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
1. This Office Action is in response to the amendment filed on 07/30/2026.
Claims 23-41 have been added.
Claims 1-41 are pending.
Response to Arguments
2. This office action has been issued in response to amendment filed 07/30/2026. Claims 1-41 are pending. Applicants’ arguments have been carefully and respectfully considered in light of the instant amendment as they relate to the claim rejections under 35 USC 102 and 112 First New Matter (Written Description) as will be discussed below. Accordingly, this action has been made final.
(1) (Independent claims 1, 10 and 19) Applicants argue that Lingelbach (US 20250244975) does not disclose, “receive event data associated with an execution of a set of tasks of the data processes, the data processes including an extraction process, a transformation process, and/or a loading process”.
(1) The Examiner respectfully disagrees. According to Google, “Event data is a record of specific actions, behaviors, or occurrences—such as a user clicking a link, a server logging an error, or a customer buying a product”. As such: [Lingelbach: Paragraphs 2 and 18 (“Operations computing systems may manage incidents triggered by events in computing systems that are registered to customer sites. Managing such incidents may be done through the use of workflows, or a set of actions, resources, services, messages, notifications, alerts, events, etc. related to resolving incidents”)] [Lingelbach: Paragraphs 19 and 33 (“create incident workflows for incidents through the use of a natural language user interface facilitated by the operations computing system and a language model. In other words, a user of the operations computing system or a user of a device in communication with the operations computing system may interact with a natural language user interface, such as to provide natural language text input, in which the natural language text input may be used to create incident workflows with custom triggers, actions, and fields”, i.e., “incidents” = ‘event data’)] [Lingelbach: Paragraph 45 (“adding more disk or memory space, managing tickets (e.g., opening tickets, updating tickets, closing tickets), healing, incident escalation, etc. Incident data storage 124 may store incident workflows comprising a set of data distribution tasks such as job scheduling, extract-transform-load (ETL), file transfers, data removal, complex workflows or rules”, i.e., ETL)].
(2) (Independent claims 1, 10 and 19) Applicants argue that Lingelbach (US 20250244975) does not disclose, “process the event data to identify at least one first task limiting performance of the data processes”.
(2) The Examiner respectfully disagrees. [Lingelbach: Paragraphs 32-33 (“Operations computing system 110 may monitor the performance of computer operations of customer sites 140. For example, operations computing system 110 may monitor whether applications or systems of customer sites 140 are operational, network performance associated with customer sites 140, trouble tickets and/or resolutions associated with customer sites 140, or the like.”)] [Lingelbach: Paragraph 50 (“Additionally, by employing computationally expensive or more powerful machine learning algorithms through API requests, the operations computing system may improve the front-end experience for users and overall system performance”)].
(3) (Independent claims 1, 10 and 19) Applicants argue that Lingelbach (US 20250244975) does not disclose, “automatically modify the at least one first task to improve performance of the data processes”.
(3) The Examiner respectfully disagrees. [Lingelbach: Paragraph 37 (“An alert (an alert object) may be created (instantiated) for anything that requires the performance (by a human or an automated task) of an action. Thus, the alert may embody or include the action to be performed..”)] [Lingelbach: Paragraph 50 (“Additionally, by employing computationally expensive or more powerful machine learning algorithms through API requests, the operations computing system may improve the front-end experience for users and overall system performance”)] [Lingelbach: Paragraph 79 (“a prompt may be “optimized” by ML model 254, in which ML model 254 may intelligently generate a prompt based on current data received by operations computing 210 and/or historical data received by operations computing 210. In some examples, ML model 254 may analyze the respective initial structured text data for each prompt received by operations computing system 210 and generate a prompt for the external machine learning model to generate the updated structured text data. As such, in some examples, API module 252 and/or other modules of operations computing system 210 may utilize one or more machine learning models, rules, conditions, logic, a combination thereof, or the like, to process or manipulate any data received or output by operations computing system”)].
Claim Rejections - 35 USC § 112
3. The following is a quotation of the first paragraph of 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
4. Claims 23-41 is rejected under 35 U.S.C. 112, first paragraph, as failing to comply with the written description requirement.
