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 .
Response to Arguments
Regarding the Remarks page 8-9 “Timing within a process”, the applicant argues that the prior art does not teach the amendments “identifying a process of the industrial machine and timing associated with the process of the industrial machine;” and “the timing associated with the process of the industrial machine”. The examiner agrees. However the prior art of Teti has taught these limitations.
Regarding the Remarks page 9-10 “Maintenance action in response to predicting the maintenance action”, the applicant argues that the prior art is silent concerning this limitation. The examiner respectfully disagrees. Ahmed teaches the severity of the event is determined and an action is recommended at Fig. 8 step S810. If the severity of the event is medium, a recommendation to watch the equipment Fig. 1, 114 is generated, and a watch is performed using a visual inspection of the equipment as described in [0079], or perform a video clip using the camera [[0080]. This is a maintenance action in response to the prediction of the maintenance action.
Response to Amendment
Claims 1-20 are pending.
Claims 1 and 13 are independent claims.
Claims 1, 13 and 15 are currently amended.
AIA Considerations Regarding Claim Rejections Under 35 USC § 102 & 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 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.
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.
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.
Claims 1, 5-11, 13 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ahmed et al., US 2016/0282872 (hereinafter Ahmed), and further in view of Teti et al., “Advanced monitoring of machining operations”, CIRP Annals - Manufacturing Technology 59 (2010) 717–739 (hereinafter Teti)
Claim 1 (Currently Amended):
Ahmed teaches a method comprising:
deploying a mobile data collector for detecting and monitoring vibration activity of at least a portion of an industrial machine, the mobile data collector comprising a drone that including includes one or more vibration sensors and one or more image sensors (Figure 1 shows the overall system with the industrial equipment 108 and 114, the autonomous vehicle or drone 116 (last sentence [0046]) which comprises FIG. 2B a camera 230 and sensors 202 which include vibration sensor, temperature sensor, optical sensor or a biosensor. Abstract);
controlling the mobile data collector to approach a location of the industrial machine within an industrial environment that includes the industrial machine. The drone is sent to a waypoint 112 located near the equipment 114 (FIG. 1, [0050], FIG. 7 S714).
causing the one or more vibration sensors of the mobile data collector to record one or more measurements of the vibration activity. When the system is set to “MEASUREMENT” the drone 116 is moved closer to the equipment 114 and is made to land at close proximity to the equipment 114, at step S714 [0079]. The autonomous vehicle 116 is made to land in close proximity to the equipment 114, at step S716, a sensor 202 is activated to collect sensor data. An example of a sensor may include, but is not limited to, vibration sensor.
transmitting the one or more measurements of the vibration activity as vibration data to a server over a network in real-time ([0081]-[0082])
Although Ahmed teaches measuring parameters related to processes [0002], Ahmed is silent concerning the collection of vibration data associated with the timing of specific processes of a machine.
Teti teaches using vibrations to monitor specific processes of machining operations. Thus the vibration data is measured associated with the timing of the process of the machine. Table 2 shows various process monitoring scopes with vibration sensors, where the process operation and the vibration sensing is correlated in time. Note that the scheduling of Ahmed allows a timing dependent of a particular process in order to monitor that process (Ahmed [0055])
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention, to use the mobile data collectors of Ahmed as the vibration sensors used to monitor specific operations at the time they occur as taught by Teti with the expected benefit, that specific machine processes can be monitored and analyzed.
This method for improving the scheduled monitoring of Ahmed was within the ordinary ability of one of ordinary skill in the art before the effective filing date of the claimed invention based on the teachings of Teti.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Ahmed and Teti to obtain the invention:
identifying a process of the industrial machine and timing associated with the process of the industrial machine (Ahmed, scheduling the monitoring [0055], using the process monitoring guidance of Teti, page 717, col 1, second paragraph and page 727 FIG. 2);
determining, at the server, a severity of the vibration activity relative to the timing associated with the process of the industrial machine (Ahmed teaches this action may indicate the use of one or more sensors 202 or the camera 230. The time 310 attribute represents the scheduled time at which the action 308 is to be performed, which was scheduled in time by the teaching of Teti. page 717, col 1, second paragraph) by processing the vibration data. (Ahmed, analyze the sensor data, FIG.7 S724 [0074], FIG. 8 step S806)
predicting, at the server, a maintenance action to perform with respect to at least the portion of the industrial machine based on the severity of the vibration activity. (Ahmed, the severity of the event is determined in Fig. 8 S808).
