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 .
Claim Objections
Claims 4, 5, 7, 11, 12, 14, 18, and 19 are objected to because of the following informalities:
Claim 4, line 3: “NaaS” should be --Network as a Service (NaaS)--;
Claim 5, line 2: “AI” should be --Artificial Intelligence (AI)--;
Claim 7, line 3: “GUI” should be --Graphical User Interface (GUI)-- and line 6: “AI” should be --Artificial Intelligence (AI)--;;
Claim 11, line 3: “NaaS” should be --Network as a Service (NaaS)--;
Claim 12, line 2: “AI” should be --Artificial Intelligence (AI)--;
Claim 14, line 2: “GUI” should be --Graphical User Interface (GUI)-- and line 5: “AI” should be --Artificial Intelligence (AI)--;
Claim 18, line 3: “NaaS” should be --Network as a Service (NaaS)--;
Claim 19, line 2: “AI” should be --Artificial Intelligence (AI)--.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 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, 2, 8, 9, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Shah et al. (US 20230244466 A1, hereinafter Shah) in view of Arora et al. (US 20220206774 A1, hereinafter Arora) and Ritchie et al. (US 20220209991 A1, hereinafter Ritchie).
With respect to claim 1:
Shah teaches A system comprising: a processor; and a memory comprising computer program code, the memory and the computer program code configured to cause the processor to (e.g., [0007], “…, a method performed by one or more computers …” and [0059], “The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data.”):
automatically build a (e.g., fig. 1 and [0034], “In stage (F), the cloud computing account 150 receives the configuration data 114 (e.g., Helm charts or other packaging data). In stage (G), the cloud computing account 150 receives the automation data 115, including scripts for creating various aspects of the deployment tools ... At this point in the process, the cloud computing account 150 contains all of the software and configuration data needed to create the environment deployment infrastructure (e.g., tools for managing and deploying environments) as well as the software to be run in the server environments being deployed.”);
configure a node cluster in the built (e.g., fig. 3 and [0047], “In stage (J), instructions 131 to create a new cluster are received, interpreted, and communicated to the deployment orchestrator 152, which creates a new cluster 160 of processing nodes, e.g., a Kubemetes cluster. The instructions 131 can include, as API call payload data, various settings or parameter values such as geographical region settings, an instance type, cluster size, and so on, and the deployment orchestrator 152 creates the cluster 160 according to the specified parameters.”);
deploy (e.g., [0025], “For example, the system 110 can deploy the container images 113 as containers in cluster of processing nodes (e.g., a Kubernetes cluster) in a target cloud computing platform.”).
Shah does not teach however Arora teaches
(e.g., fig. 1A and [0009], “FIG. 1A depicts an illustrative machine learning workflow system suitable for building and deploying a machine learning application, according to some embodiments;”) ;
(e.g., fig. 1A and [0009], “machine learning workflow system suitable for building and deploying a machine learning application”) ([0045], “ In some embodiments, each of the components in a workflow may be represented by the AI/ML workflow management system 104 as a node of a graph, and the connections between the components in the workflow may be represented as edges in the graph. …When recording a workflow to a computer readable storage medium, the workflow may be represented in any suitable way, including as a machine learning workflow specification, and/or as data that describes components and connections as nodes and edges of a graph. ”);
(e.g., fig. 1A and [0009], “machine learning workflow system suitable for building and deploying a machine learning application”); and
deploy an application (e.g., [0037], “deploying a machine learning application”) (e.g., [0038], “When the machine learning workflow is executed, the multiple operations in the workflow are automatically performed to build and deploy the machine learning application.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah with the invention of Arora to reduce manual efforts of creating a machine learning project environment and allow consistency of current and future machine learning application environments.
