DETAILED ACTION
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 .
Response to Arguments
Applicant’s following arguments with respect to the 35 U.S.C. 103 rejections (Remarks pp. 10-14) have been fully considered. Most of the arguments are moot in view of the Examiner adding secondary reference Bills (US 20170308402 A1). However, there are 2 arguments that the Examiner would like to address.
(1) Applicant states:
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The Examiner respectfully disagrees. Ibrahim teaches a “set of proposed task assignments” with the algorithm determining assignments of resources to tasks for each stage of the algorithm.
Ibrahim discloses, “In step 510, a genetic algorithm module creates an initial population in which each row of the initial population corresponds to one chromosome in a genetic algorithm, and each activity in the row of the initial population corresponds to a gene in the genetic algorithm. In step 520, the module calculates the fitness of every individual in the population and selects the fittest ones using roulette wheel selection. In step 530, the module applies a crossover process to create new chromosomes. In step 540, the module applies a mutation process to create mutated chromosomes. In step 550, the module applies an iteration process until a predetermined value is reached. In step 560, the module determines the optimum chromosome from the new population,” ¶ 0038.
Each successive set of task assignments for each stage is based on the previous stage’s set of task assignments, in order to converge towards an optimal set.
(2) Applicant states:
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The Examiner respectfully disagrees. Both Ibrahim’s algorithm and a branch-and-bound algorithm serve the same purpose of optimizing a solution. The algorithm of Ibrahim is not incompatible with the branch-and-bound method of Piazza. There are various ways to combine Ibrahim and Piazza through substitution or supplementation. For example, after Piazza is added to the combination, the algorithm from Ibrahim produces a set of solutions that can then be searched by Piazza’s branch-and-bound algorithm. As a result, the branch-and-bound algorithm fine-tunes the near-optimal algorithm results to mathematically guarantee that the final assignment of tasks is optimal.
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, 5, 9-10, 14, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Ibrahim (US 20170116522 A1) in view of Brandt (US 20030050800 A1) and Bills (US 20170308402 A1).
Regarding Claim 1, Ibrahim teaches a computer-implemented method comprising: obtaining, by one or more processors, preferences and capabilities for resources to complete tasks of a job (
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Ibrahim discloses, “The invention provides a computer implemented method for assigning tasks to a group of workers including the steps of retrieving, by a data retrieval module, information relating to a set of activities to be executed, a set resources to be utilized during the execution of the activities, a set of constraints to be satisfied, and a set of objectives to be accomplished from a database,” ¶ 0006.
The claimed “preferences” is mapped to the disclosed “working hours”-based and “travelling time and distance”-based “constraints”, which are preferences in that the assignment of tasks to a group of workers is based on the workers’ preferences for time and distance.
The claimed “capabilities” is mapped to the disclosed skillset-based “resource constraints”, as the constraints indicate the capability of the resources to execute the tasks. This is shown by the above Table 3, which shows ticket assignment being based on team skillsets, or the skills of each team member for completing the task.);
representing the tasks as separate sets of (
Ibrahim discloses, “The invention provides a computer implemented method for assigning tasks to a group of workers including the steps of retrieving, by a data retrieval module, information relating to a set of activities to be executed, a set resources to be utilized during the execution of the activities, a set of constraints to be satisfied, and a set of objectives to be accomplished from a database; assigning, by a weight assigning module, a weight value according to the set of constraints for each of the activities; sorting, by a task sorting module, the activities according to each of their respective weight value; assigning, by the task sorting module, at least one resource to each of the activities; generating, by a match matrix generator, a matrix carrying the list of activities with their corresponding resource assigned,” ¶ 0006.
The claimed “items” is mapped to the set of unassigned activities as well as the initial assignments of each of the activities to a resource, as this set will be inputted into the genetic algorithm being used in order to optimize the assignments.
This is consistent with paragraph 57 of the present application’s specification, which states “the sets of configurable items include a set of unassigned tasks identifying each of the tasks of the job to be assigned to the resources, as well as a set of proposed task assignments identifying one or more tasks of the job with proposed assignments to one or more of the resources, and with preferences and capabilities of the one or more resources identified for completing the one or more respective tasks.”
