DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
The examiner encourages Applicant to submit an authorization to communicate with the examiner via the Internet by making the following statement (from MPEP 502.03):
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Please note that the above statement can only be submitted via Central Fax, Regular postal mail, or EFS Web (PTO/SB/439).
Allowable Subject Matter
Claims 3 and 5 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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.
Claims 1-2, 7, 9, and 11 rejected under 35 U.S.C. 103 as being unpatentable over Rippenhagen et al. (US 6243612 B1) in view of Liu et al. (US 20220179689 A1).
As per claim 1, Rippenhagen teaches the invention substantially as claimed including a task scheduling method applied to a semiconductor process device (Rippenhagen, Column 5, Lines 65-67, one embodiment of a processing system is manufacturing system 100 which in one embodiment is a semiconductor manufacturing system; and Column 11, Lines 62-65, manufacturing system 100 subsequently performs processing operation 214 and processes each lot Ly at the lot's current station STx in accordance with the scheduling order determined in scheduling operation 220), wherein the semiconductor process device processes a plurality of materials in a same batch of processes (Column 1, Lines 22-26, lots of material may be any raw material, processed material, components, or other tangible item. Typically, each lot is transported to stations, and each piece of equipment at each station carries out a specific operation or process on the lots), each of the materials corresponds to one to-be-started task (Column 1, Lines 24-26, each lot is transported to stations, and each piece of equipment at each station carries out a specific operation or process on the lots), each of to-be-started tasks comprises a plurality of procedures (Column 1, Lines 24-26, each lot is transported to stations, and each piece of equipment at each station carries out a specific operation or process on the lots; and Column 1, Lines 30-38, in a semiconductor manufacturing system, lots, which in such case are generally silicon based wafers, may flow along a process path of a cleaning equipment station, photo lithographer equipment station, to an etching equipment station, to an implanter equipment station, return to the cleaning equipment station, return to the photo lithographer equipment station, and so on until a complete integrated circuit is manufactured), and the task scheduling method comprises:
acquiring task data of all the to-be-started tasks (Column 5, Lines 17-21, composite ratio may be updated in accordance upon receipt of updated process scheduling factor data. Thus, if real-time process scheduling factor data is available, the composite ratio may provide a real-time, easily interpreted solution to lot process scheduling issues; and Column 6, Lines 42-51, scheduling system 102 selects process scheduling factors for inclusion in the composite ratio CMPRLy of each lot Ly. The selection process is based on identifying process scheduling factors which are desirable to affect scheduling of lots L1:Ln queued at stations ST1:STm. In one embodiment of factor selection operation 201 shown in FIGS. 2 and 3, three such process scheduling factors are identified: processing system efficiency as affected by the hunger ratio, customer factors as affected by the critical ratio, and market factors; and Column 9, Lines 30-33, In process times determination operation 406, for each lot Ly, scheduling system 102 determines the set, PTBNSLy, of all process times by the next constraint resource BNi of lot Ly for all lots in the set WIPBNLy);
wherein the task data comprises procedure numbers (Column 7, Lines 17-19, market factors MFL1:MFLn are numbers assigned to reflect the market factor priority of each lot relative to all other lots in manufacturing system 100), processing durations (Column 9, Lines 30-33, In process times determination operation 406, for each lot Ly, scheduling system 102 determines the set, PTBNSLy, of all process times by the next constraint resource BNi of lot Ly for all lots in the set WIPBNLy), processing positions (Column 6, Lines 2-6, manufacturing system 100 utilizes a "composite ratio" based scheduling system 102 to schedule processing of each lot Ly, y=1, 2, . . . , n, queued at a respective station STx, x=1, 2, . . . , m, in the manufacturing system 100), and processing-position stop durations corresponding to all procedures corresponding to the to-be-started tasks (Column 2, Lines 36-38, The critical ratio focuses on a due date for the completion of all processing on a lot, and, thus, drives lot process scheduling toward such completion due dates), a procedure number is a procedure code (Column 7, Lines 17-19, market factors MFL1:MFLn are numbers assigned to reflect the market factor priority of each lot relative to all other lots in manufacturing system 100), a processing duration is time required for completing each of the procedures (Column 9, Lines 30-33, In process times determination operation 406, for