CTNF 18/292,879 CTNF 90815 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is responsive to preliminary amendment filed 1/26/2024. By the preliminary amendment, claims 1-3, 6-12, 17 and 20 were amended; claim 16 was cancelled. Claims 1-15 and 17-21 are presented for examination. Examiner Notes Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirely 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. Priority Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Information Disclosure Statement 06-52 The information disclosure statement (IDS) submitted on 3/4/2024. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Specification 07-29 AIA The disclosure is objected to because of the following informalities: “As inputs to the insights compliance algorithm (Block 902)” at line 1 of [0091] should be: As inputs to the insights compliance algorithm (Block 903) . Appropriate correction is required. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 18-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Regarding to Claim 18 , Claim 18 recites “a machine-readable storage medium” includes compute program code to be executed by computer. There is no evidence in the specification excluding the broadest reasonable interpretation of such claimed “a machine-readable storage medium” as signals per se. [0039] from the specification only provides certain particular examples for such claimed “a machine-readable storage medium” (see “machine-readable storage media (e.g., magnetic disks, optical disks, solid state drives, read only memory (ROM), flash memory devices, phase change memory)”). However, these are only particular examples without defining such claimed “a machine-readable storage medium” from excluding signals per se. In addition, the specification even uses term/language like “a non-transitory machine-readable storage medium” (see [0010] and [0138] from the specification. Note: “a non-transitory machine-readable storage medium” is different from “a machine-readable storage medium”). Signals are directed to a non-statutory subject matter. Thus, claim 18 is rejected under 35 U.S.C. 101 for directing to a non-statutory subject matter. Examiner suggests amend the claim element as “a non-transitory machine-readable storage medium” in order to draw the claim to non-transitory subject matter. Claims 19-20 are rejected for failing to cure the deficiency from their respective parent claim by dependency. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention. 07-15 AIA Claim s 18-20 are rejected under 35 U.S.C. 102 ( a) (1 ) as being anticipated by Vijayaraghavan et al. (US 20190050265 A1, hereafter Vijayaraghavan) . Regarding to Claim 18 , Vijayaraghavan discloses: A machine-readable storage medium storing computer program code which when executed by a computer carries out functions of an application in an executable package (see [0029]; “Flowcharts representative of example hardware logic, machine readable instructions …The program may be embodied in software stored on a non-transitory computer readable storage medium … or a memory associated with the processor 612”. Also see [0001], [0010]-[0012]; “methods and apparatus for allocating a workload to an accelerator” and “select, based on attribute(s) of the workload, an accelerator of the accelerator farm 120 to be selected for execution of the workload … the execution of the workload at one or more of the accelerators 122, 124, 126 in the example accelerator farm 120”), comprising: an executable file including computer program code representing serial logic to be executed by a general purpose processor and data-parallel logic to be executed by an accelerator (see [0010]-[0011], [0050]; “Certain machine learning workloads are better suited for particular types of hardware. Such hardware is referred to as a machine learning accelerator and may include , for example, a central processing unit (CPU), a field programmable gate array (FPGA) , a graphics processing unit (GPU)”, “CPUs are best suited to accelerating memory-intensive data (entire images) while FPGAs may be used to effectively accelerate workloads for which processing can be parallelized (e.g., image processing /edge detection algorithms, which can operate on a single pixel in an image)”, “ the workload may be segmented and/or divided into portions for execution among the different accelerator(s) . The accelerator(s) may then execute the workload and/or portions thereof in a parallel fashion and/or in a serial fashion ”. At one of the reasonable embodiments, the workload application would include a portion to be executed CPU type of accelerator in a serial fashion, i.e., claimed seria logic to be executed by one general-purpose processor, and another portion related to image processing to be executed by FPGA type of accelerator in a parallel fashion, i.e., claimed data-parallel logic ); and a set of embedded insights wherein the embedded insights include any one or more of building insights, profiling insights, and preferences insights (see [0022]; “The attribute(s) may include, for example, whether the workload can be parallelized, whether the workload is resource intensive, whether the workload deals with image