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. DETAILED ACTION This action is responsive to the Application filed on 1/09/202 3 . Claims 1- 20 are pending in the case. Claims 1, 8 and 15 are independent claims. Claim Rejections - 35 U.S.C. § 101 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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If itis determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult the 2019 PEG for more details of the analysis. Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03. Claims 1- 7 are drawn to a method , claim s 8-14 are drawn to a system and claims 15-20 are drawn to a computer program product comprising a computer readable storage medium , therefore each of these claim groups falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater; Step 1). Nonetheless, the claims are directed to a judicially recognized exception of an abstract idea without significant more (Step 2A, see below). Independent claims 1, 8 and 15 are non-verbatim but similar in claim construction, hence share the same rationale that the claimed inventions are directed to non-statutory subject matter as follows: As to claim 1 : Claim 1 recites “ Referring to claims 1 , 8 and 15 . A computer-implemented method comprising: receiving, from a user an identification of a computing task to be performed by an edge device; obtaining, a data set corresponding to the computing task; determining a supernet model space based at least in part on the computing task; creating a plurality of trained models for the computing task by training a plurality of deep learning models within the supernet model space with the data set; and deploying one of the plurality of trained models to the edge device, wherein the one of the plurality of trained models is determined based at least in part on one or more characteristics of the edge device. ” Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “ determining a supernet model space based at least in part on the computing task ” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) . See MPEP § 2106.04(a)(2)(III). Yes, the limitation “ wherein the one of the plurality of trained models is determined based at least in part on one or more characteristics of the edge device ” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) . See MPEP § 2106.04(a)(2)(III). Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d). No, this limitation “ a supernet model space ” , “ creating a plurality of trained models for the computing task by training a plurality of deep learning models within the supernet model space with the data set ”, “ deploying one of the plurality of trained models to the edge device ” are additional element s that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “ supernet model space ” and “plurality of trained model” are used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d). No, this limitation “ computer-implemented ” , “computer task” and “edge device” are additional element s that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(f)(2). No, this limitation “ receiving, from a user an identification of a computing task to be performed by an edge device ” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception to perform “ receiving ”. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g). No, this limitation “ obtaining, a data set corresponding to the computing task ” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception to perform “obtaining”. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole. Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness. Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness. No, this limitation “ a supernet model space ”, “ creating a plurality of trained models for the computing task by training a plurality of deep learning models within the supernet model space with the data set ”, “ deploying one of the plurality of trained models to the edge device ” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “supernet model space” and “plurality of trained model” are used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d). No, this limitation “ computer-implemented ”, “computer task” and “edge device” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(f)(2). No, this limitation “ receiving, from a user an identification of a computing task to be performed by an edge device ” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception to perform “receiving”. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II). No, this limitation “ obtaining, a data set corresponding to the computing task ” amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception to perform “obtaining”. Therefore, the additional limitation is insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II). Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter. Independent claims 1, 8 and 15 are non-verbatim but similar in claim construction, hence share the same rationale that the claimed inventions are directed to non-statutory subject matter as follows: In addition to the analysis of claim 1 above, claim 8 recites “A system comprising: a memory having computer readable instructions; and one or more processors for executing the computer readable instructions, the computer readable instructions controlling the one or more processors to perform operations” (this limitation “ system ” , “memory” and “processors”, “computer readable instructions” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(f)(2).) Claim 15 recites “A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations” (this limitation “computer program product”, “computer readable storage medium” and “processor” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(f)(2).) Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”. Furthermore, regarding dependent claims 2- 7 which are dependent on claim 1, claims 9 -1 4 which are dependent on claim 8 , and claims 1 6 - 20 which are dependent on claim 15 , the claims are directed to a judicial exception without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under Step 2A and 2B: Dependent claims 2, 9 and 16 Incorporates the rejection of independent claim s Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Incorporates the abstract idea of independent claims. Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No. Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No. Dependent claims 3 , 10 and 17 Incorporates the rejection of independent claims Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Incorporates the abstract idea of independent claims. Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No. Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No. Dependent claims 4 , 11 and 18 Incorporates the rejection of independent claims Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Incorporates the abstract idea of independent claims. Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No. This limitation “ wherein the one or more characteristics of the edge device include one or more of an amount of available memory on the edge device, a processing power of the edge device, a network connection of the edge device, and an operating system executing on the edge device ” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(h). Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No. This limitation “ wherein the one or more characteristics of the edge device include one or more of an amount of available memory on the edge device, a processing power of the edge device, a network connection of the edge device, and an operating system executing on the edge device ” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(h). Dependent claims 5 , 12 and 19 Incorporates the rejection of independent claims Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes, the limitation “ monitoring the one or more characteristics of the edge device ”, “ based on a determination that the one or more characteristics has changed by more than a threshold amount, deploying another one of the plurality of trained models to the edge device ” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) . See MPEP § 2106.04(a)(2)(III). Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No, this limitation “ deploying another one of the plurality of trained models to the edge device ” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “ deploying another one of the plurality of trained models to the edge device ” are used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d). Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No, this limitation “ deploying another one of the plurality of trained models to the edge device ” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “ deploying another one of the plurality of trained models to the edge device ” are used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d). Dependent claims 6 , 13 and 20 Incorporates the rejection of independent claims Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Incorporates the abstract idea of independent claims. Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No, this limitation “ displaying, to the user, one or more operating requirements for each of the plurality of trained models ” amounts to insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g). The limitation “ wherein the one of the plurality of trained models deployed to the edge device ” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “ the one of the plurality of trained models deployed to the edge device ” are used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d). Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. No, this limitation “ displaying, to the user, one or more operating requirements for each of the plurality of trained models ” amounts to insignificant extra-solution activity to the judicial exception, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(g). The limitation “ wherein the one of the plurality of trained models deployed to the edge device ” are additional elements that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “ the one of the plurality of trained models deployed to the edge device ” are used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d). Furthermore the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II). D ependent claims 7, and 14 Incorporates the rejection of independent claims Step 2A Prong 1: does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Incorporates the abstract idea of independent claims. Step 2A prong 2: the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d) No. This limitation “wherein the computing task is one of image classification, object detection, and image segmentation” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(h). Step 2B: the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. and Is the additional element recognized as well-understood, routine, and conventional? No. This limitation “wherein the computing task is one of image classification, object detection, and image segmentation” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use, and as such is deemed insufficient to transform the judicial exception to a patentable invention. See MPEP §§ 2106.04(d), 2106.05(h). The dependent claims as analyzed above, do not recite limitations that integrated the judicial exception into a practical application. In addition, the claim limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B). Therefore, the claims do not recite any limitations, when considered individually or as a whole, that recite what the courts have identified as “significantly more”, see MPEP 2106.05; and therefore, as a whole the claims are not patent eligible. As shown above, the dependent claims do not provide any additional elements that when considered individually or as an ordered combination, amount to significantly more than the abstract idea identified. Therefore, as a whole the dependent claims do not recite what the courts have identified as “significantly more” than the recited judicial exception. Therefore, claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception and does not recite, when claim elements are examined individually and as a whole, elements that the courts have identified as “significantly more” than the recited judicial exception. Claim Rejections - 35 USC § 102 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 (i.e., changing from AIA to pre-AIA) 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 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 – (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. Claim s 1-20 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Turgeman et al (US 20210012187 A1). Referring to claims 1 , 8 and 15 . A computer-implemented method comprising: receiving, from a user an identification of a computing task to be performed by an edge device; ( [0027] of Tugeman, “a user may select one of more DL models from the resulting DL model solution set 116 to be deployed in computing devices, including resource constrained edge devices. The performance functions computed for the DL models may also be stored in association with each of the DL models so that the user is able to identify which DL models satisfy the needs of the user. The user is then able to select a model according to some objective criteria preferred by the user, such as the size, accuracy, or the inference time of the DL model. In some embodiments, the user may select a single DL model based on the preferred objective. In some embodiments, the user may specify an objective criteria and the DL models may be ranked according to the specified objective criteria to facilitate identification of the suitable DL models to be deployed.” ) obtaining, a data set corresponding to the computing task; ( [0030] of Turgeman, “program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computing device 200 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network.” ) determining a supernet model space based at least in part on the computing task; ( The Specification is silent as to what is “a supernet model space”, under BRI, the Examiner will interpret it as a software module. [0030] of Turgeman, “program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computing device 200 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network.” ) creating a plurality of trained models for the computing task by training a plurality of deep learning models within the supernet model space with the data set; ( [0037]-[00 40 ] of Turgeman, “At block 302 , an initial set of Deep Learning (DL) models is trained using a set of on training data. The topology of each of the DL models is determined based on a parameters vector, which specifies attributes of the DL model such as the number of layers and the number of nodes per layer for each model in the initial set of DL models… a set of estimated performance functions are computed for each of the DL models in the initial set. The estimated performance functions are computed based on a set of edge-related metrics such as an inference time, a model size, and a test accuracy. The estimated performance functions provide sampled performance values for computing a plurality of objective functions.” And “ a final DL model set is generated based on the objective functions. The final set of DL models may be determined by identifying the model parameters that result in a specified objective, such as minimizing inference time, maximizing accuracy, and the like. In some embodiments, generating the final DL model set comprises to computing a Pareto front corresponding to a plot of DL model parameters versus DL model performance as computed by the objective functions. ” ) and deploying one of the plurality of trained models to the edge device, wherein the one of the plurality of trained models is determined based at least in part on one or more characteristics of the edge