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 .
This action is responsive to communications filed on 12/27/2023. As per claims filed on 03/28/2024
Claims 1-22 are currently pending.
Claims 1 and 12 are independent claims.
Priority
Acknowledgment is made of applicant’s claim for priority based United States provisional application number 63/478,384, filed on January 4, 2023.
Claim Objections
Claims 4, 5, 15, 16 objected to because of the following informalities:
In claim 4, line 7, “causes” should be “cause”.
In claim 5, line 8, there be should “line” after “manufacturing assembly”.
In claim 15, line 6, “causes” should be “cause”.
In claim 5, line 7, there be should “line” after “manufacturing assembly”.
Appropriate correction is required.
Prior Arts
Listed herein below are the prior art references relied upon in this office action:
Huang (US 2021/0253351 A1, which has a priority date of 02/19/2020), referred to as Huang herein.
Mignano et al. (US 12,071,310 B2, which has a priority date of 02/10/2021), referred to as Mignano herein.
Gordon et al. (US 6,332,107 B1, which has a priority date of 04/14/1999), referred to as Gordon herein.
Willison et al. (US 11,514,344 B2, which has a priority date of 03/29/2021), referred to as Willison herein.
‘Move-and-Charge System for Automatic Guided Vehicles’ by Chaoqiang Jiang, K. T. Chau, Fellow, IEEE, Chunhua Liu, Christopher H. T. Lee, Wei Han, and Wei Liu (which has a release date of 11/11/2018), referred to as Jiang herein.
Entzminger et al. (US 2021/0080941 A1, which has a priority date of 09/17/2019), referred to as Entzminger herein.
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.
Claim(s) 1 -2, 6 - 7, 10, 12 - 13, 17 - 18, 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang further in view of Mignano.
Regarding Claim 1, Huang teaches a method for optimizing operation at a manufacturing assembly line having a track on which a plurality of shuttles travel, (“The independent cart system includes multiple movers travelling along a track” (¶ 0026), meaning the disclosed movers 20 correspond to the claimed shuttles and the disclosed track corresponds to the claimed track on which a plurality of shuttles travels. This satisfies the conditional requirement of the claim);
the track comprising one or more stations at which a production task occurs and one or more track areas defined relative to the one or more stations, (“At least one station is defined along the track, where a device external to the track interacts with the movers on the track.” (¶ 0026) and “robotic assembly stations may perform various assembly and/or machining tasks on workpieces carried along by the movers 20.” (¶ 0034), meaning the disclosed stations correspond to the claimed stations at which a production task occurs. Since assembly and machining tasks performed by the robotic stations on workpieces are production tasks, and the disclosed track segments on which the stations are positioned correspond to the claimed track areas defined relative to the stations. Additionally, Fig. 9 illustrates stations 400A and 400B positioned along discrete segments of the tracks, depicting track areas defined relative to those stations. This satisfies the conditional requirement of the claim);
Huang teaches monitoring shuttle operation data associated with the plurality of shuttles on the track, and track sensor data associated with an operation state of the track; (“A position feedback system provides knowledge of the location of each mover 20 along the length of the track segment 15 to the segment controller 120.” (¶ 0037), meaning this position data corresponds to the shuttle operation data. “the position feedback system includes one or more position magnets 205 mounted to the mover 20 and an array of sensors 210 spaced along the side wall 40 of the track segment 15.” (¶ 0037) and “he magnetic field sensor 210 outputs a feedback signal provided to the segment controller 120 for the corresponding track segment 15 on which the sensor 210 is mounted.” (¶ 0037), meaning the array of sensors 210 mounted along the track segment corresponds to the claimed track sensor data associated with an operation state of the track. This satisfies the conditional requirement of the claim);
Huang does not teach detecting on operation variation at the manufacturing assembly line based on the shuttle operation data.
