DETAILED ACTION
This Office Action is in response claims filed on 08/16/2023.
Claims 1-20 are pending.
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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: "an assignment cost module configured to", "a resource selector module configured to", and "an assignment module configured to" in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof, including customizable plugins adaptable to domain-specific use ([0050]).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-10 and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kern et al. Patent No. US 7,954,106 B2 (hereinafter Kern) in view of Oliver Patent No. US 11,481,245 B1 (hereinafter Oliver).
With regard to claim 1, Kern teaches an operational planning (Abstract, A method for allocating resources of a group of resources to tasks of a group of tasks) system (FIG. 1 illustrates a suitable computer for implementing the present system.), comprising:
a memory configured for storage of processor-executable encoded instructions (Col. 5, a non-volatile memory device 3 … Software suitable for implementing the methods described herein … are loaded into the non-volatile memory 3); and
a processing environment operatively coupled to the memory and comprising (Col. 5, It includes a processing unit 1, random access memory 2):
one or more processors configurable by the encoded instructions (Col. 5, The software is then executed by processing unit 1 via the random access memory 2 so as to process the data); and
one or more modules executable on the one or more processors, the one or more modules configured for assignment of a plurality of resources for optimal completion of a plurality of tasks, the one or more modules (Col. 11, Solutions are generated by executing a planning method by means of a computer system such as that shown in FIG. 1. Software for execution by the computer executes the various steps of the planning method. The high-level algorithm of the planning method is illustrated by pseudo-code in FIG. 3) comprising:
a resource manager (Col. 14, The generated solution … can be analysed to provide information to resource managers and also serve as input into other systems that could utilize the data in the plan) configured to:
receive resource data associated with the plurality of resources (Col. 5, A set of resources with, for each resource, the attributes of that resource in each of a set of predetermined categories); and
select from the plurality of resources, based on the resource data, a set of one or more valid resources assignable to the plurality of tasks (Col. 5, One or more assessment criteria that are to be used in assess the fitness of any allocation of resources to the tasks. );
a task manager (Col. 14, The deployment plan would serve as a guide for scheduling systems) configured to:
receive task data associated with the plurality of tasks (Col. 5, A set of tasks with, for each task, the attributes of the task in each of a set of predetermined categories);
select from the plurality of tasks a set of one or more valid tasks available for completion by the set of valid resources (Col. 7, In the present system the planning process is performed over a selected planning period. Only those of the tasks that ought to be completed during or before the chosen planning period, and only those resources that are available during the chosen planning period need to be considered (Examiner notes: a valid set of tasks and resources). Other tasks and resources that are stored in the starting data are ignored during the planning process that is carried out for the period);
…; and
one or more customizable modules (Col. 13, A preferred optimisation algorithm is illustrated by the pseudo-code shown in FIG. 4), comprising:
an assignment cost module (Col. 8, The assessment criteria are defined with the aim of rating the quality of a particular solution) configured to:
receive the hierarchy [set] of selected tasks and the set of valid resources (Col. 7, The user may selecta planning period as desired: for example, half a day, a day, a week, or a month; Col. 8, A solution will involve the mapping of one or more tasks or task aggregates to each resource.);
determine assignment cost data comprising at least one cost associated with an assignment of at least one valid resource of the set to a selected task of the hierarchy [set] (Col. 9, Technical clearance score (tcs) for a resource such as a technician T is the sum of job aggregate clearance scores for all tasks or task aggregates {JA.sub.1, …, JA.sub.n} assigned to this resource … The function is a measure for the importance of the work done by the resource. Again, higher values are desirable as they indicate the clearance of more significant work. A higher score indicates a better solution.);
a resource selector module (Col. 13, Using the base-line plan as its point of start, this method applies move-to, swap, and replace operators (Examiner notes: With respect to resources) to that base-line plan.) configured to:
receive the hierarchy [set] of selected tasks and the assignment cost data (Col. 11, In the first step (101) the system identifies from the database the records of all resources that are available during the planning period under consideration. A record containing the attribute data is built for each of those resources … task aggregates, together with any remaining unaggregated tasks, are initial assigned to any resource); and
select from the set of valid resources a set of one or more selected resources by providing an assignment instruction to each selected resource of the set (Col. 11, A simple un-optimised base-line plan is generated (step 103). This is generated by the allocation of a suitable resource to some or all of the tasks; Col. 13, If any of these moves leads to an improvement of the chosen objective function then the move is accepted, otherwise it is rejected and the change are reversed); and
an assignment module (Col. 13, During each cycle of the main optimization loop, all single resources are tried to be moved, all pairs of resources are tried to be swapped and all possible chains of resources are subjected to potential replacement moves) configured to:
receive the set of selected resources, the hierarchy [set] of selected tasks, and the set of assignment cost data (Col. 11, The base-line plan is modified repeatedly (step 104) by altering the allocation of resources to tasks.); and
produce assignment data associated with an assignment of the set of selected resources to the hierarchy [set] of selected tasks, the assignment data corresponding to the optimal completion of the hierarchy [set] of selected tasks (Col. 11, This may involve allocating a resource to a task or task aggregate that was previously unallocated, allocating a different resource to a particular task or task aggregate, or removing the allocation of a resource to a task or task aggregate. The modifications may be done randomly or by the use of an algorithm that aims at efficient allocation of resources. After each modification, or after a series of modifications, the values of the objective functions are calculated for the modified plan and compared with the values for the previous version of the plan.)