Newly-added independent claims 23, 31 and 39 comprise new matter. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 23, 31 and 39 recite “responsive to detecting that the measured performance metric of the at least the first task exceeds the determined threshold, automatically invoke a generative machine learning (ML) model to generate a modified set of machine-readable instructions associated with the first task, based on i) a representation of the database schema, ii) a natural-language description of an intent of the at least the first task, and iii) at least a portion of the event data. As such, the claim lacks written description.” . However, nowhere in the specification does it disclose invoking a generative machine learning model to generate model. A regular machine learning model is not a generative machine learning model.
Dependent claims 24-30, 32-38 and 40-41 are rejected under 35 U.S. C. 101 because it fails to resolve the deficiencies of claims 23, 31 and 39.
Examiner's Note
5. Lingelbach et al, US 20250244975, [Lingelbach: Abstract and paragraphs 8-9
("receives, from a user computing device, initial natural language text input associated with an
incident and generates, based on the initial natural language text input, a set of prompts. The
operations computing system provides the set of prompts as input to a machine learning model
and receives, from the model, text output for each prompt, in which the text output includes a
clarifying question or a clarifying instruction. The operations computing system sends, to the
user computing device, the text output, and receives, from the user computing device, additional
natural language text input. The operations computing system applies the model to the natural
language text input to generate respective initial structured text data for each prompt. The
operations computing system applies the model to the respective initial structured text data for
each prompt to generate updated structured text data including instructions for creating an
incident workflow", i.e., receiving event data and processing event data)] [Lingelbach:
Paragraph 22 ("the machine learning model to an initial natural language text input received
from a user computing device and one or more prompts associated with one or more tasks
identified in the initial natural text input, i.e., process the event data to identify at least one first task ')] [Lingelbach: Paragraphs 43 and 9 and 37 ("the central piece of information that
is processed and updated throughout an incident workflow. assign each of customer sites 140
one or more identifier values to manage or group tasks associated with customer sites AND
"applying machine learning models (e.g., large language models) through API requests, overall
system performance may be further increased" AND "An alert (an alert object) may be created
(instantiated) for anything that requires the performance (by a human or an automated task) of
an action", i.e., 'automatically modify the at least one first task to improve performance of the
data processes ')] [Lingelbach: Paragraph 45 ("include multiple "jobs," or multiple tasks
executed by operations computing system 110. When responding to an incident, a user (i.e., a
responder) may document the steps taken during the response that led to a resolution. In
addition to actions or tasks such as an addition of stakeholders, a sending of a status update, a
creation of a message thread, a sending of a message thread link, an addition of responders, and
a starting of a virtual meeting, the incident workflows generated by operations computing system
110 may comprise other actions or tasks including, but not limited to, incident response
diagnostic tasks, data distribution tasks, and/or service request automation tasks. Incident data
storage 124 may store incident workflows that include a set of incident response diagnostic tasks
such as enriching existing events with relevant data, logging incidents (e.g., time, date, and/or
status of incidents), updating the status of a platform, updating the status of a service, updating
the status of third party services, restarting services, restarting servers, unlocking databases,
flushing storages, clearing files from memory, adding more disk or memory space, managing
tickets (e.g., opening tickets, updating tickets, closing tickets), healing, incident escalation, etc.
Incident data storage 124 may store incident workflows comprising a set of data distribution tasks such as job scheduling, extract-transform-load (ETL), file transfers, data removal, complex
workflows or rules", i.e., ETL and generating and updating tasks)].
Blair et al, US 20250245042, [Blair: Abstract ("assign a plurality of tasks to the plurality of
positions based on task hash values associated with the plurality of tasks; generate a random
position on the linear index, wherein the random position includes a random hash value within a
range of possible hash values associated with the plurality of tiers; select, based on the random
position, a populated position of the plurality of positions, wherein the populated position is
assigned one or more tasks of the plurality of tasks; obtain information for the one or more tasks
based on the populated position; process, based on the information, the one or more tasks")]
[Blair: Paragraphs 4, 6 and 20 ("The operations computing system may address tasks in a
random or pseudo-random manner to minimize computational and performance drawbacks
associated with addressing tasks from customers based on a number of tasks submitted by a
customer or a time a customer submits tasks. In this way, the operations computing system may
implement techniques that support a more scalable approach of multiple services addressing a
high volume of tasks from various customers, in parallel, while minimizing or reducing
computational and performance drawbacks, such as excessive processing and/or memory
utilization, a delay in processing of tasks, lock-contention, or the like ")] [Blair: Paragraph 23
("healing, incident escalation, etc. Task queues 124 may store information for a set of data
distribution tasks such as job scheduling, extract-transform-load (ETL), file transfers, data
removal, complex workflows or rules, data replication, data remodeling, database creation, etc.
