DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This final office action is responsive to the amendments filed on 07/05/2026.
Claims 1-7, 9-14 are pending.
Response to Amendment
Applicant has amended independent claims 1, 9, 10 to include new/old limitations in a form not previously presented necessitating new search and considerations. Claims 4, 8, 15 have been canceled by the Applicant previously.
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 1-7, 9-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Powers et al. (US 2009/0049443 A1, hereafter Powers) in view of Kim et al. (US 2020/0218567 A1, hereafter Kim), and further in view of Sanghvi et al. (US 2018/0189102 A1, hereafter Sanghvi).
Powers, and Kim were cited in the last office action.
As per claim 1, Powers teaches the invention substantially as claimed including a task processing apparatus ([0047] fig. 1 computing resources 110 [0051] work units or tasks, run on computing resource), the task processing apparatus being implemented and coupled to a host apparatus via a communication interface (fig. 1 control server 105 i.e. host apparatus, pool 110 [0047] control server 105 connected via a communication network with at least one pool 110 of computing resources, server 111 desktop 112 laptop 114 nodes 116) to perform task and data interactions with the host apparatus ([0050] computing resources includes an agent [0051] job, work unit, run on computing resources, [0046] agent manages the execution of work units on its computing node [0224] agent, computing resources, requests, receives, work units list from the control server), and the task processing apparatus comprising ([0047] fig. 1 pool 110 computing resources):
at least one scheduler ([0248] agent module, adapted select work units ([0232] agent, prioritizes, work unit, using the scheduling algorithm in use);
a controller configured to query whether the at least one scheduler about whether there is a data processing task to be executed in the task processing apparatus ([0052] computing resource’s agent, queries, control server to identify any work units that need to be processed, agent select appropriate work unit to execute to the computing resources, agent, starts an instance, process [0245] [0248] fig. 25 computing resource 2505 work unit queue 2515 store set of work units [0248] fig. 25 combined control server/agent module 2535 i.e. both controller and agent are within the computing resource or task processing apparatus, select work units from work unit queue 2515 for execution to the processor cores ), and
trigger execution of the data processing task if the data processing task exists ([0219] startup and initialization phase, performed by resource pool [0235] agent receives the selected work units and initiates their execution on the computational resource [0247] computing resource, initialization work units [0052] computing resource’s agent, queries, control server to identify any work units that need to be processed, agent, starts an instance, process);
at least one data processing engine configured to process operation data corresponding to the data processing task ([0047] pool 110 computing resources, server, computers, nodes within clusters [0051] job, task, work units, run on one computing resource in pool 110 [0224] agent, computing resource, receive, work unit list, attribute/requirement of the work units [0122] data required for the selected work units, transferred, to the computing resource, process the work unit) according to a configured working mode ([0137] agent, set the priority of the application [0229] agent, selects, and prioritizes work units, for executing work units [0231] agent, adjust, the concurrency attributes of a work unit [0130] hosted applications, run by agents, on the computing resources to complete work units), and generate a data processing result ([0077] application process the work unit and transfer result once the application is complete]); and
wherein the at least one scheduler is configured to ([0232] agent’s associated computing resource, prioritizes the work units, the scheduling algorithm in use on the pool of computing resources i.e. agent acting as scheduler [0085] agent core module 715 manages the activities of the distributed processing system of the computing resource, including fetching descriptions of available work units from the control server [0086] agent core module 715 in selecting appropriate work units to execute [0089] one task of the agent is selecting appropriate work units for execution by the associated computing resource, by comparing attributes of the computing resources with requirements of a work unit):
receive a task descriptor of the data processing task from the host apparatus via the communication interface (fig. 1 control server 105 pool 110 [0047] control server 105 connected via a communications network with at least one pool 110 of computing resources [0010] agent, request, work units, server, agent manage execution of work units [0224] In step 2405 of method 2400, an agent associated with a computing resource requests a list of available work units from a control server for the distributed processing system. In response to this request, the agent receives a work unit list from the control server in step 2410 work unit list provides information on the attributes or requirements of the work units included in the work unit list. Work unit attributes can include a Work unit ID; a sequence; a name; a Job ID; one or more File Overrides; substitution attributes; priority values; an affinity; and minimum hardware, software, and application and data requirements for processing the work unit);