However, Kern does not explicitly teach resolving task dependencies of a plurality of valid tasks, such that they may be designated as a hierarchy of selected tasks.
Oliver teaches resolve one or more task dependencies associated with the set of valid tasks (Col. 7, FIG. 4 is a block diagram illustrating an embodiment of a task tree and a task list. In some embodiments, the task tree and task list of FIG. 4 are associated with the filesystem structure of FIG. 3 … For example, task 1 402 and task 2 404 comprise tasks identified at a project root directory of a project. Task 3 406 and task 4 408, task 410, and task 6 412, and task 7 414 comprise tasks identified at directories subordinate to the project root directory; Col. 8, FIG. 5 is a block diagram illustrating an embodiment of task dependence. In some embodiments, the block diagram of FIG. 5 illustrates task dependencies of the tasks of task tree 400 of FIG. 4 on prerequisite tasks outside of the tasks identified in FIG. 4. In the example shown, task 1 500 depends on prerequisite task 502, which in turns depends on prerequisite task 2 504); and
select from the set of valid tasks, based on the resolved task dependencies, a hierarchy of selected tasks available for assignment (Col. 7, Task tree 400 comprises 16 tasks arranged in a hierarchical structure; Col. 10, In 816, the task list is provided. In some embodiments, the task list comprises a task tree. For example, a task tree comprises a set of tasks wherein the tasks are stored in a tree corresponding to the directory hierarchy where the tasks were located (Examiner: Such that a set of tasks may correspond to a hierarchy)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Oliver with the teachings of Kern in order to provide a system that teaches a set of tasks with dependencies, organized to form a hierarchical task structure. The motivation for applying Oliver teaching with Kern teaching is to provide a system that applies a set of tasks with the known technique of hierarchical arrangement of such tasks, because a hierarchy is merely an organizational variant of a task set that preserves the task list while structuring task relationships, such that obtains predictable results. Kern and Oliver are analogous art directed towards task dispatching arrangements. Therefore, it would have been obvious for one of ordinary skill in the art to combine Oliver with Kern to teach the claimed invention in order to provide a determination of task dependencies and construction of a hierarchical task structure.
With regard to claim 2, Kern teaches wherein the resource data includes, for at least one first resource of the plurality of, one or more of resources (Col. 5, Each resource has the following attributes):
an attribute or property associated with the first resource (Col. 9, The technician score (ts), is based on the technician clearance score. Its aim is to incorporate further business objectives into the judgement of a resource’s deployment);
positional data associated with the first resource (Col. 5, geographical location … the geographical location(s) at which the resource is or can be located), the positional data relative to at least one of a second resource of the plurality of resources or a task of the plurality of tasks (Col. 6, At least some of the categories of the tasks correspond to categories of the resources, in that they indicate information congruent in nature. In the present example, the categories “geographical location” corresponds to each other (Examiner notes: positional data of a first resource relative to a task);
timing data associated with the first resource (Col. 5, availability … the times at which the resource is available);
a functional status of the first resource; or
a collaborative capacity of the first resource (Col. 6, aggregated … flag indicating whether the resource has been aggregated into an aggregated resource).
With regard to claim 3, Kern teaches wherein the attribute or property associated with the first resource includes at least one of:
a task objective or task state effect achievable by the first resource (Col. 9, More specifically, it modifies the tcs of a resource depending on the beneficial utilization of the resource in an accordance with the resource’s mapping on to a task, for example taking into account the resource’s preference values for the assigned area, skills, and state that are associated with that task.);
a frequency associated with an achievement of the task objective or task state effect;
a base probability of completion of a task of the plurality of tasks by the first resource; or
a resource constraint associated with the first resource.