Task queues")] [Blair: Paragraph 29 ("generate the modified random position by finding the
nearest populated position on the linear index corresponding to a task hash value associated with one or more tasks stored in task queues 124. Services 126 may determine whether the
modified random position crossed a tier boundary by checking whether the modified random
position includes one or more different tier hash values (e.g., a different lier two value)
compared to the original random position. Services 126 may select a task record with the largest
hash value less than the original random hash value (e.g., if moving left on the linear index).
Services 126 may select a task record with the smallest hash value less than the original random
hash (e.g., if moving right on the linear index) ")].
Amano, US 20210065671, [Amano: Paragraph 28 (“Generative machine learning is implemented through reinforcement learning, a class of machine learning algorithm that attempts to find the optimal way to accomplish a particular goal, improve performance on a specific task, or optimize one or more prioritized parameters of the system. Reinforcement learning may repeatedly model and adjust some or all system parameters (e.g., a layer thickness, a volume fill factor, a material) to optimize one or more of the system parameters.”)].
Yang et al, US 20250068671, [Yang: Paragraph 123 (“The usage of a generative machine learning model with a verifier machine learning model of the present disclosure may be leveraged to initiate the performance of various computing tasks that improve the performance of a computing system (e.g., a computer itself, etc.) with respect to various predictive actions performed by the document analysis computing entity 106, such as for the classification of long documents, and/or the like. Example predictive actions may include the generation of a filter set of a plurality of model-assigned categorical identifiers one or more classifications of a document data object based on the filter set.”)].
Huang et al, US 20250111151, [Huang: Paragraph 17 (“Accordingly, implementing indexing split documents for data retrieval augmenting generative machine learning results can improve the performance of generative machine learning systems by optimally using computing resources (e.g., by creating efficient and perform search indexes) and provide right-sized and relevant data to guide a generative machine learning model to produce accurate results”)].
Saligrama Shreeram et al, US 20250111091, [Shreeram: Paragraph 17 (“Accordingly, implementing intent classification for executing a retrieval augmented generation pipeline for natural language tasks using a generative machine learning model can improve the performance of generative machine learning systems by optimally using computing resources when appropriate (e.g., not performing data retrieval when not needed), decontextualizing requests (e.g., to add in relevant information), and recognizing and performing tasks in multiple parts, when needed (e.g., by classifying a task as multi-part in order to determine and perform the multiple parts before providing a response)”)].
Saligrama Shreeram et al, US 20250110979, [Shreeram: Paragraphs 18 and 104 (“Implementing these techniques can improve the performance of generative machine learning systems by optimally using computing resources (e.g., using the source specific data retrievers) and coordinating data retrieval in order to gain access to the most relevant data for a received natural language task, which improves the quality and accuracy (e.g., preventing hallucinations) of generated results”)].
Claim Rejections - 35 USC § 102
6. In the event the determination of the status of the application as subject to AIA 35 U.S.C.
102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the
statutory basis for the rejection will not be considered a new ground of rejection if the prior art
relied upon, and the rationale supporting the rejection, would be the same under either status.
7. 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)(2) the claimed invention was described in a patent issued under section 151, or in an application from patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
8. Claims 1-2, 5-12, 14-20 and 22 are rejected under 35 U.S.C. 102(a)(2) as being
anticipated by Lingelbach et al (US 20250244975).
