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 .
Priority
Applicant’s claim for the benefit of the PCT application PCT/IL2023/050051 filed on 1/18/2023 which further benefits from the provisional 63/300,283 filed on 1/18/2022 is acknowledged.
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.
Claim 11 is 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. Claim 11 directs to the first and second processing circuitry being “integrated”. Processors must be coupled to the board in some capacity to be able to operate. It is unclear whether the claim directs to processing circuitry being on the same circuit board and physically connected, or on separate boards that are capable of working together. For the purposes of examining claim 11, it has been interpreted as two different processors that are capable of working together.
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) 1-5, 8-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over LOPES US2017/0091688 A1 in view of SLAGLE US2019/0130330 A1.
Regarding independent claims 1,12 and 13. LOPES discloses a system of assessing a degree of risk of schedule disruption in executing a automated maintenance process plan in an industrial facility, the automated maintenance process plan comprising a plurality of sequences of maintenance tasks, the system comprising (LOPES discloses ¶ 52 schedule with priorities, and ¶ 46-47 “takes 4 inputs: the demands for intervention: composed by any kind of maintenance or other services intervention demands such as repairs, inspections, preventive maintenance, modifications, installations, commissioning etc.”):
a) a first processing circuitry configured to, repeatedly (LOPES discloses ¶76 “In one embodiment, controller 14 can be implemented as shown in FIG. 8 by one or more processors 500”):
i) receive, from one or more monitoring units (MUs), data indicative of progress of a maintenance task of a first maintenance task sequence of the plurality of sequences of maintenance tasks, (LOPES discloses ¶129 “The recurrent cycle of planning and scheduling services depends on the feedback from the field to update the accomplishment status of activities.”) and
b) a second processing circuitry configured to, repeatedly: i) obtain and store data indicative of an updated TFV of the first maintenance task sequence, thereby giving rise to an updated TFV history of the first maintenance task sequence (LOPES discloses ¶76 “In one embodiment, controller 14 can be implemented as shown in FIG. 8 by one or more processors 500”, ¶131 “The execution management system 400 provide inputs of new maintenance and services demands to be included in the projects backlog and also provide updated accomplishment status for scheduled projects, which comprise projects already scheduled as per scheduling solutions previously generated.”),
iii) responsive to, at least, the calculated degree of schedule disruption risk meeting a threshold, perform at least one action from a group consisting of:
a. raising an alert, and b. revising the maintenance process plan (LOPES discloses ¶132 “The automated scheduling optimization 14 runs every time with updated information furnished by execution management systems. Activities that are accomplished and finished are removed from the scheduled projects list.”).
LOPES does not disclose expressly ii) determine, in accordance with the received task progress data, data indicative of an updated scheduled start time of a subsequent maintenance task of the first maintenance task sequence, thereby giving rise to an updated total float value (TFV) of the first maintenance task sequence
ii) calculate, for the first maintenance task sequence, a degree of schedule disruption risk, in accordance with, at least, the updated TFV history of the first maintenance task sequence, and
SLAGLE discloses ii) determine, in accordance with the received task progress data, data indicative of an updated scheduled start time of a subsequent maintenance task of the first maintenance task sequence, thereby giving rise to an updated total float value (TFV) of the first maintenance task sequence (The abstract, “redetermining task criticality.”, ¶36-40 total float being the planned versus the remaining float. To determine if there is enough time to prevent delay, ¶46 “over time update the tasks”, ¶ 53 “taking into account environmental factors.”, ¶102-105 determine the criticality, determining the amount of time needed to complete, and compute delay amount, if negative, project is late.); and
ii) calculate, for the first maintenance task sequence, a degree of schedule disruption risk, in accordance with, at least, the updated TFV history of the first maintenance task sequence, and (See above arguments for the updated TFV history, SLAGLE discloses ¶147, “Using a ratio as a schedule risk and prioritization signal may not be adequate in an industrial multi-project environment. Instead, the system may create a prioritization signal with multiple levels of risk and time interval until a threshold between levels is crossed. Rather than displaying that a task has 80% of the safety required for the potential variation, the system may state that the task is “green” and has 4 days until it becomes a “yellow” task. With a schedule risk being converted to a standard unit, risks across projects can be compared with a normalized score.”)