processing, whether the workload deals with text processing, and/or any other information about the execution of the workload and/or data to be processed in connection with the workload, etc”. Note: at least the attributes related to “whether the workload deals with image processing, whether the workload deals with text processing” can be considered as claimed building insights, at least the attributes related to “whether the workload is resource intensive” can be considered as claimed profiling insights and at least the attributes related to “whether the workload can be parallelized” can be considered as claimed preferences insights. Furthermore see [0034]; “The example workload attribute determiner 230 identifies attribute(s) of the workload. (Block 320). In examples disclosed herein, the example workload attribute determiner 230 inspects the workload to identify properties of the data and/or tasks that are to be performed in connection with the workload” . The attributes or properties to be used for the accelerator selection process can be determined or identified by inspecting the workload itself, and thus such attributes or properties can be considered claimed embedded insights ) Regarding to Claim 19 , the rejection of Claim 18 is incorporated and further Vijayaraghavan discloses: wherein the building insights define characteristics of the application collected in a software build process including kernel complexity, kernel identity, or kernel footprint (see [0022]; “whether the workload deals with image processing, whether the workload deals with text processing”. The workload type (i.e., whether image processing or text processing) discussed at [0022] can be considered as claimed kernel identity to identify the type of kernel. Note: it is understood that whether the software application is able to deal with image processing or text processing is defined during the software build process ). Regarding to Claim 20 , the rejection of Claim 18 is incorporated and further Vijayaraghavan discloses: wherein the profiling insights define performance of at least one kernel of the application on at least one execution environment of the accelerator (see [0022] and [0036]; “whether the workload is resource intensive ” and “the workload is considered resource intensive when the workload would cause the accelerator to use at least a threshold amount of memory. However, any other type of computing resource (e.g., hard disk space, processor cycles, network bandwidth) may additionally or alternatively be considered ”) . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Vijayaraghavan et al. (US 20190050265 A1, hereafter Vijayaraghavan) in view of Rooke et al. (US 20210303723 A1, hereafter Rooke) . Regarding to Claim 21 , the rejection of Claim 18 is incorporated and further Vijayaraghavan discloses: wherein preferences insights define information including any one or more of key performance objective and accelerator affinity (see [0011], [0022] from Vijayaraghavan; “FPGAs may be used to effectively accelerate workloads for which processing can be parallelized” and “The attribute(s) may include, for example, whether the workload can be parallelized”. The attribute indicates whether the workload can be executed parallelized or not discussed at [0022] can be considered as claimed accelerator affinity ). Vijayaraghavan does not disclose: the information is provided by a developer. However, Rooke discloses: information provided by a developer including any one or more of key performance objective and accelerator affinity to indicate associated application code is executed in serial or parallel (see [0016]; “a developer can optimize execution of an application with parallel or serially specific segments upon compilation of an FPGA application 106”). It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify the attribute of indicating whether the workload application can be executed parallelized from Vijayaraghavan by including the a developer to provide information on whether application code can be executed with parallel or serially from Rooke, and thus the combination of Vijayaraghavan and Rooke would disclose the missing limitations from Vijayaraghavan, since it would provide a preference execution manner according to user’s needs (see [0016] from Rooke) . 07-21-aia AIA Claim s 1, 10-14 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Vijayaraghavan et al. (US 20190050265 A1, hereafter Vijayaraghavan) in view of Jonnadula et al. (US 20160119661 A1, hereafter Jonnadula) . Regarding to claim 1 , Vijayaraghavan discloses: A method of an accelerator selection process implemented by a computing node, the computing node having a plurality of accelerators (see [0001], [0010]-[0012]; “methods and apparatus for allocating a workload to an accelerator” and “select, based on attribute(s) of the workload, an accelerator of the accelerator farm 120 to be selected for execution of the workload … the execution of the workload at one or more of the accelerators 122, 124, 126 in the example accelerator farm 120”), the method comprising: receiving a request for an embedded insights-based accelerator selection for an application (see [0012], [0014]; “an