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 .
DETAILED ACTION
Claims 1-20 are pending.
Examiner Notes
Examiner cites particular paragraphs and/or columns and lines in the references as applied to Applicant’s claims 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 prompt development of a clear issue requires that the replies of the Applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Authorization for Internet Communications in a Patent Application
Applicant is encouraged to file an Authorization for Internet Communications in a Patent Application form (http://www.uspto.gov/sites/default/files/documents/sb0439.pdf) along with the response to this office action to facilitate and expedite future communication between Applicant and the examiner. If the form is submitted then Applicant is requested to provide a contact email address in the signature block at the conclusion of the official reply.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 1: The claim is a process, machine, manufacture, or composition of matter:
Claim 1. A device, comprising.
Step 2A Prong One: The claim recites an abstract idea because it includes limitations that can be considered mental processes (concepts performed in the human mind including an observation, evaluation, judgment, and/or opinion). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the human mind or via pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea:
assigning the plurality of jobs to a plurality of workers in accordance with a base schedule, wherein the base schedule is derived by solving an initial scheduling model that is configured to facilitate job assignments based on worker availability, capabilities, skills, experience, or a combination thereof, while reducing or minimizing job completion time and maintaining a determined balanced load (abstract idea mental process);
detecting one or more disruptions or events after the assigning (abstract idea mental process); and
causing the base schedule to be repaired based on the detecting, wherein the causing involves solving a scheduling repairing model that is configured to repair an existing schedule based on detected disruptions or events, while reducing or minimizing an impact of the detected disruptions or events on the existing schedule (abstract idea mental process).
Step 2A Prong Two: The abstract idea is not integrated into a practical application because the abstract idea is recited but for generically recited additional computer elements (i.e. data storage, processor, memory, computer readable medium, etc.) which do not add meaningful limitations to the abstract idea amounting to simply implementing the abstract idea on a generic computer using generic computing hardware and/or software (e.g. generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The generic computing components are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using the recited generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea:
a processing system including a processor (generic computing components); and
a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations (generic computing components), the operations comprising:
receiving information regarding a plurality of jobs (generic computing components performing extra-solution activity of receiving data/information).
Step 2B: The claim includes limitations which can be considered extra-solution activity (see MPEP 2106.05(g)) insufficient to amount to significantly more than the abstract idea because the additional limitations only perform at least one of collecting, gathering, displaying, generating, modifying, updating, storing, retrieving, sending, and receiving data/information data which are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d)II. The claim further includes limitations that do not integrate the judicial exception into a practical application because they merely recite the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Therefore, the claim, and its limitations when considered separately and in combination, is directed to patent ineligible subject matter:
receiving information regarding a plurality of jobs (extra-solution activity of receiving data/information).
Claim 2. The device of claim 1, wherein the initial scheduling model and the scheduling repair model are implemented as two phases or components of an unrelated parallel machine scheduling (UPMS) system (extra-solution activity of merely reciting the words "apply it" or an equivalent with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using the computer as a tool to perform the abstract idea).
Claim 3. The device of claim 1, wherein the solving the initial scheduling model, the solving the scheduling repair model, or both are performed using a genetic algorithm (GA) (abstract idea mental process).
Claim 4. The device of claim 3, wherein the GA employs a custom initial seed that involves sorting the plurality of jobs in an ascending order based on job execution time, job weight, or a combination thereof, and allocating the plurality of jobs one at a time to workers of the plurality of workers that have the lowest load (abstract idea mental process).
Claim 5. The device of claim 3, wherein the GA includes jump operations that prevent the GA from being trapped in local optima (abstract idea mental process).
Claim 6. The device of claim 3, wherein the GA has a self-learning mechanism that implements jump selection based on a self-learning patience value (abstract idea mental process).
Claim 7. The device of claim 1, wherein the base schedule comprises individual work lists for the plurality of workers based on worker availability, capabilities, skills, experience, or a combination thereof, rather than a single shared work list for the plurality of workers (abstract idea mental process).
Claim 8. The device of claim 1, wherein the one or more disruptions or events comprise: receipt of information regarding one or more additional jobs; receipt of information regarding one or more defined high-priority jobs; unavailability of one or more of the plurality of workers; availability of one or more additional workers; one or more of the plurality of jobs being completed; a need for one or more completed jobs to be redone; manual intervention with respect to the base schedule; or a combination thereof (extra-solution activity of receiving data/information).
