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
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1, 13, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over BLAIR et al. (Pub. No: US 2024-0121125) in view of LI et al. (US Pub. No: 2019-0377624) in view of JEZEWSKI (US Pub. No: 2023-0044564)
As per Claim 1 BLAIR discloses A system comprising (Figs. 1-3):
a processor (Figs. 1-3 processor 122 [0025] [0140-0143]); and a memory coupled to the processor, wherein the memory comprises processor-executable instructions, which on execution, cause the processor to (Figs. 1-3 [0140-0143]):
receive a request for creating an artificial intelligence (AI)-based workflow from a user device (Figs. 1-3 [0026-0028] workflow and generative outputs configured by user [0102-0104] [0129-0131]); obtain an input data from a plurality of data sources based on the received request (Figs. 1-3 obtained data input 110 in different forms and formats dependent on the source [0026-0028, 0055-0056] [0102-0104] [0129-0131]);
pre-process the obtained data using an artificial intelligence (AI) based pre-processing model (Figs. 1-3 model pre-processing [0026-0028, 0041] [0055-0056] [0082-0083] [0086-0088] [0102-0104]);
identify a plurality of AI and Generative AI service nodes to be executed on the pre-processed data based on the received request (Figs. 1-3 - for nodes [0086-0088] aggregated nodes [0129-0131] [0136, 0138-0139]), wherein the plurality of AI and Generative AI service nodes comprise a functional task to be executed on the pre-processed data and wherein the plurality of AI and Generative AI service nodes comprise a plurality of processing nodes (Figs. 1-3 [0041] [0055-0056] data modeling tasks [0082-0083] [0086-0088] [0102-0104] [0129-0131] servicing nodes [0138-0139]);
generate an AI-based workflow by connecting each of the identified plurality of AI and Generative AI service nodes in a pre-determined manner (Figs. 1-3 [0041] [0055-0056] [0082-0083] further node connection and process flow [0086-0088] [0102-0104] [0129-0131] servicing nodes [0138-0139]), wherein the AI-based workflow comprises the identified plurality of AI and Generative AI service nodes to be executed, an order of execution, and a service configuration (Figs. 1-3 logical sequential workflow [0041] [0055-0056] [0082-0083] [0086-0088] [0102-0104] [0129-0131] [0138-0139]);
plurality of AI and Generative AI service nodes by executing each of the identified plurality of AI and Generative AI service nodes comprised in the generated AI-based workflow (Figs. 1-3 - for nodes [0086-0088] aggregated nodes and generative workflow [0129-0131] [0136, 0138-0139]),
perform the determined set of actions on the generated AI-based workflow (Figs. 1-3 action based upon entity recognition dictate workflow [0023] [0040-0041, 0045] [0055-0056] [0068-0070] based on identified outcome further [0086-0088] [0102-0104]);
and deploy the generated AI-based workflow onto at least one external system based on a set of configuration parameters (Figs. 1-3 external deployment [0025, 0027-0029] – and see pipeline outputs 350 [0070-0071] [0080-0081] [0100-0104])
BLAIR does not disclose but LI discloses generate a metadata for each of identified AI node, wherein the metadata is generated at each stage of execution of the of AI node (Figs. 1-5 single node [0018] – generated metadata for validation [0027] [0042] [0070] [0074-0079]); validate the generated metadata based on a plurality of AI-based rules (Figs. 1-5 [0042] [0070] – rule processor 410 [0074-0079]); determine a set of actions to be performed on the generated metadata based on results of validation (Figs. 1-5 based on results from rule validation steps 650, 655, 660 [0074-0079] [0105-0107])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include generate a metadata for each of identified AI node, wherein the metadata is generated at each stage of execution of the of AI node; validate the generated metadata based on a plurality of AI-based rules; determine a set of actions to be performed on the generated metadata based on results of validation as taught by LI into the system of BLAIR because of the benefit taught by LI to include advanced data validation for AI processed data to ensure proper workflow outcomes which will improve BLAIR which is directed towards AI workflow generative systems and would benefit from the additional related features for data validation prior to output.