each lot Ly, scheduling system 102 determines the set, PTBNSLy, of all process times by the next constraint resource BNi of lot Ly for all lots in the set WIPBNLy), a processing position is a position of a material in the semiconductor process device when each of the procedures is performed (Column 6, Lines 2-6, manufacturing system 100 utilizes a "composite ratio" based scheduling system 102 to schedule processing of each lot Ly, y=1, 2, . . . , n, queued at a respective station STx, x=1, 2, . . . , m, in the manufacturing system 100), and a processing-position stop duration is a stop duration of a material at a corresponding processing position after each of the procedures is completed (Column 2, Lines 36-38, The critical ratio focuses on a due date for the completion of all processing on a lot, and, thus, drives lot process scheduling toward such completion due dates);
inputting the task data of each of the to-be-started tasks to a task scheduling model (Column 2, Lines 44-49, a scheduling system combines the hunger ratio with at least one other process scheduling influencing factor such as the critical ratio and/or market factor of each lot to generate respective composite ratios which are used to determine the processing order of lots queued among stations in a processing system);
wherein … constraint conditions of the task scheduling model are associated with the task data (Column 7, Lines 24-39, A composite ratio, CMPRLy, is determined by scheduling system 102 where CMPRLy is a function of process scheduling factors such as HRLy, CRLy, and MFLy. In the embodiment as illustrated by composite ratio determination operation 208, CRLy=(.X)HRLy+(.Y)CRLy+(.Z)MFLy, where process scheduling weighting factors X, Y, and Z are real numbers and .X+.Y+.Z=1. If any of the process scheduling factors HRLy, CRLy, and MFLy are not selected in factor selection operation 201, the weighting factors of such nonselected process scheduling factor is set to zero. Thus, CMPRLy may be a function of any one or more process scheduling factors. It will be recognized by persons of ordinary skill in the art that other functions including other combinations of process scheduling factors may be used to determine CMPRLy such as multiplying combinations of process scheduling factors);
Rippenhagen fails to specifically teach, inputting the task data of each of the to-be-started tasks to a task scheduling model to obtain a start moment of each of the to-be-started tasks which is output by the task scheduling model; wherein the task scheduling model takes minimizing total time required for performing all the to-be-started tasks as an objective function; and performing task scheduling on the to-be-started tasks according to the start moment of each of the to-be-started tasks.
However, Liu teaches, inputting the task data of each of the to-be-started tasks to a task scheduling model to obtain a start moment of each of the to-be-started tasks which is output by the task scheduling model (Abstract, dynamic characteristics of each of jobs to be scheduled and system dynamic characteristics into a scheduling model to obtain a job execution sequence or batch execution sequence of the jobs in each production stage; and [0013], inputting the static characteristics and dynamic characteristics of multiple batches of jobs and the system dynamic characteristics into a first sequence determining submodel to obtain a batch execution sequence of multiple batches in the production stage; for each batch, calculating, according to the dynamic characteristics of multiple jobs of the batch and the batch execution sequence, start execution moment and completion execution moment of the jobs of the batch in the production stage; and [0014], for each of jobs, calculating, according to the dynamic characteristics of the job and the job execution sequence, start execution moment and completion execution moment of the job in the production stage);
wherein the task scheduling model takes minimizing total time required for performing all the to-be-started tasks as an objective function ([0173], the goal of the dynamic production scheduling method based on deep reinforcement learning provided by the embodiments of the present invention is to minimize the overall completion time C.sub.max, and the scheduling of jobs needs to meet the following constraints), and constraint conditions of the task scheduling model are associated with the task data ([0010], the scheduling model is: a model obtained after training a first actor network based on static characteristics and dynamic characteristics of multiple sample jobs); and
performing task scheduling on the to-be-started tasks according to the start moment of each of the to-be-started tasks ([0042], determining submodel to obtain a job execution sequence of the jobs in the production stage; for each of jobs, calculating, according to the dynamic characteristics of the job and the job execution sequence, start execution moment and completion execution moment of the job in the production stage, and updating the dynamic characteristics of the job and the system dynamic characteristics).