Claim 1:
Lingelbach suggests a computing system for managing data processes, comprising: one or more processors; and a memory in communication with the one or more processors, the memory storing machine-executable instructions which, when executed by the one or more processors, cause the one or more processors to: receive event data associated with an execution of a set of tasks of the data processes, the data processes including an extraction process, a transformation process, and/or a loading process [Lingelbach: Abstract and paragraphs 8-9 ("receives, from a user computing device, initial natural language text input associated with an incident and generates, based on the initial natural language text input, a set of prompts. The operations computing system provides the set of prompts as input to a machine learning model and receives, from the model, text output for each prompt, in which the text output includes a clarifying question or a clarifying instruction. The operations computing system sends, to the user computing device, the text output, and receives, from the user computing device, additional natural language text input. The operations computing system applies the model to the natural language text input to generate respective initial structured text data for each prompt. The operations computing system applies the model to the respective initial structured text data for each prompt to generate updated structured text data including instructions for creating an incident workflow", i.e., receiving event data and processing event data)] [Lingelbach: Paragraph 45 ("include multiple "jobs," or multiple tasks executed by operations computing system 110. Incident data storage 124 may store incident workflows comprising a set of data distribution tasks such as job scheduling, extract-transform-load (ETL), file transfers, data removal, complex workflows or rules", i.e., ETL and generating and updating tasks)]. Lingelbach suggests processing the event data to identify at least one first task limiting performance of the data processes [Lingelbach: Paragraphs 43 and 9 and 37 ("the central piece of information that is processed and updated throughout an incident workflow. assign each of customer sites 140 one or more identifier values to manage or group tasks associated with customer sites " AND "applying machine learning models (e.g., large language models) through API requests, overall system performance may be further increased" AND "An alert (an alert object) may be created (instantiated) for anything that requires the performance (by a human or an automated task) of an action", i.e., "automatically modify the at least one first task to improve performance of the data processes')]. Lingelbach suggests automatically modifying the at least one first task to improve performance of the data processes [Lingelbach: Paragraphs 43 and 9 and 37 ("the central piece of information that is processed and updated throughout an incident workflow. assign each of customer sites 140 one or more identifier values to manage or group tasks associated with customer sites " AND "applying machine learning models (e.g., large language models) through API requests, overall system performance may be further increased" AND "An alert (an alert object) may be created (instantiated) for anything that requires the performance (by a human or an automated task) of an action", i.e., 'automatically modify the at least one first task to improve performance of the data processes')].
Claim 2:
Lingelbach suggests wherein the machine-executable instructions, when executed by the
one or more processors, cause the one or more processors to: in response to identifying the first
task is limiting performance of the data processes as a result of an error, obtain a database schema and a natural language representation of a user intent associated with the first task; and
automatically modify, using a machine learning model, the set of task instructions, based on the
database schema and the natural language representation [Lingelbach: Paragraph 36 ("Typically, incidents may be a failure or error that occurs in the operation of a managed network and/or computing environment. One or more events may be associated with one or more incidents. However, not all events may be associated with incidents. The term "incident workflow" as used herein can refer to the actions, resources, services, messages, notifications, alerts, events, or the like, related to resolving one or more incidents. ")] [Lingelbach: Abstract ("The operations computing system provides the set of prompts as input to a machine learning model and receives, from the model")].
Claim 3:
Lingelbach suggests wherein the set of task instructions represents code associated with
one or more of the data processes [Lingelbach: Paragraph 65 ("determine tasks related to jobs
or processes of executing scripts, commands, or plugins that address incidents. Workflow engine
232 may determine tasks related to runbooks or a compilation of routine operating procedures
for managing computing systems")].
Claim 5:
Lingelbach suggests wherein the machine-executable instructions, when executed by the
one or more processors, cause the one or more processors to: prior to receiving the event data:
receive a natural language description of a requirement of the data processes; and generate, by a
machine learning model, the set of tasks of the data processes, based on the natural language
description [Lingelbach: Abstract ("initial natural language text input associated with an incident and generates, based on the initial natural language text input, a set of prompts. The operations computing system provides the set of prompts as input to a machine learning model and receives, from the model, text output for each prompt")].
Claim 6:
Lingelbach suggests wherein the machine-executable instructions, when executed by the
one or more processors, cause the one or more processors to: in response to identifying the at least one first task limiting performance of the data processes, determine whether one or more alerts should be sent; and in response to determining that one or more alerts should be sent, generate the one or more alerts for notifying a user of the first operating condition of the data processes [Lingelbach: Paragraph 36 ("Typically, incidents may be a failure or error that occurs in the operation of a managed network and/or computing environment. One or more events may be associated with one or more incidents. However, not all events may be associated with incidents. The term "incident workflow" as used herein can refer to the actions, resources, services, messages, notifications, alerts, events, or the like, related to resolving one or more incidents. ")] [Lingelbach: Abstract ("The operations computing system provides the set of prompts as input to a machine learning model and receives, from the model")].