BLAIR and LI do not disclose but JEZEWSKI discloses wherein the AI-based workflow comprises a workflow description (contained in the data structure [0897] [0940-0941] [1113-1115] [1132])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the AI-based workflow comprises a workflow description as taught by JEZEWSKI into the system of BLAIR and LI because of the benefit taught by JEZEWSKI to include additional generative automation and analysis techniques and solutions to improve AI based workflow outcomes for AI training systems as disclosed in for the systems of BLAIR and LI.
As per Claim 13 BLAIR discloses A method comprising (Fig. 1 [Abstract]):
receiving, by a processor (See said analysis for Claim 1), a request for creating an artificial intelligence (AI)-based workflow from a user device (See said analysis for Claim 1); obtaining, by the processor, an input data from a plurality of data sources based on the received request (See said analysis for Claim 1);
pre-processing, by the processor, the obtained data using an artificial intelligence (AI) based pre-processing model (See said analysis for Claim 1); identifying, by the processor, a plurality of AI and Generative AI service nodes to be executed on the pre-processed data based on the received request (See said analysis for Claim 1), wherein the plurality of AI and Generative AI service nodes comprise a functional task to be executed on the pre-processed data and wherein the plurality of AI and Generative AI service nodes comprise a plurality of processing nodes (See said analysis for Claim 1);
generating, by the processor, an AI-based workflow by connecting each of the identified plurality of AI and Generative AI service nodes in a pre-determined manner (See said analysis for Claim 1), wherein the AI-based workflow comprises the identified plurality of AI and Generative AI service nodes to be executed, an order of execution, and a service configuration (See said analysis for Claim 1);
plurality of AI and Generative AI service nodes by executing each of the identified plurality of AI and Generative AI service nodes comprised in the generated AI-based workflow (See said analysis for Claim 1)
performing, by the processor, the determined set of actions on the generated AI-based workflow (See said analysis for Claim 1); and deploying, by the processor, the generated AI-based workflow onto at least one external system based on a set of configuration parameters (See said analysis for Claim 1)
BLAIR does not disclose but LI discloses generating, by the processor (Fig. 5 processor 505) a metadata for each of identified AI node (See said analysis for Claim 1), wherein the metadata is generated at each stage of execution of the of AI node (See said analysis for Claim 1); validating, by the processor (Fig. 5 processor 505), the generated metadata based on a plurality of AI-based rules (See said analysis for Claim 1); determining, by the processor (Fig. 5 processor 505), a set of actions to be performed on the generated metadata based on results of validation (See said analysis for Claim 1) (The motivation that applied in Claim 1 applies equally to Claim 13)
BLAIR and LI do not disclose but JEZEWSKI discloses wherein the AI-based workflow comprises a workflow description (See said analysis for Claim 1)
As per Claim 20 BLAIR discloses A non-transitory computer-readable medium comprising machine-readable instructions that are executable by a processor to (Figs. 1-3 [0140-0143]):
receive a request for creating an artificial intelligence (AI)-based workflow from a user device (See said analysis for Claim 1); obtain an input data from a plurality of data sources based on the received request (See said analysis for Claim 1); pre-process the obtained data using an artificial intelligence (AI) based pre-processing model (See said analysis for Claim 1); identify a plurality of AI and Generative AI service nodes to be executed on the pre-processed data based on the received request, wherein the plurality of AI and Generative AI service nodes comprise a functional task to be executed on the pre-processed data and wherein the plurality of AI and Generative AI service nodes comprise a plurality of processing nodes (See said analysis for Claim 1); generate an AI-based workflow by connecting each of the identified plurality of AI and Generative AI service nodes in a pre-determined manner, wherein the AI-based workflow comprises the identified plurality of AI and Generative AI service nodes to be executed, an order of execution, and a service configuration (See said analysis for Claim 1); plurality of AI and Generative AI service nodes by executing each of the identified plurality of AI and Generative AI service nodes comprised in the generated AI-based workflow (See said analysis for Claim 1) perform the determined set of actions on the generated AI-based workflow (See said analysis for Claim 1); and deploy the generated AI-based workflow onto at least one external system based on a set of configuration parameters (See said analysis for Claim 1)
BLAIR does not disclose but LI discloses generate a metadata for each of identified AI node (See said analysis for Claim 1), wherein the metadata is generated at each stage of execution of the of AI node (See said analysis for Claim 1); validate the generated metadata based on a plurality of AI-based rules (See said analysis for Claim 1); determine a set of actions to be performed on the generated metadata based on results of validation (See said analysis for Claim 1)