The “preferences and capabilities” are indicated by the weight value representing the constraints, in particular the working hours”-based and “travelling time and distance”-based “resource constraints”, and the skillset-based “resource constraints”, for each activity.);
obtaining a configurator to execute on the one or more processors to generate an optimized solution which identifies for the one set of unassigned tasks an optimal set of assigned tasks to the resources based on possible combinations of task-to-resource assignments obtained, at least in part, with reference to the other set of proposed task assignments, where the optimized solution contains task-to-resources assignments based, at least in part, on the capabilities of the resources, while meeting a satisfaction goal for completion of the job based, at least in part, on the preferences of the resources (
Ibrahim discloses, “The invention relates to a task scheduling system and method. More particularly, the invention relates to an automation system and method in assigning duties to a group of workers via an improved genetic algorithm that satisfies all constraints,” ¶ 0002, “The invention provides a computer implemented method for assigning tasks to a group of workers including the steps of retrieving, by a data retrieval module, information relating to a set of activities to be executed, a set resources to be utilized during the execution of the activities, a set of constraints to be satisfied, and a set of objectives to be accomplished from a database… applying, by a genetic algorithm module, a genetic algorithm process on the generated matrix to produce an optimum solution for the assignment of the at least one resource to each of the activities,” ¶ 0006, and “In step 510, a genetic algorithm module creates an initial population in which each row of the initial population corresponds to one chromosome in a genetic algorithm, and each activity in the row of the initial population corresponds to a gene in the genetic algorithm. In step 520, the module calculates the fitness of every individual in the population and selects the fittest ones using roulette wheel selection. In step 530, the module applies a crossover process to create new chromosomes. In step 540, the module applies a mutation process to create mutated chromosomes. In step 550, the module applies an iteration process until a predetermined value is reached. In step 560, the module determines the optimum chromosome from the new population,” ¶ 0038.
The claimed “optimal set of assigned tasks” is mapped to the disclosed “optimum solution for the assignment of the at least one resource to each of the activities”, which is a set of most optimal assignments between each activity and a resource that is outputted by the genetic algorithm. This is consistent with paragraph 54 of the present application’s specification, which states that “in one or more embodiments, a configurator workflow is presented herein for generating an optimized solution which identifies, for a set of unassigned tasks, the optimal set of assigned tasks to the resources based on possible combinations of task-to-resource assignments obtained, at least in part, with reference to a set of proposed task assignments. The optimized solution contains task-to-resource assignments based, at least in part, on the capabilities of the resources, while meeting a satisfaction goal for completion of the job based, at least in part, on the preferences of the resources.”
As paragraph 2 of Ibrahim states, the optimized solution has to be based on the constraints of the problem, which include the “preferences of the resources” (“working hours”-based and “travelling time and distance”-based “resource constraints”) to ensure that the solution can be satisfied.);
and executing the configurator on the one or more processors to automatically generate the optimized solution which identifies for the one set of unassigned tasks the optimal set of assigned tasks to the resources, wherein the optimized solution contains the task-to-resource assignments based, at least in part, on the capabilities of the resources, while meeting the satisfaction goal for completion of the job based, at least in part, on the preferences of the resources (
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Ibrahim discloses, “applying, by a genetic algorithm module, a genetic algorithm process on the generated matrix to produce an optimum solution for the assignment of the at least one resource to each of the activities,” ¶ 0006, and “Therefore, a need exists to design an engine based on an improved genetic algorithm to overcome the aforementioned drawbacks and to replace the prior manual task assignment system with a fully automated engine that can assign multiple tasks with multiple objectives to multiple groups of workers. The present invention provides such a method and system thereof,” ¶ 0005.
Here, the optimal solution will be based on the constraints, which include “working hours”-based and “travelling time and distance”-based “resource constraints” (preferences) to meet a satisfaction goal, and skillset-based “resource constraints” (capabilities) to ensure that the solution can be completed.).
Ibrahim does not teach that the tasks are represented as sets of configurable items, or wherein tasks of the one set of unassigned tasks include task representations with one or more associated resource-related attributes with undetermined values and tasks of the other set of proposed task assignments include task representations with one or more associated resource-related attributes with determined values.
However, Brandt teaches that the tasks are represented as sets of configurable items (
Brandt discloses, “The designer of software application 60 provides interfaces and functions for the accomplishment of real world tasks 17 (FIG. 5). By building a solution which exposes discrete work steps as configurable items, and by building a user-friendly, graphical design environment which represents each exposed work step as image element 350 (FIG. 1) and minimizes the work required by the designer to incorporate that step within a process, it is possible to create reengineered work flow processes that can be successfully executed by a partnership between software application 60 and work flow engine 10,” ¶ 0054.).