---However, Mignano teaches detecting an operation variation at the manufacturing assembly line based on the shuttle operation data; (“the OBC 480 collects data from the one or more cart sensors 470 to determine whether measurements taken of the operational cart 400 as the operational cart 400 moves along the track 110 are within a predetermined range of values that corresponds to normal operation of the conveyor system 100.” (Col 11, line 42-47) and “If the current operating parameters 940 are not in range of the normal operating parameters 840, in step 960, an anomaly in the conveyor system 100 is identified.” (Col 12, line 5-7), meaning that when cart sensor measurements fall outside the predetermined normal operation range, an anomaly, which is operation variation, is identified based on the shuttle operation data);
Mignano teaches in response to detecting the operation variation, analyzing the shuttle operation data and the track sensor data to determine one or more degradation causes resulting in the operation variation, each degradation cause relating to one or more of at least one shuttle, the one or more stations and the one or more track areas; (“if two track sensors 150 both indicate that a mechanical fault has occurs, the processor 170 can determine that the mechanical fault occurred in an area of the track 110 between these two track sensors 150.” (Col 10, line 1-4), meaning the processor localizes an anomaly to a specific track area by correlating which track sensors reported the anomalous reading. Additionally, “If there is a difference between the measured vibrations of the first and second rails 112, 114, the processor 170 can determine that a mechanical fault exists which is particular to one set of wheels 420, 430 of the cart 400 (i.e., a first set of wheels 420, 430 that engages the first rail 112 or a second set of wheels 420, 430 that engages the second rail 114).” (Col 10, line 15-21), meaning the processor likewise localizes an anomaly to a specific cart or shuttle. In regards to station, combining Huang’s disclosed stations with Mignano’s sensor correlation technique, an irregularity in that external device’s interaction with a mover would manifest as an anomalous variation in mover behavior detectable and localizable at that station using the same technique. This satisfies the conditional requirement of the claim);
Mignano teaches and generating an operation recommendation for resolving at least one degradation cause of the one or more degradation causes. (“the processor 170 reports the anomaly to a user, for example with an alert on a display 610, as shown in FIG. 5. The user can then modify operation of the conveyor system 100 as needed.” (Col 10, line 25-28), meaning that reporting the identified anomaly together with a prompt to modify system operation establishes the claimed operation recommendation for resolving the identified degradation cause).
At the time of the invention, it would have been obvious to a person of ordinary skills in art to combine the independent cart manufacturing assembly line of Huang with the track sensor based fault localization and reporting system of Mignano. Specifically, Mignano’s track sensors, cart sensors, central controller, and processor collect position and vibration data and check it against normal ranges to find and report problems. These would be added to Huang’s track parts and movers, improving Huang’s current sensors and controllers with Mignano’s ability to diagnose and report issues.
The motivation for doing so would have been that both references address the same underlying problem of minimizing operational downtime in these systems. Huang discloses an independent cart manufacturing line but does not disclose diagnosing the specific cause of a detected irregularity. Whereas, Mignano discloses a sensor architecture and processor that automatically localize a detected anomaly to a specific cart or track area and report it to a user for corrective action for a structurally analogous cart and track system. Mignano also discloses that its monitoring approach is broadly applicable to conveyor systems having any number of carts moving along a track. A person of ordinary skill seeking to reduce undiagnosed downtime in Huang’s manufacturing line would have looked to Mignano’s known fault localization technique and applied it to Huang’s cart and track architecture, yielding predictable result of an automated diagnostic and recommendation capability.
Regarding Claim 2, Huang teaches the method of claim 1, wherein detecting the operation variation at the manufacturing assembly line based on the shuttle operation data comprises detecting a variation in a cycle time for one or more shuttles with respect to a station of the one or more stations. (“The segment controller 120 may, therefore, maintain a running average for settling time of a mover.” (¶ 0067) and “the segment controller 120 may determine a standard deviation for each new settling time from the running average stored in memory 235.” (¶ 0069), meaning the running average and standard deviation of a mover’s settling time relative to a station discloses monitoring and detecting variation in a cycle time type metric for a shuttle with respect to station. This satisfies the conditional requirement of the claim).
Regarding Claim 6, Mignano teaches the method of claim 1, wherein analyzing the shuttle operation data and the track sensor data to determine the one or more degradation causes resulting in the operation variation comprises: detecting a number of an operation error associated with the one or more shuttles exceeds a maximum operation error threshold. (“the OBC 480 compares the analyzed acceleration forces from the accelerometer cart sensor 470 of the operational cart 400 with the predetermined range of acceleration forces stored in the OBC 480 from analysis of the test cart 400 to determine whether the acceleration forces are within the predetermined range of acceleration forces of the cart.” (Col 11, line 48-54), meaning the acceleration force value measured for a shuttle, which is the claimed number associated with an operation error, is compared against a predetermined range. That is a maximum operation error threshold, to determine whether the shuttle’s measured value exceeds it. This satisfies the conditional requirement of the claim).