configure the working mode of the data processing engine based on the task descriptor after the execution of the data processing task is triggered ([0137] agent, set the priority of the application i.e. setting the working mode processing the work unit on a computing resource, priority determines how the computing resource divided its processing between primary user and the work unit [0089] agent, comparing attributes specifying the capabilities of the computing resources with attributes specifying the requirements of a work unit, [0092] set of attributes can also include information about the hardware and software configuration of the computing resource such as CPU type/speed, network connection speed, available memory/disk storage, operating system, installed application [0095] each work unit, attributes, affinity indicating one or more pools, minimum hardware /software and data requirements for processing the work unit [0099] adjust the weighting [0101] [0122] once the data required for the selected work units, transferred, to the computing resource, agent executes the application, and instructs it to process the work unit, agent, executes application, application, application control object [0229] agent, selects, and prioritizes work units, for executing work units [0231] agent, adjust, the concurrency attributes of a work unit [0130] hosted applications, run by agents, on the computing resources to complete work units);
control transmission of the operation data corresponding to the data processing task from the host apparatus to the data processing engine via the communication interface ([0077] agent, responsible, transferring and installing application and data, for processing work units [0085] agent core module 715, managing activities of the distributed processing system, computing resources, fetching description of available work units from the control server [0047] control server 105 connected via a communications network with at least one pool 110 of computing resources [0232] agent’s associated computing resource, the scheduling algorithm in use on the pool of computing resources i.e. acting as scheduler [0047] control server 105 connected via a communications network with at least one pool 110 of computing resources; msgAgentcheckinresult - sent from the server to the agent, contain the job table for a pool [0122] once the data required for the selected work units, transferred, to the computing resource, agent executes the application, and instructs it to process the work unit, agent, executes application, application, application control object); and
control transmission of the data processing result from the data processing engine to the host apparatus via the communication interface, after the data processing engine has completed the processing of the operation data and generated the data processing result ([0077] agent, run on each individual computing resource, coordinate, control server, agent responsible for transferring, result once the application is complete [0135] agent, determine the progress [0139] agent to determine when the application has completed processing the work unit [0047] control server 105 connected via a communications network with at least one pool 110 of computing resources [0143] message communicated between control servers and agents; msgnotifywork status - to notify the server of the progress/completion of a work unit), wherein the task processing apparatus performs the data processing task with the host apparatus ([0047] pool 110 computing resources, server, computers, nodes within clusters [0051] job, task, work units, run on one computing resource in pool 110).
Power doesn’t specifically teach task processing apparatus being implemented as an express card or an acceleration card; task processing apparatus comprising: at least one scheduler implemented as a hardware circuit.
Kim, however, teaches task processing apparatus being implemented as an express card or an acceleration card (fig. 1 task processing device 110/120 [0049] task processing device 110 include a processor 111 [0055] processor 111, include one or more CPUs / GPUs i.e. accelerator e.g. like GPU in 101 may perform GPU accelerated computing [0055]);
configure the working mode of the data processing engine based on the task descriptor ([0057] configure i.e. mode edge computing device, task processing device edges [0061] task descriptor, task processing algorithm [0079] task processing device, process, task, basis of the task descriptor [0080] task, include code, processing algorithm, apply an image classification algorithm [0121] [0007] set the maximum number of tasks that each of the plurality of task processing devices is capable of processing [0126] task, computational intensive, select a GPU edge having the largest amount of idle resources [0127] task, not computational intensive, select a non-GPU edge group, priority, set, main memory bandwidth / LLC hit ratio);
at least one data processing engine configured to process operation data corresponding to the data processing task according to a configured working mode ([0072] task processing device 110, task, processor, process the task, processing input data [0057] configure i.e. mode edge computing device, task processing device edges ).
It would have been obvious to one of ordinary skills in the art before the effective filing date of the invention was made to combine the teachings of Powers with the teachings of Kim of task processing device comprising processor including one or more GPU to improve efficiency and allow task processing apparatus being implemented as an express card or an acceleration card to the method of Powers as in the instant invention.
The combination of analogous arts would have been obvious because substituting /adding the GPU taught by Kim to the computing resources taught by Powers to yield expected result and improved efficiency and speed.
Power and Kim, in combination, do not specifically teach task processing apparatus comprising: scheduler implemented as a hardware circuit.
Sanghvi, however, teaches task processing apparatus comprising: scheduler implemented as a hardware circuit ([0022] fig. 2 vision preprocessing accelerators VPAC 112, four hardware accelerator 202-208 connected to hardware thread scheduler 200 [0024] hardware thread scheduler, schedule, execution, single / multiple concurrent threads of task by nodes of the VPAC 112).