Claim 9. The device of claim 1, wherein the scheduling repair model comprises a fitness function for penalizing repairs that deviate from the existing schedule (abstract idea mental process).
Claim 10. The device of claim 1, wherein the assigning and the causing are performed based on an assumption that the plurality of workers are each capable of working on only one job at a time (abstract idea mental process).
As per claim 11, it has similar limitations as claim 1 and is therefore rejected using the same rationale.
As per claim 12, it has similar limitations as claim 2 and is therefore rejected using the same rationale.
As per claim 13, it has similar limitations as claim 3 and is therefore rejected using the same rationale.
As per claim 14, it has similar limitations as claim 4 and is therefore rejected using the same rationale.
As per claim 15, it has similar limitations as claim 5 and is therefore rejected using the same rationale.
Step 1: The claim is a process, machine, manufacture, or composition of matter:
Claim 16. A method, comprising.
Step 2A Prong One: The claim recites an abstract idea because it includes limitations that can be considered mental processes (concepts performed in the human mind including an observation, evaluation, judgment, and/or opinion). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the human mind or via pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea:
identifying, by a processing system including a processor, a plurality of jobs that are to be assigned (abstract idea mental process);
solving, by the processing system, an initial scheduling model that is configured to facilitate job assignments based on worker availability, capabilities, skills, experience, or a combination thereof, while reducing or minimizing job completion time and maintaining a determined balanced load, resulting in a base schedule (abstract idea mental process);
assigning, by the processing system, the plurality of jobs to a plurality of workers in accordance with the base schedule (abstract idea mental process);
causing, by the processing system, the base schedule to be repaired based upon detection of one or more disruptions or events, resulting in a repaired schedule (abstract idea mental process); and
reassigning, by the processing system, some or all of the plurality of jobs to some or all of the plurality of workers based on the repaired schedule (abstract idea mental process).
Step 2A Prong Two: The abstract idea is not integrated into a practical application because the abstract idea is recited but for generically recited additional computer elements (i.e. data storage, processor, memory, computer readable medium, etc.) which do not add meaningful limitations to the abstract idea amounting to simply implementing the abstract idea on a generic computer using generic computing hardware and/or software (e.g. generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The generic computing components are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using the recited generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea:
a processing system including a processor (generic computing components);
Step 2B: The claim does not include any limitations which can be considered extra-solution activity sufficient to amount to significantly more than the abstract idea. The claim does not further include any limitations that integrate the judicial exception into a practical application. Therefore, the claim, and its limitations when considered separately and in combination, is directed to patent ineligible subject matter:
Claim 17. The method of claim 16, wherein the causing involves solving a scheduling repairing model that is configured to repair an existing schedule based on detected disruptions or events, while reducing or minimizing an impact of the detected disruptions or events on the existing schedule (abstract idea mental process).
Claim 18. The method of claim 17, wherein the scheduling repair model comprises a fitness function for penalizing repairs that deviate from the existing schedule (abstract idea mental process).
Claim 19. The method of claim 16, wherein the base schedule comprises individual work lists for the plurality of workers based on worker availability, capabilities, skills, experience, or a combination thereof, rather than a single shared work list for the plurality of workers (abstract idea mental process).
Claim 20. The method of claim 16, wherein one or more of the solving, the assigning, the causing, and the reassigning are performed based on an assumption that the plurality of workers are each capable of working on only one job at a time (abstract idea mental process).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 8, 11, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Azar et al. (US 2009/0007101) (hereinafter Azar) in view of Colena et al. (US 2022/0027861) (hereinafter Colena).
As per claim 1, Azar primarily teaches the invention as claimed including a device, comprising:
a processing system including a processor (fig. 12, block 1204); and
a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations (fig. 12, block 1206), the operations comprising:
receiving information regarding a plurality of jobs ([0057] receive information determined at timing component and distributed process component to calculate task inefficiency for each task on each device);
assigning the plurality of jobs to a plurality of workers in accordance with a base schedule ([0078] scheduling of independent tasks assigned by selfish entities so as to decrease the overall global time of completion of all scheduled tasks), wherein the base schedule is derived by solving an initial scheduling model ([0037] solutions to an unrelated system scheduling policy associated with a global optimism defined as a particular assignment of jobs to devices that minimizes the time to complete the jobs and [0047] an optimal solution to scheduling tasks to devices according to pure Nash equilibrium) that is configured to facilitate job assignments based on worker availability, capabilities, skills, experience, or a combination thereof, while reducing or minimizing job completion time ([0028] schedule each of the plurality of tasks for processing on a machine in a non-decreasing order of task inefficiency as compared to a minimum time required to complete the task on any suitable available device; [0039] if a set of tasks has converged to a Nash equilibrium, no agent can unilaterally re-schedule its task in order to decrease the completion time of the task at the expense of the global task completion time; and [0053] determine inefficiency parameters associated with each task assigned to each device and enforce an inefficiency-based local policy to lower global completion times as compared to conventional local and strongly local policy mechanisms) and maintaining a determined balanced load ([0052] scheduling policies enforced by distributed load balancing system based on available resources/processing and task completion history list i.e., experience and [0075] assist a distributed load balancing system in adjusting task scheduling as task inefficiencies change as a function of processing capabilities of devices).