BLAIR and LI do not disclose but JEZEWSKI discloses wherein the AI-based workflow comprises a workflow description (See said analysis for Claim 1)
Allowable Subject Matter
Claims 2-12, 14-19 is/are objected to as being dependent upon the rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 2-12, 14-19 is/are allowed. The following is an examiner’s statement of reasons for allowance:
As per Claim 2 the prior art of record either alone or in reasonable combination fails to teach or suggest “The system of claim 1, wherein the processor is to pre-process the obtained data using the artificial intelligence (AI) based pre-processing model by: identifying a type of data format associated with the obtained data, wherein the type of data format comprises a multi-media data format; classifying the obtained data into a plurality of categories based on content of the obtained data; and segmenting the obtained data into a plurality of multi-media files based on the plurality of categories, wherein each of the plurality of multi-media files comprise data objects and data object descriptors” These limitations in combination with the other limitations of the independent claim are thus deemed allowable.
As per Claim 3 the prior art of record either alone or in reasonable combination fails to teach or suggest “The system of claim 1, wherein the processor is to identify the plurality of AI and Generative AI service nodes to be executed on the pre-processed data based on the received request by: determining a plurality of functional tasks to be performed for each type of the plurality of multi-media files based on the received request; tagging the determined plurality of functional tasks to each type of the plurality of multi-media files; determining the plurality of processing nodes corresponding to the determined plurality of functional tasks, wherein the plurality of processing nodes is to perform a computation within the determined plurality of functional tasks; configuring the determined plurality of processing nodes based on the received request; and identifying the plurality of AI and Generative AI service nodes corresponding to the configured plurality of processing nodes” These limitations in combination with the other limitations of the independent claim are thus deemed allowable.
As per Claim 4 the prior art of record either alone or in reasonable combination fails to teach or suggest “The system of claim 1, wherein the processor is to generate the AI-based workflow by connecting each of the identified plurality of AI and Generative AI service nodes in the pre-determined manner by: determining a service configuration of the identified plurality of AI and Generative AI service nodes based on a type of an AI service node; identifying an order of execution for the identified plurality of AI and Generative AI service nodes based on a data flow of the pre-processed data and a type of the plurality of functional tasks; determining a flow path between the identified plurality of AI and Generative AI service nodes based on the identified order of execution and the determined service configuration, wherein the identified plurality of AI and Generative AI service nodes are dragged and dropped at a plurality of node locations; connecting each of the identified plurality of AI and Generative AI service nodes based on the determined flow path; and generating the AI-based workflow comprising the identified plurality of AI and Generative AI service nodes to be executed, the order of execution, and the service configuration based on the connection, wherein the AI-based workflow comprises the workflow description and wherein the AI-based workflow comprises a starting service node, an intermediate service node and an ending service node connected in the order of execution and based on the determined flow path” These limitations in combination with the other limitations of the independent claim are thus deemed allowable.
As per Claim 5 the prior art of record either alone or in reasonable combination fails to teach or suggest “The system of claim 1, wherein the processor is to execute each of the identified plurality of AI and Generative AI service nodes comprised in the generated AI-based workflow by: analyzing the workflow descriptor associated with each of the identified plurality of AI and Generative AI service nodes, wherein the workflow descriptor comprises data objects in a human-readable format; instantiating each of the plurality of AI and Generative AI service nodes in the generated AI-based workflow; performing a functional task associated with each of the plurality of AI and Generative AI service nodes in the order of execution; generating the metadata for each of the identified plurality of AI and Generative AI service nodes at each stage of execution of the functional task; fusing the metadata generated at each stage with corresponding data objects of an AI service node; and generating a fused metadata output at each stage of execution of the functional task” These limitations in combination with the other limitations of the independent claim are thus deemed allowable.
