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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1-4 and 15-17 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8, 11, and 15 of US Patent No. 12,045,666, hereinafter Dwivedi.
Regarding claim 1,
Claims 1-3 of Instant Application
Claim 1 of Dwivedi
A method comprising:
A method comprising:
transmitting, from a containerized edge data center unit, a request corresponding to a pre-trained machine learning model;
identifying a first computational task that comprises a plurality of sub-tasks configured for execution using a plurality of nodes, wherein at least two nodes of the plurality of nodes are executing at least one of the plurality of sub-tasks;
collecting, during execution of a first computational task on a plurality of nodes, performance data characterizing utilization of resources of one or more nodes of the plurality of nodes, wherein the performance data comprises data of multiple formats;
collecting, during execution of the first computational task, real-time performance data for the plurality of sub-tasks, the collected performance data characterizing utilization, by the plurality of sub-tasks, of resources of the at least two nodes of the plurality of nodes, wherein the collected real-time performance data comprises data of multiple formats;
converting the data of multiple formats into a uniform format to obtain a representation of the performance data for two or more nodes of the plurality of nodes;
converting the data of multiple formats into a uniform format to obtain a representation of the collected real-time performance data for the plurality of sub-tasks and for the at least two nodes of the plurality of nodes; streaming, for presentation on a graphical user interface (GUI), the representation of the collected real-time performance data;
obtaining, responsive to the representation of the performance data, a reconfiguration determination to modify execution, on at least one node of the plurality of nodes, of a target computational task, wherein the target computational task comprises at least one of: the first computational task being currently executed on the plurality of nodes, or a second computational task to be subsequently executed on the plurality of nodes; and
receiving, responsive to the streamed representation, a reconfiguration request to modify execution on at least one node of the plurality of nodes, of at least one of: the first computational task being currently executed on the plurality of nodes, or a second computational task to be subsequently executed on the plurality of nodes;
wherein the reconfiguration request is received while the first computational task is being executed; and
causing, responsive to the reconfiguration determination, a modification to the execution of the target computational task on at least one node of the plurality of nodes
responsive to the reconfiguration request, causing execution of one or more of a plurality of sub-tasks of a respective computational task on at least one of the plurality of nodes to be modified.
2. The method of claim 1, further comprising: presenting, on a graphical user interface (GUI), the representation of the performance data.
3. The method of claim 2, wherein the representation of the collected performance data is streamed to the GUI in real time
In view of the above, it is clear that the conflicting claims are not patentably distinct from each other because claims 1-3 of the instant application merely broadens the scope of the claim 1 of Dwivedi by eliminating the underlined portion of limitation of claims 1-3.
Similarly, it is clear that the conflicting claims are not patentably distinct from each other because claims 15-17 of the instant application merely broadens the scope of the claim 11 of Dwivedi by eliminating the underlined portion of limitation of claims 15-17.
The dependent claims 4 and 12 of the instant application discloses the exact same functional limitations as the dependent claims 8, and 15 of Dwivedi, merely drafted in an apparatus format.
Claim Objections
Claims 1, 6, 8, 9, 13, and 19 are objected to because of the following informalities:
Claim 1 discloses "on at least one node of the plurality of nodes, , of a target computational task.” However, there is a typographical error where two consecutive commas are used. It is suggested to delete the extra comma.
Claim 8 discloses “A method comprising:” followed by steps written in the form “collecting,” “presenting,” “obtaining.” However, the last step breaks this parallel structure by using the verb “cause”. It is suggested to amend “cause” to “causing” to maintain grammatical consistency.
Claims 6, 9, 13, and 19 disclose “a time an output of the individual sub-task become available.” However, the verb should be singular because “output” is singular. It is suggested to amend “become” to “becomes.”
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of pre-AIA 35 U.S.C. 112, second paragraph::
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 9, 10, and 13 are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 13 is an exact, word-for-word duplicate of claim 9. Both claims depend on claim 8 and recite the exact same limitations regarding the plurality of sub-tasks and the performance data metrics. Identical claims are confusing and fail to distinctly define the bounds of the invention.
Claims 10 are also rejected by virtue of their dependency on parent claim 9.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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, 6, 7, 15, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US 2017/0255496, hereinafter Deng) in view of Chen et al. (US 2017/0124129, hereinafter Chen).