accelerator selection processor 114 that utilizes a neural network processor 118 to select, based on attribute(s) of the workload , an accelerator of the accelerator farm 120 to be selected for execution of the workload” and “ receives a workload from the workload provider 105 and, using machine learning, selects one or more accelerator(s) of the accelerator farm 120 on which the workload is to be executed”. The workload received is reasonable to be considered as a request for selecting accelerator for the workload based on attributes, i.e., claimed insights. Also see [0010]; “Machine learning workloads (e.g., training a machine learning model on a large dataset”. The workload received at [0014] can be machine learning type of workloads, it is understood that such type of workloads can be implemented as software application . Furthermore see [0034]; “The example workload attribute determiner 230 identifies attribute(s) of the workload. (Block 320). In examples disclosed herein, the example workload attribute determiner 230 inspects the workload to identify properties of the data and/or tasks that are to be performed in connection with the workload”. The attributes or properties to be used for the accelerator selection process can be determined or identified by inspecting the workload itself, and thus such attributes or properties can be considered claimed embedded insights ); determining embedded insights (see [0022] and [0034]; “The example workload attribute determiner 230 identifies attributes and/or other properties of the workloads received at the workload orchestrator 110” and “The example workload attribute determiner 230 identifies attribute(s) of the workload. (Block 320). In examples disclosed herein, the example workload attribute determiner 230 inspects the workload to identify properties of the data and/or tasks that are to be performed in connection with the workload”); and selecting at least one of the plurality of accelerators based on the embedded insights to execute the application (see [0015]; “The example accelerator selection processor 114 evaluates attribute(s) of a workload to select an appropriate accelerator to be used to execute the workload”. Also see [0022]; “The example workload attribute determiner 230 identifies attributes and/or other properties of the workloads received at the workload orchestrator 110”). Vijayaraghavan does not disclsoes: determining whether the application includes embedded insights as part of an executable package of the application. However, Jonnadula discloses: determining whether the object includes embedded insights as part of package of the object (see claim 2; “determining whether the metadata is pre-packaged with the alternate content”). It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify the process of identifying the attributes of workloads for executing the workload from Vijayaraghavan by including the process of determining whether metadata of an object is prepackaged with the object from Jonnadula, and thus the combination of Vijayaraghavan and Jonnadula would discloses the missing limitations from Vijayaraghavan, since it would provide a mechanism of ensuring attributes or metadata is embedded with an object itself (see claims 1-2 from Jonnadula). Regarding to Claim 10 , the rejection of Claim 1 is incorporated and further the combination of Vijayaraghavan and Jonnadula disclose: training an artificial intelligence model (see [0015] from Vijayaraghavan; “stores the attributes of the workload in connection with the selected accelerator such that the associations of workload attributes and selected accelerator may be used as training data for a neural network implemented by the neural network processor 118 ”), wherein the training comprises: selecting embedded insights for training the artificial intelligence model (see [0015] from Vijayaraghavan; “the example accelerator selection processor 114 uses the attributes of the subsequent workload as an input to the neural network (e.g., the neural network implemented by the neural network processor 118)”); collecting profiling insights for different execution environments and accelerators for the application; and training the artificial intelligence model using the selected embedded insights and collected profiling insights (see [0016], [0050] from Vijayaraghavan; “during creation of the training data associating workload attributes with selected accelerator(s) , the accelerator selection processor 114 causes the workload (and/or a portion thereof) to be executed at each of the potential accelerators, and selects an appropriate accelerator based on performance metric(s) associated with the execution of the workload ”, “The example accelerator selection processor 114 obtains the results of the workload execution via the example accelerator interface 240. (Block 550). The example accelerator selection processor 114 collects performance metrics resulting from the execution of the workload ”. The performance metrics discussed at [0016] and [0050] can be considered as claimed profiling insights ). Regarding to Claim 11 , the rejection of Claim 1 is incorporated and further the combination of Vijayaraghavan and Jonnadula