device. ( [0038]-[0040] of Turgeman, “ a user may select one or more of the DL models from the final DL model set for deployment to an edge device. For example, a user interface may enable a user to specify an objective and generate a ranked list of top ranked DL models, which are ranked in accordance with the specified objective. The top ranked models may be displayed to the user, allowing the user to select the one or more of the DL models for deployment. If a plurality of DL models are deployed to the edge device, each DL models make predictions based on a common DL model input, with a final prediction to be determined based on a voting scheme.” ) Referring to claims 2 , 9 and 16 , Turgeman discloses the computer-implemented method of claim 1, wherein the one or more characteristics of the edge device are provided by the user. ( [0038]-[0042] of Turgerman, user interface allow the user to select a specific objective to generate a list of DL models where objective functions are computed based on a set of estimated performance values based on a set of edge-related metrics ) Referring to claims 3 , 10 and 17 , Turgeman discloses the computer-implemented method of claim 1, wherein the one or more characteristics of the edge device are determined by performing a test operation on the edge device. ( [003 4 ] -[0038] and as shown in Fig. 1 of Turgerman, one or more characteristics of the edge device is based on training/testing data 106 to estimate performance function of the edge device ) Referring to claims 4 , 11 and 18 , Turgeman discloses the computer-implemented method of claim 1, wherein the one or more characteristics of the edge device include one or more of an amount of available memory on the edge device, a processing power of the edge device, a network connection of the edge device, and an operating system executing on the edge device. ( [0013] of Turgeman, “model performance metrics of interest (Si) are evaluated for the given training and testing sets, with respect to different values of the DL model's parameters. Those metrics of interest refer to different resource constraints of edge devices, such as DL model size, inference time, accuracy, and others. The DL model's performance is optimized over multiple objectives which refer to the metrics of interest (multi-objective optimization), thereby adapting the model to a given edge. In the multi-objective optimization process, each objective corresponds to an optimal solution. Since the optimization objectives may be inconsistent, even conflicting, one cannot identify a single solution that is optimal on all objectives. Therefore, the different trade-offs are incorporated among the multiple objectives. This results in a set of non-dominated DL model solutions, each of which are optimal according to different trade-offs among multiple objectives. Users can then flexibly construct various predictive models from the solution set for a given edge, considering its resource constraints.” ) Referring to claims 5 , 12 and 19 , Turgeman discloses the computer-implemented method of claim 1, further comprising: monitoring the one or more characteristics of the edge device; and based on a determination that the one or more characteristics has changed by more than a threshold amount, deploying another one of the plurality of trained models to the edge device. ( [0025]-[0026] of Turgeman, where if the specific perfor mance exceeds a threshold/edge related error, then the modified MOP runs again and new set of DL models are generated ) Referring to claims 6 , 13 and 20 , Turgeman discloses the computer-implemented method of claim 1, further comprising: displaying, to the user, one or more operating requirements for each of the plurality of trained models, wherein the one of the plurality of trained models deployed to the edge device is determined based at least in part on a selection by the user. ( [0042] of Turgeman, “a user may select one or more of the DL models from the final DL model set for deployment to an edge device. For example, a user interface may enable a user to specify an objective and generate a ranked list of top ranked DL models, which are ranked in accordance with the specified objective. The top ranked models may be displayed to the user, allowing the user to select the one or more of the DL models for deployment. If a plurality of DL models are deployed to the edge device, each DL models make predictions based on a common DL model input, with a final prediction to be determined based on a voting scheme.” ) Referring to claims 7 , and 14 , Turgeman discloses the computer-implemented method of claim 1, wherein the computing task is one of image classification, object detection, and image segmentation. ( [0028] of Turgeman, computing task is image classification such as face recognition ) The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure: Yuan et al ( CN 114861935 A ): The invention claims a method for obtaining optimal deep learning model, comprising: obtaining the parameter input by the scientific research personnel, comprising a task, corresponding to the deep learning model of the name NameModel, realizing the corresponding to the deep learning model deep learning and the structure parameter of the deep learning model, obtaining scientific research staff input, data related to the parameter, pre-processing the data to obtain the pre-processed data, processing the NameFramework name NameFramework deep learning model NameModel deep learning to establish the needed deep learning model inputting the pre-processed data into the established deep learning model for training, to obtain the final optimal deep learning model The invention can solve the problem that the existing deep learning in the application process of the numerical control machine tool field, deep learning algorithm the code, the algorithm environment configuration, model debugging process wastes time and labour, and the cost is high. “W hen Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning ”, Wang et al, 4/14/2018: Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradient descent based approaches. We analyze the convergence rate of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best trade-off between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions . Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action. It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck , 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson , 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)). In the interests of compact prosecution, Applicant is invited to contact the examiner via electronic media pursuant to USPTO policy outlined MPEP § 502.03. All electronic communication must be authorized in writing. Applicant may wish to file an Internet Communications Authorization Form PTO/SB/439. Applicant may wish to request an interview using the Interview Practice website: http://;www.uspto.gov/patent/laws-and-regulations/interview-practice. Applicant is reminded Internet e-mail may not be used for communication for matters under 35 U.S.C. § 132 or which otherwise require a signature. A reply to an Office action may NOT be communicated by Applicant to the USPTO via Internet e- mail. If such a reply is submitted by Applicant via Internet e-mail, a paper copy will be placed in the appropriate patent application file with an indication that the reply is NOT ENTERED. See MPEP § 502.03(II). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT HAIMEI JIANG whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-1590 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F 9-5pm . 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) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FILLIN "SPE Name?" \* MERGEFORMAT Mariela D Reyes can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571-270-1006 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /HAIMEI JIANG/ Primary Examiner, Art Unit 2142