Ibrahim and Brandt are both considered to be analogous to the claimed invention because they are in the same field of task scheduling. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ibrahim to incorporate the teachings of Brandt and provide that the tasks are represented as sets of configurable items. Doing so would help allow for increased flexibility in workflows and reduce the amount of effort required in scheduling tasks. (Brandt discloses, “minimizes the work required by the designer to incorporate that step within a process, it is possible to create reengineered work flow processes that can be successfully executed by a partnership between software application 60 and work flow engine 10,” ¶ 0054.).
Ibrahim in view of Brandt does not teach wherein tasks of the one set of unassigned tasks include task representations with one or more associated resource-related attributes with undetermined values and tasks of the other set of proposed task assignments include task representations with one or more associated resource-related attributes with determined values.
However, Bills teaches wherein tasks of the one set of unassigned tasks include task representations with one or more associated resource-related attributes with undetermined values (
Bills discloses, “Associations 124 are data structures that describe relationships between object 121-123. An association 124 may be a discrete data structure, or an association may be a logical construct derived from correlating objects 121-123 based on identifiers found in objects 121-123. For instance, one type of association is an attachment. A task object may, for instance, include a number of attachment fields specifying identifiers of other data objects 121-123 that have been attached to the task object. Or, a separate association table may include records that specify a task object identifier for a task object, and another object identifier that corresponds to an object that has been attached to the task object. Associations 124 may describe many other types of relationships to a given task object, such as an assigned user object, an allocated resource object, an originating report or event object, a follow-up task object, and so forth,” ¶ 0055,
“System 100 comprises a task object generation component 130. Task object generation component 130 receives requests to generate task objects 121, and generates task objects 121 responsive to those requests. In response to certain requests, task object generation component 130 may initially generate ‘empty’ task objects that comprise trivial or null values for each task object field,” ¶ 0058,
“Block 340 comprises identifying, in the repository of data objects, a particular data object to associate with the task object. For instance, the particular data object may be a report object, event object, asset object, user object, resource object, or any other suitable data object,” ¶ 0103,
“In an embodiment, block 340 comprises receiving a second request to attach the particular data object to the task object, subsequent to the first request. For instance, a graphical user interface may display a representation of the task object. The graphical user interface may also display various representations of other data objects, such as a second icon representing the particular data object. In response to receiving input that drags the second icon over the representation of the task object, the second request may be generated, including an identifier for the particular data object. A link between the task object and the particular data object may optionally be created,” ¶ 0105.
Here, tasks can be generated with attributes (disclosed “object fields”) with undetermined or null values. According to paragraphs 103 and 105 of Bills, the set of unassigned tasks consists of all tasks that have not been assigned to an allocated resource object. Each of these tasks has an object field representing an allocated resource object that is assigned to it, with a null or undetermined value meaning that no allocated resource object is currently assigned to it.)
and tasks of the other set of proposed task assignments include task representations with one or more associated resource-related attributes with determined values (
Bills discloses, “In response to other requests, task object generation component 130 may populate various fields of newly generated task objects 121 with values derived from parameters included with the request or supplied by a data importing component 132,” ¶ 0058,
“Block 340 comprises identifying, in the repository of data objects, a particular data object to associate with the task object. For instance, the particular data object may be a report object, event object, asset object, user object, resource object, or any other suitable data object,” ¶ 0103,
“In an embodiment, block 340 comprises receiving a second request to attach the particular data object to the task object, subsequent to the first request. For instance, a graphical user interface may display a representation of the task object. The graphical user interface may also display various representations of other data objects, such as a second icon representing the particular data object. In response to receiving input that drags the second icon over the representation of the task object, the second request may be generated, including an identifier for the particular data object. A link between the task object and the particular data object may optionally be created,” ¶ 0105.
Here, tasks can be generated with attributes (disclosed “object fields”) with determined or populated values. According to paragraphs 103 and 105 of Bills, the set of assigned tasks consists of all tasks that have been assigned to an allocated resource object. Each of these tasks has an object field representing an allocated resource object that is assigned to it, with a populated, non-null/determined value meaning that an allocated resource object has been assigned to it.).