disclose: wherein the application includes serial logic to be executed by at least one general-purpose processor, and data-parallel logic to be executed by the at least one of the plurality of accelerators (see [0010]-[0011], [0050] from Vijayaraghavan; “Certain machine learning workloads are better suited for particular types of hardware. Such hardware is referred to as a machine learning accelerator and may include , for example, a central processing unit (CPU), a field programmable gate array (FPGA) , a graphics processing unit (GPU)”, “CPUs are best suited to accelerating memory-intensive data (entire images) while FPGAs may be used to effectively accelerate workloads for which processing can be parallelized (e.g., image processing /edge detection algorithms, which can operate on a single pixel in an image)”, “ the workload may be segmented and/or divided into portions for execution among the different accelerator(s) . The accelerator(s) may then execute the workload and/or portions thereof in a parallel fashion and/or in a serial fashion ”. At one of the reasonable embodiments, the workload application would include a portion to be executed CPU type of accelerator in a serial fashion, i.e., claimed seria logic to be executed by one general-purpose processor, and another portion related to image processing to be executed by FPGA type of accelerator in a parallel fashion, i.e., claimed data-parallel logic ). Regarding to Claim 12 , the rejection of Claim 1 is incorporated and further the combination of Vijayaraghavan and Jonnadula disclose: wherein the embedded insights include any one or more of building insights, profiling insights, and preferences insights (see [0022] from Vijayaraghavan; “The attribute(s) may include, for example, whether the workload can be parallelized, whether the workload is resource intensive, whether the workload deals with image processing, whether the workload deals with text processing, and/or any other information about the execution of the workload and/or data to be processed in connection with the workload, etc”. Note: at least the attributes related to “whether the workload deals with image processing, whether the workload deals with text processing” can be considered as claimed building insights, at least the attributes related to “whether the workload is resource intensive” can be considered as claimed profiling insights and at least the attributes related to “whether the workload can be parallelized” can be considered as claimed preferences insights). Regarding to Claim 13 , the rejection of Claim 12 is incorporated and further the combination of Vijayaraghavan and Jonnadula disclose: wherein the building insights define characteristics of the application collected in a software build process include kernel complexity, kernel identity, or kernel footprint (see [0022] from Vijayaraghavan; “whether the workload deals with image processing, whether the workload deals with text processing”. The workload type (i.e., whether image processing or text processing) discussed at [0022] can be considered as claimed kernel identity to identify the type of kernel. Note: it is understood that whether the software application is able to deal with image processing or text processing is defined during the software build process ). Regarding to Claim 14 , the rejection of Claim 12 is incorporated and further the combination of Vijayaraghavan and Jonnadula disclose: wherein the profiling insights define performance of at least one kernel of the application on at least one execution environment of one of the plurality of accelerators (see [0022] and [0036] from Vijayaraghavan; “whether the workload is resource intensive ” and “the workload is considered resource intensive when the workload would cause the accelerator to use at least a threshold amount of memory. However, any other type of computing resource (e.g., hard disk space, processor cycles, network bandwidth) may additionally or alternatively be considered ”). Regarding to Claim 17 , Claim 17 is a system claim corresponds to method Claim 1 and is rejected for the same reason set forth in the rejection of Claim 1 above (note: also see [0028]-[0030] from Vijayaraghavan for claimed “a machine readable storage medium”) . 07-21-aia AIA Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Vijayaraghavan et al. (US 20190050265 A1, hereafter Vijayaraghavan) in view of Jonnadula et al. (US 20160119661 A1, hereafter Jonnadula) and further in view of Kim et al. (US 20200125390 A1, hereafter Kim) . Regarding to Claim 2 , the rejection of Claim 1 is incorporated and further the combination of Vijayaraghavan and Jonnadula discloses: an artificial intelligence model for generating profiling insights for the application; and inferring the profiling insights based on the embedded insights including building insights and preferences insights (see [0015], [0016], [0024] from Vijayaraghavan; “during creation of the training data associating workload attributes with selected accelerator(s) , the accelerator selection processor 114 causes the workload (and/or a portion thereof) to be executed at each of the potential accelerators, and selects an appropriate accelerator based