Claim 7:
Lingelbach suggests wherein the one or more alerts is selected from the group consisting
of: a system alert; a text alert; an email alert; a phone alert; and a notification channel alert
[Lingelbach: Paragraph 36 ("Typically, incidents may be a failure or error that occurs in the
operation of a managed network and/or computing environment. One or more events may be
associated with one or more incidents. However, not all events may be associated with incidents.
The term "incident workflow" as used herein can refer to the actions, resources, services,
messages, notifications, alerts, events, or the like, related to resolving one or more incidents. ")]
[Lingelbach: Abstract ("The operations computing system provides the set of prompts as input to
a machine learning model and receives, from the model")].
Claim 8:
Lingelbach suggests wherein the event data comprises at least one of: an execution log
associated with an execution of the first task; and a connection data status for a connection
associated with the first task [Lingelbach: Paragraph 27 ("network links of network 130 may include Ethernet, ATM or other network connections. Such connections may include wireless
and/or wired connections. ")] [Lingelbach: Paragraphs 36 and 42 ("maintaining the services may also be added to the incident workflow. Further, log entries, journal entries, notes, timelines, task lists, status information, or the like, AND "action log ")].
Claim 9:
Lingelbach suggests wherein the event data enabling identification of the at least one
first task limiting performance of the data processes comprises at least one of: an error associated
with an allocation of resource that is insufficient to perform the data processes; an error in a set
of task instructions associated with the first task; and a failed connection to a data source
associated with the first task [Lingelbach: Paragraph 36 ("Typically, incidents may be a failure
or error that occurs in the operation of a managed network and/or computing environment. One
or more events may be associated with one or more incidents. However, not all events may be
associated with incidents. The term "incident workflow" as used herein can refer to the actions,
resources, services, messages, notifications, alerts, events, or the like, related to resolving one or
more incidents. ")] [Lingelbach: Abstract ("The operations computing system provides the set of
prompts as input to a machine learning model and receives, from the model")].
Claim 10:
Claim 10 is essentially the same as claim 1 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 11:
Claim 11 is essentially the same as claim 2 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 12:
Claim 12 is essentially the same as claim 3 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 14:
Claim 14 is essentially the same as claim 5 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 15:
Claim 15 is essentially the same as claim 6 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 16:
Claim 16 is essentially the same as claim 7 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 17:
Claim 17 is essentially the same as claim 8 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 18:
Claim 18 is essentially the same as claim 9 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 19:
Claim 19 is essentially the same as claim 10 except that it sets forth the claimed invention
as a system rather than a method and rejected under the same reasons as applied above.
Claim 20:
Claim 20 is essentially the same as claim 12 except that it sets forth the claimed invention
as a system rather than a method and rejected under the same reasons as applied above.
Claim 22:
Claim 22 is essentially the same as claim 14 except that it sets forth the claimed invention
as a system rather than a method and rejected under the same reasons as applied above.
Claim Rejections - 35 USC § 103
9. In the event the determination of the status of the application as subject to AIA 35 U.S.C.
102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the
statutory basis for the rejection will not be considered a new ground of rejection if the prior art
relied upon, and the rationale supporting the rejection, would be the same under either status.
10. 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.
11. Claims 4, 13 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over
Lingelbach et al (US 20250244975), in view of Nautiyal et al (US 20170068595).
Claim 4:
The combined teachings of Lingelbach and Nautiyal suggest wherein the code is SQL
code or Python code associated with the data processes [Nautiyal: Paragraph 56 ("scenario is
designed to put a source component (mapping, package, procedure, variable) into production. A
scenario results from the generation of code (SQL, shell, and so forth) for this component ")]
[Nautiyal: Paragraph 47 ("A DI may be a Java-based application that uses one or more databases to perform set-based data integration tasks. In addition, a DI can extract data, provide transformed data through Web services and messages, and create integration processes that respond to and create events in service-oriented architectures. A DI may be based at least in
part on an ELT [extract-Load and Transform] architecture rather than conventional ETL
[extract-transform-load/ architectures")].
Both references (Lingelbach and Nautiyal) taught features that were directed to
analogous art and they were directed to the same field of endeavor, such as data processing. It
would have been obvious to one of ordinary skill in the art at the time the invention was made,
having the teachings of Lingelbach and Nautiyal before him/her, to modify the system of
Lingelbach with the teaching of Nautiyal in order to implement data process in SQL code
[Nautiyal: Paragraph 56].