Ibrahim in view of Brandt, and Bills are both considered to be analogous to the claimed invention because they are in the same field of computer resource scheduling. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ibrahim in view of Brandt to incorporate the teachings of Bills and provide wherein tasks of the one set of unassigned tasks include task representations with one or more associated resource-related attributes with undetermined values and tasks of the other set of proposed task assignments include task representations with one or more associated resource-related attributes with determined values. Doing so would help ensure that the attributes of the task representations do not have to be filled in during task initialization, and can instead be populated later when suitable values for the attributes are found (Bills discloses, “In response to certain requests, task object generation component 130 may initially generate ‘empty’ task objects that comprise trivial or null values for each task object field. In response to other requests, task object generation component 130 may populate various fields of newly generated task objects 121 with values derived from parameters included with the request or supplied by a data importing component 132,” ¶ 0058.).
Claims 10 and 17 are a computer system claim and a computer program product claim, respectively, corresponding to the method Claim 1 (Ibrahim ¶ 0025.). Therefore, Claims 10 and 17 are rejected for the same reasons set forth in the rejection of Claim 1.
Regarding Claim 5, Ibrahim in view of Brandt and Bills teaches the computer-implemented method of claim 1, wherein representing the tasks as the separate sets of configurable items for processing by the configurator includes splitting an unassigned task of the one set of unassigned tasks that extends across multiple defined time intervals into separate unassigned tasks with non-overlapping time intervals, and splitting a proposed task assignment of the other set of proposed task assignments that extends across multiple defined time intervals into separate proposed task assignments with non-overlapping time intervals (
Brandt discloses, “many work flow engines 10 (FIG. 1) feature a graphical design environment or graphical work flow process configuration environment 100 (not shown in the figures) that allows a work flow process to be defined and/or manipulated, e.g. visually by creating a flowchart of work flow steps to be accomplished (shown at 300 in FIG. 1 and in more detail in FIG. 3),” ¶ 0033, and “When work flow engine 10 is executing and reaches a step in the flowchart, e.g. 354 in FIG. 3, work flow engine 10 will put a descriptor of a work process step or task, e.g. a line item, in a work list (not shown in the figures). A user can select that line item in the work list to start the step or task. In a preferred embodiment, selecting a line item in a work list takes the user to a predetermined interface, e.g. information shown on display 12, which may then be used to trigger an automatic program control. Completion of the task using that interface results in a notification to work flow engine 10 of completion of the task, along with any relevant state data. Work flow engine 10 may then proceed with a next step in the work flow process configuration and may use the provided relevant data to determine how to proceed,” ¶ 0034.
Here, the workflow is split into different steps, and the different steps of the workflow are completed in a way so that they do not overlap in terms of the time intervals during which they execute. In other words, none of the steps of the workflow execute at the same time.).
Ibrahim and Brandt are both considered to be analogous to the claimed invention because they are in the same field of task scheduling. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ibrahim to incorporate the teachings of Brandt and provide wherein representing the tasks as the separate sets of configurable items for processing by the configurator includes splitting an unassigned task of the one set of unassigned tasks that extends across multiple defined time intervals into separate unassigned tasks with non- overlapping time intervals, and splitting a proposed task assignment of the other set of proposed task assignments that extends across multiple defined time intervals into separate proposed task assignments with non-overlapping time intervals. Doing so would help allow for flexibility in scheduling the different subtasks of each task on different resources.
Claim 14 is a computer system claim corresponding to the method Claim 5 (Ibrahim ¶ 0025.). Therefore, Claim 14 is rejected for the same reasons set forth in the rejection of Claim 5.
Regarding Claim 9, Ibrahim in view of Brandt and Bills teaches the computer-implemented method of claim 1, further comprising initiating execution of the tasks of the job using the optimized solution identifying the optimal set of assigned tasks to resources (
Ibrahim discloses, “In step 600, the genetic algorithm module determines the optimum solution and applies the outcomes to a task assigner for assigning resources to each task. In step 700, the outcomes of the assignment are outputted to a display,” ¶ 0035.).
Claims 2-5, 11-14, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ibrahim (US 20170116522 A1) in view of Brandt (US 20030050800 A1), Bills (US 20170308402 A1), and Zhu (US 20210096911 A1)
Regarding Claim 2, Ibrahim in view of Brandt and Bills teaches the computer-implemented method of claim 1. Ibrahim in view of Brandt and Bills does not teach wherein representing the tasks as the separate sets of configurable items for processing by the configurator to execute on the one or more processors includes defining the associated resource-related attributes for respective task representations, the associated resource-related attributes comprising a resource for the task, a resource preference for the task, and a suitability of the resource to the task.