Liu also teaches, acquiring task data of all the to-be-started tasks ([0016], inputting the static characteristics and dynamic characteristics of multiple sample jobs and the system dynamic characteristics into a first actor network to obtain the job execution sequence or batch execution sequence of the multiple sample jobs in each production stage).
wherein the task data comprises … processing durations ([0036], the static characteristics of the job comprise …time required for completion), processing positions ([0089], the scheduling scheme may also include device numbers corresponding to jobs or batches in each production stage. During processing of the jobs or batches, it is processed on the device corresponding to the device number).
Rippenhagen and Liu are analogous because they are each related to task scheduling. Rippenhagen teaches a method of scheduling tasks in manufacturing systems. (Abstract, processing system includes a scheduling system to scheduling processing of lots which are distributed among various processing system stations. Process scheduling is determined in accordance with lot specific composite ratios which are a function of process scheduling factors. Such process scheduling factors include, for example, a processing system efficiency, customer factors, and market factors. Processing system efficiency is influenced by a hunger ratio which is a ratio of the time when a particular lot is needed by a next constraint resource in the lot's process flow and the planned cycle time of a select lot to such next constraint resource; and Column 11, Lines 52-61, scheduling system 102 may recalculate scheduling order based on the availability of new data used to determine each hunger ratio HRLy such as lot processing stage data, WIP service time data, equipment down time, insertion of new lots into the process flow, and reevaluation of constraint resource processing times, and new data used to determine CRLy and MFLy as described above. Thus, if real time data is available, real time scheduling recalculations and any resulting adjustments may be made by scheduling system 102). Liu teaches a task scheduling method using deep learning that considers task and system constraints. (Abstract, dynamic production scheduling method, apparatus and electronic device based on deep reinforcement learning, which relate to the technical field of Industrial Internet of Things, and can reduce the overall processing time of jobs on the basis of not exceeding the processing capacity of production device. The embodiments of the present invention includes: acquiring static characteristics, dynamic characteristics of each of jobs and system dynamic characteristics, inputting the static characteristics, dynamic characteristics of each of jobs to be scheduled and system dynamic characteristics into a scheduling model to obtain a job execution sequence or batch execution sequence of the jobs in each production stage, wherein, the static characteristics of the job include an amount of tasks and time required for completion). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention that based on the combination, Rippenhagen’s scheduling method would be modified with the Liu’s scheduling model resulting in a system that inputs tasks and system constraints into a scheduling model to increase scheduling efficiency. Therefore, it would have been obvious to combine the teachings of Rippenhagen and Liu.
As per claim 2, Rippenhagen teaches, wherein before inputting the task data of each of the to-be-started tasks to the task scheduling model to obtain the start moment of each of the to-be-started tasks which is output by the task scheduling model, the method further comprises:
constructing the objective function comprising a minimization function related to time required for performing all the to-be-started tasks (Column 6, Lines 12-18, The hunger ratio of each lot Ly allows scheduling system 102 to schedule processing of lots to feed each constraint resource BNi, i=1, 2, . . . , p, based on a ratio of the time, TNBNLy, in which lot Ly is needed at the next constraint resource of lot Ly to the planned cycle time of a select lot, such as lot Ly; and Column 6, Lines 53-57, If in factor selection operation 201, efficiency is selected as a process scheduling factor, scheduling system 102 determines in hunger ratio determination operation 202 the hunger ratios HRL1:HRLn of lots L1:Ln, respectively, as described below with reference to hunger ratio determination and lot scheduling process 400);
constructing a constraint condition set comprising constraint conditions related to the task data of all the to-be-started tasks (Column 2, Lines 25-38, a scheduling system utilizes a composite ratio for each lot which is a function of respective process scheduling influencing factors that determine a process scheduling order for the lots on a station by station basis…Another illustrative factor is a `critical ratio` of each lot. The critical ratio focuses on a due date for the completion of all processing on a lot, and, thus, drives lot process scheduling toward such completion due dates); and
constructing the task scheduling model according to the objective function and the constraint condition set (Column 2, Lines 44-49, a scheduling system combines the hunger ratio with at least one other process scheduling influencing factor such as the critical ratio and/or market factor of each lot to generate respective composite ratios which are used to determine the processing order of lots queued among stations in a processing system; and Column 11, Lines 31-37, The composite ratio, therefore, provides a single measure to schedule processing of lots L1:Ln at the stations ST1:STm at which such lots are queued in order to, for example, balance minimization of the remaining available production capacity of each of constraint resources BN1:BNp, completion due dates, and market factor influences on lot process scheduling).