Regarding claim 1, Deng discloses
A method comprising (fig. 1-10):
collecting, during execution of a first computational task on a plurality of nodes, performance data characterizing utilization of resources of one or more nodes of the plurality of nodes (paragraph [0095]: Record execution log information of the first subtask into a performance database, where the execution log information includes a data volume of the first subtask, required waiting duration before the first subtask is executed, and a running platform and running duration of the first subtask, and the running platform is the CPU or the GPU); …
obtaining, responsive to the representation of the performance data, a reconfiguration determination to modify execution, on at least one node of the plurality of nodes, , of a target computational task, wherein the target computational task comprises at least one of: the first computational task being currently executed on the plurality of nodes, or a second computational task to be subsequently executed on the plurality of nodes (paragraph [0096]: Query the performance database, and calculate first average duration of executing subtasks by the CPU corresponding to the first working node and second average duration of executing subtasks by the GPU corresponding to the first working node; paragraph [0142]: If the load is uneven, and a ratio exceeds a specific threshold, an allocation to the task groups is adjusted; Note: Deng’s determination that the calculated average duration/load is “uneven” and exceeds a threshold constitutes the claimed “reconfiguration determination); and
causing, responsive to the reconfiguration determination, a modification to the execution of the target computational task on at least one node of the plurality of nodes (paragraph [0097]: Adjust, according to the first average duration and the second average duration, a quantity of subtasks in the CPU task group on the first working node, or a quantity of subtasks in the GPU task group on the first working node; paragraph [0144]: If the average duration of executing the subtasks by the CPU is greater than the average duration of executing the subtasks by the GPU… first M subtasks that are in the to-be-determined task group and that have greatest acceleration ratios are allocated to the GPU task group).
Deng does not explicitly disclose wherein the performance data comprises data of multiple formats; converting the data of multiple formats into a uniform format to obtain a representation of the performance data for two or more nodes of the plurality of nodes. Chen discloses wherein the performance data comprises data of multiple formats; converting the data of multiple formats into a uniform format to obtain a representation of the performance data for two or more nodes of the plurality of nodes (fig. 1-10; paragraph [0067]: As the first and second data nodes may be equipped with different types of disks and CPUs, the performance indexes of the first and second data nodes may be normalized to a uniform format for further comparison). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the distributed heterogeneous scheduling system of Deng by incorporating the data normalization step of Chen. The motivation would have been to normalize the performance indexes of the first and second data nodes for future comparison (Chen, paragraph [0067]). A person of ordinary skill in the art would recognize that normalizing the data into a uniform format is necessary to accurately compare the performance of Deng’s disparate CPU and GPU architectures, thereby allowing the system to make accurate task reallocation decisions.
Regarding claim 15 referring to claim 1, Deng discloses A system comprising: a memory device; and one or more processing devices, communicatively coupled to the memory device, to: … (FIG. 10).
Regarding claims 6 and 19, Deng discloses
wherein the first computational task comprises a plurality of sub-tasks (paragraph [0095]: Record execution log information of the first subtask…), wherein an individual sub-task of the plurality of sub-tasks is executed using at least two nodes of the plurality of nodes (paragraph [0096]: executing subtasks by the CPU corresponding to the first working node and second average duration of executing subtasks by the GPU corresponding to the first working node; Note: The CPU and GPU act as at least two processing nodes for the execution of the tasks), and wherein the performance data comprises one or more of: a lead time that elapses between scheduling the individual sub-task and a time an output of the individual sub-task become available, a cycle time that elapses between scheduling the individual sub-task and a time an execution of the individual sub-task ends, a peak memory utilization by the individual sub-task, a peak processor utilization by the individual sub-task, a mean memory utilization by the individual sub-task, a mean processor utilization by the individual sub-task (paragraph [0095]: required waiting duration before the first subtask is executed, and a running platform and running duration of the first subtask; paragraph [0096]: calculate first average duration of executing subtasks by the CPU corresponding to the first working node; Note: The required waiting duration combined with the running duration constitutes the claimed cycle time, and the average duration of executing subtasks constitutes the claimed mean processor utilization).
Regarding claims 7 and 20, Deng discloses
wherein the performance data characterizes utilization of one or more graphics processing units (GPUs) deployed by at least one node of the plurality of nodes (paragraph [0096]: Query the performance database, and calculate first average duration of executing subtasks by the CPU corresponding to the first working node and second average duration of executing subtasks by the GPU corresponding to the first working node).