As per Claim 6 the prior art of record either alone or in reasonable combination fails to teach or suggest “The system of claim 1, wherein the processor is to validate the generated metadata based on the plurality of AI-based rules by: obtaining a list of the generated metadata, policy set identifiers (IDs) and parameters for metadata processing; segmenting each of the generated metadata in the list into a plurality of data segments using a sliding window; determining the plurality of AI-based rules associated with the plurality of data segments based on a pre-stored rule database; validating the generated metadata by applying the determined plurality of AI-based rules to the generated metadata; and generating a confidence score for the generated metadata based on the validation, wherein the confidence score comprises one of a low confidence score and a high confidence score” These limitations in combination with the other limitations of the independent claim are thus deemed allowable.
As per Claim 7 the prior art of record either alone or in reasonable combination fails to teach or suggest “The system of claim 6, wherein the processor is to: determine the set of actions to be performed on the generated metadata based on the generated confidence score, wherein the confidence score corresponds to the high confidence score, and wherein the set of actions comprise at least one of a locally executable part of code within a system and integrations with the at least one external system; and route the received request to an agent system for resolution based on the generated confidence score, wherein the confidence score corresponds to the low confidence score, and wherein a processor at the agent system is to resolve the received request by: assessing the received request based on a description, a priority level, a business line, and product information; determining a request description score and a request priority score for the received request based on the assessment; identifying issue resolution pain-points for the received request to be resolved by the agent system; determining an appropriate agent corresponding to the received request based on at least one of the determined request description score, the request priority score, the priority level, identified issue resolution pain points, a resolution method, and a resolution sequence, wherein the appropriate agent is determined by constructing a working agent finding model; assigning the received request to the determined appropriate agent; periodically monitoring a request progress at the agent system based on feedback from the agent system, interaction logs and a status report; and continuously updating the rule database with learnings from the agent system upon resolving the received request, wherein the learnings comprise at least one of an issue category, knowledge base records, and operational support records” These limitations in combination with the other limitations of the independent claim are thus deemed allowable.
As per Claim 8 the prior art of record either alone or in reasonable combination fails to teach or suggest “The system of claim 6, wherein the processor is to: generate the plurality of AI-based rules based on at least one of a metadata existence, a data formatting and logic inconsistencies between an existing rule and an updated rule, wherein the plurality of AI-based rules are configured with updated metadata; and periodically modify the plurality of AI-based rules based on the updated metadata, a plurality of events detected by an AI service node, the received request, and the plurality of AI and Generative AI service nodes, wherein each of the modified plurality of AI-based rules are assigned with corresponding confidence scores and actions to be performed” These limitations in combination with the other limitations of the independent claim are thus deemed allowable.
As per Claim 9 the prior art of record either alone or in reasonable combination fails to teach or suggest “The system of claim 1, wherein the processor is to: analyze workflow descriptors associated with each of the identified plurality of AI and Generative AI service nodes, wherein the workflow descriptors comprise data objects in a human-readable format; instantiate each of the plurality of AI and Generative AI service nodes in the generated AI-based workflow; perform the functional task associated with each of the plurality of AI and Generative AI service nodes in the order of execution; measure an execution time of each of the processing nodes within the plurality of AI and Generative AI service nodes; validate the generated AI-based workflow based on at least one of the measured execution time, a processing node description, code functions, and the analyzed workflow descriptors; generate an updated AI-based workflow based on results of validation by modifying the AI-based workflow with updated processing nodes and corresponding AI-based service nodes; re-compute the execution time of each of the updated processing nodes; tune the updated AI-based workflow based on the re-computed execution time using an AI-based optimization method; generate a ranked list of workflows and node configurations based on the tuned AI-based workflow; and modify container implementation information for each of the AI-based service nodes comprised within each of the generated ranked list of workflows and the node configurations” These limitations in combination with the other limitations of the independent claim are thus deemed allowable.