It would have been obvious to one of ordinary skills in the art before the effective filing date of the invention was made to combine the teachings of Powers and Kim with the teachings of Sanghvi of vision pre-processing accelerator comprising hardware thread scheduler scheduling thread execution by accelerator nodes to improve efficiency and allow scheduler implemented as hardware circuit to the method of Powers and Kim as in the instant invention. The combination would have been obvious because supplementing / substituting the hardware scheduler taught by Sanghvi to the method of Powers and Kim to yield predictable result with improved efficiency.
As per claim 2, Kim teaches wherein the task descriptor at least contains information indicative of ([0061] task descriptor, information):
a type of the data processing task ([0061] information associated with task processing algorithm), a storage location of operation data corresponding to the data processing task ([0061] information associated with input data, information associated with a source from which input data is to be obtained), and a storage location of the data processing result generated after the processing of the data processing task is completed ([0061] information associated with an address to which a processing result is to be output ).
As per claim 3, Powers teaches task processing apparatus comprising at least one scheduler ([0232] agent, using the scheduling algorithm, i.e. acting as scheduler).
Kim teaches remaining claim elements of the task processing apparatus of claim 2, wherein the at least one scheduler is further configured to:
configure the working mode of the data processing engine according to the information of the type of the data processing task ([0057] configure i.e. mode edge computing device, task processing device edges [0061] task descriptor, task processing algorithm [0079] processing device, process, task, basis of the task descriptor [0121]);
control acquisition of the operation data from a memory of the host apparatus based on the information of the storage location of the operation data ([0061] information associated with input data, information associated with a source from which input data is to be obtained); and
transmit the data processing result to the memory of the host apparatus based on the information of the storage location of the data processing result ([0061] information associated with an address to which a processing result is to be output [0072] processor, transfer, processing result, external port, output connector [0055] communication circuit 113/ 123, transfer a task performance result, external device).
As per claim 4, Kim teaches wherein the task descriptor further contains information indicative of an operation command required for executing the data processing task([0061] task descriptor, information associated with task processing algorithm [0071] task descriptor, input command associated with input, processing algorithm command); and the at least one scheduler is further configured to acquire the operation command from a memory of the host apparatus based on the information of the operation command ([0072] execute, processor, processing algorithm 322, obtained processing algorithm [0071] task descriptor, input command associated with input, processing algorithm command).
As per claim 5, Powers teaches wherein the task processing apparatus comprises a scheduler, and the controller is further configured to poll the plurality of schedulers to query whether there is a data processing task to be executed in the plurality of schedulers ([0052] computing resource’s agent, queries, control server to identify any work units that need to be processed, agent select appropriate work unit to execute to the computing resources, agent, starts an instance, process [0248] fig. 25 control server/agent module 2535 select work units from work unit queue 2515 for execution to the processor cores).
Powers and Kim, in combination, do not specifically teach wherein the task processing apparatus comprises a plurality of schedulers.
Sanghvi, however, teaches wherein the task processing apparatus comprises a plurality of schedulers ([0026] fig. 2 vision pre-processing accelerator VPAC 112 hardware thread scheduler 200 hardware task schedulers 210-218 schedulers 204 206 208 fig. 6 execution environment 600 processing units, schedulers 608).
As per claim 6, Powers teaches wherein the at least data processing engine comprises a plurality of data processing engines ([0045] agent, associated, several computers, executed by a head node of a computing cluster that includes two or more computers), and the scheduler is further configured to select specific data processing engine from the plurality of processing engine ( [0045] agent coordinates the assignment of distributed computing tasks to all of the computers in the computing cluster [0086] availability, computing resources, used, agent, selecting work units [0232] scheduling algorithm, pool of computing resources [0248] select work units and distribute for execution) according to the task descriptor to execute the data processing task corresponding to the task descriptor ([0052] agent, select, work unit, execute, computing resource, based on computing resources’ capabilities, processing capability, amount of memory / disk space, bandwidth, availability, [0224] agent, receives a work unit list, provides information on the attributes or requirements of the work units included in the work unit list);
As per claim 7, Kim teaches further comprising:
an input buffer and an output buffer corresponding to the data processing engine (fig. 3 input connector 115 output connector 116), wherein the input buffer is configured to buffer operation data ([0072] obtain sensing data via input connector), and the output buffer is configured to buffer data processing results ([0072] transfer the processing result, via output connector 116).
