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 application filed on 05/12/2025.
Claims 1-22 are pending.
Priority
2. This application is a Continuation-In-Part of 18/583,653 (Patent US 12,298,995), which was filed on 02/21/2024, was acknowledged and considered
Claim Rejections - 35 USC § 101
3. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
4. Claims 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims 1, 10 and 19 recite:
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;
process the event data to identify at least one first task limiting
performance of the data processes; and
automatically modify the at least one first task to improve
performance of the data processes.
Step 2A Prong One:
The limitations of receiving data associated with a set of tasks, processing said data to identify task and automatically modifying said tasks to improve performance, which, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting, “computer method’; nothing in the claim element precludes the step from practically being performed in a human mind. Note that the limitations are done by the generically recited computer components under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes’ grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Step 2A Prong Two:
The judicial exception is not integrated into a practical application. Claims 1, 10 and 19 recite the additional element, “receiving, processing and modifying data” these limitation is a mere generic transmission and presentation of collected and analyzed data (MPEP 2106.05(g)). The limitations amount to a data gathering step and a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)).
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitation “receiving, processing and modifying data”, are recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(qd)/(II) (iv) transferring and/or displaying information, Versata Dev. Group Inc.
Dependent Claims 2-9, 11-18 and 20-22
The limitations as recited in dependent claims 2 and 11 recite “identifying … automatically modify ..” which further describes the concepts performed in the human mind including an observation, evaluation, judgment, and opinion, in step 2A prong one.
Claims 3, 4, 12, 13, 20 and 21 recite “wherein the code is SQL code or Python code ...” which is recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(qd)/(II) (iv) transferring and/or displaying information, Versata Dev. Group Inc., in step 2B prong two.
Claims 5, 14 and 22 recite “receive, …generate ..” which further describes the concepts performed in the human mind including an observation, evaluation, judgment, and opinion, in step 2A prong one.
Claims 6, 7 and 15-16 recite “: identifying …determining...notifying ..” which further describes the concepts performed in the human mind including an observation, evaluation, judgment, and opinion, in step 2A prong one.
Claims 8 and 17 recite “an execution log ... a connection data status …” which is recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(qd)/(II) (iv) transferring and/or displaying information, Versata Dev. Group Inc., in step 2B prong two.
Claims 9 and 18 recite “… an error ...” which further describes the
concept is mere generating or identifying error in data under prong 2 (insignificant extra solution activity— MPEP 2106.06g) and WURC under 2b (using gather data - MPEP 2106.05d).
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 tier 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)”)].
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 for 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. 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
03/30/2026
/HUNG D LE/Primary Examiner, Art Unit 2161
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