As per Claim 10 the prior art of record either alone or in reasonable combination fails to teach or suggest “The system of claim 1, wherein the processor is to deploy the generated AI-based workflow onto the at least one external systems at the real-time based on the set of configuration parameters by: analyzing workflow descriptors associated with each of the identified plurality of AI and Generative AI service nodes, wherein the workflow descriptors comprise data objects in a human-readable format; mapping the analyzed workflow descriptors to a target external system; performing network connection tests at the target external system for deploying the generated AI-based workflow onto the target external system; instantiating AI-based services corresponding to the generated AI-based workflow as containers at the target external system; executing each of the identified plurality of AI and Generative AI service nodes at the target external system in the pre-determined manner based on the generated AI-based workflow; validating the execution of each of the identified plurality of AI and Generative AI service nodes at the target external system; generating a deployment successful message upon successful validation of the execution of each of the identified plurality of AI and Generative AI service nodes at the target external system; generating a deployment failure message upon failure of the execution of each of the identified plurality of AI and Generative AI service nodes at the target external system, wherein the deployment failure message comprises one or more execution errors detected during execution; and performing one or more actions to rectify the one or more execution errors at the target external system” These limitations in combination with the other limitations of the independent claim are thus deemed allowable.
As per Claim 11 the prior art of record either alone or in reasonable combination fails to teach or suggest “The system of claim 1, wherein the processor is to: obtain one of a streaming data and a batch data associated with the generated AI-based workflow; instantiate the generated AI-based workflow based on the obtained one of the streaming data and the batch data; deploy the AI-based workflow onto at least one external systems at real-time based on the set of configuration parameters; create a plurality of cases for the deployed AI-based workflow using an AI-detection model; generate AI-based insights and visualizations for a plurality of events detected and processing performed on the plurality of cases; and output the generated AI-based insights and visualizations on a graphical user interface of a user device” These limitations in combination with the other limitations of the independent claim are thus deemed allowable.
As per Claim 12 the prior art of record either alone or in reasonable combination fails to teach or suggest “The system of claim 1, wherein the processor is to perform the determined set of actions on the generated AI-based workflow by: generating an action code relevant to the at least one external system based on the determined set of actions; determining action parameters associated with the determined set of actions; converting the determined action parameters into action descriptors, wherein the action descriptors correspond to a human-readable format; determining an order of execution associated with the determined set of actions; triggering action APIs associated with the determined set of actions based on the determined order of execution; monitoring an action execution at the at least one external system; and reporting an action execution status at the real-time based on the monitoring, wherein the action execution status comprises one of a successful execution status and errors detected status” These limitations in combination with the other limitations of the independent claim are thus deemed allowable.
As per Claim 14 the prior art of record either alone or in reasonable combination fails to teach or suggest “The method of claim 13, wherein identifying the plurality of AI and Generative AI service nodes to be executed on the pre-processed data based on the received request comprises: determining, by the processor, a plurality of functional tasks to be performed for each type of the plurality of multi-media files based on the received request; tagging, by the processor, the determined plurality of functional tasks to each type of the plurality of multi-media files; determining, by the processor, the plurality of processing nodes corresponding to the determined plurality of functional tasks, wherein the plurality of processing nodes is to perform a computation within the determined plurality of functional tasks; configuring, by the processor, the determined plurality of processing nodes based on the received request; and identifying, by the processor, the plurality of AI and Generative AI service nodes corresponding to the configured plurality of processing nodes” These limitations in combination with the other limitations of the independent claim are thus deemed allowable.