Shah as modified by Arora does not teach, however Ritchie teaches
provision network access (e.g., [0030], “In some examples, network provisioning may include automating connections to a virtual private network (VPN), to a public cloud provider, to a private cloud provider, etc. The NCO system 150 may operate to automate the building of a network path between network resources 105 between the edge compute instance and the service that is being provided to the edge compute instance, whether that service is an Internet connection, a VPN, or another type of networking service.”) and connectivity to the nodes of the node cluster using a network configuration (e.g., [0050], “automatically provision the network to provide connectivity to the server instance 225”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah as modified by Arora with the invention of Ritchie to increase efficiency of automatically deploying a machine learning application by reducing manual network configuration.
With respect to claim 2:
Shah teaches
generating a (e.g., [0024], “generate configuration data 114.”)
Shah does not teach, however Arora teaches
(e.g., fig. 1A and [0009], “machine learning workflow system suitable for building and deploying a machine learning application”); and
(e.g., fig. 1A and [0009], “machine learning workflow system suitable for building and deploying a machine learning application”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah with the invention of Arora to increase consistency and efficiency of machine learning project environments for development.
Shah as modified by Arora does not teach, however Ritchie teaches
(e.g., [0027], “a network configuration orchestration (NCO) system 150, which may sometimes be referred to as a Network-as-a-Service (NaaS) system.)
automatically configuring a network endpoint (e.g., [0030], “The NCO system 150 may operate to automate the building of a network path between network resources 105 between the edge compute instance … the network configurator 112 may operate to interrogate network elements, reserve network resources in the network 106, configure ports on network resources, create a domain path between server instances, delete domain paths”) (e.g., [0050], “to provide connectivity to the server instance 225”) cluster (e.g.., [0037], “clusters of bare metal machines, virtual machines, or other instances that may be useful in a number of use cases.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah modified by Arora with the invention of Ritchie to reduce manual network configuration and simplify the deployment process of machine learning applications.
With respect to claim 8:
Shah teaches
automatically building a (e.g., fig. 1A and [0034], “In stage (F), the cloud computing account 150 receives the configuration data 114 (e.g., Helm charts or other packaging data). In stage (G), the cloud computing account 150 receives the automation data 115, including scripts for creating various aspects of the deployment tools ... At this point in the process, the cloud computing account 150 contains all of the software and configuration data needed to create the environment deployment infrastructure (e.g., tools for managing and deploying environments) as well as the software to be run in the server environments being deployed.”);
configuring a node cluster in the built (e.g., [0047], “In stage (J), instructions 131 to create a new cluster are received, interpreted, and communicated to the deployment orchestrator 152, which creates a new cluster 160 of processing nodes, e.g., a Kubemetes cluster. The instructions 131 can include, as API call payload data, various settings or parameter values such as geographical region settings, an instance type, cluster size, and so on, and the deployment orchestrator 152 creates the cluster 160 according to the specified parameters.”);
deploying (e.g., [0025], “For example, the system 110 can deploy the container images 113 as containers in cluster of processing nodes (e.g., a Kubernetes cluster) in a target cloud computing platform.”).
Shah does not teach however Arora teaches
(e.g., fig. 1A and [0009], “FIG. 1A depicts an illustrative machine learning workflow system suitable for building and deploying a machine learning application, according to some embodiments;”) ;
(e.g., fig. 1A and [0009], “machine learning workflow system suitable for building and deploying a machine learning application”) ([0045], “ In some embodiments, each of the components in a workflow may be represented by the AI/ML workflow management system 104 as a node of a graph, and the connections between the components in the workflow may be represented as edges in the graph. …When recording a workflow to a computer readable storage medium, the workflow may be represented in any suitable way, including as a machine learning workflow specification, and/or as data that describes components and connections as nodes and edges of a graph. ”);
(e.g., fig. 1A and [0009], “machine learning workflow system suitable for building and deploying a machine learning application”); and
deploying an application (e.g., [0037], “deploying a machine learning application”) (e.g., [0038], “When the machine learning workflow is executed, the multiple operations in the workflow are automatically performed to build and deploy the machine learning application.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah with the invention of Arora to reduce manual efforts of creating a machine learning project environment and allow consistency of current and future machine learning application environments.