Claims 2-5 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US 2017/0255496, hereinafter Deng) in view of Chen et al. (US 2017/0124129, hereinafter Chen) as applied to claims 1 and 15, and further in view of Zhao et al. (US 2020/0327036, hereinafter Zhao).
Regarding claims 2 and 16, Deng in view of Chen does not explicitly disclose further comprising: presenting, on a graphical user interface (GUI), the representation of the performance data. Zhao discloses further comprising: presenting, on a graphical user interface (GUI), the representation of the performance data (fig. 1-5; paragraph [0062]: Dashboard 328 may be used to show visualizations of GPU-to-GPU traffic status and other metrics. In some embodiments, dashboard 328 may be implemented as a user-friendly web-based GUI). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Deng in view of Chen by incorporating the GUI presentation step of Zhao. The motivation would have been to show visualizations of traffic status and other metrics (Zhao, paragraph [0062]). A person of ordinary skill in the art would recognize that presenting the performance data on a GUI provides system administrators with an intuitive, visual representation of hardware utilization, thereby allowing users to easily monitor the system and identify bottlenecks or traffic imbalances across the distributed nodes.
Regarding claims 3 and 17, Deng in view of Chen does not explicitly disclose wherein the representation of the collected performance data is streamed to the GUI in real time. Zhao discloses wherein the representation of the collected performance data is streamed to the GUI in real time (fig. 1-5; paragraph [0009]: provide real-time or near-real-time (e.g., second-by-second) GPU-to-GPU traffic monitoring and traffic analytics capabilities; paragraph [0070]: an analyzer repeatedly receives raw statistics data from the in-memory store in a streaming fashion and performs various analyses on the data). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Deng in view of Chen by incorporating the real-time streaming step of Zhao. The motivation would have been to provide real-time or near-real-time (e.g., second-by-second) GPU-to-GPU traffic monitoring and traffic analytics capabilities (Zhao, paragraph [0009]). A person of ordinary skill in the art would recognize that streaming the data to the GUI in real time ensures that the system administrator is provided with up-to-the-second performance metrics, thereby facilitating immediate troubleshooting and dynamic task reallocation.
Regarding claim 4, Deng in view of Chen does not explicitly disclose wherein the GUI comprises at least one of a web browser or a mobile application. Zhao discloses wherein the GUI comprises at least one of a web browser or a mobile application (fig. 1-5; paragraph [0062]: dashboard 328 may be implemented as a user-friendly web-based GUI; Note: A web-based GUI implicitly comprises a web browser for presentation). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Deng in view of Chen by incorporating the web-based GUI of Zhao. The motivation would have been to implement the dashboard as a user-friendly web-based GUI (Zhao, paragraph [0062]). A person of ordinary skill in the art would recognize that utilizing a web browser allows the performance data to be easily accessed remotely from various client devices without requiring specialized, locally installed software.
Regarding claims 5 and 18, Deng in view of Chen does not explicitly disclose wherein the performance data comprises one or more of: control flow instruction data for the resources of the one or more nodes, arithmetic instruction data for the resources of the one or more nodes, memory load instruction data for the resources of the one or more nodes, memory store instruction data for the resources of the one or more nodes, or processing thread occupation data for resources of the one or more nodes. Zhao discloses wherein the performance data comprises one or more of: control flow instruction data for the resources of the one or more nodes, arithmetic instruction data for the resources of the one or more nodes, memory load instruction data for the resources of the one or more nodes, memory store instruction data for the resources of the one or more nodes, or processing thread occupation data for resources of the one or more nodes (fig. 1-5; paragraph [0055]: Typical information in in-memory store 332 may include metrics such as jobID, timestamp, source GPU (srcGPU), target GPU (tgtGPU), and monitor metrics (e.g., read size, write size, etc.); Note: read size and write size metrics correspond to memory load data and memory store data, respectively). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Deng in view of Chen by incorporating the specific monitor metrics of Zhao. The motivation would have been to include monitor metrics such as read size and write size (Zhao, paragraph [0055]). A person of ordinary skill in the art would recognize that tracking read and write sizes provides a granular view of memory bandwidth and I/O utilization, allowing the system to better identify specific hardware bottlenecks when making reconfiguration determinations.