However, Zhu teaches wherein representing the tasks as the separate sets of configurable items for processing by the configurator to execute on the one or more processors includes defining the associated resource-related attributes for respective task representations, the associated resource-related attributes comprising a resource for the task, a resource preference for the task, and a suitability of the resource to the task (
Zhu discloses, “For workflows that have designated edge servers, priority is given to the designated edge server, otherwise, it is assigned to the edge server with the first recommendation score; for workflows that do not specify an edge server, The distribution is directly based on the recommendation scores of each edge server,” ¶ 0083, “When the system matches user requirements with a specific template, it needs to detect three user-specified indicators, namely the type, the constraints of various resources, and the running time of the total task. Only when the three requirements are met, the original task is transformed into the task set corresponding to the template,” ¶ 0089, “The dynamic resource monitoring module mainly monitors the real-time resources and task status of each edge server, thereby helping the real-time task scheduling module to make decisions. The monitored information mainly includes the common resources of each edge server, such as CPU occupancy rate, memory occupancy rate and current task waiting queue, etc.,” ¶ 0091, and “Step C, The intelligent judgment module calculates the node computing capability and the database information through the edge computing node to determine the processing capability of the edge computing node; and if it is determined that the current edge computing node can implement the face recognition and behavior determination, send the execution command to the current node, and then the next step jumps to the step D,” ¶ 0097.
The claimed “resource for the task” is mapped to the disclosed edge computing node that is selected for running a task.
The claimed “resource preference for the task” is mapped to the disclosed “current task waiting queue” that is monitored to determine the edge computing node’s current schedule.
The claimed “suitability of the resource to the task” is mapped to the disclosed “recommendation score” of the edge computing node that determines the suitability of the node for running the task.).
Ibrahim in view of Brandt and Bills, and Zhu are both considered to be analogous to the claimed invention because they are in the same field of resource scheduling. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ibrahim in view of Brandt and Bills to incorporate the teachings of Zhu and provide wherein representing the tasks as the separate sets of configurable items for processing by the configurator to execute on the one or more processors includes defining the associated resource-related attributes for respective task representations, the associated resource-related attributes comprising a resource for the task, a resource preference for the task, and a suitability of the resource to the task. Doing so would help ensure that the selected resources can process the task more efficiently, and provide a better match between each resource and each task (Zhu discloses, “the edge computing node is provided to select the upload server, having a good scheduling characteristics, thereby solving the resource scheduling problem of the edge computing node, and improving the overall performance of the cloud computing center,” ¶ 0020.).
Claims 11 and 18 are a computer system claim and a computer program product claim, respectively, corresponding to the method Claim 2 (Ibrahim ¶ 0025.). Therefore, Claims 11 and 18 are rejected for the same reasons set forth in the rejection of Claim 2.
Regarding Claim 3, Ibrahim in view of Brandt, Bills, and Zhu teaches the computer-implemented method of claim 2, wherein the respective task representations of the one set of unassigned tasks each have identified values for a task type and a task duration (
Zhu discloses, “The original task mapping module is to translate the user requirements entered by the system in a preset format into a set of corresponding task units. When the system matches user requirements with a specific template, it needs to detect three user-specified indicators, namely the type, the constraints of various resources, and the running time of the total task,” ¶ 0089.),
and undetermined values for the resource for the task, the resource preference for the task, and the suitability of the resource to the task (
Zhu discloses, “For workflows that have designated edge servers, priority is given to the designated edge server, otherwise, it is assigned to the edge server with the first recommendation score; for workflows that do not specify an edge server, The distribution is directly based on the recommendation scores of each edge server,” ¶ 0083, “When the system matches user requirements with a specific template, it needs to detect three user-specified indicators, namely the type, the constraints of various resources, and the running time of the total task. Only when the three requirements are met, the original task is transformed into the task set corresponding to the template,” ¶ 0089, “The dynamic resource monitoring module mainly monitors the real-time resources and task status of each edge server, thereby helping the real-time task scheduling module to make decisions. The monitored information mainly includes the common resources of each edge server, such as CPU occupancy rate, memory occupancy rate and current task waiting queue, etc.,” ¶ 0091, and “Step C, The intelligent judgment module calculates the node computing capability and the database information through the edge computing node to determine the processing capability of the edge computing node; and if it is determined that the current edge computing node can implement the face recognition and behavior determination, send the execution command to the current node, and then the next step jumps to the step D,” ¶ 0097.