Shah as modified by Arora does not teach, however Ritchie teaches
provision network access (e.g., [0030], “In some examples, network provisioning may include automating connections to a virtual private network (VPN), to a public cloud provider, to a private cloud provider, etc. The NCO system 150 may operate to automate the building of a network path between network resources 105 between the edge compute instance and the service that is being provided to the edge compute instance, whether that service is an Internet connection, a VPN, or another type of networking service.”) and connectivity to the nodes of the node cluster using a network configuration (e.g., [0050], “automatically provision the network to provide connectivity to the server instance 225”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah as modified by Arora with the invention of Ritchie to increase efficiency of automatically deploying a machine learning application by reducing manual network configuration.
With respect to claim 9:
Shah teaches
generating a (e.g., [0024], “generate configuration data 114.”)
Shah does not teach, however Arora teaches
(e.g., fig. 1A and [0009], “machine learning workflow system suitable for building and deploying a machine learning application”); and
(e.g., fig. 1A and [0009], “machine learning workflow system suitable for building and deploying a machine learning application”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah with the invention of Arora to increase consistency and efficiency of machine learning project environments for development.
Shah as modified by Arora does not teach, however Ritchie teaches
(e.g., [0027], “a network configuration orchestration (NCO) system 150, which may sometimes be referred to as a Network-as-a-Service (NaaS) system.)
automatically configuring a network endpoint (e.g., [0030], “The NCO system 150 may operate to automate the building of a network path between network resources 105 between the edge compute instance … the network configurator 112 may operate to interrogate network elements, reserve network resources in the network 106, configure ports on network resources, create a domain path between server instances, delete domain paths”) (e.g., [0050], “to provide connectivity to the server instance 225”) cluster (e.g.., [0037], “clusters of bare metal machines, virtual machines, or other instances that may be useful in a number of use cases.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah modified by Arora with the invention of Ritchie to reduce manual network configuration and simplify the deployment process of machine learning applications.
With respect to claim 15:
Shah teaches A computer storage medium has computer-executable instructions that, upon execution by a processor, cause the processor to at least (e.g., [0059], “Computer readable media suitable for storing computer program instructions and data include all forms of nonvolatile memory, media and memory devices,”):
automatically build a (e.g., fig. 1 and [0034], “In stage (F), the cloud computing account 150 receives the configuration data 114 (e.g., Helm charts or other packaging data). In stage (G), the cloud computing account 150 receives the automation data 115, including scripts for creating various aspects of the deployment tools ... At this point in the process, the cloud computing account 150 contains all of the software and configuration data needed to create the environment deployment infrastructure (e.g., tools for managing and deploying environments) as well as the software to be run in the server environments being deployed.”);
configure a node cluster in the built (e.g., [0047], “In stage (J), instructions 131 to create a new cluster are received, interpreted, and communicated to the deployment orchestrator 152, which creates a new cluster 160 of processing nodes, e.g., a Kubemetes cluster. The instructions 131 can include, as API call payload data, various settings or parameter values such as geographical region settings, an instance type, cluster size, and so on, and the deployment orchestrator 152 creates the cluster 160 according to the specified parameters.”);
deploy (e.g., [0025], “For example, the system 110 can deploy the container images 113 as containers in cluster of processing nodes (e.g., a Kubernetes cluster) in a target cloud computing platform.”).
Shah does not teach however Arora teaches
(e.g., fig. 1A and [0009], “FIG. 1A depicts an illustrative machine learning workflow system suitable for building and deploying a machine learning application, according to some embodiments;”) ;
(e.g., fig. 1A and [0009], “machine learning workflow system suitable for building and deploying a machine learning application”) ([0045], “ In some embodiments, each of the components in a workflow may be represented by the AI/ML workflow management system 104 as a node of a graph, and the connections between the components in the workflow may be represented as edges in the graph. …When recording a workflow to a computer readable storage medium, the workflow may be represented in any suitable way, including as a machine learning workflow specification, and/or as data that describes components and connections as nodes and edges of a graph. ”);
(e.g., fig. 1A and [0009], “machine learning workflow system suitable for building and deploying a machine learning application”); and
deploy an application (e.g., [0037], “deploying a machine learning application”) (e.g., [0038], “When the machine learning workflow is executed, the multiple operations in the workflow are automatically performed to build and deploy the machine learning application.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah with the invention of Arora to reduce manual efforts of creating a machine learning project environment and allow consistency of current and future machine learning application environments.