With regard to claim 4, Kern teaches wherein the timing data associated with the first resource includes at least one of:
a remaining functional time of the first resource; or
a remaining assignable time of the first resource (Col. 12, Assigning areas, skills, and an availability state to each resource involves the allocation of unassigned tasks or task aggregates to these resources. As long as a resource has free capacity, i.e. as long as the work assigned to that resource so far will take the resource less time to complete than the time for which the resource is available and unassigned during the period, under consideration, the system can try shifting more tasks or task aggregates to the resource (Examiner notes: Such that availability data is a reflection of the free capacity of time of a resource).
With regard to claim 5, Kern teaches wherein the task data includes, for at least one first task of the plurality of tasks, one or more of (Col. 5, In the present example, each task has the following categories):
a capability associated with the first task (Col. 5, skills required …. the skills required to perform the task, including the quantity thereof);
a completion status of the first task (Col. 5, time window … window of time during which the task must be completed);
a task dependency associated with the first task;
positional data associated with the first task (Col. 5, geographical location … at which the task must be performed), the positional data relative to at least one of a second task of the plurality of tasks or a resource of the plurality of tasks or a resource of the plurality of resources Col. 6, At least some of the categories of the tasks correspond to categories of the resources, in that they indicate information congruent in nature. In the present example, the categories “geographical location” corresponds to each other (Examiner notes: positional data of a first resource relative to a task);
timing data associated with the first task;
a required status of the first task (Col. 5, importance … the organization’s level of interest in the job (carry-over jobs, i.e., jobs whose completion is over-due, may be given a higher importance than future jobs); or
a collaborative capacity of the first task (Col. 5, aggregated … flag indicating whether the task has been aggregated into an aggregated task).
With regard to claim 6, Oliver teaches wherein the task dependency corresponds to at least one necessary task state associated with a second task of the plurality of tasks, the at least one necessary task state required for at least one of 1) commencement of the first task or 2) completion of the first task (Col. 2-Col. 3, A system for executing a dependency task tree comprises … a processor configured to determine a set of tasks of the dependency task tree such that a task of the set of tasks does not depend on any other task, add the set of tasks to a task queue, begin executing tasks from the task queue, receive an indication of a completed task, determine a dependent task that depends on the completed task, in response to determining that all dependencies of the dependent tasks are completed, add the dependent task to the task queue and continue executing tasks from the task queue until all tasks are completed).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Oliver with the teachings of Kern in order to provide a system that teaches the flow of task dependencies associated with the task state of an origin task. The motivation for applying Oliver teaching with Kern teaching is to provide a system that allows for a dependent task state to signal completion of a preceding dependent task, thereby enabling correct execution flow for dependent task chaining (Oliver, Col. 3). Kern and Oliver are analogous art directed towards task dispatching arrangements. Therefore, it would have been obvious for one of ordinary skill in the art to combine Oliver with Kern to teach the claimed invention in order to maintain execution flow of task dependencies through the use completed task state of independent tasks.
With regard to claim 7, Kern teaches wherein the capability associated with the first task includes one or more of:
a task state effect required for completion of the first task;
a frequency with which the first task may be performed;
a probability of the first task being performed; or
a task constraint associated with the first task (Col. 6, At least some of the categories of the tasks correspond to categories of the resources, in that they indicate information of a congruent nature … Skills: Resources possess skills while tasks require skills. To handle a task successfully, a resource must be correctly skilled. Skills of similar nature are grouped into so called work types.).
With regard to claim 8, Kern teaches wherein the assignment cost data includes, for at least one assignment of at least one valid resource to a selected task (Col. 10, This function is therefore is a measure for the quality of the resource deployments with regard to the formulated business objectives … Various business goals affect and influence the objective function and planning system. Their weighting and thus their interaction can be controlled by a user):
a probability of completion of the selected task by the at least one valid resource;
a capability associated with a performance of the selected task by the at least one valid resource;
timing data associated with the performance of the selected task by the at least one valid resource (Col. 10, Satisfying demand within agreed time limits. The aggregation of single jobs into ob aggregates includes, as discussed earlier, an importance modification with regard to carry-over, demand and future work.);
an indicator of whether the selected task is performable by the at least one valid resource; or
a rationale associated with the performance of the selected task by the at least one valid resource (Col. 10, Satisfying demand with correctly equipped and placed resources. This goal is satisfied by correctly matching resources to demand, i.e. by assigning the right type of job aggregates to resources, with regard to area and skill).