on performance metric(s) associated with the execution of the workload. The selected accelerator and the attributes of the workload are used as training data for a neural network implemented by the example neural network processor 118” and “the example training data store 245 stores information used to train the neural network parameters … Such information may include, for example, attribute(s) of workload(s) and their corresponding selected accelerator”. Also see [0022], [0061]; “The attribute(s) may include, for example, whether the workload can be parallelized, whether the workload is resource intensive, whether the workload deals with image processing, whether the workload deals with text processing”, “train a machine learning model based on an association between the first accelerator and the first attribute of the first workload, and a neural network processor to process, using the machine learning model, the second attribute to select one of the at least two accelerators to execute the second workload”. The associations between attributes of workloads and their corresponding selected accelerator, i.e., claimed profiling insights, are inferred or generated based on the attributes, i.e., claimed embedded insights including building insights (like the attribute indicating “whether the workload deals with image processing, whether the workload deals with text processing”) and preferences insights (like the attribute indicating “whether the workload can be parallelized” )). The combination of Vijayaraghavan and Jonnadula does not disclose: determining whether the artificial intelligence model is available. However, Kim discloses: determining whether an artificial intelligence model is available for generating result (see [0122]; “Whether a machine learning model exists in the machine learning model DB is checked (S703), an algorithm is downloaded from the machine learning engine manager in response to non-existence of the machine learning model (S704), and the user behavior is analyzed in response to existence of the machine learning model”). It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify the processes of utilizing neural network to generate associations between attributes and workload and their corresponding selected accelerators from the combination of Vijayaraghavan and Jonnadula by including the process of determining whether a machine learning model exists or not before utilizing the machine learning model to generate a result from Kim, and thus the combination of Vijayaraghavan, Jonnadula and Kim would disclose the missing limitations from the combination of Vijayaraghavan and Jonnadula, since it would provide a mechanism of ensuring existence of an object prior to utilizing the object (see [0122] of Kim) . 07-21-aia AIA Claim s 3-8 are rejected under 35 U.S.C. 103 as being unpatentable over Vijayaraghavan et al. (US 20190050265 A1, hereafter Vijayaraghavan) in view of Jonnadula et al. (US 20160119661 A1, hereafter Jonnadula) and further in view of Munshi et al. (US 8341611 B2, hereafter Munshi) . Regarding to Claim 3 , the rejection of Claim 1 is incorporated, the combination of Vijayaraghavan and Jonnadula does not disclose: wherein the selecting the at least one of the plurality of accelerators further comprising: estimating insights compliance scores for each of the plurality of accelerators for the application; and selecting the at least one of the plurality of accelerators based on a best insights compliance score. However, Munshi discloses: wherein the selecting the at least one of the plurality of resources further comprising: estimating insights compliance scores for each of the plurality of resources for the application; and selecting the at least one of the plurality of resources based on a best insights compliance score (see lines 63-2 of cols. 4-5, lines 4-9, 17-21 of col. 8; “An application may select and specify capability requirements for performing a processing task … determine a configuration for physical compute devices to allocate and initialize processing resources from the attached CPUs 117 and/or GPUs 115 for the processing task”, “a list of required capabilities for requesting processing resources to perform a task for the application”, “determine a matching score according to the number of compute capabilities matched between a physical compute device and the compute capability requirement. In one embodiment, process 400 may select a plurality of physical compute devices with highest matching scores ”. Also see claim 1; “match the processing capability requirement specified in the first API and the predetermined capabilities for the parallel processors to select one or more of the parallel processors for the processing task, wherein said matching selects the at least one processor based on … the predetermined capabilities for the selected processor scoring a highest matching score indicating highest compatibility with the processing capability requirements”). It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify the processes of selecting best or proper accelerator resources based on the identified attributes or properties of workload application from the combination of Vijayaraghavan