As per claim 9, Powers teaches the invention substantially as claimed including a task processing system, comprising (fig. 1 distributed processing system 100 ):
a host apparatus (fig. 1 control server 105); and
at least one task processing apparatus (fig 1 pool 100 of computing resources) implemented and coupled to the host apparatus via a communication interface (fig. 1 control server 105 i.e. host apparatus, pool 110 [0047] control server 105 connected via a communication network with at least one pool 110 of computing resources, server 111 desktop 112 laptop 114 nodes 116) to perform task and data interactions with the host apparatus ([0050] computing resources includes an agent [0051] job, work unit, run on computing resources, [0046] agent manages the execution of work units on its computing node [0224] agent, computing resources, requests, receives, work units list from the control server),
wherein the hosts apparatus is configured to (fig. 1 control server 105 ):
receive a data processing task from a user program executed on the host apparatus ([0048] control server 105, software application, supports user control and monitoring [0051] users submits one or more jobs to the control server via administrative control 107);
allocate the data processing task to a virtual function queue ([0210] queue of pending distributed computing jobs);
generate a task descriptor corresponding to the data processing task ([0093] attributes specifying requirement of a work unit, work unit id, sequence, name, job id);
transmit the task descriptor to the at least one task processing apparatus for execution ([0049] server 105, job manager, allocating, task, computing resource pool 100 [0077] transferring data for processing work units [0095] agent retrieves list of available work units from the control servers, Job Manager responds with a "job table"; job table includes the length of time that each work unit of a job is expected to take and the requirements each work unit [0093] attributes specifying requirement of a work unit, work unit id, sequence, name, job id); and
receive from the at least one task processing apparatus a data processing result generated after operation data is processed ([0085] fetching descriptors from the control server, agent, communicating work unit results [0077] agent, run on individual computing resources, transferring the results once the application is complete);
wherein the task processing apparatus comprising ([0047] fig. 1 pool 110 computing resources):
at least one scheduler ([0248] agent module, adapted select work units ([0232] agent, prioritizes, work unit, using the scheduling algorithm in use);
a controller configured to query the at least one scheduler about whether there is a data processing task to be executed in the task processing apparatus ([0052] computing resource’s agent, queries, control server to identify any work units that need to be processed, agent select appropriate work unit to execute to the computing resources, agent, starts an instance, process [0245] [0247 ]fig. 25 computing resource 2505 work unit queue 2515 store set of work units [0248] fig. 25 combined control server/agent module 2535 i.e. both controller and agent are within the computing resource or task processing apparatus, select work units from work unit queue 2515 for execution to the processor cores ), and
trigger execution of the data processing task if the data processing task exists ([0219] startup and initialization phase, performed by resource pool [0235] agent receives the selected work units and initiates their execution on the computational resource [0247] computing resource, initialization work units [0052] computing resource’s agent, queries, control server to identify any work units that need to be processed, agent, starts an instance, process);
at least one data processing engine configured to process operation data corresponding to the data processing task ([0047] pool 110 computing resources, server, computers, nodes within clusters [0051] job, task, work units, run on one computing resource in pool 110 [0224] agent, computing resource, receive, work unit list, attribute/requirement of the work units [0122] data required for the selected work units, transferred, to the computing resource, process the work unit) according to a configured working mode ([0137] agent, set the priority of the application i.e. setting the working mode processing the work unit on a computing resource, priority determines how the computing resource divided its processing between primary user and the work unit [0089] agent, comparing attributes specifying the capabilities of the computing resources with attributes specifying the requirements of a work unit, [0092] set of attributes can also include information about the hardware and software configuration of the computing resource such as CPU type/speed, network connection speed, available memory/disk storage, operating system, installed application [0095] each work unit, attributes, affinity indicating one or more pools, minimum hardware /software and data requirements for processing the work unit [0099] adjust the weighting [0101] [0122] once the data required for the selected work units, transferred, to the computing resource, agent executes the application, and instructs it to process the work unit, agent, executes application, application, application control object [0229] agent, selects, and prioritizes work units, for executing work units [0231] agent, adjust, the concurrency attributes of a work unit [0130] hosted applications, run by agents, on the computing resources to complete work units), and generate a data processing result ([0077] application process the work unit and transfer result once the application is complete]); and
wherein the at least one scheduler is configured to ([0232] agent’s associated computing resource, prioritizes the work units, the scheduling algorithm in use on the pool of computing resources i.e. agent acting as scheduler [0085] agent core module 715 manages the activities of the distributed processing system of the computing resource, including fetching descriptions of available work units from the control server [0086] agent core module 715 in selecting appropriate work units to execute [0089] one task of the agent is selecting appropriate work units for execution by the associated computing resource, by comparing attributes of the computing resources with requirements of a work unit):