With regard to claim 9, Kern teaches wherein the assignment instruction is selected from a group including:
an instruction to stop a current task assignment (Fig. 4, IF improvement MoveTo(OptimisedPlan, Resource[i]); Col. 12, A fundamental element of optimization process is the concept of a move … “Move-to” moves: This move type changes the assignment of a single resource … While the initially assigned tasks or task aggregates are removed from the resource’s record and are placed back into the pool of unassigned work, the resource is allocated one or more tasks or task aggregates for new assignment);
an instruction to continue a current task assignment (Fig. 4, IF improvement Replace-Move(OptimisedPlan, Technician[i], Technician[j], …); Col. 13, “Replace” moves: The replace move involves two or more resources that form a endless chain. Each resource in the chain apart from the last is allocated one or more tasks/aggregates that were previous allocated to the next resource in the chain (Examiner notes: maintains a current task assignment); and
an instruction to receive a new task assignment (Fig. 3, 104. OptimisedPlan[i] = optimisePlan(BaseLinePlan[i], JobAggr[i]); Col. 11, Software for execution by the computer executes the various steps of the planning method. The high-level algorithm of the planning method is illustrated by pseudo-code in FIG. 3 … The base-line plan is modified repeatedly (step 104) by altering the allocation of resources to tasks. This may involve allocating a resource to a task or task aggregate that was previously unallocated, allocating a different resource to a particular task or task aggregate)
With regard to claim 10, Kern teaches wherein the assignment data includes one or more of:
a mapping of at least one selected resource of the set of selected resources to a selected task of the hierarchy of selected tasks (Col. 12, After iteratively over all planning periods, the respective output plans an important characteristic of the algorithm is the iterative construction of the solution. Rather than generating a single solution for the whole planning window at once, the method iteratively constructs a partial solution for each planning periods. At the end, the combination of all partial data yields a complete solution. Solving smaller sub-problems reduces the considered problem size per step and accelerate the overall planning process);
one or more assignable resources of the set of selected resources, each assignable resource mappable to a selected task;
one or more assignable tasks of the hierarchy of selected tasks, each assignable task capable of accepting mapped selected resources;
timing data associated with a completion of each selected task by its mapped selected resources;
probability data associated with a completion of each selected task by its mapped selected resources; or
the set of assignment cost data.
With regard to claim 14, Kern teaches a computer-assisted method for assignment of a set of resources for optimal completion of a set of tasks (Col. 11, Solutions are generated by executing a plan method by means of a computer system such as that shown in FIG. 1. Software for execution by the computer executes the various steps of the planning method), the method comprising:
It is a computer-assisted method having similar limitations as claim 1. Thus, claim 14 is rejected for the same rationale as applied to claim 1.
With regard to claim 15, it is a computer-assisted method having similar limitations as claim 2. Thus, claim 15 is rejected for the same rationale as applied to claim 2.
With regard to claim 16, it is a computer-assisted method having similar limitations as claim 3. Thus, claim 16 is rejected for the same rationale as applied to claim 3.
With regard to claim 17, it is a computer-assisted method having similar limitations as claim 5. Thus, claim 17 is rejected for the same rationale as applied to claim 5.
With regard to claim 18, it is a computer-assisted method having similar limitations as claim 7. Thus, claim 18 is rejected for the same rationale as applied to claim 7.
With regard to claim 19, it is a computer-assisted method having similar limitations as claim 8. Thus, claim 19 is rejected for the same rationale as applied to claim 8.
With regard to claim 20, it is a computer-assisted method having similar limitations as claim 10. Thus, claim 20 is rejected for the same rationale as applied to claim 10.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Kern in view of Oliver as applied to claim 10 above, and further in view of Laithwaite et al. GB 2,457,320 A (hereinafter Laithwaite).