Shah as modified by Arora does not teach, however Ritchie teaches
provision network access (e.g., [0030], “In some examples, network provisioning may include automating connections to a virtual private network (VPN), to a public cloud provider, to a private cloud provider, etc. The NCO system 150 may operate to automate the building of a network path between network resources 105 between the edge compute instance and the service that is being provided to the edge compute instance, whether that service is an Internet connection, a VPN, or another type of networking service.”) and connectivity to the nodes of the node cluster using a network configuration (e.g., [0050], “automatically provision the network to provide connectivity to the server instance 225”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah as modified by Arora with the invention of Ritchie to increase efficiency of automatically deploying a machine learning application by reducing manual network configuration.
With respect to claim 16:
Shah teaches
generating a (e.g., [0024], “generate configuration data 114.”)
Shah does not teach, however Arora teaches
(e.g., fig. 1A and [0009], “machine learning workflow system suitable for building and deploying a machine learning application”); and
(e.g., fig. 1A and [0009], “machine learning workflow system suitable for building and deploying a machine learning application”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah with the invention of Arora to increase consistency and efficiency of machine learning project environments for development.
Shah as modified by Arora does not teach, however Ritchie teaches
(e.g., [0027], “a network configuration orchestration (NCO) system 150, which may sometimes be referred to as a Network-as-a-Service (NaaS) system.)
automatically configuring a network endpoint (e.g., [0030], “The NCO system 150 may operate to automate the building of a network path between network resources 105 between the edge compute instance … the network configurator 112 may operate to interrogate network elements, reserve network resources in the network 106, configure ports on network resources, create a domain path between server instances, delete domain paths”) (e.g., [0050], “to provide connectivity to the server instance 225”) cluster (e.g.., [0037], “clusters of bare metal machines, virtual machines, or other instances that may be useful in a number of use cases.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah modified by Arora with the invention of Ritchie to reduce manual network configuration and simplify the deployment process of machine learning applications.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Shah as modified by Arora and Ritchie as applied to claims 1, 8, and 15 above and further in view of Zafar et al. (US 20190129702 A1, hereinafter Zafar).
With respect to claim 3:
Shah further teaches
validating a prerequisite associated with a gate agent process (e.g., [0023], “The computer system 110 can run various tests on the generated container images 113 as well. Unit tests and other automated tests can be triggered to ensure that each container image 113 can be started and run without errors, and that the desired functionality (e.g., services, APIs, etc.) of the container is provided with expected performance characteristics”).
Shah as modified by Arora and Ritchie does not teach, however Zafar teaches generating a manifest file (e.g., [0019], “the client device 110 may generate a manifest file”) configured for use during address allocation to nodes in the ML project environment; and
initiating the gate agent process using the generated manifest file (e.g., [0022], “The deployment engine 120 may receive a manifest file and parse the manifest file to determine which software tools need to be deployed, which machines the software tools should be deployed on, the order that the software tools should be deployed, and additional configurations of the tools to interoperate and interconnect with one another.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah as modified by Arora and Ritchie with the invention of Zafar to ensure the machine learning project environment has been properly set up.
With respect to claim 10:
Shah further teaches
validating a prerequisite associated with a gate agent process (e.g., [0023], “The computer system 110 can run various tests on the generated container images 113 as well. Unit tests and other automated tests can be triggered to ensure that each container image 113 can be started and run without errors, and that the desired functionality (e.g., services, APIs, etc.) of the container is provided with expected performance characteristics”).