The claimed “undetermined values” is mapped to the disclosed values of the current task waiting queue, edge computing node, and recommendation score of the edge computing node not being determined until after the task type and task duration are identified. This means that these undetermined values must be solved for in order to compute the optimal assignments of each task to a resource.
This is supported by paragraph 58 of the present application’s specification, which states that “task representations of the set of unassigned tasks can each have identified values for the task type (t) and the task duration (d), and undetermined values initially for the resource for the task (a), the resource preference for the task (p), and the suitability of the resource to the task (s), while task representations of the set of proposed task assignments can each have identified values for the task type (t), task duration (d), the resource for the task (a), the resource preference for the task (p), and the suitability of the resource to the task (s).”
The “resource for the task” is the disclosed edge computing node that is selected for running a task.
The “resource preference for the task” is the disclosed “current task waiting queue” that is monitored to determine the edge computing node’s current schedule.
Ibrahim teaches scheduling based on “preferences” is mapped to the disclosed “working hours”-based and “travelling time and distance”-based “constraints”, which are preferences in that the assignment of tasks to a group of workers is based on the workers’ preferences for time and distance.
The “suitability of the resource to the task” is the disclosed “recommendation score” of the edge computing node that determines the suitability of the node for running the task.).
Ibrahim in view of Brandt and Bills, and Zhu are both considered to be analogous to the claimed invention because they are in the same field of computer resource scheduling. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ibrahim in view of Brandt and Bills to incorporate the teachings of Zhu and provide wherein the respective task representations of the one set of unassigned tasks each have identified values for a task type and a task duration, and undetermined values for the resource for the task, the resource preference for the task, and the suitability of the resource to the task. Doing so would help ensure that the resources can be selected accordingly in order to process the task more efficiently, and provide a better match between each resource and each task. (Zhu discloses, “the edge computing node is provided to select the upload server, having a good scheduling characteristics, thereby solving the resource scheduling problem of the edge computing node, and improving the overall performance of the cloud computing center,” ¶ 0020.).
Claims 12 and 19 are a computer system claim and a computer program product claim, respectively, corresponding to the method Claim 3 (Ibrahim ¶ 0025.). Therefore, Claims 12 and 19 are rejected for the same reasons set forth in the rejection of Claim 3.
Regarding Claim 4, Ibrahim in view of Brandt, Bills, and Zhu teaches the computer-implemented method of claim 3, wherein the respective task representations of the other set of proposed task assignments each have identified values for task type, and task duration, and the resource for the task, the resource preference for the task and the suitability of the resource to the task (
Zhu discloses, “For workflows that have designated edge servers, priority is given to the designated edge server, otherwise, it is assigned to the edge server with the first recommendation score; for workflows that do not specify an edge server, The distribution is directly based on the recommendation scores of each edge server,” ¶ 0083, “When the system matches user requirements with a specific template, it needs to detect three user-specified indicators, namely the type, the constraints of various resources, and the running time of the total task. Only when the three requirements are met, the original task is transformed into the task set corresponding to the template,” ¶ 0089, “The dynamic resource monitoring module mainly monitors the real-time resources and task status of each edge server, thereby helping the real-time task scheduling module to make decisions. The monitored information mainly includes the common resources of each edge server, such as CPU occupancy rate, memory occupancy rate and current task waiting queue, etc.,” ¶ 0091, and “Step C, The intelligent judgment module calculates the node computing capability and the database information through the edge computing node to determine the processing capability of the edge computing node; and if it is determined that the current edge computing node can implement the face recognition and behavior determination, send the execution command to the current node, and then the next step jumps to the step D,” ¶ 0097.
When the task is assigned an edge computing node, the type, duration, resource (edge computing node), resource preference (current task waiting queue, which indicates the schedule of the edge computing node), and suitability (recommendation score) will all have identified values.).
Ibrahim in view of Brandt and Bills, and Zhu are both considered to be analogous to the claimed invention because they are in the same field of computer resource scheduling. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ibrahim in view of Brandt and Bills to incorporate the teachings of Zhu and provide wherein the respective task representations of the other set of proposed task assignments each have identified values for task type, and task duration, and the resource for the task, the resource preference for the task and the suitability of the resource to the task. Doing so would help provide a more optimal assignment of the resource to the task, and provide a better match between each resource and each task. (Zhu discloses, “Step C, The intelligent judgment module calculates the node computing capability and the database information through the edge computing node to determine the processing capability of the edge computing node; and if it is determined that the current edge computing node can implement the face recognition and behavior determination, send the execution command to the current node, and then the next step jumps to the step D,” ¶ 0097).