Claim 13:
Claim 13 is essentially the same as claim 4 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 21:
Claim 21 is essentially the same as claim 13 except that it sets forth the claimed invention
as a system rather than a method and rejected under the same reasons as applied above.
12. Claims 23, 25-31 and 33-41 are rejected under 35 U.S.C. 103 as being unpatentable over
Lingelbach et al (US 20250244975), in view of Amano (US 20210065671).
Claim 23:
Lingelbach suggests a computing system for managing data processes, comprising:mone or more processors; and a memory in communication with the one or more processors, the memory storing machine-executable instructions which, when executed by the one or more processors, cause the one or more processors to: receive event data associated with an execution of a set of tasks of a data pipeline performing at least one of the data processes, the at least one of the data processes including an extraction process, a transformation process, or a loading process, the at least one of the data processes being associated with a database having a database schema [Lingelbach: Abstract and paragraphs 8-9 ("receives, from a user computing device, initial natural language text input associated with an incident and generates, based on the initial natural language text input, a set of prompts. The operations computing system provides the set of prompts as input to a machine learning model and receives, from the model, text output for each prompt, in which the text output includes a clarifying question or a clarifying instruction. The operations computing system sends, to the user computing device, the text output, and receives, from the user computing device, additional natural language text input. The operations computing system applies the model to the natural language text input to generate respective initial structured text data for each prompt. The operations computing system applies the model to the respective initial structured text data for each prompt to generate updated structured text data including instructions for creating an incident workflow", i.e., receiving event data and processing event data)] [Lingelbach: Paragraph 45 ("include multiple "jobs," or multiple tasks executed by operations computing system 110. Incident data storage 124 may store incident workflows comprising a set of data distribution tasks such as job scheduling, extract-transform-load (ETL), file transfers, data removal, complex workflows or rules", i.e., ETL and generating and updating tasks)]. Lingelbach suggests executing a monitoring process that detects, based on the event data, that a measured performance metric of at least a first task of the set of tasks exceeds a determined threshold, the at least the first task being associated with a first set of machine-readable instructions [Lingelbach: Paragraph 7 ("As such, users of an operations computing system may prefer more user-friendly and streamlined processes for monitoring and managing events, incidents, incident workflows, etc. associated with their organization. Furthermore, even users with prior IT experience may find it quicker, easier, and/or less error-prone to build incident workflows using the operations computing system described herein versus manually writing scripts or the like. As such, the techniques described herein, which combine the expertise of operations computing systems with the power of generative artificial intelligence, may provide more assistance to users and an overall better user experience when interacting with the operations computing system. For example, to edit or create incident workflows, rather than being required to heavily interact with buttons, tables, or other user interface elements, a user may simply provide natural language input in accordance with their specific needs. Furthermore, by applying machine learning models (e.g., large language models) through API requests, overall system performance may be further increased”)] [Lingelbach: Paragraph 33 (“Other non-limiting examples of events may include a monitored operating system process not running, a virtual machine restarting, a disk space on a device is low, processor utilization on a device is higher than a threshold, a shopping cart service of an e-commerce site is unavailable, a digital certificate has expired or is expiring, a certain web server returning a 503 error code (indicating that web server is not ready to handle requests), a customer relationship management (CRM) system is down (e.g., unavailable) such as because it is not responding to ping requests, etc”)]. Lingelbach suggests responsive to detecting that the measured performance metric of the at least the first task exceeds the determined threshold, automatically invoke a generative machine learning (ML) model to generate a modified set of machine-readable instructions associated with the first task, based on i) a representation of the database schema, ii) a natural-language description of an intent of the at least the first task, and iii) at least a portion of the event data [Lingelbach: Abstract and paragraphs 8, 23 and 49 (“generating, by the computing system and based on the initial natural language text input, a set of prompts including one or more prompts, and providing, by the computing system, the set of prompts as input to a machine learning model. The method may further include receiving, by the computing system and from the machine learning model, text output for each prompt from the set of prompts, wherein the text output includes one or more of a clarifying question and a clarifying instruction, sending, by the computing system and to the user computing device, the text output, and receiving, by the computing system, and from the user computing device, additional natural language text input. The method may further include applying, by the computing system, the machine learning model to the initial natural language text input and the additional natural language text input to generate respective initial structured text data for each prompt from the set of prompts, and applying”)]. Lingelbach suggests automatically deploy the modified set of machine-readable instructions, with effect that the modified set of machine-readable instructions replaces the first set of machine-readable instructions and subsequent execution of the at least the first task is based on the modified set of machine-readable instructions. [Lingelbach: Paragraphs 43 and 9 and 37 ("the central piece of information that is processed and updated throughout an incident workflow. assign each of customer sites 140 one or more identifier values to manage or group tasks associated with customer sites " AND "applying machine learning models (e.g., large language models) through API requests, overall system performance may be further increased" AND "An alert (an alert object) may be created (instantiated) for anything that requires the performance (by a human or an automated task) of an action", i.e., "automatically modify the at least one first task to improve performance of the data processes')].