Claims 8, 9, and 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US 2017/0255496, hereinafter Deng) in view of Zhao et al. (US 2020/0327036, hereinafter Zhao).
Regarding claim 8, Deng discloses
A method comprising (fig. 1-10):
collecting, during execution of a first computational task on a plurality of nodes, performance data characterizing utilization of processing resources of the plurality of nodes, … (paragraph [0095]: Record execution log information of the first subtask into a performance database, where the execution log information includes a data volume of the first subtask, required waiting duration before the first subtask is executed, and a running platform and running duration of the first subtask, and the running platform is the CPU or the GPU);
obtaining, responsive to the presented representation, a reconfiguration determination to modify execution, on at least one node of the plurality of nodes, of a target computational task, wherein the target computational task comprises at least one of: the first computational task being currently executed on the plurality of nodes, or a second computational task to be subsequently executed on the plurality of nodes (paragraph [0096]: Query the performance database, and calculate first average duration of executing subtasks by the CPU corresponding to the first working node and second average duration of executing subtasks by the GPU corresponding to the first working node; paragraph [0142]: If the load is uneven, and a ratio exceeds a specific threshold, an allocation to the task groups is adjusted; Note: Deng discloses obtaining the reconfiguration determination responsive to the performance data representation, which in the combination discussed below is presented on the GUI); and
cause, responsive to the reconfiguration determination, a modification to the execution of the target computational task on at least one node of the plurality of nodes (paragraph [0097]: Adjust, according to the first average duration and the second average duration, a quantity of subtasks in the CPU task group on the first working node, or a quantity of subtasks in the GPU task group on the first working node; paragraph [0144]: If the average duration of executing the subtasks by the CPU is greater than the average duration of executing the subtasks by the GPU… first M subtasks that are in the to-be-determined task group and that have greatest acceleration ratios are allocated to the GPU task group).
Deng does not explicitly disclose wherein the performance data comprises one or more of: control flow instruction data for the processing resources, arithmetic instruction data for the processing resources, memory load instruction data for the processing resources, memory store instruction data for the processing resources, or processing thread occupation data for the processing resources; presenting, on a graphical user interface (GUI), a representation of the performance data. Zhao discloses wherein the performance data comprises one or more of: control flow instruction data for the processing resources, arithmetic instruction data for the processing resources, memory load instruction data for the processing resources, memory store instruction data for the processing resources, or processing thread occupation data for the processing resources (fig. 1-5; paragraph [0055]: Typical information in in-memory store 332 may include metrics such as jobID, timestamp, source GPU (srcGPU), target GPU (tgtGPU), and monitor metrics (e.g., read size, write size, etc.); Note: “read size” and “write size” metrics correspond to memory load data and memory store data, respectively); presenting, on a graphical user interface (GUI), a representation of the performance data (paragraph [0062]: Dashboard 328 may be used to show visualizations of GPU-to-GPU traffic status and other metrics. In some embodiments, dashboard 328 may be implemented as a user-friendly web-based GUI). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Deng by incorporating the specific performance metrics and GUI presentation step of Zhao. The motivation would have been to include monitor metrics such as read size and write size (Zhao, paragraph [0055]) and to show visualizations of traffic status and other metrics on a user-friendly web-based GUI (Zhao, paragraph [0062]). A person of ordinary skill in the art would recognize that tracking memory read/write sizes provides a granular view of I/O utilization, and presenting this data on a GUI provides system administrators with an intuitive, visual representation of hardware utilization, thereby allowing users to easily monitor the system and intelligently trigger or oversee the dynamic task reallocation.
Regarding claims 9 and 13, Deng discloses
wherein the first computational task comprises a plurality of sub-tasks (paragraph [0095]: Record execution log information of the first subtask…), wherein an individual sub-task of the plurality of sub-tasks is executed using at least two nodes of the plurality of nodes (paragraph [0096]: executing subtasks by the CPU corresponding to the first working node and second average duration of executing subtasks by the GPU corresponding to the first working node; Note: The CPU and GPU act as at least two processing nodes for the execution of the tasks), and wherein the performance data comprises one or more of: a lead time that elapses between scheduling the individual sub-task and a time an output of the individual sub-task become available, a cycle time that elapses between scheduling the individual sub-task and a time an execution of the individual sub-task ends, a peak memory utilization by the individual sub-task, a peak processor utilization by the individual sub-task, a mean memory utilization by the individual sub-task, a mean processor utilization by the individual sub-task (paragraph [0095]: required waiting duration before the first subtask is executed, and a running platform and running duration of the first subtask; paragraph [0096]: calculate first average duration of executing subtasks by the CPU corresponding to the first working node; Note: The required waiting duration combined with the running duration constitutes the claimed cycle time, and the average duration of executing subtasks constitutes the claimed mean processor utilization).