Shah as modified by Arora and Ritchie does not teach, however Zafar teaches generating a manifest file (e.g., [0019], “the client device 110 may generate a manifest file”) configured for use during address allocation to nodes in the ML project environment; and
initiating the gate agent process using the generated manifest file (e.g., [0022], “The deployment engine 120 may receive a manifest file and parse the manifest file to determine which software tools need to be deployed, which machines the software tools should be deployed on, the order that the software tools should be deployed, and additional configurations of the tools to interoperate and interconnect with one another.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah as modified by Arora and Ritchie with the invention of Zafar to ensure the machine learning project environment has been properly set up.
With respect to claim 17:
Shah further teaches
validating a prerequisite associated with a gate agent process (e.g., [0023], “The computer system 110 can run various tests on the generated container images 113 as well. Unit tests and other automated tests can be triggered to ensure that each container image 113 can be started and run without errors, and that the desired functionality (e.g., services, APIs, etc.) of the container is provided with expected performance characteristics”).
Shah as modified by Arora and Ritchie does not teach, however Zafar teaches generating a manifest file (e.g., [0019], “the client device 110 may generate a manifest file”) configured for use during address allocation to nodes in the ML project environment; and
initiating the gate agent process using the generated manifest file (e.g., [0022], “The deployment engine 120 may receive a manifest file and parse the manifest file to determine which software tools need to be deployed, which machines the software tools should be deployed on, the order that the software tools should be deployed, and additional configurations of the tools to interoperate and interconnect with one another.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah as modified by Arora and Ritchie with the invention of Zafar to ensure the machine learning project environment has been properly set up.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Shah as modified by Arora and Ritchie as applied to claims 3, 10, and 17 above and further in view of Bringhenti et al. (NPL “Security automation for multi-cluster orchestration in Kubernetes”, hereinafter Bringhenti).
With respect to claim 4:
Shah further teaches
generating a control cluster pipeline of the node cluster, wherein the control cluster pipeline is configured to enable control of the node cluster within the ML project environment (e.g., [0037], “For example, the deployment orchestrator 152 can enable functions such as cluster creation and management, cluster upgrade, configuration and setup for the deployment tools (e.g., for the deployment orchestrator 152 and the deployment controller 153), and so on.”).
Shah as modified by Arora and Ritchie does not teach, however Bringhenti teaches
generating a cluster NaaS document for the node cluster (e.g., pg. 482: right column: second paragraph, “First, the orchestrator automatically creates a Global Configuration, characterized by general information about all the communications that must be allowed. Then, it derives a Single Configuration for each cluster, refining the elements of the Global Configuration that pertain the specific cluster.”), wherein the cluster NaaS document is configured for use with NaaS policies of the node cluster (e.g., pg. 483: left column: first full paragraph, “2) Single Configurations: Once a Global Configuration is created, the Multi-Cluster Orchestrator creates a Single Configuration for every cluster and applies it to them. Each Single Configuration includes:
• the parameters that are required to set up the presence of
the cluster in the cluster mesh;
• the network policies to be installed for the services or
pods of the cluster;
• the commands to be used to create new services in
the cluster, with the objective resolve names of services
deployed in external clusters.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah as modified by Arora and Ritchie with the invention of Bringhenti to reduce human-made configuration errors.
With respect to claim 11:
Shah further teaches
generating a control cluster pipeline of the node cluster, wherein the control cluster pipeline is configured to enable control of the node cluster within the ML project environment (e.g., [0037], “For example, the deployment orchestrator 152 can enable functions such as cluster creation and management, cluster upgrade, configuration and setup for the deployment tools (e.g., for the deployment orchestrator 152 and the deployment controller 153), and so on.”).
Shah as modified by Arora and Ritchie does not teach, however Bringhenti teaches
generating a cluster NaaS document for the node cluster (e.g., pg. 482: right column: second paragraph, “First, the orchestrator automatically creates a Global Configuration, characterized by general information about all the communications that must be allowed. Then, it derives a Single Configuration for each cluster, refining the elements of the Global Configuration that pertain the specific cluster.”), wherein the cluster NaaS document is configured for use with NaaS policies of the node cluster (e.g., pg. 483: left column: first full paragraph, “2) Single Configurations: Once a Global Configuration is created, the Multi-Cluster Orchestrator creates a Single Configuration for every cluster and applies it to them. Each Single Configuration includes:
• the parameters that are required to set up the presence of
the cluster in the cluster mesh;
• the network policies to be installed for the services or
pods of the cluster;
• the commands to be used to create new services in
the cluster, with the objective resolve names of services
deployed in external clusters.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah as modified by Arora and Ritchie with the invention of Bringhenti to reduce human-made configuration errors.