As per claim 7, Rippenhagen teaches, wherein before acquiring the task data of all the to-be-started tasks (Column 11, Lines 52-61, scheduling system 102 may recalculate scheduling order based on the availability of new data used to determine each hunger ratio HRLy such as lot processing stage data, WIP service time data, equipment down time, insertion of new lots into the process flow, and reevaluation of constraint resource processing times, and new data used to determine CRLy and MFLy as described above. Thus, if real time data is available, real time scheduling recalculations and any resulting adjustments may be made by scheduling system 102; Examiner Note: Rippenhagen’s system receives task data dynamically: Column 15, Lines 37-39, scheduling system 502 receives updated lot data when available and recalculates the process scheduling order using the updated lot data), the method further comprises:
analyzing the semiconductor process device to obtain device information (Column 8, Lines 23-27, PCTLy may also include times for estimated down times of intervening stations and other factors that persons of ordinary skill in the art will recognize as useful in determining planned cycle times);
wherein the device information at least comprises: hardware information of the semiconductor process device (Column 10, Lines 29-33, the scheduling system 102 performs time needed at next constraint resource 414 where TNBNLy equals .SIGMA.PTBNSLy plus .SIGMA.PTWIPQi. Thus, TNBNLy provides an assessment of when each of the constraint resources BNi will need additional work to prevent starvation), the materials processed by the semiconductor process device (Column 9, Lines 54-55, queues of lots, WIPi, awaiting processing by constraint resource BNi; and Column 10, Lines 1-8, each constraint resource BNi is shown having various queued WIPi, and each queued WIPi has cumulative process time that represents the time needed by the constraint resource BNi to process the respective queued WIPi. Queued WIPi process times by constraint resource BNi may be expressed in any unit of, for example, time or normalized based on, for example, the smallest PTBNSLy; Examiner Note: Rippenhagen’s lots represent material: Column 1, Lines 22-26, The lots of material may be any raw material, processed material, components, or other tangible item. Typically, each lot is transported to stations, and each piece of equipment at each station carries out a specific operation or process on the lots), and
a plurality of procedures included in the to-be-started tasks (Column 1, Lines 24-26, each lot is transported to stations, and each piece of equipment at each station carries out a specific operation or process on the lots; and Column 1, Lines 30-38, in a semiconductor manufacturing system, lots, which in such case are generally silicon based wafers, may flow along a process path of a cleaning equipment station, photo lithographer equipment station, to an etching equipment station, to an implanter equipment station, return to the cleaning equipment station, return to the photo lithographer equipment station, and so on until a complete integrated circuit is manufactured).
Rippenhagen fails to specifically teach, the device information is configured to construct the task scheduling model.
However, Liu teaches, and the device information is configured to construct the task scheduling model (Abstract, … system dynamic characteristics, inputting the … system dynamic characteristics into a scheduling model to obtain a job execution sequence or batch execution sequence of the jobs in each production stage, wherein, the … the system dynamic characteristics include a remaining amount of tasks that can be performed by the device in each production stage).
The same motivation used in the rejection of claim 1 is applicable to the instant claim.
As per claim 9, this is the “semiconductor process device claim” corresponding to claim 1 and is rejected for the same reasons. The same motivation used in the rejection of claim 1 is applicable to the instant claim.
As per claim 11, this claim is similar to claim 7 and is rejected for the same reasons.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Rippenhagen-Liu as applied to claim 1 and in further view of Garric et al. (US 5388945).
As per claim 4, Rippenhagen teaches, wherein the semiconductor process device is a cleaning machine, the materials are wafers (Column 1, Lines 30-33, in a semiconductor manufacturing system, lots, which in such case are generally silicon based wafers, may flow along a process path of a cleaning equipment station), and the processing positions comprise a … a scanning module (Column 1, Lines 30-34, in a semiconductor manufacturing system, lots, which in such case are generally silicon based wafers, may flow along a process path of a … photo lithographer equipment station)…[and], a processing module (Column 1, Lines 32-34, flow along a process path of … an etching equipment station).
The combination of Rippenhagen-Liu fails to specifically teach, the processing positions comprise a wafer holder …a temporary storage module, a verification module,…and a wafer transferring manipulator.