the maintenance action comprising at least one of calibration, diagnostic testing, or visual inspection. Ahmed teaches the severity of the event is determined and an action is recommended at Fig. 8 step S810. If the severity of the event is medium, a recommendation to watch the equipment Fig. 1, 114 is generated, and a watch is performed using a visual inspection of the equipment as described in [0079], or perform a video clip using the camera [[0080].
transmitting a signal indicative of the maintenance action to the mobile data collector in response to predicting the maintenance action to cause the mobile data collector to perform the maintenance action utilizing at least the one or more image sensors. Ahmed teaches, in response to a maintenance action being required (Ahmed teaches the recommendation to watch the equipment [0089]), the trajectory is sent to the autonomous-vehicle/drone [0054] with instructions for either MEASUREMENT or PATROL [0078] where both involve taking measurements using the camera [0079], [0080].
storing a record of the predicted maintenance action within a ledger associated with the industrial machine. Where the Plant Management System (PIM) uses its databases, FIG 2B 228 and 240, and records/stores the report Fig. 8 S812 [0090].
Claim 13 (Currently Amended):
Regarding claim 13, the claim is similar to claim 1 and is rejected similarly, except that the additional details of the processor and memory are further taught in Fig. 10 and [0100].
Claim 5 (Original):
The combined art of Ahmed and Teti make obvious the method of claim 1, wherein the vibration activity is indicative of a waveform derived from a vibration envelope associated with the industrial machine, wherein the one or more vibration sensors detect the vibration activity when the mobile data collector is in near proximity to the industrial machine.
Ahmed teaches making vibration measurements using a vibration sensor where the measurement is made by taking the drone to the machine and measuring the vibration sensor data which is effectively a waveform within a vibration range/envelope [0079].
Claim 6 (Original):
The combined art of Ahmed and Teti make obvious the method of claim 1, wherein the vibration activity represents velocity information for at least the portion of the industrial machine.
Ahmed teaches making vibration measurements using a vibration sensor where the measurement is made by taking the drone to the machine and measuring the vibration sensor data at the portion of the machine near the sensor [0079].
Claim 7 (Original):
Regarding Claim 7 the combined art of Ahmed and Teti make obvious the method of claim 1, wherein the vibration activity represents frequency information for at least the portion of the industrial machine.
Ahmed teaches making vibration measurements using a vibration sensor where the measurement is made by taking the drone to the machine and measuring the vibration sensor data at the portion of the machine near the sensor [0079]. Thus the vibration data which inherently includes frequency information would be for the portion of the machine which has been measured.
Claim 8 (Original):
Regarding Claim 8 the combined art of Ahmed and Teti make obvious the method of claim 1.
Ahmed discloses the claimed invention except for the mobile data collector is one of a plurality of mobile data collectors of a mobile data collector swarm. It would have been obvious to one having ordinary skill in the art at the time the invention was made to simply duplicate the mobile-device/drone to produce a swarm of such devices, since it has been held that mere duplication of the essential working parts of a device involves only routine skill in the art. St Regis Paper Co. v. Bemis Co., 193 USPQ 8.
Claim 17 (Previously Presented):
Claim 17 is rejected similarly to claim 8.
Claim 9 (Original):
Regarding Claim 9 the combined art of Ahmed and Teti make obvious the method of claim 8, further comprising:
using self-organization systems of the mobile data collector swarm to control movements of the mobile data collector within an industrial environment that includes the industrial machine. Ahmed teaches having a schedule for performing the monitoring for the autonomous-vehicle/drone [0055].
wherein the one or more vibration sensors detect the vibration activity when the mobile data collector is in near proximity to the industrial machine. Ahmed measures vibration by placing the autonomous-vehicle/drone near the machine [0079].
Claim 18 (Previously Presented):
Claim 18 is rejected similarly to claim 9.
Claim 10 (Original):
Regarding Claim 10 the combined art of Ahmed and Teti make obvious the method of claim 9, wherein using the self-organization systems of the mobile data collector swarm to control the movements of the mobile data collector within the industrial environment comprises:
controlling the movements of the mobile data collector within the industrial environment based on movements of at least one other mobile data collector of the plurality of mobile data collectors.
Ahmed teaches making vibration measurements using a vibration sensor where the measurement is made by taking the drone to the machine and measuring the vibration sensor data at the portion of the machine near the sensor [0079].
Claim 19 (Previously Presented):
Claim 19 is rejected similarly to claim 10.
Claim 11 (Previously Presented):
Regarding Claim 11 the combined art of Ahmed and Teti make obvious the method of claim 8, wherein the mobile data collector is a mobile robot and at least one other mobile data collector of the plurality of mobile data collectors is a mobile vehicle or a mobile robot.