and Jonnadula by including selecting best or proper resource based on scores determined for candidate resources for executing a task that determined from processing capability requirements of the task from Munshi, and thus the combination of Vijayaraghavan, Jonnadula and Munshi would disclose the missing limitations from the combination of Vijayaraghavan and Jonnadula, since it would provide a specific standard to determine best resource that meeting certain job requirements for executing a job (see lines 17-21 of col. 8 and claim 1 from Munshi). Regarding to Claim 4 , the rejection of Claim 3 is incorporated and further the combination of Vijayaraghavan, Jonnadula and Munshi discloses: wherein the insights compliance scores are generated from the embedded insights and a capacity specification for each of the plurality of accelerators (see [0011] from Vijayaraghavan and lines 4-9, 17-21 of col. 8 from Munshi; “to dynamically determine the type(s) of machine learning accelerator(s) to be used for a particular workload based on attribute(s) of the workload” and “determine a matching score according to the number of compute capabilities matched between a physical compute device and the compute capability requirement ”). Regarding to Claim 5 , the rejection of Claim 4 is incorporated and further the combination of Vijayaraghavan, Jonnadula and Munshi discloses: wherein the insights compliance scores are further generated from profiling insights determined by an artificial intelligence model (see [0016] from Vijayaraghavan and lines 4-9, 17-21 of col. 8 from Munshi; “ The selected accelerator and the attributes of the workload are used as training data for a neural network implemented by the example neural network processor 118” and “determine a matching score according to the number of compute capabilities matched between a physical compute device and the compute capability requirement”. The associations between attributes of workloads and their corresponding selected accelerator, i.e., claimed profiling insights, are determined from a neural network model can be used to generate matching score to determine proper resource ). Regarding to Claim 6 , the rejection of Claim 3 is incorporated and further the combination of Vijayaraghavan, Jonnadula and Munshi discloses: wherein estimating the insights compliance scores further comprising: calculating a first compliance value by comparing a capacity of each of the plurality of accelerators to fulfill building insights in the embedded insights (see [0011], [0022] from Vijayaraghavan and lines 4-9, 17-21 of col. 8 from Munshi; “identifies attributes and/or other properties of the workloads received at the workload orchestrator 110. The attribute(s) may include, for example … whether the workload deals with image processing, whether the workload deals with text processing” and “determine a matching score according to the number of compute capabilities matched between a physical compute device and the compute capability requirement ”. The matching or not, i.e., claimed first compliance value, is made by comparing a capacity of each of the candidate resources to fulfil workload attribute indicating image processing or text processing, i.e., claimed building insights in embedded insights ). Regarding to Claim 7 , the rejection of Claim 6 is incorporated and further the combination of Vijayaraghavan, Jonnadula and Munshi discloses: wherein estimating the insights compliance score further comprising: calculating a second compliance value by comparing a capacity of each of the plurality of accelerators to fulfill preference insights in the embedded insights (see [0011], [0022] from Vijayaraghavan and lines 4-9, 17-21 of col. 8 from Munshi; “identifies attributes and/or other properties of the workloads received at the workload orchestrator 110. The attribute(s) may include, for example … whether the workload can be parallelized” and “determine a matching score according to the number of compute capabilities matched between a physical compute device and the compute capability requirement ”. The matching or not, i.e., claimed second compliance value, is made by comparing a capacity of each of the candidate resources to fulfil workload attribute indicating parallel execution, i.e., claimed preference insights in embedded insights ). Regarding to Claim 8 , the rejection of Claim 7 is incorporated and further the combination of Vijayaraghavan, Jonnadula and Munshi discloses: wherein estimating the insights compliance score further comprising: calculating a third compliance value by comparing a capacity of each of the plurality of accelerators to fulfill profiling insights derived from the embedded insights or an artificial intelligence model (see [0011], [0022], [0036] from Vijayaraghavan and lines 4-9, 17-21 of col. 8 from Munshi; “identifies attributes and/or other properties of the workloads received at the workload orchestrator 110. The attribute(s) may include, for example … whether the workload is resource intensive” and “determine a matching score according to the number of compute capabilities matched between a physical compute device and the compute capability requirement ”. The matching or not, i.e., claimed third compliance value, is made by comparing a capacity of each of the candidate resources to fulfil workload attribute indicating resource intensive, i.e., claimed profiling insights derived from the embedded insights ) . 