Regarding claim 11, Deng does not explicitly disclose wherein the representation of the collected performance data is streamed to the GUI in real time. Zhao discloses wherein the representation of the collected performance data is streamed to the GUI in real time (paragraph [0009]: provide real-time or near-real-time (e.g., second-by-second) GPU-to-GPU traffic monitoring and traffic analytics capabilities; paragraph [0070]: an analyzer repeatedly receives raw statistics data from the in-memory store in a streaming fashion and performs various analyses on the data). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Deng by incorporating the real-time streaming step of Zhao. The motivation would have been to provide real-time or near-real-time (e.g., second-by-second) GPU-to-GPU traffic monitoring and traffic analytics capabilities (Zhao, paragraph [0009]). A person of ordinary skill in the art would recognize that streaming the data to the GUI in real time ensures that the system administrator is provided with up-to-the-second performance metrics, thereby facilitating immediate troubleshooting and dynamic task reallocation.
Regarding claim 12, Deng does not explicitly disclose wherein the GUI comprises at least one of a web browser or a mobile application. Zhao discloses wherein the GUI comprises at least one of a web browser or a mobile application (fig. 1-5; paragraph [0062]: dashboard 328 may be implemented as a user-friendly web-based GUI; Note: A web-based GUI implicitly comprises a web browser for presentation). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Deng by incorporating the web-based GUI of Zhao. The motivation would have been to implement the dashboard as a user-friendly web-based GUI (Zhao, paragraph [0062]). A person of ordinary skill in the art would recognize that utilizing a web browser allows the performance data to be easily accessed remotely from various client devices without requiring specialized, locally installed software.
Regarding claim 14, Deng discloses
wherein the performance data characterizes utilization of one or more graphics processing units (GPUs) deployed by at least one node of the plurality of nodes (paragraph [0096]: Query the performance database, and calculate first average duration of executing subtasks by the CPU corresponding to the first working node and second average duration of executing subtasks by the GPU corresponding to the first working node).
Claims 10 are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US 2017/0255496, hereinafter Deng) in view of Zhao et al. (US 2020/0327036, hereinafter Zhao) as applied to claim 8, and further in view of Chen et al. (US 2017/0124129, hereinafter Chen).
Regarding claim 10, Deng in view of Zhao does not explicitly disclose wherein the performance data comprises data of multiple formats, the method further comprising: converting the data of multiple formats into a uniform format to obtain the representation of the performance data. Chen discloses wherein the performance data comprises data of multiple formats, the method further comprising: converting the data of multiple formats into a uniform format to obtain the representation of the performance data (fig. 1-10; paragraph [0045]: The collected telemetry data from various heterogeneous nodes may arrive in different native formats. The data processing engine normalizes and converts these multiple formats into a standardized, uniform format for rendering on the dashboard). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Deng in view of Zhao by incorporating the data conversion step of Chen. The motivation would have been to normalize and convert multiple formats into a standardized, uniform format for rendering on the dashboard (Chen, paragraph [0045]). A person of ordinary skill in the art would recognize that converting heterogeneous data into a uniform format ensures compatibility and seamless integration when aggregating and presenting performance metrics from diverse processing resources on a single graphical user interface.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Toukmaji et al. (US 2012/0185864) discloses obtaining task execution data, showing a scheduler view on a display, receiving a modification request for a selected task execution, reassigning the tasks based on the modification request, and updating the scheduler view on the display (see Abstract; paragraphs [0022]-[0024], [0031]-[0033]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SISLEY N. KIM whose telephone number is (571)270-7832. The examiner can normally be reached M-F 11:30AM -7:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, April Y. Blair can be reached on (571)270-1014. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SISLEY N KIM/Primary Examiner, Art Unit 2196 10/12/2026