Amano suggests implementing a generative machine learning model [Amano: Paragraph 28 (“Generative machine learning is implemented through reinforcement learning, a class of machine learning algorithm that attempts to find the optimal way to accomplish a particular goal, improve performance on a specific task, or optimize one or more prioritized parameters of the system. Reinforcement learning may repeatedly model and adjust some or all system parameters (e.g., a layer thickness, a volume fill factor, a material) to optimize one or more of the system parameters.”)].
Both references (Lingelbach and Amano) taught features that were directed to
analogous art and they were directed to the same field of endeavor, such as data processing. It
would have been obvious to one of ordinary skill in the art at the time the invention was made,
having the teachings of Lingelbach and Amano before him/her, to modify the system of
Lingelbach with the teaching of Amano in order to implement a generative machine learning model in data processing [Amano: Paragraph 28].
Claim 25:
The combined teachings of Lingelbach and Amano suggest wherein the first set of machine-readable instructions and the modified set of machine-readable instructions represent code associated with one or more of the data processes [Lingelbach: Figures 1, 2 and 5].
Claim 26:
The combined teachings of Lingelbach and Amano suggest wherein the machine-executable instructions, when executed by the one or more processors, cause the one or more processors to: prior to receiving the event data: receive a natural language description of a requirement of the at least one of the data processes; and generate, by the generative ML model, the first set of machine-readable instructions, based on the natural language description n [Lingelbach: Abstract ("initial natural language text input associated with an incident and generates, based on the initial natural language text input, a set of prompts. The operations computing system provides the set of prompts as input to a machine learning model and receives, from the model, text output for each prompt")] [Lingelbach: Abstract and paragraphs 8, 23 and 49 (“generating, by the computing system and based on the initial natural language text input, a set of prompts including one or more prompts, and providing, by the computing system, the set of prompts as input to a machine learning model. The method may further include receiving, by the computing system and from the machine learning model, text output for each prompt from the set of prompts, wherein the text output includes one or more of a clarifying question and a clarifying instruction, sending, by the computing system and to the user computing device, the text output, and receiving, by the computing system, and from the user computing device, additional natural language text input. The method may further include applying, by the computing system, the machine learning model to the initial natural language text input and the additional natural language text input to generate respective initial structured text data for each prompt from the set of prompts, and applying”)].
Claim 27:
The combined teachings of Lingelbach and Amano suggest wherein the machine-executable instructions, when executed by the one or more processors, cause the one or more processors to: in response to identifying the at least one first task limiting performance of the data processes, determine whether one or more alerts should be sent; and in response to determining that one or more alerts should be sent, generate the one or more alerts for notifying a user of the first operating condition of the data processes [Lingelbach: Paragraph 36 ("Typically, incidents may be a failure or error that occurs in the operation of a managed network and/or computing environment. One or more events may be associated with one or more incidents. However, not all events may be associated with incidents. The term "incident workflow" as used herein can refer to the actions, resources, services, messages, notifications, alerts, events, or the like, related to resolving one or more incidents. ")] [Lingelbach: Abstract ("The operations computing system provides the set of prompts as input to a machine learning model and receives, from the model")].