Azar does not explicitly teach:
detecting one or more disruptions or events after the assigning; and
causing the base schedule to be repaired based on the detecting, wherein the causing involves solving a scheduling repairing model that is configured to repair an existing schedule based on detected disruptions or events, while reducing or minimizing an impact of the detected disruptions or events on the existing schedule.
However, Colena teaches:
detecting one or more disruptions or events after the assigning ([0053] interruption of the production schedule and [0056] disruption to production schedules); and
causing the base schedule to be repaired based on the detecting ([0164] a GUI interface element configured to receive user input accepting, rejecting, and/or further modifying a proposed maintenance schedule see also [0298] and [0321]), wherein the causing involves solving a scheduling repairing model that is configured to repair an existing schedule based on detected disruptions or events ([0050] the iterative process to obtain a proposed maintenance schedule may be interrupted to request a solution associated with a lowest total cost value thus far. The iterative process may thereafter resume to continue finding solutions with even lower total cost values; [0159 a proposed maintenance schedule may be a revision from a current maintenance schedules for the set of maintenance tasks; and [0291] constraint programming solver determines a solution based on a data model and uses the solution to return a proposed maintenance schedule and [0271] if all values from the domain of the particular task element are attempted and do not satisfy the constraint, then re-assignment of one or more task elements previously traversed is performed. In particular, a value from the domain of a previously-traversed task element was preliminarily assigned to the previously-traversed task element. Based on the need for re-assignment, another value from the domain of the previously-traversed task element is now preliminarily assigned to the previously-traversed task element. Then, assignment of the particular task element is re-attempted), while reducing or minimizing an impact of the detected disruptions or events on the existing schedule ([0053] minimize negative impacts to production schedules).
Colena and Azar are both concerned with task/job scheduling in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Azar in view of Colena because it would provide a way for iteratively applying a constraint programming data model to a constraint programming solver to obtain a proposed maintenance schedule. A total cost value associated with a prior solution obtained by the constraint programming solver is used as an upper bound for the domain of the total cost element in a next iteration of the constraint programming solver. The iterative process may be interrupted to request a solution associated with a lowest total cost value thus far. The iterative process may thereafter resume to continue finding solutions with even lower total cost values. The iterative process is complete when the domain of the total cost element is so modified with the lowering of the upper bound for the domain of the total cost element that no valid solution satisfying all constraints exists. Then the last-determined solution is the “best” solution associated with the lowest total cost value. A proposed maintenance schedule is determined based on the last-determined solution. By modifying the constraint programming data model based on a lowest total cost value thus far in each iteration, a set of solutions with total cost values higher than the lowest total cost value thus far are omitted and never attempted. The constraint programming solver may thus more efficiently arrive at a solution with a lowest total cost value possible.
As per claim 8, Colena teaches wherein the one or more disruptions or events comprise: receipt of information regarding one or more additional jobs; receipt of information regarding one or more defined high-priority jobs; unavailability of one or more of the plurality of workers ([0083] Server C may be loaned to a different user on Friday and therefore unavailable for performing any maintenance tasks on Friday); availability of one or more additional workers; one or more of the plurality of jobs being completed; a need for one or more completed jobs to be redone; manual intervention with respect to the base schedule ([0164] a GUI interface element configured to receive user input accepting, rejecting, and/or further modifying a proposed maintenance schedule see also [0298] and [0321]); or a combination thereof.
As per claim 11, it has similar limitations as claim 1 and is therefore rejected using the same rationale.
As per claim 16, it has similar limitations as claim 1 and is therefore rejected using the same rationale.