With respect to claim 18:
Shah further teaches
generating a control cluster pipeline of the node cluster, wherein the control cluster pipeline is configured to enable control of the node cluster within the ML project environment (e.g., [0037], “For example, the deployment orchestrator 152 can enable functions such as cluster creation and management, cluster upgrade, configuration and setup for the deployment tools (e.g., for the deployment orchestrator 152 and the deployment controller 153), and so on.”).
Shah as modified by Arora and Ritchie does not teach, however Bringhenti teaches
generating a cluster NaaS document for the node cluster (e.g., pg. 482: right column: second paragraph, “First, the orchestrator automatically creates a Global Configuration, characterized by general information about all the communications that must be allowed. Then, it derives a Single Configuration for each cluster, refining the elements of the Global Configuration that pertain the specific cluster.”), wherein the cluster NaaS document is configured for use with NaaS policies of the node cluster (e.g., pg. 483: left column: first full paragraph, “2) Single Configurations: Once a Global Configuration is created, the Multi-Cluster Orchestrator creates a Single Configuration for every cluster and applies it to them. Each Single Configuration includes:
• the parameters that are required to set up the presence of
the cluster in the cluster mesh;
• the network policies to be installed for the services or
pods of the cluster;
• the commands to be used to create new services in
the cluster, with the objective resolve names of services
deployed in external clusters.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah as modified by Arora and Ritchie with the invention of Bringhenti to reduce human-made configuration errors.
Claims 5, 6, 12, 13, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shah as modified by Arora and Ritchie as applied to claims 1, 8, and 15 above and further in view of Polleri et al. (WO 2021050391 A1, hereinafter Polleri).
With respect to claim 5:
Shah as modified by Arora and Ritchie does not teach, however Polleri teaches
collect training data (e.g., [0272], “training data received in steps 1102 and 1104.”);
engineer features using the collected training data (e.g., [0296], “In various embodiments, the techniques can review the one or more labels that characterize the data to determine the one or more features from the data. The techniques can extract the one or more features, store the features and the associated data locations (e.g., data addresses) in a memory. The techniques can also identify and select the features that are predictive for each individual use case (i.e., one client), effectively making the machine learning solution client agnostic for the application developer. In some embodiments, the features are can be extracted by the metadata contained within each of the categories of stored data.”);
train an AI model using the engineered features (e.g., [0300], “At 1412, the functionality includes feeding features to a machine learning solution.”) and collected training data (e.g., [0272], “at 1106 the models may be trained using machine-learning algorithms based on training data sets including any, some, or all of the code integration request/outcome data received in steps 1102-1104.”); and
deploy the trained AI model to perform a model operation in response to input from another application (e.g., [0230], “In various embodiments, the computer-implemented method can include deploying the machine learning architecture via an intelligent assistant interface”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah as modified by Arora and Ritchie with the invention of Polleri to enable developers to build models without needing detailed knowledge of different tools and programming languages utilized by various AI models.
With respect to claim 6:
Shah as modified by Arora and Ritchie does not teach, however Polleri teaches
monitor operations of the deployed AI model (e.g., [0046], “A monitoring engine 156 can monitor operation of the machine learning applications 112 according to the KPI/QoS metrics 160 to assure the machine learning application 112 is performing according to requirements.”);
collect feedback data based on the monitored operations (e.g., [0053], “The monitoring engine 156 can provide feedback to the model composition engine 132. The feedback can include adjustments to one or more variables or selected machine learning model used in the machine learning model 112.”); and adjust training of a next version of the AI model using the collected feedback data (e.g., [0097], “At 322, the functionality includes auto-adjusting the model as needed. In various embodiments, the values of the algorithm can be automatically adjusted to achieve a desired QoS/KPI outcome. In various embodiments, the values can be adjusted within a defined range of values. The adjustments can include selecting different pipelines 136, microservices routines 140, software modules 144, and infrastructure modules 148.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah as modified by Arora and Ritchie with the invention of Polleri to simplify the process of fixing and adjusting the AI model.