Regarding Claim 7, Mignano teaches the method of claim 1, wherein analyzing the shuttle operation data and the track sensor data to determine the one or more degradation causes resulting in the operation variation comprises: detecting a number of an operation error associated with the one or more track areas exceeds a maximum operation error threshold. (“the processor 170 compares the analyzed vibration data from the track sensors 150 with the predetermined range of frequencies of vibration stored in the database 600 to determine whether the frequency of the vibrations is within the predetermined range of frequencies of vibrations.” (Col 9, line 42-46), meaning the vibration frequency value measured by the track sensors associated with a given track area, which is the claimed number associated with an operation error, is compared against a predetermined range. That is a maximum operation error threshold, to determine whether the shuttle’s measured value exceeds it. This satisfies the conditional requirement of the claim).
Regarding Claim 10, Mignano teaches the method of claim 1, wherein generating the operation recommendation for resolving the at least one degradation cause comprises: defining the operation recommendation to include adjusting two or more of the at least one shuttle, the one or more stations and the one or more track areas. (“the processor 170 provides control signals 620 to automatically correct operation and maintain safe operation of the conveyor system 100, as also shown in FIG. 5.” (Col 10, line 28-31), discloses that a single corrective action taken in response to a diagnosed anomaly can automatically adjust operation broadly across the system rather than being confined to one category. This general corrective control capability, combined with Jiang’s shuttle position adjustment and Willison’s station or device level fault handling, together span all three categories recited in the claim. This satisfies the conditional requirement of the claim).
Regarding Claim 12, a system claim that incorporates the method of Claim 1, is being rejected under the same rationale as claim 1.
Regarding Claim 13, is being rejected under the same rationale as claim 2.
Regarding Claim 17, is being rejected under the same rationale as claim 6.
Regarding Claim 18, is being rejected under the same rationale as claim 7.
Regarding Claim 21, is being rejected under the same rationale as claim 10.
Claim(s) 3, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang and Mignano, and further in view of Gordon.
Regarding Claim 3, Huang and Mignano do not teach one or more degradation causes comprises at least one bottleneck at the track resulting from a delayed arrival of the one or more shuttles at the one or more stations.
However, Gordon teaches the method of claim 2, wherein the one or more degradation causes comprises at least one bottleneck at the track resulting from a delayed arrival of the one or more shuttles at the one or more stations; (-“In either case, when a train stops outside of the station, the present algorithm recognizes that a delay has occurred and calculates reduced speed commands for any approaching trains to prevent them from stopping.” (Col 12, line 40-44) and “If any train approaching the backup is forced to stop while the backup is clearing, then a reduced speed command will be calculated for that train in the same way.” (Col 14, line 7-9), meaning a train stopping short of a station is recognized as a delay condition. This is a bottleneck resulting from delayed arrival at a station. This satisfies the conditional requirement of the claim);
Gordon teaches and generating the operation recommendation for resolving the at least one degradation cause comprises: identifying one or more idle track areas from the one or more track areas, the one or more idle track areas being associated with the one or more stations at which the one or more shuttles consistently arrive early; (“In addition, the speed commands of all moving trains are decreased by subtracting the term
V
t
r
a
i
n
2
*
T
d
e
l
a
y
D
s
t
a
t
i
o
n
, where V.sub.train is the train speed, T.sub.delay is the delay time since the expected station dispatch time (or the last time the speed was adjusted), and D.sub.station is the distance from the train to the station.” (Col 14, line 17-26), meaning the algorithm identifies trains, and by extension the track areas they occupy, that are running ahead of the currently achievable pace relative to a station and reduces their speed commands accordingly, corresponding to identification of an idle track area associated with early shuttle arrival. This satisfies the conditional requirement of the claim);
Gordon teaches identifying one or more bottleneck track areas from the one or more track areas, the one or more bottleneck track areas being associated with the one or more stations at which the one or more shuttles are consistently delayed; (“if a train that had been restarted has stopped again between stations, then the backup has recurred and the entire process begins again starting from step 302.” (Col 14, line 37-40), meaning the algorithm tracks and re-identifies the location of a recurring delay or backup condition along the route, corresponding to identification of a bottleneck track area associated with consistently delayed shuttles);
Gordon teaches and decreasing a velocity of the one or more shuttles when travelling within the one or more idle track areas and increasing the velocity of the one or more shuttles when travelling within the one or more bottleneck track areas. (“In addition, the speed commands of all moving trains are decreased by subtracting the term
V
t
r
a
i
n
2
*
T
d
e
l
a
y
D
s
t
a
t
i
o
n
, where V.sub.train is the train speed, T.sub.delay is the delay time since the expected station dispatch time (or the last time the speed was adjusted), and D.sub.station is the distance from the train to the station.” (Col 14, line 17-26) and “In step 314 when the lead train departs the station, the next train behind it becomes the new "lead" train.” (Col 14, line 29-30), meaning trains running ahead of the achievable pace (idle areas) receive decreased speed commands, while trains recovering from a delay (bottleneck areas) accelerate to make up lost time. This satisfies the conditional requirement of the claim).