07-21-aia AIA Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Vijayaraghavan et al. (US 20190050265 A1, hereafter Vijayaraghavan) in view of Jonnadula et al. (US 20160119661 A1, hereafter Jonnadula) and Munshi et al. (US 8341611 B2, hereafter Munshi) and further in view of Edwards et al. (US 7483618 B1; hereafter Edwards) . Regarding to Claim 9 , the rejection of Claim 8 is incorporated, the combination of Vijayaraghavan, Jonnadula and Munshi does not discloses: wherein estimating the insights compliance score further comprising: weighting the first compliance value, the second compliance value, and the third compliance value based on weighting specified by the embedded insights or an accelerator selection algorithm. However, Edwards discloses: weighting the first compliance value, the second compliance value, and the third compliance value based on weighting specified by the embedded insights or a candidate object selection algorithm (see lines 7-29 of col. 6, lines 15-22 of col. 9, lines 15-20 of col. 20; “each candidate clip is evaluated … a score for each candidate clip can be determined as a combination of multiple sub-scores computed for that clip … sub-scores can be computed for each candidate clip that measure the desirability of the candidate clip with respect to several different properties of the candidate clip … the score for the candidate clip computed as a weighted average of those sub-scores ”, “a format template can include … the weight(s) to be assigned to criter(ia)”, “a specified set of criteria weights to be used in selecting candidate clips for inclusion in the visual recording summary”). It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify an overall score based on multiple requirements or factors from the combination of Vijayaraghavan, Jonnadula and Munshi by including a weighted score based on multiple sub-scores determined from multiple factors or criteria from Edwards, and thus the combination of combination of Vijayaraghavan, Jonnadula, Munshi and Edwards would disclose the missing limitations from the combination of Vijayaraghavan, Jonnadula and Munshi, since it would provide a well-known mechanism to prioritize certain factor or criteria over other factors or criteria when there are multiple factor or criteria work together . 07-21-aia AIA Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Vijayaraghavan et al. (US 20190050265 A1, hereafter Vijayaraghavan) in view of Jonnadula et al. (US 20160119661 A1, hereafter Jonnadula) and further in view of Rooke et al. (US 20210303723 A1, hereafter Rooke) . Regarding to Claim 15 , the rejection of Claim 12 is incorporated and further the combination of Vijayaraghavan and Jonnadula discloses: wherein preferences insights define information including any one or more of key performance objective and accelerator affinity (see [0011], [0022] from Vijayaraghavan; “FPGAs may be used to effectively accelerate workloads for which processing can be parallelized” and “The attribute(s) may include, for example, whether the workload can be parallelized”. The attribute indicates whether the workload can be executed parallelized or not discussed at [0022] can be considered as claimed accelerator affinity ). The combination of Vijayaraghavan and Jonnadula does not discloses: the information is provided by a developer. However, Rooke discloses: information provided by a developer including any one or more of key performance objective and accelerator affinity to indicate associated application code is executed in serial or parallel (see [0016]; “a developer can optimize execution of an application with parallel or serially specific segments upon compilation of an FPGA application 106”). It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify the attribute of indicating whether the workload application can be executed parallelized from the combination of Vijayaraghavan and Jonnadula by including the a developer to provide information on whether application code can be executed with parallel or serially from Rooke, and thus the combination of Vijayaraghavan, Jonnadula and Rooke would disclose the missing limitations from the combination of Vijayaraghavan and Jonnadula, since it would provide a preference execution manner according to user’s needs (see [0016] from Rooke) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Martin et al. (US 20140310695 A1) discloses: an application includes serial logic to be executed by at least one general-purpose processor, and parallel logic to be executed by at least one GPU accelerator (see [0015]-[0016], [0061] and claim 29). Mine et al. (US 20180095745 A1) discloses: flags within the software package to indicates certain attributes of the software package embedded within the software package (see [0056]-[0060]). Jung et al. (US 20080155673 A1) discloses: program package includes program header having predetermined metadata and program code to be executed (see [0081]). Secomandi et