Claims 13 and 20 are a computer system claim and a computer program product claim, respectively, corresponding to the method Claim 4 (Ibrahim ¶ 0025.). Therefore, Claims 13 and 20 are rejected for the same reasons set forth in the rejection of Claim 4.
Claims 6-7 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Ibrahim (US 20170116522 A1) in view of Brandt (US 20030050800 A1), Bills (US 20170308402 A1), Zhu (US 20210096911 A1), and Diaz (US 20120130915 A1).
Regarding Claim 6, Ibrahim in view of Brandt, Bills, and Zhu teaches the computer-implemented method of claim 1. Ibrahim in view of Brandt, Bills, and Zhu does not teach further comprising prespecifying a satisfaction score threshold for the optimized solution, and wherein executing the configurator on the one or more processors to automatically generate the optimized solution includes confirming by the configurator that the optimized solution has a satisfaction score associated therewith which at least meets the prespecified satisfaction score threshold.
However, Diaz teaches further comprising prespecifying a satisfaction score threshold for the optimized solution, and wherein executing the configurator on the one or more processors to automatically generate the optimized solution includes confirming by the configurator that the optimized solution has a satisfaction score associated therewith which at least meets the prespecified satisfaction score threshold (
Diaz discloses, “If a resource is not fully qualified for a job, the matching engine 102 accesses transition rules for the job to determine whether the resource can become qualified. For example, if the rules specify that if the resource has a score greater than a threshold and the resource has a certain skill set, then the rules indicate that with certain training the resource can become qualified for the job.,” ¶ 0015.
Here, it can be seen that a satisfaction score associated with a resource is required to satisfy a threshold in order for a selected resource to be accepted. After the combination of Ibrahim in view of Brandt, Bills, and Zhu, with Diaz, said satisfaction score and threshold are used to ensure that the optimized solution of task-to-resource assignments satisfies the threshold.).
Ibrahim in view of Brandt, Bills, and Zhu, and Diaz, are both considered to be analogous to the claimed invention because they are in the same field of computer resource scheduling. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ibrahim in view of Brandt, Bills, and Zhu to incorporate the teachings of Diaz and provide further comprising prespecifying a satisfaction score threshold for the optimized solution, and wherein executing the configurator on the one or more processors to automatically generate the optimized solution includes confirming by the configurator that the optimized solution has a satisfaction score associated therewith which at least meets the prespecified satisfaction score threshold. Doing so would help ensure that a more optimal configuration of resource to task assignments is selected in a way that satisfies users’ needs.
Claim 15 is a computer system claim corresponding to the method Claim 6 (Ibrahim ¶ 0025.). Therefore, Claim 15 is rejected for the same reasons set forth in the rejection of Claim 6.
Regarding Claim 7, Ibrahim in view of Brandt, Bills, and Zhu teaches the computer-implemented method of claim 1. Ibrahim in view of Brandt, Bills, and Zhu does not teach further comprising: prespecifying an efficiency score threshold for the optimized solution, wherein executing the configurator on the one or more processors to automatically generate the optimized solution includes confirming by the configurator that the optimized solution has an efficiency score associated therewith which at least meets the prespecified efficiency score threshold;
and prespecifying a preference score threshold for the optimized solution, wherein executing the configurator on the one or more processors to automatically generate the optimized solution further includes confirming by the configurator that the optimized solution has a preference score threshold associated therewith which at least meets the prespecified preference score threshold.
However, Diaz teaches further comprising: prespecifying an efficiency score threshold for the optimized solution, wherein executing the configurator on the one or more processors to automatically generate the optimized solution includes confirming by the configurator that the optimized solution has an efficiency score associated therewith which at least meets the prespecified efficiency score threshold (
Diaz discloses, “If a resource is not fully qualified for a job, the matching engine 102 accesses transition rules for the job to determine whether the resource can become qualified. For example, if the rules specify that if the resource has a score greater than a threshold and the resource has a certain skill set, then the rules indicate that with certain training the resource can become qualified for the job,” ¶ 0015.