At the time of the invention, it would have been obvious to a person of ordinary skills in art to combine Gordon’s vehicle speed adjustment algorithm with the Huang and Mignano combination from above. Gordon’s algorithm for calculating reduced or increased speed commands based on a vehicle’s projected arrival time relative to a station would be incorporated into Huang’s segment controllers, operating on the same settling time and position data those controllers collect for each mover to determine when a mover is running ahead of or behind the achievable pace at a given station.
The motivation for doing so would have been that Huang’s segment controllers already collect and track per mover, per station timing data, for example, settling time. But Huang does not disclose using that data to proactively adjust shuttle velocity based on a pattern of early or delayed arrivals. Gordon discloses an algorithm that uses per vehicle timing data relative to a station to identify vehicles running ahead of or behind schedule and to decrease or increase speed commands accordingly to recover throughput for an analogous track guided vehicle system stopping at fixed stations. A person of ordinary skill seeking to make active use of Huang’s already collected timing data would have looked to Gordon’s known speed recovery algorithm and applied it to the already combined system of Huang and Mignano for improvement, yielding the predictable result of automated early or late zone velocity correction.
Regarding Claim 14, is being rejected under the same rationale as claim 3.
Claim(s) 4 - 5, 8, 15 - 16, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang and Mignano, and further in view of Willison.
Regarding Claim 4, Huang and Mignano do not teach wherein generating the operation recommendation for resolving the at least one degradation cause comprises: evaluating an urgency level of the one or more degradation causes for the manufacturing assembly line.
However, Willison teaches wherein generating the operation recommendation for resolving the at least one degradation cause comprises: evaluating an urgency level of the one or more degradation causes for the manufacturing assembly line; (----“Furthermore, when problems inevitably arise in the manufacturing assembly line, it can be difficult to evaluate which faults should be remedied first. For example, it may be unclear which faults are critical to the operation of the manufacturing assembly line.” (Col 10, line 56-60), this discloses evaluating the manufacturing assembly line’s faults to determine which should be addressed first. This is evaluating an urgency level of the degradation causes);
Willison teaches and generating the operation recommendation for resolving the at least one degradation causes based on the urgency level. (“The processor 112 may assign a relatively high priority score to these faults due to the decrease in production level.” (Col 25, line 17-19) and “The processor may be configured to generate the at least one operator alert based on a device fault corresponding to the at least one high priority fault.” (Col 9, line 24-26), meaning the operator alert, which is claimed operation recommendation, is generated based on the fault’s assigned priority score, that is based on the urgency level. This satisfies the conditional requirement of the claim).
At the time of the invention, it would have been obvious to a person of ordinary skills in art to combine Willison’s fault prioritization and alert generation technique with the Huang and Mignano combination. Specifically, Willison’s processor assigns priority score to each detected fault based on its effect on production level and generates an operator alert accordingly. This would be incorporated into Mignano’s anomaly reporting mechanism, so that anomalies localized by Mignano’s processor are ranked and surfaced to the user in order of urgency rather than reported uniformly.
The motivation for doing so would have been that Mignano already reports a detected anomaly to a user for corrective action, but treats every anomaly the same way regardless of its impact on the line. Whereas, Willison addresses the same underlying problem, a manufacturing assembly line with multiple simultaneous or recurring faults, by scoring each fault according to its effect on production and generating an alert prioritized accordingly. Therefore, a person of ordinary skill combining the already established Huang and Mignano anomaly reporting system with a manufacturing line generating multiple diagnosable faults across its stations would recognize the practical need to prioritize among them and would apply Willison’s known priority scoring technique to Mignano’s known reporting mechanism, yielding the predictable result of an urgency ranked recommendation.