Claim 28:
The combined teachings of Lingelbach and Amano suggest wherein the one or more alerts is selected from the group consisting of: a system alert; a text alert; an email alert; a phone alert; and a notification channel alert [Lingelbach: Paragraph 36 ("Typically, incidents may be a failure or error that occurs in the operation of a managed network and/or computing environment. One or more events may be associated with one or more incidents. However, not all events may be associated with incidents. The term "incident workflow" as used herein can refer to the actions, resources, services, messages, notifications, alerts, events, or the like, related to resolving one or more incidents. ")] [Lingelbach: Abstract ("The operations computing system provides the set of prompts as input to a machine learning model and receives, from the model")].
Claim 29:
The combined teachings of Lingelbach and Amano suggest wherein the event data comprises at least one of: an execution log associated with an execution of the first task; and a connection data status for a connection associated with the first task [Lingelbach: Paragraph 27 ("network links of network 130 may include Ethernet, ATM or other network connections. Such connections may include wireless and/or wired connections. ")] [Lingelbach: Paragraphs 36 and 42 ("maintaining the services may also be added to the incident workflow. Further, log entries, journal entries, notes, timelines, task lists, status information, or the like, AND "action log ")].
Claim 30:
The combined teachings of Lingelbach and Amano suggest wherein the event data enabling identification of the at least one first task limiting performance of the data processes comprises at least one of: an error associated with an allocation of resource that is insufficient to perform the data processes; an error in a set of task instructions associated with the first task; and a failed connection to a data source associated with the first task [Lingelbach: Paragraph 36 ("Typically, incidents may be a failure or error that occurs in the operation of a managed network and/or computing environment. One or more events may be associated with one or more incidents. However, not all events may be associated with incidents. The term "incident workflow" as used herein can refer to the actions, resources, services, messages, notifications, alerts, events, or the like, related to resolving one or more incidents. ")] [Lingelbach: Abstract ("The operations computing system provides the set of prompts as input to a machine learning model and receives, from the model")].
Claim 31:
Claim 31 is essentially the same as claim 23 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 33:
Claim 33 is essentially the same as claim 25 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 34:
Claim 34 is essentially the same as claim 26 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 35:
Claim 35 is essentially the same as claim 27 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 36:
Claim 36 is essentially the same as claim 28 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 37:
Claim 37 is essentially the same as claim 29 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 38:
Claim 38 is essentially the same as claim 30 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 39:
Claim 39 is essentially the same as claim 23 except that it sets forth the claimed invention
as a program product rather than a system and rejected under the same reasons as applied above.
Claim 40:
Claim 40 is essentially the same as claim 25 except that it sets forth the claimed invention
as a program product rather than a system and rejected under the same reasons as applied above.
Claim 41:
Claim 41 is essentially the same as claim 26 except that it sets forth the claimed invention
as a program product rather than a system and rejected under the same reasons as applied above.
13. Claims 24 and 32 are rejected under 35 U.S.C. 103 as being unpatentable over
Lingelbach et al (US 20250244975), in view of Amano (US 20210065671), and further in view of Castellanos et al (US 20110047525).
Claim 24:
The combined teachings of Lingelbach, Amano and Castellanos suggest automatically execute the modified set of machine-readable instructions during a subsequent invocation of the data pipeline [Castellanos: Abstract and paragraph 17 (“quality objective-based ETL pipeline optimization can be provided. An improvement objective may be obtained from user input into a computing system. The improvement objective may represent a priority optimization desired by a user for improved ETL flows for a target application designed to run in memory of the computing system. Computing components available for processing ETL flows are determined and an ETL flow can be created in the memory of the computing system.”)].
Three references (Lingelbach, Amano and Castellanos) taught features that were directed to analogous art and they were directed to the same field of endeavor, such as data processing. It would have been obvious to one of ordinary skill in the art at the time the invention was made, having the teachings of Lingelbach, Amano and Castellanos before him/her, to modify the system of Lingelbach and Amano with the teaching of Castellanos in order to implement a data pipeline [Castellanos: Abstract and paragraph 17].
Claim 32:
Claim 32 is essentially the same as claim 24 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Conclusion
14. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136 (a)
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filled within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
15. Any inquiry concerning this communication or earlier communications from the examiner should be directed to [Hung D. Le], whose telephone number is [571-270-1404]. The examiner can normally be communicated on [Monday to Friday: 9:00 A.M. to 5:00 P.M.].
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on [571-272-4080]. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Hung Le
09/18/2026
/HUNG D LE/Primary Examiner, Art Unit 2161
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