As per claim 17, it has similar limitations as claim 1 and is therefore rejected using the same rationale.
Claim 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Azar in view of Colena in view of Kimbrel et al. (US 2011/0246994) (hereinafter Kimbrel).
As per claim 2, Azar in view of Colena do not explicitly teach wherein the initial scheduling model and the scheduling repair model are implemented as two phases or components of an unrelated parallel machine scheduling (UPMS) system.
However, Kimbrel teaches wherein the initial scheduling model and the scheduling repair model are implemented as two phases or components of an unrelated parallel machine scheduling (UPMS) system ([0053] unrelated parallel machines scheduling).
Kimbrel and Azar are both concerned with task/job scheduling in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Azar in view of Colena in view of Kimbrel because it would provide for an automated solution to obtaining quality scheduling for users of computing resources. The system, implemented in an enterprise software test center, collects information from test-shop personnel about test machine features and availability, test jobs, and tester preferences and constraints. The system reformulates this testing information as a system of constraints. An optimizing scheduling engine computes efficient schedules whereby all the jobs are feasibly scheduled while satisfying the users' time preferences to the greatest extent possible. The method and system achieves fairness: if all preferences cannot be met, it is attempted to evenly distribute violations of preferences across the users. The test scheduling is generated according to a first application of a greedy algorithm that finds an initial feasible assignment of jobs. The second is a local search algorithm that improves the initial greedy solution.
As per claim 12, it has similar limitations as claim 2 and is therefore rejected using the same rationale.
Claims 3, 5, 13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Azar in view of Colena in view of Joslin et al. (US 6,272,483) (hereinafter Joslin).
As per claim 3, Azar in view of Colena do not explicitly teach wherein the solving the initial scheduling model, the solving the scheduling repair model, or both are performed using a genetic algorithm (GA).
However, Joslin teaches wherein the solving the initial scheduling model, the solving the scheduling repair model, or both are performed using a genetic algorithm (GA) (col. 8, ll. 5-8 a commercial scheduler called OPTIFLEX from i2 Technologies of Irving Texas is based on an approach that uses genetic algorithms).
Joslin and Azar are both concerned with task/job scheduling in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Azar in view of Colena in view of Joslin because it would provide a way for determining schedules and processing other optimization problems includes a local optimization engine and a global optimization engine. The local optimization engine operates based on heuristics, and includes a prioritizer, a constructor, and an analyzer to make large "coherent" moves in the search space, thus helping to avoid local optima without relying entirely on random moves. The global optimization engine takes the individual schedules produced by the local optimization engine and optimizes them using Linear Programming/Integer Programming techniques.
As per claim 5, Joslin teaches wherein the GA includes jump operations that prevent the GA from being trapped in local optima (col. 10, ll. 9-20 system can be thought of as searching two coupled spaces: the space of solutions, and the space of job sequences. Note that in the space of job sequences, the only "local optima" are those in which all jobs are assigned the same penalty, which in practice does not occur. Because of this, system tends to avoid getting trapped in local optima in the solutions generated by constructor, since analysis and prioritization will always (in practice) suggest changes in the sequence, thus changing the solution generated on the next iteration and col. 15, ll. 6-8 TABU search, which is a local search algorithm in which moves that increase the cost are permitted to avoid getting trapped at a local optimum.).
As per claim 13, it has similar limitations as claim 3 and is therefore rejected using the same rationale.
As per claim 15, it has similar limitations as claim 5 and is therefore rejected using the same rationale.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Azar in view of Colena in view of Joslin in view of Suzuki et al. (US 2012/0331474) (hereinafter Suzuki) in view of Mabuchi et al. (US 2008/0228779) (hereinafter Mabuchi).
As per claim 4, Azar in view of Colena in view of Joslin do not explicitly teach wherein the GA employs a custom initial seed that involves sorting the plurality of jobs in an ascending order based on job execution time, job weight, or a combination thereof, and allocating the plurality of jobs one at a time to workers of the plurality of workers that have the lowest load.
However, Suzuki teaches wherein the GA employs a custom initial seed that involves sorting the plurality of jobs in an ascending order based on job execution time, job weight, or a combination thereof ([0137]-[0138] the execution times of the non-fixed tasks are sorted in ascending order).