Regarding Claim 5, Willison teaches the method of claim 4, wherein evaluating the urgency level of the one or more degradation causes comprises: predict an effect of the one or more degradation causes to the operation at the manufacturing assembly line at a future time; (“a predictive model may be used to determine the effect of a malfunctioning heating device of the mold forming cell 202 on the manufacturing assembly line 120A. For example, erroneously decreasing the curing temperature may decrease the rate of mold production, which may decrease the production level of the manufacturing assembly line 120A.” (Col 25, line 11-17), discloses applying a predictive model to determine the effect a fault will have on the manufacturing assembly line’s production level);
Willison teaches and assigning a higher urgency level to the one or more degradation causes associated with a greater effect to the operation at the manufacturing assembly line at the future time and a lower urgency level to the one or more degradation causes associated with a lower effect to the operation at the manufacturing assembly at the future time. (“The processor 112 may assign a relatively high priority score to these faults due to the decrease in production level.” (Col 25, line 17-19), meaning the priority score assigned to a fault rises with the size of its predicted effect on production level, such that faults predicted to cause a smaller decrease in production level are correspondingly assigned a lower priority score. This satisfies the conditional requirement of the claim).
Regarding Claim 8, Willison teaches the method of claim 1, wherein analyzing the shuttle operation data and the track sensor data to determine the one or more degradation causes resulting in the operation variation comprises: detecting a number of an operation error associated with the one or more stations exceeds a maximum operation error threshold. (“Each fault may include at least one device fault, each device fault corresponding to a device of a cell corresponding to that fault.” (Col 8, line 10-12) and “the processor may be further configured to determine the priority level for that fault based on whether the defect level meets a predetermined production quota.” (Col 9, line 16-19), meaning each fault is tied to a specific device within a specific station, and the defect level associated with that station specific fault (number associated with an operation error) is compared against a predetermined production quota. That is a maximum operation error threshold. This satisfies the conditional requirement of the claim).
At the time of the invention, it would have been obvious to a person of ordinary skills in art to combine Willison’s threshold based fault evaluation into the manufacturing assembly line monitoring system of Huang and Mignano so that station related degradation causes could be identified based on whether an associated operation error value exceeds a predetermined threshold.
The motivation for doing so would have been to provide a reliable and objective mechanism for determining when a station related condition represents a significant operational issue requiring corrective action. Applying Willison’s known threshold comparison technique to the detected degradation causes of Huang and Mignano would have yielded the predictable result of improved fault detection and prioritization. A threshold based evaluation provides a quantitative basis for assessing the severity of station related errors rather than relying solely on the occurrence of individual fault events. As a results, the system can more effectively identify degradation conditions that are likely to impact production performance and initiate corrective measures before those conditions lead to increased defects or operational interruptions.
Regarding Claim 15, is being rejected under the same rationale as claim 4.
Regarding Claim 16, is being rejected under the same rationale as claim 5.
Regarding Claim 19, is being rejected under the same rationale as claim 8.
Claim(s) 9, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang and Mignano, and further in view of Jiang.
Regarding Claim 9, Huang and Mignano do not teach detecting the operation variation at the manufacturing assembly line based on the shuttle operation data comprises detecting a current draw when at least one shuttle arrives at a station is above an expected current threshold.
However, Jiang teaches detecting the operation variation at the manufacturing assembly line based on the shuttle operation data comprises detecting a current draw when at least one shuttle arrives at a station is above an expected current threshold; (-------“The key is to make use of the dc–dc converter to maintain the equivalent load resistance and then detect the charging current variation to conduct the desired traction under constant ac input current for the rail transmitter. In order to avoid frequent corrections of the forward direction and dc–dc converter regulation, both the straight rail and turning rail conditions are analyzed to determine the threshold value of the charging current.” (Page 1, Abstract), discloses detecting a variation in charging current as the AGV moves along the rail and comparing that current against a determined threshold value);
Jiang teaches in response to detecting the current draw is above the expected current threshold, determining the one or more degradation causes comprises a misalignment between the at least one shuttle and the station; (“By newly adopting the equivalent resistance adjustment and current threshold control method, it can simultaneously provide wireless power and misalignment correction, hence achieving the desired traction.” (Page 5), meaning the current threshold triggered condition Jiang detects is expressly identifies as a misalignment between the AGV and the rail);
Jiang teaches and generating the operation recommendation for resolving the at least one degradation cause comprises automatically adapting a position offset between the at least one shuttle and the station. (“Thus, when the reference current of 2.25 A is set as the threshold value to conduct the direction correction, the correction allowances above the straight and turning rail can achieve 20 and 30 mm, respectively.” (Page 4), meaning the AGV’s forward direction is automatically corrected in response to the current exceeding the threshold, that is a position offset is automatically).