With regard to claim 11, Laithwaite teaches wherein the probability data includes one or more of:
a probability of the completion of each selected task by its mapped selected resources; or
a probability of the completion of the hierarchy of selected tasks (Col. 16, The resource allocation system calculates a time-dependent “cost function” for each task. This takes into account evaluation cost criteria such as the penalty for failing to meet an agreed time (which is the same whoever does it) and the probability of the task failing (which varies from one resource to another). This probability depends on the projected finishing time of the resource’s current tour, the amount of travelling time needed to get to the new task, the estimated duration of the new task, and the target time by which the new task must be done.).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Laithwaite with the teachings of Kern and Oliver in order to provide a system that teaches probability data including a probability of completion of selected tasks on a resource. The motivation for applying Laithwaite teaching with Kern and Oliver teaching is to provide a system that allows for calculation of the probability of task failing to meet a completion time on a particular resource, such that the cost can be computed across a plurality of resources in order to inform a best resource for allocation of the set of tasks (Laithwaite, Col. 17). Kern and Oliver and Laithwaite are analogous art directed towards resource and workflow management. Therefore, it would have been obvious for one of ordinary skill in the art to combine Laithwaite with Kern and Oliver to teach the claimed invention in order to provide a metric to calculate the completion of a set of task on a resource in order to compare and inform assignment descisions.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Kern in view of Oliver as applied to claim 1 above, and further in view of Pendar et al. Pub. No. US 2021/0055973 A1 (hereinafter Pendar).
With regard to claim 12, Pendar teaches wherein:
the resource selector module is configured by artificial intelligence training according to one or more simulations of the plurality of task ([0002], Accordingly, there is a need for an efficient way to optimize task/resource assignment to entities … The embodiments described herein provide for efficient task/resource assignment using artificial intelligence and machine learning. Tasks/resources are initially randomly assigned a number of times and a score for each solution of the random assignment is calculated. Using machine learning and artificial intelligence, a subset of the solutions is selected. Assignment of task/resource within the subset of the solutions may be randomly changed … During each iteration the process is improved and optimized. In some embodiments, the process repeats until a certain condition is met, e.g., a number of iterations, time out, improvement between two iterations is less than a certain threshold, etc.)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Pendar with the teachings of Kern and Oliver in order to provide a system that teaches an task resource selection configured through training provided by artificial intelligence over one or more simulations of the plurality of tasks. The motivation for applying Pendar teaching with Kern and Oliver teaching is to provide a system that allows for an artificial intelligence to perform the optimizations of assignments between tasks and resource in a time efficient manner such that would save CPU power and cost (Pendar, [0002]). Kern and Oliver and Pendar are analogous art directed towards task dispatching arrangements. Therefore, it would have been obvious for one of ordinary skill in the art to combine Pendar with Kern and Oliver to teach the claimed invention in order to provide a machine learning algorithm configured to efficiently and accurately output an optimized task-resource mapping.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Kern in view of Oliver as applied to claim 1 above, and further in view of Zavlanos et al. "A Distributed Auction Algorithm for the Assignment Problem" (hereinafter Zavlanos).
With regard to claim 13, Zavlanos teaches wherein:
the assignment module is configured to produce the optimal assignment of the set of selected resources to the hierarchy of selected tasks according to one of more auction algorithms (Pg. 1212, Given two sets consisting of agents and tasks, respectively, the linear assignment problem searches for a one-to-one matching between agents and the tasks so that the total assignment benefit is maximized. In this paper, we investigate the linear assignment problem in the context of networked systems, where computation and memory resources are distributed among a set of agents with limited communication capabilities seeking an assignment with a desired set of tasks … we propose a distributed auction algorithm for the assignment problem and discuss its convergence and complexity properties).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to apply the teachings of Zavlanos with the teachings of Kern and Oliver in order to provide a system that teaches an auction algorithm applied as the assignment module configured to produce an optimal mapping of resources to tasks. The motivation for applying Zavlanos teaching with Kern and Oliver teaching is to provide a system that allows for a distributed system of agents to bid for task assignment, thereby eliminating the need for a global communication among agents while maintaining allocation efficiency and resource utilization (Zavlanos, Abstract). Kern and Oliver and Zavlanos are analogous art directed towards task dispatching arrangements. Therefore, it would have been obvious for one of ordinary skill in the art to combine Zavlanos with Kern and Oliver to teach the claimed invention in order to provide an auction algorithm assignment module for mapping tasks to respective resources.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2002/0188597 A1
teaches
Methods and Systems for Linking Tasks to Workflow
US 2008/0183538 A1
teaches
Allocating Resources to Tasks in Workflows
US 2011/0321051 A1
teaches
Task Scheduling Based on Dependencies and Resources
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/I.A.C./Examiner, Art Unit 2195
/Aimee Li/Supervisory Patent Examiner, Art Unit 2195