receive a task descriptor of the data processing task from the host apparatus via the communication interface (fig. 1 control server 105 pool 110 [0047] control server 105 connected via a communications network with at least one pool 110 of computing resources [0010] agent, request, work units, server, agent manage execution of work units [0224] In step 2405 of method 2400, an agent associated with a computing resource requests a list of available work units from a control server for the distributed processing system. In response to this request, the agent receives a work unit list from the control server in step 2410 work unit list provides information on the attributes or requirements of the work units included in the work unit list. Work unit attributes can include a Work unit ID; a sequence; a name; a Job ID; one or more File Overrides; substitution attributes; priority values; an affinity; and minimum hardware, software, and application and data requirements for processing the work unit);
configure the working mode of the data processing engine based on the task descriptor after the execution of the data processing task is triggered ([0137] agent, set the priority of the application i.e. setting the working mode processing the work unit on a computing resource, priority determines how the computing resource divided its processing between primary user and the work unit [0089] agent, comparing attributes specifying the capabilities of the computing resources with attributes specifying the requirements of a work unit, [0092] set of attributes can also include information about the hardware and software configuration of the computing resource such as CPU type/speed, network connection speed, available memory/disk storage, operating system, installed application [0095] each work unit, attributes, affinity indicating one or more pools, minimum hardware /software and data requirements for processing the work unit [0099] adjust the weighting [0101] [0122] once the data required for the selected work units, transferred, to the computing resource, agent executes the application, and instructs it to process the work unit, agent, executes application, application, application control object [0229] agent, selects, and prioritizes work units, for executing work units [0231] agent, adjust, the concurrency attributes of a work unit [0130] hosted applications, run by agents, on the computing resources to complete work units);
control transmission of the operation data corresponding to the data processing task from the host apparatus to the data processing engine via the communication interface ([0077] agent, responsible, transferring and installing application and data, for processing work units [0085] agent core module 715, managing activities of the distributed processing system, computing resources, fetching description of available work units from the control server [0047] control server 105 connected via a communications network with at least one pool 110 of computing resources [0232] agent’s associated computing resource, the scheduling algorithm in use on the pool of computing resources i.e. acting as scheduler [0047] control server 105 connected via a communications network with at least one pool 110 of computing resources; msgAgentcheckinresult - sent from the server to the agent, contain the job table for a pool); and
control transmission of the data processing result from the data processing engine to the host apparatus via the communication interface, after the data processing engine has completed the processing of the operation data and generated the data processing result ([0077] agent, run on each individual computing resource, coordinate, control server, agent responsible for transferring, result once the application is complete [0135] agent, determine the progress [0139] agent to determine when the application has completed processing the work unit [0047] control server 105 connected via a communications network with at least one pool 110 of computing resources [0143] message communicated between control servers and agents; msgnotifywork status - to notify the server of the progress/completion of a work unit),
wherein the task processing apparatus performs the data processing task with the host apparatus ([0047] pool 110 computing resources, server, computers, nodes within clusters [0051] job, task, work units, run on one computing resource in pool 110 [0224] agent, computing resource, receive, work unit list, attribute/requirement of the work units [0122] data required for the selected work units, transferred, to the computing resource, process the work unit).
Power doesn’t specifically teach task processing apparatus being implemented as an express card or an acceleration card; allocate the data processing task to a virtual function queue; generate a task descriptor corresponding to the data processing task according to a type of the data processing task; scheduler implemented as a hardware circuit.
Kim, however, teaches task processing apparatus implemented as an express card or an acceleration card (fig. 1 task processing device 110/120 [0049] task processing device 110 include a processor 111 [0055] processor 111, include one or more CPUs / GPUs i.e. accelerator e.g. like GPU in 101 may perform GPU accelerated computing [0055]);
allocate the data processing task to a virtual function queue ([0074] fig. 4 task pool 412, task descriptors 413-415);
generate a task descriptor corresponding to the data processing task according to a type of the data processing task ([0074] task descriptors, input/processing/output information [0061] task, information, expressed, task descriptor, information processing algorithm fig. 4 task pool 412 task descriptor 413)
configure the working mode of the data processing engine based on the task descriptor ([0057] configure i.e. mode edge computing device, task processing device edges [0061] task descriptor, task processing algorithm [0079] processing device, process, task, basis of the task descriptor [0121]);
at least one data processing engine configured to process operation data corresponding to the data processing task according to a configured working mode ([0072] task processing device 110, task, processor, process the task, processing input data [0057] configure i.e. mode edge computing device, task processing device edges ).