Here, it can be seen that an efficiency score (which is the same as the satisfaction score from Claim 6, as the score both indicates satisfaction and efficiency; if the resource is not qualified or does not have the ideal skill set for the job, then the job cannot be efficiently done) associated with a resource is required to satisfy a threshold in order for a selected resource to be accepted. After the combination of Ibrahim in view of Brandt, Bills, and Zhu, with Diaz, said efficiency score and threshold are used to ensure that the optimized solution of task-to-resource assignments satisfies the threshold.);
and prespecifying a preference score threshold for the optimized solution, wherein executing the configurator on the one or more processors to automatically generate the optimized solution further includes confirming by the configurator that the optimized solution has a preference score threshold associated therewith which at least meets the prespecified preference score threshold (
Diaz discloses, “If a resource is not fully qualified for a job, the matching engine 102 accesses transition rules for the job to determine whether the resource can become qualified. For example, if the rules specify that if the resource has a score greater than a threshold and the resource has a certain skill set, then the rules indicate that with certain training the resource can become qualified for the job,” ¶ 0015.
Here, it can be seen that a preference score (skill set requirements being satisfied) associated with a resource is required to satisfy a threshold in order for a selected resource to be accepted. After the combination of Ibrahim in view of Brandt, Bills, and Zhu, with Diaz, said preference score and threshold are used to ensure that the optimized solution of task-to-resource assignments satisfies the threshold.).
Ibrahim in view of Brandt, Bills, and Zhu, and Diaz, are both considered to be analogous to the claimed invention because they are in the same field of scheduling. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ibrahim in view of Brandt, Bills, and Zhu to incorporate the teachings of Diaz and provide further comprising: prespecifying an efficiency score threshold for the optimized solution, wherein executing the configurator on the one or more processors to automatically generate the optimized solution includes confirming by the configurator that the optimized solution has an efficiency score associated therewith which at least meets the prespecified efficiency score threshold; and prespecifying a preference score threshold for the optimized solution, wherein executing the configurator on the one or more processors to automatically generate the optimized solution further includes confirming by the configurator that the optimized solution has a preference score threshold associated therewith which at least meets the prespecified preference score threshold. Doing so would help ensure that a more optimal configuration of resource to task assignments is selected in a way that satisfies users’ preferences.
Claim 16 is a computer system claim corresponding to the method Claim 7 (Ibrahim ¶ 0025.). Therefore, Claim 16 is rejected for the same reasons set forth in the rejection of Claim 7.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Ibrahim (US 20170116522 A1) in view of Brandt (US 20030050800 A1), Bills (US 20170308402 A1), Zhu (US 20210096911 A1), and Piazza (US 20110161964 A1).
Regarding Claim 8, Ibrahim in view of Brandt, Bills, and Zhu teaches the computer-implemented method of claim 1. Ibrahim in view of Brandt, Bills, and Zhu does not teach wherein the configurator is a branch-and-bound configurator which implements branch-and-bound processing of unassigned tasks of the one set of unassigned tasks in determining the optimized solution.
However, Piazza teaches wherein the configurator is a branch-and-bound configurator which implements branch-and-bound processing of unassigned tasks of the one set of unassigned tasks in determining the optimized solution (
Piazza discloses, “Other examples of methods suitable for determining an optimal task schedule may include any of a number of deterministic methods (e.g., interval optimization and branch and bound methods),” ¶ 0030.
After the combination of Ibrahim in view of Brandt, Bills, and Zhu, with Piazza, a branch and bound method is used to process the unassigned tasks to determine the optimized solution.).
Ibrahim in view of Brandt, Bills, and Zhu, and Piazza, are both considered to be analogous to the claimed invention because they are in the same field of computer resource scheduling. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ibrahim in view of Brandt, Bills, and Zhu to incorporate the teachings of Piazza and provide wherein the configurator is a branch-and-bound configurator which implements branch-and-bound processing of unassigned tasks of the one set of unassigned tasks in determining the optimized solution. Doing so would help provide a widely-used and supported algorithm for determining the optimized solution and enhance reliability and/or reduce development cost.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Benbassat et al. (US 20230004922 A1): Method and System for Solving Large Scale Optimization Problems Including Integrating Machine Learning With Search Processes
Liberty et al. (US 11537439 B1): Intelligent Compute Resource Selection For Machine Learning Training Jobs
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ANDREW NMN SUN/Examiner, Art Unit 2195
/Aimee Li/Supervisory Patent Examiner, Art Unit 2195
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