Suzuki and Azar are both concerned with task/job scheduling in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Azar in view of Colena in view of Joslin in view of Suzuki because it would provide for an automatic optimization system capable of searching for an allocation with a good performance from among a plurality of task allocations which can be scheduled in a system of a development target configured with a plurality of periodic tasks. A task allocation optimization system for a multi-core processor including a plurality of cores calculates a response time of each of a plurality of tasks which are core allocation decision targets, and outputs an accumulative value of the calculated response time as an evaluation function value which is an index representing excellence of a task allocation performing optimization to minimize an accumulative value of a response time of each of a plurality of tasks which are core allocation decision targets.
Azar in view of Colena in view of Suzuki do not explicitly teach allocating the plurality of jobs one at a time to workers of the plurality of workers that have the lowest load.
However, Mabuchi teaches allocating the plurality of jobs one at a time to workers of the plurality of workers that have the lowest load ([0050] processing units sequentially accept and process inquiries e.g., host retrieval requests one at a time from the call control severs. When a next retrieval request is transmitted before one processing unit finishes the current retrieval process, another processing unit performs the other host retrieval process in parallel. Methods for process distribution are set in advance to allow selection of a round-robin method and other methods, e.g., a method in which a process is allocated to the processing unit having the lightest load at the point in time of interest).
Mabuchi and Azar are both concerned with task/job scheduling in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Azar in view of Colena in view of Joslin in view of Suzuki in view of Mabuchi because it would provide a way of efficiently relocating data when additional processors are provided in the retrieval server. This is advantageous because data can be relocated without stopping services provided by the retrieval server while efficiently relocating data when additional processors are provided in the retrieval server.
As per claim 14, it has similar limitations as claim 4 and is therefore rejected using the same rationale.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Azar in view of Colena in view of Joslin in view of Han et al. (US 2020/0401950) (hereinafter Han).
As per claim 6, Azar in view of Colena in view of Joslin do not explicitly teach wherein the GA has a self-learning mechanism that implements jump selection based on a self-learning patience value.
However, Han teaches wherein the GA has a self-learning mechanism that implements jump selection based on a self-learning patience value ([0062] new machine learning pipelines may be generated by crossover and mutation similar to the Genetic Algorithm (GA). The idea of crossover in the genetic algorithm indicates that the combination of excellent individuals has a high probability of generating more excellent individuals, so that the individuals tend to optimal values. The idea of mutation in the genetic algorithm indicates that individual mutations may produce more excellent individuals or worse individuals, the worse individuals may be thrown away in later iterations, and the excellent individuals may be retained to produce more excellent individuals. The mutation may cause the optimization path to jump out of the local optimal value and to tend to the global optimal value. The models and parameters corresponding to the machine learning pipelines are modified by means of the ideas of crossover and mutation in the genetic algorithm, so that new machine learning pipelines are continuously generated in the automatic modeling process for subsequent selection and use. Later, better models may be generated by looping execution of the model generation steps).
Han and Azar are both concerned with task/job scheduling in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Azar in view of Colena in view of Joslin in view of Han because it would provide a way to improve the efficiency of model training, so that the model training may be performed on the target number of initial machine learning pipelines and the target number of new machine learning pipelines in a distributed manner. Thus, the training efficiency of the models can be greatly improved by iteratively executing the model generation steps, thereby improving the effect of automatically generating a model.
Claims 7 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Azar in view of Colena in view of Rodriguez-Vera et al. (US 2023/0121776) (hereinafter Rodriguez-Vera).
As per claim 7, Azar in view of Colena do not explicitly teach wherein the base schedule comprises individual work lists for the plurality of workers based on worker availability, capabilities, skills, experience, or a combination thereof, rather than a single shared work list for the plurality of workers.
However, Rodriguez-Vera teaches wherein the base schedule comprises individual work lists for the plurality of workers based on worker availability, capabilities, skills, experience, or a combination thereof, rather than a single shared work list for the plurality of workers ([0048] the task port may also allow the input of meta-data to the tasks. Such meta-data may relate to the skills/expertise required to execute the task, the location where the task needs to be executed, the duration of the task, the priority of the task, material needs to execute task, and the like. In addition, the task port may allow the input of information on workers, such as availability, task preferences, training ambitions, skills, expertise, location, and/or the like. These pieces of information may be considered in generating a list of tasks for each worker).