LOPES and SLAGLE are analogous art because they are from the same field of endeavor of maintenance task scheduling and criticality determination.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify the maintenance task scheduler of LOPES by adding the float and schedule risk calculations of SLAGLE to determine an updated float value and a schedule risk to better optimize the maintenance plan.
The suggestion/motivation for doing so would have been to use float to allow calculations that drive down uncertainty (SLAGLE ¶52).
Therefore, It would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine LOPES and SLAGLE for the benefit of determine, in accordance with the received task progress data, data indicative of an updated scheduled start time of a subsequent maintenance task of the first maintenance task sequence, thereby giving rise to an updated total float value (TFV) of the first maintenance task sequence and calculate, for the first maintenance task sequence, a degree of schedule disruption risk, in accordance with, at least, the updated TFV history of the first maintenance task sequence to obtain the invention as specified in the claims 1, 12 and 13.
Regarding Claim 2, the limitations of claim 1 have been discussed above.
LOPES discloses the system of claim 1.
LOPES does not disclose expressly, wherein the second processing circuitry is configured to calculate
SLAGLE discloses, wherein the second processing circuitry is configured to calculate the degree of schedule disruption risk in accordance with a metric of current TFV decrease. (¶39 “Float is the difference between the amount of time available to complete a task and the amount of time needed to complete the task. ¶40 This algorithm is used to determine if there is enough time between these tasks to prevent delaying their successors and/or the project as a whole. The level of risk for variation to affect a task's successors or the project end date can be quantified and compared to other tasks. This measurement can be used to prioritize tasks based upon variability risk and the current state of the project. Work is then authorized based upon the priority list”, ¶46 “At regular intervals or as desired, performance data may be converted into a format where they can be fed back into the criticality computations in order to update the prioritization of project tasks and generate a new task planning bill.”)
LOPES and SLAGLE are analogous art because they are from the same field of endeavor of maintenance task scheduling and criticality determination.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify the maintenance task scheduler of LOPES by adding the float and schedule risk calculations of SLAGLE to determine a metric of the current decrease to better optimize the maintenance plan.
The suggestion/motivation for doing so would have been to use float to allow calculations that drive down uncertainty (SLAGLE ¶52).
Therefore, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine LOPES and SLAGLE for the benefit wherein the second processing circuitry is configured to calculate the degree of schedule disruption risk in accordance with a metric of current TFV decrease to obtain the invention as specified in the claim 2.
Regarding claim 3, the limitations of claim 2 are discussed above.
LOPES discloses the system of claim 1.
LOPES does not disclose expressly wherein the second processing circuitry is configured to calculate the degree of
SLAGLE discloses wherein the second processing circuitry is configured to calculate the degree of schedule disruption risk in accordance with a difference between an initial TFV and a current TFV (¶104 “Float can now be calculated by subtracting the amount of time needed for tasks to be worked from the amount of time available for those tasks to be worked in. If the result is a negative value, the task is said to have negative float and indicates the task (and, if the task is part of the critical chain, likely the project) will be late to deliver, and ¶147 “With a schedule risk being converted to a standard unit, risks across projects can be compared with a normalized score.”).
LOPES and SLAGLE are analogous art because they are from the same field of endeavor of maintenance task scheduling and criticality determination.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify the maintenance task scheduler of LOPES by adding the float and schedule risk calculations of SLAGLE to determine a difference between the initial and the current value to better optimize the maintenance plan.
The suggestion/motivation for doing so would have been to use float to allow calculations that drive down uncertainty (SLAGLE ¶52).
Therefore, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine LOPES and SLAGLE for the benefit calculate the degree of schedule disruption risk in accordance with a difference between an initial TFV and a current TFV to obtain the invention as specified in the claim 3.
Regarding claim 4, the limitations of claim 3 have been discussed above.
LOPES discloses the system of claim 1.
LOPES does not disclose expressly wherein the initial TFV is a TFV indicated by the maintenance process plan, and the current TFV is a
SLAGLE discloses wherein the initial TFV is a TFV indicated by the maintenance process plan, and the current TFV is a most recently received TFV (¶40 “This algorithm is used to determine if there is enough time between these tasks to prevent delaying their successors and/or the project as a whole. The level of risk for variation to affect a task's successors or the project end date can be quantified and compared to other tasks. This measurement can be used to prioritize tasks based upon variability risk and the current state of the project.” Prevent successors is read as using the most recent data to determine the float calculation).