In claim 8 the creation of the swarm was a duplication of parts, where the parts were the autonomous-vehicles/drones of Ahmed. Thus the swarm comprises mobile vehicle/robots.
Claim 20 (Previously Presented):
Claim 20 is rejected similarly to claim 11.
Claims 2-4, 12 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Ahmed, and further in view of Teti, and Discenzo, US 6,434,512
Claim 2 (Original):
Regarding Claim 2 the combined art of Ahmed and Teti make obvious the method of claim 1, determining the severity of the vibration data relative to the timing by processing the vibration data.
However Ahmed and Teti are silent concerning:
wherein determining the severity of the vibration data relative to the timing by processing the vibration data comprises:
determining a frequency of the vibration activity by processing the vibration data;
determining, based on the frequency, a segment of a multi-segment vibration frequency spectra that bounds the vibration activity; and
calculating a severity unit for the vibration activity based on the determined segment of the multi-segment vibration frequency spectra.
Note although Discenzo describes embodiments directed to commercial transport vehicles he explicitly states that the diagnostics/prognostics can be applied to other dynamic systems, e.g. industrial machines (Abstract).
Discenzo teaches performing a vibration analysis of an industrial machine Figs. 4e, 5 & 6, col. 13 lines 57-62. Discenzo further teaches performing FFT analyses col 13, lines 38-50. Discenzo teaches analyses can be performed over select frequencies attributed to features such as critical bearing ball pass frequencies and outer race frequencies, col 9 lines 36-43. Discenzo teaches using the measured vibrations and their analysis, determining the state of the motor from the frequency information, col 9 lines 36-43. This information is then used to determine needed maintenance (col 14 lines 24-28)
Discenzo further teaches analyzing across fundamental, side band and harmonic frequencies of the vibrations (col 9 lines 36-43). This information is collected by measuring accelerometers (col 9 lines 58-62).
Last, Discenzo, FIG. 14A, teaches gathering the data generate health signals, step 544, and when maintenance is required generate a notification signal, step 550, and transmit the notification to an indication unit and store the information, step 551, (col 22 lines 27-35). Discenzo teaches using signature analysis using stored tables of prior data associated with particular machine states, e.g. a database (Table 340 FIG. 7, col 14 lines 50-67).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention, to use the detailed analysis of Discenzo to improve the ability to determine the health of a vibrating machine.
This method for improving the vibration analysis of Ahmed, was within the ordinary ability of one of ordinary skill in the art before the effective filing date of the claimed invention based on the teachings of Discenzo.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Ahmed, Teti and Discenzo to obtain the invention:
wherein determining the severity of the vibration data relative to the timing by processing the vibration data comprises:
determining a frequency of the vibration activity by processing the vibration data (Discenzo teaches performing FFT analyses col 13, lines 38-50);
determining, based on the frequency, a segment of a multi-segment vibration frequency spectra that bounds the vibration activity (Discenzo teaches analyses can be performed over select frequencies attributed to features such as critical bearing ball pass frequencies and outer race frequencies, col 9 lines 36-43); and
calculating a severity unit for the vibration activity based on the determined segment of the multi-segment vibration frequency spectra (Discenzo teaches determining the state of the motor from the frequency information, col 9 lines 36-43).
Claim 14 (Previously Presented):
Claim 14 is rejected similarly to claim 2.
Claim 3 (Original):
The combined art of Ahmed, Teti and Discenzo in claim 2 make obvious the method of claim 2, wherein calculating the severity unit for the vibration activity based on the determined segment of the multi-segment vibration frequency spectra comprises:
mapping the vibration activity to the severity unit based on the determined segment of the multi-segment vibration frequency spectra by: mapping the vibration activity to a first severity unit when the frequency of the vibration activity corresponds to a below a low-end knee threshold-range of the multi-segment vibration frequency spectra; mapping the vibration activity to a second severity unit when the frequency of the vibration activity corresponds to a mid-range of the multi-segment vibration frequency spectra; and mapping the vibration activity to a third severity unit when the frequency of the vibration activity corresponds to an above a high-end knee threshold-range of the multi-segment vibration frequency spectra.
Discenzo teaches analyzing across fundamental, side band and harmonic frequencies of the vibrations (col 9 lines 36-43).
Claim 15 (Currently Amended):
Claim 15 is rejected similarly to claim 3.
Claim 4 (Original):
Regarding Claim 4 the combined art of Ahmed and Teti make obvious the method of claim 1, predicting the one or more maintenance actions to perform with respect to at least the portion of the industrial machine based on the severity of the vibration activity ([0089]).