However, Garric teaches, the processing positions comprise a wafer holder (Column 38, Lines 35-38, the movable arm 217A is extended through port window 205A and the vacuum operated fork-shaped gripper 218A is engaged underneath the wafer 138 within wafer holder 130; and Column 43, Line 64-Column 44, Line 6, When a processing equipment, which performs a specified processing step, is going to be available (e.g. no more container 100 in the IN section of the corresponding interface apparatus 200), the host computer 601 knows what wafers are waiting for this step in the stocker 302 of dispatching apparatus 300. Thus, according to scheduling defined by the FCS logistic management (depending on the equipment availability, the current wafer priority, the equipment set-up parameters, . . . ), host computer 601 decides which wafer and thus which container has to be moved to this equipment)…a temporary storage module (Column 29, Lines 36-42, Once holder 130 fully enclosing wafer 138 is totally inserted in receptacle 103B and cover 124 closed for hermetic sealing, container 100 may be transported or stored. It may be transported either manually by an operator or automatically, for instance by the intelligent flexible conveyor 400 or stored in a dispatching apparatus 300), a verification module (Column 47, Lines 37-48, When the container 100 is pushed on the IN section rest zone of interface apparatus 201-1 of processing equipment 501-1, it is immediately clamped and connected to the neutral gas supply installation 700-1. Its identification is sent to the host computer 601 by reader 604A-1. If necessary, host computer 601 first checks if equipment 501-1 matches well the process step planned to be done on the enclosed wafer to avoid any misprocessing),…and a wafer transferring manipulator (Column 5, Lines 15-18, the cassette containing the wafers is withdrawn from the SMIF box and transferred by an elevator/manipulator assembly to the close vicinity of the said input/output port for processing; and Column 43, Line 64-Column 44, Line 6, When a processing equipment, which performs a specified processing step, is going to be available (e.g. no more container 100 in the IN section of the corresponding interface apparatus 200), the host computer 601 knows what wafers are waiting for this step in the stocker 302 of dispatching apparatus 300. Thus, according to scheduling defined by the FCS logistic management (depending on the equipment availability, the current wafer priority, the equipment set-up parameters, . . . ), host computer 601 decides which wafer and thus which container has to be moved to this equipment).
The combination of Rippenhagen-Liu and Garric are analogous because they are each related to task scheduling. Rippenhagen teaches a method of scheduling tasks in manufacturing systems. Liu teaches a task scheduling method using deep learning that considers task and system constraints. Garric teaches a method of automated task scheduling in a manufacturing environment. (Column 1, Lines 8-17, present invention relates to automatic container handling and transporting systems in a factory. It more particularly relates to a plurality of fully automated and computerized conveyor based manufacturing line architectures adapted to store, handle and transport pressurized sealable transportable containers. Each container, encloses a workpiece, typically a semiconductor wafer, in a protective gaseous environment in view of its treatment in a processing equipment, without breaking the said protective gaseous environment; and Column 45 Lines 58-63, FCS moves the containers to . . . equipments for further wafer processing based on availability and wafer processing scheduling. The FCS must be real time in nature and must operate without human intervention to avoid misprocessing errors that are a major yield detractor). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention that based on the combination, the scheduling method taught by the combination of Rippenhagen-Liu would be modified with the Garric’s wafter processing resources resulting in a system that inputs tasks and system constraints into a scheduling model to increase scheduling efficiency among wafer processing resources. Therefore, it would have been obvious to combine the teachings of the combination of Rippenhagen-Liu and Garric.
Claims 6 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Rippenhagen-Liu as applied to claim 1 and in further view of Sinclair et al. (US 20020035465 A1).
As per claim 6, Liu teaches, wherein inputting the task data to the task scheduling model to obtain the start moment of each of the to-be-started tasks which is output by the task scheduling model comprises:
inputting the task data to the task scheduling model to obtain the start moment of each of the to-be-started tasks ([0013], inputting the static characteristics and dynamic characteristics of multiple batches of jobs and the system dynamic characteristics into a first sequence determining submodel to obtain a batch execution sequence of multiple batches in the production stage; for each batch, calculating, according to the dynamic characteristics of multiple jobs of the batch and the batch execution sequence, start execution moment).
The combination of Rippenhagen-Liu fails to specifically teach, inputting the task data to the task scheduling model to obtain …a minimum total time required for performing all the to-be-started tasks, which are obtained through a calculation of the task scheduling model.