LOPES and SLAGLE are analogous art because they are from the same field of endeavor of maintenance task scheduling and criticality determination.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify the maintenance task scheduler of LOPES by adding the float and schedule risk calculations of SLAGLE to determine the most recent information to better optimize the maintenance plan.
The suggestion/motivation for doing so would have been to use float to allow calculations that drive down uncertainty (SLAGLE ¶52).
Therefore, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine LOPES and SLAGLE for the benefit wherein the initial TFV is a TFV indicated by the maintenance process plan, and the current TFV is a most recently received TFV to obtain the invention as specified in the claim 4.
Regarding claim 5, the limitations of claim 4 have been discussed above.
LOPES discloses the system of claim 1.
LOPES does not disclose expressly wherein the threshold is in accordance with a difference between a scheduled current TFV and the initial TFV, wherein the scheduled current TFV is a TFV indicated by the maintenance project plan for
SLAGLE discloses wherein the threshold is in accordance with a difference between a scheduled current TFV and the initial TFV, wherein the scheduled current TFV is a TFV indicated by the maintenance project plan for a time corresponding to the current TFV (See above arguments for difference of initial and current TFV, SLAGLE discloses ¶104 “If the result is a negative value, the task is said to have negative float and indicates the task (and, if the task is part of the critical chain, likely the project) will be late to deliver.” The negative value implies that the float value is compared to a limit/threshold that reveals the project is above or below that limit. This is further read as calculating a time value for the project and it its negative then the project is not on time.)
LOPES and SLAGLE are analogous art because they are from the same field of endeavor of maintenance task scheduling and criticality determination.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify the maintenance task scheduler of LOPES by adding the float and schedule risk calculations of SLAGLE to compare the float values to a threshold to better optimize the maintenance plan.
The suggestion/motivation for doing so would have been to use float to allow calculations that drive down uncertainty (SLAGLE ¶52).
Therefore, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine LOPES and SLAGLE for the benefit wherein the threshold is in accordance with a difference between a scheduled current TFV and the initial TFV, wherein the scheduled current TFV is a TFV indicated by the maintenance project plan for a time corresponding to the current TFV to obtain the invention as specified in the claim 5.
Regarding claim 8, wherein the second processing circuitry is configured to perform the at least one action additionally responsive to, at least, a criticality of equipment maintained in the first maintenance task sequence (LOPES discloses ¶ 84, “Assuming that the priority indicated in each project is related to the risk of a potential failure in the associated equipment, the sorting criterion establishes that the most critical projects have to be allocated first.”).
Regarding claim 9, wherein the second processing circuitry is configured to perform the at least one action additionally responsive to, at least, an operational importance of the maintenance tasks of the first maintenance task sequence. (LOPES discloses ¶ 85 “taking a selected project from the list of sorted projects defined by the projects sequencer 102, the tasks sequencer 104 defines the sequence of tasks of this project to be allocated. A project may have one or more tasks. The tasks sorting criterion considers that the tasks that are in the critical path of the project have to be allocated first, obeying also the interrelationship among them (tasks precedencies and dependencies). The tasks sequencer 104 delivers tasks to the global manager for allocation.”)
Regarding claim 10, wherein the second processing circuitry is configured to perform the at least one action additionally responsive to, at least, a duration of the first maintenance task sequence (LOPES discloses ¶84 “In order to eliminate the risk of failures faster, taking projects with the same priority, the ones with shorter duration shall be allocated first. That generates the list of projects (backlog) sorted by priority and duration.”).
Regarding claim 11, wherein the first processing circuitry and the second processing circuitry are integrated (Claim 11 was interpreted to direct to two processors that are capable of working together, LOPES discloses ¶76 “In one embodiment, controller 14 can be implemented as shown in FIG. 8 by one or more processors 500”).
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over LOPES US2017/0091688 A1 in view of SLAGLE US2019/0130330 A1 further in view of ALVES US20200293392 A1.
Regarding claim 6, the limitations of claim 1 have been discussed above.
LOPES and SLAGLE discloses the updated TFV of claim 1.
