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 .
The rejection related to 35 USC § 101 regarding to claims 1-20 is withdrawn.
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.
Claims 1-3, 5-10, 12-17, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Karli et al. (Karli) US 11436777 in view of Nistala et al. (Nistala) US 2022/0214474 and Devine et al. (Devine) US 2021/0210187
In regard to claim 1, Karli disclose A computer-implemented method for predicting weather risk using artificial intelligence, the method comprising: (abstract, col. 1, line 42-col. 3, line 7, predicting hazard including weather using AI)
obtaining, using at least one hardware processor, historical data associated with operational suspension events corresponding to respective locations; (abstract, col. 1, line 42-col. 3, line 37, col. 5, line 20-col. 6, line 50, obtain, using the processor, hazard data related to hazard events corresponding to geographic locations)
training, using at least one hardware processor, a machine learning model to predict a suspension time period using a training dataset comprising the historical data associated with operational suspension events; (col. 1, line 42-col. 3, line 37, col. 5, line 20-col. 6, line 50, col. 9, line 57- col. 11, line 18, training a ML model to predict a hazard time period using a training dataset with obtained data related to the hazard events)
predicting, using the at least one hardware processor, a suspension time period corresponding to a location using the trained machine learning model. (col. 1, line 42-col. 3, line 37, col. 5, line 20-col. 6, line 50, col. 9, line 57- col. 11, line 18, predict a hazard time period corresponding to a location using the trained ML model)
But Karli fail to explicitly disclose “evaluating the trained machine learning model by determining a mean absolute percentage error (MAPE) of the trained machine learning model, and iteratively re-training the trained machine learning model when the MAPE satisfies a predetermined threshold.”
Nistala disclose comprising evaluating the trained machine learning model by determining a mean absolute percentage error (MAPE) of the trained machine learning model, and re-training the trained machine learning model when the MAPE satisfies a predetermined threshold. ([0034]-[0037] [0056] determine the performance of the model based on MAPE and re-train iteratively the model when the MQI (MAPE) is below a threshold)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Nistala‘s ML model into Karli’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Nistala‘s retraining the ML model based a condition would help to provide ML re-train trigger condition into Karli’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing ML retraining trigger condition would help to improve accuracy of prediction of the ML model.
But Karli and Nistala fail to explicitly disclose “and automatically generating an operational schedule that excludes lifting tasks for the location during the predicted suspension time period.”
Devine disclose and automatically generating an operational schedule that excludes lifting tasks for the location during the predicted suspension time period. ([0009]-[0014][0047] [0055][0056] [0079] [0080][0110] modifying a scheduled activity by removing a task from the schedule in response to predicting the adverse effect during the particular time period and location, and cause display of a calendar with the modified schedule, as for the particular task which is an implementation choice, but not an invention)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Devine‘s predicting an adverse effect based on a ML model into Nistala and Karli’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Devine‘s modifying a schedule based on predicting an adverse effect using a ML model would help to provide more predictions using ML into Nistala and Karli’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that modifying a schedule based on the ML prediction would help to improve planning with the help of ML model.
In regard to claim 2, Karli and Nistala, Devine disclose The computer implemented method of claim 1, Karli disclose wherein the training dataset comprises historical suspension time periods labeled by respective dates and respective locations. (col. 9, line 57- col. 11, line 18, the training dataset include historical hazard time periods annotated by the dates and corresponding locations)
In regard to claim 3, Karli and Nistala, Devine disclose The computer implemented method of claim 1,
Karli disclose wherein the training dataset comprises historical suspension time periods and corresponding weather conditions. (col. 9, line 57- col. 11, line 18, the training dataset include historical hazard time periods annotated by the dates and corresponding weather conditions)
In regard to claim 5, Karli and Nistala, Devine disclose The computer implemented method of claim 1,
Karli disclose comprising planning oil and gas operations based on the predicted suspension time period. (col. 11, line 19- col. 18, line 58, cause the operator to take appropriate action based on the predicted hazard time period related to the oil and gas)
In regard to claim 6, Karli and Nistala, Devine disclose The computer implemented method of claim 1,
Karli disclose comprising avoiding lifting tasks in oil and gas operation planning during the predicted suspension time period. (col. 8, line 34-col. 56, col. 11, line 19- col. 18, line 58, cause the operator to take appropriate action based on the predicted hazard time period related to the oil and gas, “notify technicians of location(s) of existing equipment or other infrastructure that may pose a danger to the technicians”)
In regard to claim 7, Karli and Nistala, Devine disclose The computer implemented method of claim 1,
Karli disclose wherein the historical data is pre-processed. (col. 9, line 57- col. 11, line 18, historical hazard data is corresponding to a particular geographic location and at times with certain conditions etc. therefore it is preprocessed)
In regard to claims 8-10, 12-14, claims 8-10, 12-14 are apparatus claims corresponding to the method claims 1-3, 5-7 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-3, 5-7.
In regard to claims 15-17, 19-20, claims 15-17, 19-20 are system claims corresponding to the method claims 1-3, 5-6 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-3, 5-6.
Response to Arguments
Applicant’s arguments with respect to claims filed on 4/23/2026 with respect to claims 1-20 have been considered but are moot because the arguments do not apply to the current rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
PATENT PUB. # PUB. DATE INVENTOR(S) TITLE
US 20220334870 A1 2022-10-20 Chen et al.
Automatic Container Migration System
Chen et al. disclose A method, apparatus, system, and computer program product for container migration. A set of processors operates to identify a set of containers for a set of applications for a migration using a set of application performance metrics. The set of processors operates to create a set of tasks following a migration strategy to move the set of containers for the set of applications identified for the migration from a set of current physical host computers to a set of target physical host computers using the set of application performance metrics. The set of processors operates to move the set of containers for the set of applications from the set of current physical host computers to the set of target physical host computers using the set of tasks following the migration strategy… see abstract.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUYANG XIA whose telephone number is (571)270-3045. The examiner can normally be reached Monday-Friday 8am-4pm.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
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