However, Sinclair teaches, inputting the task data to the task scheduling model to obtain …a minimum total time required for performing all the to-be-started tasks, which are obtained through a calculation of the task scheduling model ([0009], determining a minimum cycle time for each of said items of equipment for processing said batch; and [0056], the present invention enables continuous models of utilities identifying instantaneous demand for utilities to be incorporated within discrete models of other processes. This is achieved by the modelling module 17 determining for each step within a model, firstly whether any continuous processes are to be modelled and then utilizing a suitable time increment for the model by selecting the smaller time increment of either the minimum time required to complete an active process or a default time increment).
The combination of Rippenhagen-Liu and Sinclair are analogous because they are each related to task scheduling. Rippenhagen teaches a method of scheduling tasks in manufacturing systems. Liu teaches a task scheduling method using deep learning that considers task and system constraints. Sinclair teaches scheduling method for industrial processes that minimizes processing time. ([0012], By determining for an industrial process the greatest of the cycle times for items of equipment within a production line and the minimum processing time for a new batch and then scheduling the initiation of a new batch after the determined greatest cycle time, a means is provided to ensure that the processing of a product using equipment having the greatest cycle time is scheduled to occur separately for different batches of products. When an industrial process corresponding to that simulated is performed, the batches of material being processed are then scheduled corresponding to the scheduling within the mode; and [0094], By calculating an initial next batch being due value equal to the greater of either the current maximum cycle times for equipment within the model or the minimum cycle time necessary to process the latest batch but a means is provided to ensure that the processing of the newly initiated batch is completed by simulated equipment within the model before the next batch is started throughout the simulation). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention that based on the combination, the scheduling method taught by the combination of Rippenhagen-Liu would be modified with the Sinclair’s mechanism for determining a minimum processing time resulting in a system that inputs tasks and system constraints into a scheduling model and increases scheduling efficiency in accordance with a minimum processing time. Therefore, it would have been obvious to combine the teachings of the combination of Rippenhagen-Liu and Sinclair.
As per claim 10, Liu teaches, a solver, which is configured to perform a calculation based on the task scheduling model to obtain the start moment of each of the to-be-started tasks ([0013], inputting the static characteristics and dynamic characteristics of multiple batches of jobs and the system dynamic characteristics into a first sequence determining submodel to obtain a batch execution sequence of multiple batches in the production stage; for each batch, calculating, according to the dynamic characteristics of multiple jobs of the batch and the batch execution sequence, start execution moment).
The combination of Rippenhagen-Liu fails to specifically teach, a solver, which is configured to perform a calculation based on the task scheduling model to obtain… a minimum total time required for performing all the to-be-started tasks.
However, Sinclair teaches, a solver, which is configured to perform a calculation based on the task scheduling model to obtain… a minimum total time required for performing all the to-be-started tasks. ([0009], determining a minimum cycle time for each of said items of equipment for processing said batch; and [0056], the present invention enables continuous models of utilities identifying instantaneous demand for utilities to be incorporated within discrete models of other processes. This is achieved by the modelling module 17 determining for each step within a model, firstly whether any continuous processes are to be modelled and then utilizing a suitable time increment for the model by selecting the smaller time increment of either the minimum time required to complete an active process or a default time increment).
The same motivation used in the rejection of claim 6 is applicable to the instant claim.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Rippenhagen-Liu as applied to claim 1 and in further view of Varadan et al. (US 12190266 B2).
As per claim 8, the combination of Rippenhagen-Liu fails to specifically teach, wherein constructing the task scheduling model according to the objective function and the constraint condition set comprises: performing mathematical modeling on the objective function and the constraint condition set based on mixed integer programming, so as to obtain the task scheduling model.