LOPES and SLAGLE does not disclose expressly wherein the second processing circuitry is configured to calculate the degree of schedule disruption risk in accordance with
ALVES discloses wherein the second processing circuitry is configured to calculate the degree of schedule disruption risk in accordance with a machine learning model classification of data that is in accordance with at least 2 TFVs of the updated TFV history of the first task sequence (¶0020, “A training sample to train the machine learning model to predict the float number of the device can include a set of values based on data retrieved within a selected interval of time. The selected interval of time can be between the first day of a first detected event after a first maintenance intervention and a second day of a second detected event before a second maintenance intervention. Each training sample can be labeled with a float number that is a time interval between the second detected event and the last detected event before the second maintenance intervention to allow the machine learning training process to identify patterns in the time leading up to the maintenance intervention…” This is read as taking in two different machine maintenance events which are characterized added to the memory creating an updated history with a float number that is a time interval).
LOPES and SLAGLE are analogous art because they are from the same field of endeavor of maintenance task scheduling and criticality determination.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify the maintenance task scheduler of LOPES by adding the float and schedule risk calculations of SLAGLE to use two data points that can be used to create a history to better optimize the maintenance plan.
The suggestion/motivation for doing so would have been to use float to allow calculations that drive down uncertainty (SLAGLE ¶52).
LOPES, SLAGLE and ALVES are analogous art because they are from same field of endeavor of predictive maintenance.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to take the system from LOPES and SLAGLE and add the machine learning capabilities of ALVES that uses two maintenance events to predict future maintenance events with the updated TFV.
The suggestion/motivation for doing so would have been in ALVES ¶0007, “Maintenance intervention predicting, as described herein, can provide insight to when devices will most likely fail in a particular time period, which can improve maintenance scheduling”.
Therefore, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine LOPES, SLAGLE and ALVES for the benefit of wherein the second processing circuitry is configured to calculate the degree of schedule disruption risk in accordance with a machine learning model classification of data that is in accordance with at least 2 TFVs of the updated TFV history of the first task sequence to obtain the invention as specified in the claim 6.
Regarding claim 7, the limitations of claim 6 have been discussed above.
LOPES and SLAGLE discloses the system of claim 1 and the updated TFV.
LOPES and SLAGLE does not disclose expressly wherein the second processing circuitry is further configured to calculate the degree of schedule disruption risk in accordance with
ALVES discloses wherein the second processing circuitry is further configured to calculate the degree of schedule disruption risk in accordance with a machine learning model classification of data that is in accordance with additional data associated with the first task sequence (See claim 6 arguments for the machine learning model ¶0038, “A maintenance intervention can be related to a device failure, where the device failure can be based on the device exceeding an estimated lifetime. As described herein, monitoring the first maintenance intervention related to the device can include tracking each maintenance intervention of the device. The system log information can include information relating to the events and device usage leading up to the maintenance intervention.” This is read as on top of tracking the machine interventions it can additionally track the device usage and related events).
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify the maintenance task scheduler of LOPES by adding the float and schedule risk calculations of SLAGLE to capture additional data like the usage and other events that can be used to create a history to better optimize the maintenance plan.
The suggestion/motivation for doing so would have been to use float to allow calculations that drive down uncertainty (SLAGLE ¶52).
LOPES, SLAGLE and ALVES are analogous art because they are from same field of endeavor of predictive maintenance.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to take the system of LOPES and SLAGLE and add the machine learning capabilities of ALVES that uses maintenance events and additional data to predict future maintenance events.
The suggestion/motivation for doing so would have been in ALVES ¶0007, “Maintenance intervention predicting, as described herein, can provide insight to when devices will most likely fail in a particular time period, which can improve maintenance scheduling”.
Therefore, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine LOPES, SLAGLE and ALVES for the benefit of wherein the second processing circuitry is further configured to calculate the degree of schedule disruption risk in accordance with a machine learning model classification of data that is in accordance with additional data associated with the first task sequence to obtain the invention as specified in the claim 6.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CONNOR D COUSINEAU whose telephone number is (571)447-9620. The examiner can normally be reached Monday-Friday 8am-5pm.
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/C.D.C./Examiner, Art Unit 2115
/KAMINI S SHAH/Supervisory Patent Examiner, Art Unit 2115
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