As per Claim 15 the prior art of record either alone or in reasonable combination fails to teach or suggest “The method of claim 13, wherein generating the AI-based workflow by connecting each of the identified plurality of AI and Generative AI service nodes in the pre-determined manner comprises: determining, by the processor a service configuration of the identified plurality of AI and Generative AI service nodes based on a type of an AI service node; identifying, by the processor, an order of execution for the identified plurality of AI and Generative AI service nodes based on a data flow of the pre-processed data and a type of the plurality of functional tasks; determining, by the processor, a flow path between the identified plurality of AI and Generative AI service nodes based on the identified order of execution and the determined service configuration, wherein the identified plurality of AI and Generative AI service nodes are dragged and dropped at a plurality of node locations; connecting, by the processor, each of the identified plurality of AI and Generative AI service nodes based on the determined flow path; and generating, by the processor, the AI-based workflow comprising the identified plurality of AI and Generative AI service nodes to be executed, the order of execution, and the service configuration based on the connection, wherein the AI-based workflow comprises the workflow description and wherein the AI-based workflow comprises a starting service node, an intermediate service node and an ending service node connected in the order of execution and based on the determined flow path” These limitations in combination with the other limitations of the independent claim are thus deemed allowable.
As per Claim 16 the prior art of record either alone or in reasonable combination fails to teach or suggest “The method of claim 13, wherein executing each of the identified plurality of AI and Generative AI service nodes comprised in the generated AI-based workflow comprises: analyzing, by the processor, the workflow descriptor associated with each of the identified plurality of AI and Generative AI service nodes, wherein the workflow descriptor comprises data objects in a human-readable format; instantiating, by the processor, each of the plurality of AI and Generative AI service nodes in the generated AI-based workflow; performing, by the processor, a functional task associated with each of the plurality of AI and Generative AI service nodes in the order of execution, generating, by the processor, the metadata for each of the identified plurality of AI and Generative AI service nodes at each stage of execution of the functional task; fusing, by the processor, the metadata generated at each stage with corresponding data objects of an AI service node; and generating, by the processor, fused metadata output at each stage of execution of the functional task” These limitations in combination with the other limitations of the independent claim are thus deemed allowable.
As per Claim 17 the prior art of record either alone or in reasonable combination fails to teach or suggest “The method of claim 13, wherein validating the generated metadata based on the plurality of AI-based rules comprises: obtaining, by the processor, a list of the generated metadata, policy set identifiers (IDs) and parameters for metadata processing; segmenting, by the processor, each of the generated metadata in the list into a plurality of data segments using a sliding window; determining, by the processor, the plurality of AI-based rules associated with the plurality of data segments based on a pre-stored rule database; validating, by the processor, the generated metadata by applying the determined plurality of AI-based rules to the generated metadata; generating, by the processor, a confidence score for the generated metadata based on the validation, wherein the confidence score comprises one of a low confidence score and a high confidence score; determining, by the processor, the set of actions to be performed on the generated metadata based on the generated confidence score, wherein the confidence score corresponds to the high confidence score, and wherein the set of actions comprise at least one of a locally executable part of code within a system and integrations with the at least one external system; and routing, by the processor, the received request to an agent system for resolution based on the generated confidence score, wherein the confidence score corresponds to the low confidence score, and wherein the received request is resolved by the agent system by: assessing, by the processor, the received request based on a description, a priority level, a business line, and product information; determining, by the processor, a request description score and a request priority score for the received request based on the assessment; identifying, by the processor, issue resolution pain-points for the received request to be resolved by the agent system; determining, by the processor, an appropriate agent corresponding to the received request based on at least one of the determined request description score, the request priority score, the priority level, identified issue resolution pain points, a resolution method, and a resolution sequence, wherein the appropriate agent is determined by constructing a working agent finding model; assigning, by the processor, the received request to the determined appropriate agent; periodically monitoring, by the processor, monitor a request progress at the agent system based on feedback from the agent system, interaction logs and a status report; and continuously updating, by the processor, the rule database with learnings from the agent system upon resolving the received request, wherein the learnings comprise at least one of an issue category, knowledge base records, and operational support records” These limitations in combination with the other limitations of the independent claim are thus deemed allowable.