However, Varadan teaches, wherein constructing the task scheduling model according to the objective function and the constraint condition set comprises:
performing mathematical modeling on the objective function and the constraint condition set based on mixed integer programming, so as to obtain the task scheduling model (Claim 16, execute a resource-constrained project scheduling (RCPS) model to identify an optimal portfolio of asset-related tasks that satisfies all of the one or more constraints, according to an objective and based on the parameter values for the plurality of asset-related tasks, wherein the optimal portfolio comprises at least a subset of the plurality of asset-related tasks, wherein each of the at least a subset of the plurality of asset-related tasks is assigned to a time span comprising one or more of the plurality of time periods within the time window, wherein the parameter values comprise a benefit value, wherein the objective is to maximize a sum of the benefit values associated with ones of the plurality of asset-related tasks to be included in the optimal portfolio, using mixed integer linear programming; and Claim 17, identify an optimal portfolio of asset-related tasks that satisfies all of the one or more constraints, according to an objective and based on the parameter values for the plurality of asset-related tasks, wherein the optimal portfolio comprises at least a subset of the plurality of asset-related tasks, wherein each of the at least a subset of the plurality of asset-related tasks is assigned to a time span comprising one or more of the plurality of time periods within the time window, wherein the parameter values comprise a benefit value, wherein the objective is to maximize a sum of the benefit values associated with ones of the plurality of asset-related tasks to be included in the optimal portfolio, using mixed integer linear programming, and wherein the one or more constraints comprise, for each binary one of the plurality of asset-related tasks, a constraint that the binary asset-related task can only be started in a single one of the plurality of time periods; schedule the at least a subset of the plurality of asset-related tasks based on the optimal portfolio of asset-related tasks; and for at least one asset-related task in the at least a subset of the plurality of asset-related tasks, when a current time reaches a start of the time span to which the at least one asset-related task is assigned).
The combination of Rippenhagen-Liu and Varadan are analogous because they are each related to task scheduling. Rippenhagen teaches a method of scheduling tasks in manufacturing systems. Liu teaches a task scheduling method using deep learning that considers task and system constraints. Varadan teaches model-based distributed task scheduling in accordance with task and system constraints. (Abstract, asset-related tasks for physical equipment are received, along with a time window. Each of the asset-related tasks is associated with parameter values for each time period within the time window. A resource-constrained project scheduling model is executed to identify an optimal portfolio of the asset-related tasks that satisfies a set of constraints, according to an objective, based on the parameter values for the asset-related tasks. In the optimal portfolio, each asset-related task is assigned to a time span comprising one or more time periods in the time window. The optimal portfolio may then be used to schedule the asset-related tasks in the optimal portfolio, in order to facilitate repair, maintenance, and capital tasks, for example, by automatically dispatching work orders or resources, automatically configuring a state of the physical equipment, informing an asset management system, and/or the like; and Claim 1, the objective is to maximize a sum of the benefit values associated with ones of the plurality of asset-related tasks to be included in the optimal portfolio, using mixed integer linear programming). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention that based on the combination, the scheduling method taught by the combination of Rippenhagen-Liu would be modified with the Varadan’s modeling mechanism using mixed integer linear programming resulting in a system that inputs tasks and system constraints into a scheduling model and increases scheduling efficiency. Therefore, it would have been obvious to combine the teachings of the combination of Rippenhagen-Liu and Varadan.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is as follows:
Inventor
Application No.
Teaches
Zhang et al.
US 20190096724 A1
Task scheduling in a production environment:
Abstract- A method and a system for scheduling apparatuses in a production line are disclosed. Each of the apparatuses on the production line includes one or more functional modules, each of the functional modules have a task sequence, and a task sequence includes one or more tasks. The method includes: obtaining a queuing time required for executing each of the tasks in the task sequence of the functional module; identifying a task that has a shortest queuing time in the task sequence as a target task; executing the target task; removing the target task from the task sequence and obtaining an updated queuing time required for executing each of the tasks in the task sequence. The method divides the apparatuses on the production line apparatus into a plurality of independent functional modules and executes the task having the shortest queuing time first with respect to each of the functional modules
KOCHHAR et al.
US 20230026409 A1
Abstract- a management service: receives task execution data and corresponding user data for multiple client devices. The management service inputs target user context data along with the task execution data and the corresponding user data into a prediction model to identify a task and a task schedule that indicates a time to download and cache task content. Instructions are transmitted for a management agent to download and cache the task content according to the task schedule
Englhardt et al.
US 20070144439 A1
Task scheduling in wafer manufacturing system:
Abstract: generally provides an apparatus and method for processing substrates using a multi-chamber processing system (e.g., a cluster tool) that is easily configurable, has an increased system throughput, increased system reliability, improved device yield performance, a more repeatable wafer processing history (or wafer history), and a reduced footprint
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