With respect to claim 12:
Shah as modified by Arora and Ritchie does not teach, however Polleri teaches
collect training data (e.g., [0272], “training data received in steps 1102 and 1104.”);
engineer features using the collected training data (e.g., [0296], “In various embodiments, the techniques can review the one or more labels that characterize the data to determine the one or more features from the data. The techniques can extract the one or more features, store the features and the associated data locations (e.g., data addresses) in a memory. The techniques can also identify and select the features that are predictive for each individual use case (i.e., one client), effectively making the machine learning solution client agnostic for the application developer. In some embodiments, the features are can be extracted by the metadata contained within each of the categories of stored data.”);
train an AI model using the engineered features (e.g., [0300], “At 1412, the functionality includes feeding features to a machine learning solution.”) and collected training data (e.g., [0272], “at 1106 the models may be trained using machine-learning algorithms based on training data sets including any, some, or all of the code integration request/outcome data received in steps 1102-1104.”); and
deploy the trained AI model to perform a model operation in response to input from another application (e.g., [0230], “In various embodiments, the computer-implemented method can include deploying the machine learning architecture via an intelligent assistant interface”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah as modified by Arora and Ritchie with the invention of Polleri to enable developers to build models without needing detailed knowledge of different tools and programming languages utilized by various AI models.
With respect to claim 13:
Shah as modified by Arora and Ritchie does not teach, however Polleri teaches
monitor operations of the deployed AI model (e.g., [0046], “A monitoring engine 156 can monitor operation of the machine learning applications 112 according to the KPI/QoS metrics 160 to assure the machine learning application 112 is performing according to requirements.”);
collect feedback data based on the monitored operations (e.g., [0053], “The monitoring engine 156 can provide feedback to the model composition engine 132. The feedback can include adjustments to one or more variables or selected machine learning model used in the machine learning model 112.”); and adjust training of a next version of the AI model using the collected feedback data (e.g., [0097], “At 322, the functionality includes auto-adjusting the model as needed. In various embodiments, the values of the algorithm can be automatically adjusted to achieve a desired QoS/KPI outcome. In various embodiments, the values can be adjusted within a defined range of values. The adjustments can include selecting different pipelines 136, microservices routines 140, software modules 144, and infrastructure modules 148.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah as modified by Arora and Ritchie with the invention of Polleri to simplify the process of fixing and adjusting the AI model.
With respect to claim 19:
Shah as modified by Arora and Ritchie does not teach, however Polleri teaches
collect training data (e.g., [0272], “training data received in steps 1102 and 1104.”);
engineer features using the collected training data (e.g., [0296], “In various embodiments, the techniques can review the one or more labels that characterize the data to determine the one or more features from the data. The techniques can extract the one or more features, store the features and the associated data locations (e.g., data addresses) in a memory. The techniques can also identify and select the features that are predictive for each individual use case (i.e., one client), effectively making the machine learning solution client agnostic for the application developer. In some embodiments, the features are can be extracted by the metadata contained within each of the categories of stored data.”);
train an AI model using the engineered features (e.g., [0300], “At 1412, the functionality includes feeding features to a machine learning solution.”) and collected training data (e.g., [0272], “at 1106 the models may be trained using machine-learning algorithms based on training data sets including any, some, or all of the code integration request/outcome data received in steps 1102-1104.”); and
deploy the trained AI model to perform a model operation in response to input from another application (e.g., [0230], “In various embodiments, the computer-implemented method can include deploying the machine learning architecture via an intelligent assistant interface”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah as modified by Arora and Ritchie with the invention of Polleri to enable developers to build models without needing detailed knowledge of different tools and programming languages utilized by various AI models.