Power and Kim, in combination, do not specifically teach scheduler implemented as a hardware circuit.
Sanghvi, however, teaches scheduler implemented as hardware circuit ([0022] fig. 2 vision preprocessing accelerators VPAC 112, four hardware accelerator 202-208 connected to hardware thread scheduler 200 [0024] hardware thread scheduler, schedule, execution, single / multiple concurrent threads of task by nodes of the VPAC 112).
Claim 10 recites a task processing method for elements similar to claim 1. Therefore, it is rejected for the same rationales.
Claim 11 recites the task processing method for elements similar to claim 2. Therefore, it is rejected for the same rationales.
Claim 12 recites the task processing method for elements similar to claim 3. Therefore, it is rejected for the same rationales.
Claim 13 recites the task processing method for elements similar to claim 4. Therefore, it is rejected for the same rationales.
Claim 14 recites the task processing method for elements similar to claim 6
before configuring the working mode of the data processing engine based on the task descriptor (Kim [0014] select edge, task descriptor transferred i.e. selection before processing ). Therefore, it is rejected for the same rationales.
Examiners Note
Applicant is further reminded of the cited paragraphs and in the references as applied to the claims above for the convenience of the applicant(s) and although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested by the applicant in preparing responses, to fully consider all of the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Response to Arguments
The previous objections under 35 USC 101 abstract idea have been withdrawn.
The previous 112(b) objections have been withdrawn.
Applicant's arguments filed on 07/05/2026 have been fully considered but they are not persuasive. Applicant argues the following:
As can be seen, Powers explicitly discloses the computing resource’s agent periodically queries the control server 105, which is external to computing resource and not a component thereof, to identify any work units that need to be processed. In contrast, in the amended claim 1, the task processing apparatus includes the scheduler and the controller, and the controller is configured to query the scheduler (rather than the host apparatus) about whether there is data processing task to be executed in the task processing apparatus, which is distinctly different from the disclosure of Powers. Thus, Powers fails to discloses “a controller configured to query the at least one scheduler about whether there is a data processing task to be executed in the task processing apparatus, and trigger execution of the data processing task if the data processing task exists” as recited in claim 1.
HWS 129 of Blinzer differs from the agent of Powers in both its position within the computing system and the function it performs.
Examiner respectfully indicate that the argument is not persuasive for the following reasons:
With respect to point i.) Examiner respectfully indicates that Powers teaches agent in computing resources queries control server to identify any work units that need to be processed ([0052]) i.e. Powers teaches mechanism to query a server to identify any work units that need to be processed by an agent. Applicant’s appears to agree with such an inference, but argues that the control server is external to the task processing resource, which is in contrast to the corresponding claimed elements. Examiner respectfully indicates that Powers further teaches, in another embodiment, multiprocessor and multicore computing resources comprising combined control server and agent module distribute the work units to one or more processor or processor cores ([0245] fig. 25). Computing resource 2505 comprising work unit queue 2515 storing set of work units ([0247]) and combined control server / agent module 2535 is adapted to select work unit from the work unit queue and distribute the work units for execution to the processor core 2510 ([0248]), which clearly provides teachings of having the controller and the scheduler within the task processing apparatus. Therefore, it would have been obvious to one of ordinary skills in the art to combine the embodiment in which the control server and agent is within the task processing apparatus (fig. 25[0245] [0247]-[0248] ) with the function of agent querying the control server for any existing task to be processed ([0052]) to be similar to the argued limitations. In addition, Sanghvi also teaches the processing resource (fig. 2 VPAC) comprising plurality of schedulers 200, 210-218 for scheduling threads on the accelerator nodes (fig. 2 200, 210-218, 202-208) ([0030] [0033]).
With respect to point ii.) Argument is moot in view of new grounds of rejections.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Else et al. (US 20210200583 A1) teaches method for scheduling task
Lee et al. (US 2021/0133179 A1) teaches method for processing database updates
Venkataraman et al. (US 2018/0321983 A1) teaches method for job pre-scheduling by distributed job manager in multi-processor system
Authorization for Internet Communication
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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).
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/ABU ZAR GHAFFARI/Primary Examiner, Art Unit 2195
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