of machines, key performance indicators (KPIs), control instructions for directing the operation of machines. In some examples, the operational data for some KPIs may comprise dynamic charts or trends, real-time video, or some other graphical content [0015, 0020-0022]. Reichard does not explicitly teaches obtaining and displaying device health metrics categories which comprise categorizations of a health of the industrial device based on one or more of the device health metrics.
However, Miklosovic teaches a system and method for providing condition monitoring in industrial environments, especially, Miklosovic teaches obtaining and displaying device health metrics and device health metrics categories associated with the industrial device, wherein the device health metrics comprise contextualized performance metrics associated with the industrial device, and wherein the device health metrics categories comprise categorizations of a health of the industrial device based on one or more of the device health metrics ([0047] 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; [0048] Sensors 230 may include vibration, temperature, acoustic, or other external sensors that collect data related to operation of machine 220 and provide the data to select and capture module 212. Select and capture module 212 collects data from a drive signal or sensors 230, depending on what signal is selected, and provides the captured data to metrics module 213. Select and capture module 212 is used during baseline and runtime captures. Metrics module 213 processes the data to generate metrics data that can be utilized for fault detection; [0056] 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….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. ….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; [0067] thresholds 541 compares metrics and differences against predetermined thresholds to identify if any metrics exceed thresholds for healthy conditions. In other examples, detection 540 may implement machine learning techniques to recognize healthy or unhealthy conditions based on the metrics [0081] Machine learning module 1020 may use the fault signature information provided by drive 1010 as input to produce output 1030, in which a status is identified. The status may include a number of broken rotor bars (i.e., 1 BRB, 2 BRB, 3 BRB, 4 BRB, or healthy).
Reichard and Miklosovic are analogous art because they are from the same field of endeavor of industrial operations management. At the time before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to obtain and display different type of industrial status data to operator for industrial processing control. The suggestion for doing so would have been obvious to prepare a detail operation report includes different machine operation metrics and associated healthy status conditions to provide a better industrial operations management. Therefore, it would have been obvious to an ordinary person skilled in the art before the effective filing date of the invention to incorporate the teachings of Miklosovic with the teachings of Reichard for the purpose of providing industrial report includes different machine operation metrics and associated healthy status conditions as specified in the claim 20.
Conclusion
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/ZHIPENG WANG/Primary Examiner, Art Unit 2115
1 [0042] Machine system 160 continually produces operational data over time. The operational data indicates the current status of machine system 160, such as parameters, pressure, temperature, speed, energy usage, operational equipment effectiveness (OEE), mean time between failure (MTBF), mean time to repair (MTTR), voltage, throughput volumes, times, tank levels, or any other performance status metrics. The operational data may comprise dynamic charts or trends, real-time video, or some other graphical content. Machine system 160 and/or its associated controller system is capable of transferring the operational data over a communication link to database system 150 and application server 140, typically via a communication network.
2 [0015] Implementations disclosed herein provide for improved management, monitoring, and control of industrial operations from remote locations. In at least one implementation, a portable human-machine interface (HMI) device can be connected to general-purpose display systems such as modern high-definition televisions (HDTVs) and monitors. The portable HMI device typically has one or more applications stored thereon for display on the display system, enabling a user to navigate through and select among the applications using a remote navigation device, such as an infrared remote control, smartphone, tablet, or some other wireless device. In concert with a server, the applications on the portable HMI device can be executed to request and receive content for the portable HMI device to provide visualizations and control of various industrial automation operations. For example, the portable HMI device could receive and display key performance indicators (KPIs) related to operational data associated with machines in an industrial automation environment, such as parameters, pressures, temperatures, speeds, production rates, or some other status metrics. In some examples, the operational data for some KPIs may comprise dynamic charts or trends, real-time video, or some other graphical content.
3 [0031] As shown on the display screen of the remote navigation device of FIG. 4, the user has selected the “machine status” and “drill speed” KPIs for delivery to the portable HMI device to display on its connected display system. The user would typically also identify the target HMI device in the request to the navigation server by specifying a device identifier associated with the portable HMI device, an internet protocol (IP) address, a uniform resource identifier (URI), or any other identifying information associated with the target HMI device. The user could also specify the particular machine system, assembly line, production plant, or any other data source for the requested content, along with any unattended delivery preferences such as timed intervals for receiving updated data, event triggers for delivering data or alerts such as alarms, scheduled navigations, shift changes, thresholds, or other events, and any other content delivery preferences.