However Ahmed and Teti are silent concerning:
wherein predicting the one or more maintenance actions to perform with respect to at least the portion of the industrial machine based on the severity of the vibration activity comprises:
using intelligent systems associated with the server to process the vibration data against pre-recorded data for the industrial machine, wherein processing the vibration data against the pre-recorded data for the industrial machine includes identifying the pre- recorded data for the industrial machine within a knowledge base associated with the industrial environment:
identifying an operating characteristic of at least the portion of the machine based on the pre-recorded data for the industrial machine within the Knowledge base; and
predicting the one or more maintenance actions based on the operating characteristic.
Note although Discenzo describes embodiments directed to commercial transport vehicles he explicitly states that the diagnostics/prognostics can be applied to other dynamic systems, e.g. industrial machines (Abstract).
Discenzo teaches performing a vibration analysis of an industrial machine Figs. 4e, 5 & 6, col. 13 lines 57-62. Discenzo further teaches performing FFT analyses col 13, lines 38-50. Discenzo teaches analyses can be performed over select frequencies attributed to features such as critical bearing ball pass frequencies and outer race frequencies, col 9 lines 36-43. Discenzo teaches using the measured vibrations and their analysis, determining the state of the motor from the frequency information, col 9 lines 36-43. This information is then used to determine needed maintenance (col 14 lines 24-28)
Discenzo further teaches analyzing across fundamental, side band and harmonic frequencies of the vibrations (col 9 lines 36-43). This information is collected by measuring accelerometers (col 9 lines 58-62).
Last, Discenzo, FIG. 14A, teaches gathering the data generate health signals, step 544, and when maintenance is required generate a notification signal, step 550, and transmit the notification to an indication unit and store the information, step 551, (col 22 lines 27-35). Discenzo teaches using signature analysis using stored tables of prior data associated with particular machine states, e.g. a database (Table 340 FIG. 7, col 14 lines 50-67).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention, to use the detailed analysis of Discenzo to improve the ability to determine the health of a vibrating machine.
This method for improving the vibration analysis of Ahmed, was within the ordinary ability of one of ordinary skill in the art before the effective filing date of the claimed invention based on the teachings of Discenzo.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Ahmed, Teti and Discenzo to obtain the invention:
wherein predicting the one or more maintenance actions to perform with respect to at least the portion of the industrial machine based on the severity of the vibration activity comprises:
using intelligent systems associated with the server to process the vibration data against pre-recorded data for the industrial machine, wherein processing the vibration data against the pre-recorded data for the industrial machine includes identifying the pre- recorded data for the industrial machine within a knowledge base associated with the industrial environment: identifying an operating characteristic of at least the portion of the machine based on the pre-recorded data for the industrial machine within the Knowledge base;
Discenzo teaches analysis based on signatures, e.g. historical data, to make determinations concerning the health of the motor (col 13, lines 57-62).
predicting the one or more maintenance actions based on the operating characteristic. . Discenzo teaches the data received from the diagnostic module (which uses the signature analysis) is used to determine motor maintenance based on the state of the motor (col 14 lines 24-28).
Claim 16 (Previously Presented):
Claim 16 is rejected similarly to claim 4.
Claim 12 (Original):
Regarding Claim 12 the combined art of Ahmed and Teti make obvious the method of claim 11.
However Ahmed is silent concerning:
wherein the ledger uses a blockchain structure to track transaction records for predicted maintenance actions for the industrial machine, wherein each of the transaction records is stored as a block in the blockchain structure.
Discenzo teaches using signature analysis using stored tables of prior data associated with particular machine states, e.g. a database (Table 340 FIG. 7, col 14 lines 50-67, and also storage of the maintenance information step 551, FIG. 14). Block chain structure is simply a relational database as demonstrated by the stored data in Table 340.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention, to use the maintenance database of Discenzo in the industrial monitoring system of Ahmed with the expected benefit that the history used to determine a maintenance event will have more robust information on which to make a decision.
This method for improving industrial monitoring system of Ahmed was within the ordinary ability of one of ordinary skill in the art before the effective filing date of the claimed invention based on the teachings of Discenzo.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Ahmed and Discenzo to obtain the invention:
wherein the ledger uses a blockchain structure to track transaction records for predicted maintenance actions for the industrial machine, wherein each of the transaction records is stored as a block in the blockchain structure.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REGIS J BETSCH whose telephone number is (571)270-7101. The examiner can normally be reached Monday through Friday 8:00 am - 4:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached on (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/REGIS J BETSCH/Primary Examiner, Art Unit 2857
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