As per Claim 18 the prior art of record either alone or in reasonable combination fails to teach or suggest “The method of claim 13, further comprising: analyzing, by the processor, workflow descriptors associated with each of the identified plurality of AI and Generative AI service nodes, wherein the workflow descriptors comprise data objects in a human-readable format; instantiating, by the processor, each of the plurality of AI and Generative AI service nodes in the generated AI-based workflow; performing, by the processor, the functional task associated with each of the plurality of AI and Generative AI service nodes in the order of execution; measuring, by the processor, an execution time of each of the processing nodes within the plurality of AI and Generative AI service nodes; validating, by the processor, the generated AI-based workflow based on at least one of the measured execution time, a processing node description, code functions, and the analyzed workflow descriptors; generating, by the processor, an updated AI-based workflow based on results of validation by modifying the AI-based workflow with updated processing nodes and corresponding AI-based service nodes; re-computing, by the processor, the execution time of each of the updated processing nodes; tuning, by the processor, the updated AI-based workflow based on the re-computed execution time using an AI-based optimization method; generating, by the processor, a ranked list of workflows and node configurations based on the tuned AI-based workflow; and modifying, by the processor, container implementation information for each of the AI-based service nodes comprised within each of the generated ranked list of workflows and the node configurations” These limitations in combination with the other limitations of the independent claim are thus deemed allowable.
As per Claim 19 the prior art of record either alone or in reasonable combination fails to teach or suggest “The method of claim 13, wherein deploying the generated AI-based workflow onto the at least one external systems at the real-time based on the set of configuration parameters comprise: analyzing, by the processor, workflow descriptors associated with each of the identified plurality of AI and Generative AI service nodes, wherein the workflow descriptors comprise data objects in a human-readable format; mapping, by the processor, the analyzed workflow descriptors to a target external system; performing, by the processor, network connection tests at the target external system for deploying the generated AI-based workflow onto the target external system; instantiating, by the processor, AI-based services corresponding to the generated AI-based workflow as containers at the target external system; executing, by the processor, each of the identified plurality of AI and Generative AI service nodes at the target external system in the pre-determined manner based on the generated AI-based workflow; validating, by the processor, the execution of each of the identified plurality of AI and Generative AI service nodes at the target external system; generating, by the processor, a deployment successful message upon successful validation of the execution of each of the identified plurality of AI and Generative AI service nodes at the target external system; generating, by the processor, a deployment failure message upon failure of the execution of each of the identified plurality of AI and Generative AI service nodes at the target external system, wherein the deployment failure message comprises one or more execution errors detected during execution; and performing, by the processor, one or more actions to rectify the one or more execution errors at the target external system” These limitations in combination with the other limitations of the independent claim are thus deemed allowable.
The closest prior art of record BLAIR et al. (Pub. No: US 2024-0121125) for Claims 2-12, 14-19 does not teach all the elements in combination with the other limitations of the independent claim. BLAIR only discloses receiving a request for creating an artificial intelligence (AI)-based workflow from a user device and obtaining an input data from a plurality of data sources based on the received request. The prior art further discloses pre-processing the obtained data using an artificial intelligence (AI) based pre-processing model, identifying a plurality of AI and Generative AI service nodes to be executed on the pre-processed data based on the received request, wherein the plurality of AI and Generative AI service nodes comprise a functional task to be executed on the pre-processed data and wherein the plurality of AI and Generative AI service nodes comprise a plurality of processing nodes, and generating an AI-based workflow by connecting each of the identified plurality of AI and Generative AI service nodes in a pre-determined manner, wherein the AI-based workflow comprises the identified plurality of AI and Generative AI service nodes to be executed, an order of execution, and a service configuration. The prior art also discloses a plurality of AI and Generative AI service nodes executing each of the identified plurality of AI and Generative AI service nodes in the generated AI-based workflow, and perform the determined set of actions on the generated AI-based workflow. Finally, the prior art teaches deploying the generated AI-based workflow onto at least one external system based on a set of configuration parameters.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EILEEN M ADAMS whose telephone number is 571-270-3688. The examiner can normally be reached on Monday-Friday from 8:30am-5:00pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, William Vaughn can be reached on (571) 272-3922. The fax phone number for the organization where this application or proceeding is assigned is 571-270-4688.
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/EILEEN M ADAMS/Primary Examiner, Art Unit 2481
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