With respect to claim 20:
Shah as modified by Arora and Ritchie does not teach, however Polleri teaches
monitor operations of the deployed AI model (e.g., [0046], “A monitoring engine 156 can monitor operation of the machine learning applications 112 according to the KPI/QoS metrics 160 to assure the machine learning application 112 is performing according to requirements.”);
collect feedback data based on the monitored operations (e.g., [0053], “The monitoring engine 156 can provide feedback to the model composition engine 132. The feedback can include adjustments to one or more variables or selected machine learning model used in the machine learning model 112.”); and adjust training of a next version of the AI model using the collected feedback data (e.g., [0097], “At 322, the functionality includes auto-adjusting the model as needed. In various embodiments, the values of the algorithm can be automatically adjusted to achieve a desired QoS/KPI outcome. In various embodiments, the values can be adjusted within a defined range of values. The adjustments can include selecting different pipelines 136, microservices routines 140, software modules 144, and infrastructure modules 148.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah as modified by Arora and Ritchie with the invention of Polleri to simplify the process of fixing and adjusting the AI model.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Shah as modified by Arora and Ritchie as applied to claims 1, 8, and 15 above and further in view of Duggan et al. (US 20170178019 A1, hereinafter Duggan).
With respect to claim 7:
Shah fails to teach but Arora teaches
deploy an AI model (e.g., [0048], “deploying a machine learning application”).
verify operation of the deployed AI model (e.g., [0048], “verifying ... the machine learning model in the deployed machine learning application.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah with the invention of Arora to reduce manual efforts of creating a machine learning project environment and allow consistency of current and future machine learning application environments.
Shah as modified by Arora and Ritchie does not teach, however Duggan teaches
display a dashboard GUI for use by a user (e.g., [0040], “The local GUI may be used to present a control dashboard, actionable insights and/or other information to a maintenance engineer.”);
receive model deployment instructions from the user (e.g., [0067], “Once the analytical model is trained, the user interface circuitry 114 may be configured to accept a deployment input configured to cause deployment of the trained analytical model on the compute engine 118 by the model deployment circuitry 104 (616).”) via the displayed dashboard GUI;
respond to the received model deployment instructions to provide model access to the user (e.g., fig. 7 and [0087], “The GUI 700 may also provide a current status of the particular analytical model. For example, the first analytical model 716 is shown as presently deployed via status indicator 732,”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah as modified by Arora and Ritchie with the invention of Duggan to increase customization and flexibility of deploying models among users.
With respect to claim 14:
Shah fails to teach but Arora teaches
deploy an AI model (e.g., [0048], “deploying a machine learning application”).
verify operation of the deployed AI model (e.g., [0048], “verifying ... the machine learning model in the deployed machine learning application.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah with the invention of Arora to reduce manual efforts of creating a machine learning project environment and allow consistency of current and future machine learning application environments.
Shah as modified by Arora and Ritchie does not teach, however Duggan teaches
display a dashboard GUI for use by a user (e.g., [0040], “The local GUI may be used to present a control dashboard, actionable insights and/or other information to a maintenance engineer.”);
receive model deployment instructions from the user (e.g., [0067], “Once the analytical model is trained, the user interface circuitry 114 may be configured to accept a deployment input configured to cause deployment of the trained analytical model on the compute engine 118 by the model deployment circuitry 104 (616).”) via the displayed dashboard GUI;
respond to the received model deployment instructions to provide model access to the user (e.g., fig. 7 and [0087], “The GUI 700 may also provide a current status of the particular analytical model. For example, the first analytical model 716 is shown as presently deployed via status indicator 732,”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Shah as modified by Arora and Ritchie with the invention of Duggan to increase customization and flexibility of deploying models among users.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHELLE XUE HAN whose telephone number is (571)270-3362. The examiner can normally be reached Mon-Fri (7:30-5), Every 2nd Fri off.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hyung Sough can be reached at (571) 272-6799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHELLE XUE HAN/Examiner, Art Unit 2192
/S. Sough/SPE, Art Unit 2192
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