At the time of the invention, it would have been obvious to a person of ordinary skills in art to combine Jiang’s current threshold based misalignment detection and correction technique with the Huang and Mignano combination. Jiang’s current monitoring and direction correction logic detects when charging current exceeds a determined threshold and automatically corrects the vehicle’s position in response. This would be incorporated into Huang’s mover to station docking interface at each station position, supplementing the position feedback sensors Huang already discloses with an independent, current based check at the moment of docking.
The motivation for doing so would have been that Huang’s movers already come to rest at a precise station position to interact with external station equipment, which is exactly the kind of powered and position sensitive docking interface that Jiang’s technique is designed to keep aligned. A person of ordinary skill would recognize that Jiang’s current based misalignment detection, proven for AGVs traveling on a rail and interacting with fixed trackside infrastructure, is a known technique directly transferable to Huang’s mover to station interface to ensure accurate positioning without adding new sensors, yielding the predictable result of automatic misalignment detection and correction at the station.
Regarding Claim 20, is being rejected under the same rationale as claim 9.
Claim(s) 11, 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang and Mignano, and further in view of Entzminger.
Regarding Claim 11, Huang and Mignano do not teach generating one or more predictive models based on at least one of the shuttle operation data and track sensor data.
However, Entzminger teaches generating one or more predictive models based on at least one of the shuttle operation data and track sensor data; (--------------“a computing system receives a plurality of industrial automation process variables associated with at least one industrial asset employed in an industrial automation process. The industrial automation process variables are fed into a machine learning model associated with the at least one industrial asset to generate a future maintenance event prediction for the at least one industrial asset.” (Abstract), discloses generating a machine learning model based on process variables associated with an industrial asset. This satisfies the conditional requirement of the claim);
Entzminger teaches detecting the operation variation at the manufacturing assembly line based on the shuttle operation data comprises applying the one or more predictive models to predict the operation variation; (“The industrial automation process variables are fed into a machine learning model associated with the at least one industrial asset to generate a future maintenance event prediction for the at least one industrial asset.” (Abstract), meaning the machine learning model is applied to the process variables to generates a future prediction. This is to predict the operation variation before it fully materializes);
Entzminger teaches and generating the operation recommendation for resolving the at least one degradation cause comprises providing the operation recommendation for preventing the operation variation. (“The future maintenance event prediction for the at least one industrial asset is provided to an industrial controller that controls the at least one industrial asset.” (Abstract), meaning the predictive output is provided to a controller that controls the asset so that action can be taken before the predicted event occurs. This satisfies the conditional requirement of the claim).
At the time of the invention, it would have been obvious to a person of ordinary skills in art to combine Entzminger’s predictive machine learning maintenance technique with the Huang and Mignano combination. Specifically, Entzminger’s machine learning model, which is trained on industrial automation process variables to generate a future maintenance event prediction and provide it to a controller, would be incorporated into the combined system using the shuttle operation data and rack sensor data collected by Huang’s and Mignano’s sensors as the model’s input variables.
The motivation for doing so would have been that Mignano’s fault detection technique identifies an anomaly only after the measured data actually falls outside the normal range. Whereas Entzminger addresses the same problem by predicting a maintenance event before it occurs from the same general category of process data Huang’s system already generates. A person of ordinary skill seeking to move from reactive detective to proactive prevention would apply Entzminger’s known predictive modeling technique to the combined system’s shuttle and track sensor data, yielding the predictable result of a predictive and preventive recommendation.
Regarding Claim 22, is being rejected under the same rationale as claim 11.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHANDNI PATEL whose telephone number is (571)272-9661. The examiner can normally be reached Monday-Friday 7am-4pm, every other Friday off.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman can be reached at (571)272-3644. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHANDNI PATEL/Examiner, Art Unit 2118
/SCOTT T BADERMAN/Supervisory Patent Examiner, Art Unit 2118
Prosecution Insights