al. (US 20080228889 A1) discloses: software package starts with a header including different attributes (see [0032]). Graf et al. (US 20210248116 A1) discloses: determining whether the remaining job properties can be identified in the payload portion of the job summary (see [0051]). Prosso et al. (US 8321857 B1) discloses: determining whether the application package includes a subpackage; and responsive at least in part to the application package including a subpackage: mounting the application package on a filesystem, extracting the subpackage, identifying a predetermined file within the subpackage, parsing the predetermined file to identify metadata, extracting the metadata (see claim 1). Schlesinger et al. (US 20050125353 A1) discloses: updated course packages can be processed to determine whether the course package includes an update to the course content, the course metadata, or both (see [0020]). Bai et al. (US 10318159 B1) discloses: a determination is made about whether there are any remaining L1 metadata segments in the selected container to process (see lines 53-55 of col. 6). Petit et al. (US 20220046095 A1) discloses: the IOT gateways 500-1 to 500-N can receive embedded application metadata and the embedded applications as embedded application images from the IOT server 310 located in the cloud environment 310. The embedded application metadata can include deployment configurations for the embedded applications in the IOT gateways 500-1 to 500-N (see [0022]). Kirchhofer et al. (US 10298515 B1) discloses: each potential set of resources may be scored or weighted using policy requirements (e.g., a number of compute and/or storage resources needed, resource size, resource performance, resource affinity, etc.). In this example, using the scores or weights computed or determined, tenant cloud engine 104 may select the best set of resources to use for a tenant cloud based on the highest score or weight (see lines 46-58 of col. 9). Flavin (US 7050963 B2) discloses: determining a score based on one or more performance criteria including: CPU usage, memory usage, whether or not the server is functioning, server MIPS, available server MIPS, available server memory and any other convenient criteria (see lines 11-44 of col. 14). Adogla et al. (US 20220327007 A1) discloses: the various features (e.g., number of CPU cores (e.g., 40 CPU cores), a number of memory units (e.g., 512 Gigabytes), data bus types, disk storage types, network card types, other processor types (e.g., GPU resource), etc.) of a hardware profile may be weighted together and/or individually assessed with a numeric score (e.g., included within a vector of scores) (see [0126]). Witt (US 20040012607 A1) discloses: allows the software developer to split up the work between different parts of the CPU and the vector units can be used in either serial or parallel connection (see [0046]). MacDonald et al. (US 20200363104 A1) discloses: Setting affinity of a given task (or process) to a specific core (or SMT thread) is a well-known option for software developers (see [0066]). Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHI CHEN whose telephone number is (571)272-0805. The examiner can normally be reached on M-F from 9:30AM to 5:30PM. 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. Information regarding the status of an application may be obtained from Patent Center and the Private Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from Patent Center or Private PAIR. Status information for unpublished applications is available through Patent Center and Private PAIR to authorized users only. Should you have questions about access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form. /Zhi Chen/ Patent Examiner, AU2196 /APRIL Y BLAIR/Supervisory Patent Examiner, Art Unit 2196 Application/Control Number: 18/292,879 Page 2 Art Unit: 2196 Application/Control Number: 18/292,879 Page 3 Art Unit: 2196 Application/Control Number: 18/292,879 Page 4 Art Unit: 2196 Application/Control Number: 18/292,879 Page 5 Art Unit: 2196 Application/Control Number: 18/292,879 Page 6 Art Unit: 2196 Application/Control Number: 18/292,879 Page 7 Art Unit: 2196 Application/Control Number: 18/292,879 Page 8 Art Unit: 2196 Application/Control Number: 18/292,879 Page 9 Art Unit: 2196 Application/Control Number: 18/292,879 Page 10 Art Unit: 2196 Application/Control Number: 18/292,879 Page 11 Art Unit: 2196 Application/Control Number: 18/292,879 Page 12 Art Unit: 2196 Application/Control Number: 18/292,879 Page 13 Art Unit: 2196 Application/Control Number: 18/292,879 Page 15 Art Unit: 2196 Application/Control Number: 18/292,879 Page 16 Art Unit: 2196 Application/Control Number: 18/292,879 Page 17 Art Unit: 2196 Application/Control Number: 18/292,879 Page 18 Art Unit: 2196 Application/Control Number: 18/292,879 Page 19 Art Unit: 2196 Application/Control Number: 18/292,879 Page 20 Art Unit: 2196 Application/Control Number: 18/292,879 Page 21 Art Unit: 2196 Application/Control Number: 18/292,879 Page 22 Art Unit: 2196 Application/Control Number: 18/292,879 Page 23 Art Unit: 2196 Application/Control Number: 18/292,879 Page 24 Art Unit: 2196
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