Rodriguez-Vera and Azar are both concerned with task/job scheduling in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Azar in view of Colena in view of Rodriguez-Vera because it would provide for a task planning model and the task and training planning model which may be a deterministic or probabilistic model that puts the various types of meta-data in relation to each other and/or generates an optimized lists of tasks for each working which implements the best schedule of tasks with regards to minimum delay, minimum planning costs, satisfaction of workers preferences, travelling costs, travelling distances, and/or match of required skills in relation to the skills of each worker. Known minimization or optimization models/algorithms may be used for the task and training planning model or the task and training planning model. The system solves the maintenance planning optimization problem while considering simultaneously multiple optimization objectives, some of them configurable, that is, that can be independently activated, deactivated, and partially weighted. The configuration flexibility allows the alignment of the invention with the user's strategy. The system may include as configurable optimization objectives the reduction of the emissions in the factory (machinery, maintenance travels, materials, etc.), the increase in maintenance task preference matching, the increase in maintenance training assignation as part of the day-to-day planning, and the optimized allocation of the tasks that could be executed remotely to increase the number of workers working remotely. solves the maintenance planning optimization problem while considering simultaneously multiple optimization objectives, some of them configurable, that is, that can be independently activated, deactivated, and partially weighted. That configuration flexibility allows the alignment of the tool with the user's strategy.
As per claim 19, it has similar limitations as claim 7 and is therefore rejected using the same rationale.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Azar in view of Colena in view of Elzinga et al. (US 2003/0065544) (hereinafter Elzinga).
As per claim 9, Azar in view of Colena do not explicitly teach wherein the scheduling repair model comprises a fitness function for penalizing repairs that deviate from the existing schedule.
However, Elzinga teaches wherein the scheduling repair model comprises a fitness function for penalizing repairs that deviate from the existing schedule ([0049] the fitness of the solution is affected by the number of minutes elapsed since the specified point. A penalty is assessed by determining the actual start time from its scheduled start time).
Elzinga and Azar are both concerned with task/job scheduling in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Azar in view of Colena in view of Elzinga because it would provide a way for optimizing a schedule that is filled with a plurality of events by defining a framework to serve as the schedule and means for determining an optimization value. The means for defining a framework populates the framework with the plurality of events, selected of the plurality of events being immutable. The means for determining an optimization value makes its determination based on the placement of the plurality of events after placement of the immutable events and it also determines whether the optimization value achieves a threshold best-solution value. If the threshold value is not reached, the framework defining means will perform event swapping to improve the optimization value.
As per claim 18, it has similar limitations as claim 9 and is therefore rejected using the same rationale.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Azar in view of Colena in view of Kodialam et al. (US 2012/0284727) (hereinafter Kodialam).
As per claim 10, Azar in view of Colena do not explicitly teach wherein the assigning and the causing are performed based on an assumption that the plurality of workers are each capable of working on only one job at a time.
However, Kodialam teaches wherein the assigning and the causing are performed based on an assumption that the plurality of workers are each capable of working on only one job at a time ([0024] it is assumed that each processor can process at most one task at a time).
Kodialam and Azar are both concerned with task/job scheduling in computing environments and are therefore combinable/modifiable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Azar in view of Colena in view of Kodialam because it would provide a way for a Server to receive groups of jobs in bulk or receive individual jobs one at a time. As jobs arrive at server, the jobs are processed such that all tasks associated with each job are assigned to clients. The tasks may be assigned based on several factors, such as a current workload of a particular client or based on the capabilities of a particular client. After all tasks are assigned to clients, server determines an optimal scheduling of the tasks with each client in order to minimize total job completion times across all of the clients. The completion time of a job is defined as the time needed for all tasks belonging to a respective job to be performed by clients.
As per claim 20, it has similar limitations as claim 10 and is therefore rejected using the same rationale.
Citation of Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Brown et al. (US 2020/0411170) disclose a machine-learning framework for coordinating and optimizing resource utilization and services across a system.
Chen et al. (US 2017/0337492) disclose a workflow scheduling optimization tool.
Shao et al. (US 2015/0324740) disclose generating an optimal schedule.
Khan et al. (US 2024/0112133) disclose management of events and disruptions.
Guo et al. (US 11,206,221) disclose online task dispatching and scheduling.
Luciani (US 2022/0170758) disclose smart scheduling and mitigating disruptions based on environment conditions.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Adam Lee whose telephone number is (571) 270-3369. The examiner can normally be reached on M-TH 8AM-5PM.
If attempts to reach the above noted Examiner by telephone are unsuccessful, the Examiner’s supervisor, Pierre Vital, can be reached at the following telephone number: (571) 272-4215. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Adam Lee/Primary Examiner, Art Unit 2198 September 24, 2026
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