CHARACTERIZING COMPUTER INFRASTRUCTURE USING MACHINE LEARNING TECHNIQUES

Application No. 18/099,586

• Filed: Jan 20, 2023
• Examiner: GORMLEY, AARON PATRICK
• Art Unit: 2148
• Tech Center: 2100 — Computer Architecture & Software
• Assignee: Dell Products L.P.
• OA Round: 3 (Non-Final)

Office Action

DETAILED ACTION
This action is in response to the amendments and remarks filed 5/18/2026. Claims 1-8 and 10-21 are pending and have been examined.

Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/18/2026 has been entered.

Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .

Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.

Claims 1-8 and 10-21 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to non-statutory subject matter without significantly more.

Claim 1
Step 1: The claim recites “A computer-implemented method”, and is therefore directed to the statutory category of process
Step 2A Prong 1: The claim recites the following judicial exception(s)
generating, using the at least one machine learning model, at least one additional label for at least one additional computer infrastructure element using information corresponding to one or more user interactions with the at least one additional computer infrastructure element and configuration information associated with the at least one additional computer infrastructure element: This can be performed as a mental process. One can merely imagine a label for the infrastructure element related to user interactions and configuration info related to it.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the following additional element(s)
obtaining at least one machine learning model, wherein the at least one machine learning model is trained, using a set of training data, to generate one or more labels for one or more of a plurality of computer infrastructure elements, and wherein the training data is based at least in part on information corresponding to one or more user interactions with at least a portion of the plurality of computer infrastructure elements and configuration information associated with at least a portion of the plurality of computer infrastructure elements: This constitutes mere data reception and is insignificant extra-solution activity (MPEP 2106.05(g)).
generating, using the at least one machine learning model, at least one additional label for at least one additional computer infrastructure element using information corresponding to one or more user interactions with the at least one additional computer infrastructure element and configuration information associated with the at least one additional computer infrastructure element: This is mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).
performing one or more automated actions related to the at least one additional computer infrastructure element based at least in part on the at least one additional label, wherein the at least one additional computer infrastructure element comprises at least one software infrastructure element, and wherein at least one of the one or more automated actions comprises at least one maintenance operation performed on the at least one software infrastructure element, the at least one maintenance operation comprising at least one of: updating executable code of the at least one software infrastructure element from a first version to a second version; restoring the at least one software infrastructure element to a prior state; and rebooting the at least one software infrastructure element: This is mere instruction to perform a maintenance operation based on a judicial exception in a generic manner (MPEP 2106.05(f)).
wherein the method is performed by at least one processing device comprising a processor coupled to a memory: This is mere instruction to execute the judicial exception with generic computing hardware (MPEP 2106.05(f)).
Step 2B: The following additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
obtaining at least one machine learning model, wherein the at least one machine learning model is trained, using a set of training data, to generate one or more labels for one or more of a plurality of computer infrastructure elements, and wherein the training data is based at least in part on information corresponding to one or more user interactions with at least a portion of the plurality of computer infrastructure elements and configuration information associated with at least a portion of the plurality of computer infrastructure elements: This is an instance of retrieving data from memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.)
generating, using the at least one machine learning model, at least one additional label for at least one additional computer infrastructure element using information corresponding to one or more user interactions with the at least one additional computer infrastructure element and configuration information associated with the at least one additional computer infrastructure element: This is mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).
performing one or more automated actions related to the at least one additional computer infrastructure element based at least in part on the at least one additional label, wherein the at least one additional computer infrastructure element comprises at least one software infrastructure element, and wherein at least one of the one or more automated actions comprises at least one maintenance operation performed on the at least one software infrastructure element, the at least one maintenance operation comprising at least one of: updating executable code of the at least one software infrastructure element from a first version to a second version; restoring the at least one software infrastructure element to a prior state; and rebooting the at least one software infrastructure element: This is mere instruction to perform a maintenance operation based on a judicial exception in a generic manner (MPEP 2106.05(f)).
wherein the method is performed by at least one processing device comprising a processor coupled to a memory: This is mere instruction to execute the judicial exception with generic computing hardware (MPEP 2106.05(f)).

Claim 2
Step 1: The claim recites a process, as in claim 1
Step 2A Prong 1: The claim recites no further judicial exception(s)
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the further additional element(s)
wherein the set of training data comprises a set of existing labels associated with one or more of the plurality of computer infrastructure elements, and wherein the at least one additional label is different than each of the existing labels: Obtaining at least one machine learning model is still mere data reception and is insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B: The further additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
wherein the set of training data comprises a set of existing labels associated with one or more of the plurality of computer infrastructure elements, and wherein the at least one additional label is different than each of the existing labels: Obtaining at least one machine learning model is still an instance of retrieving data from memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.)

Claim 3
Step 1: The claim recites a process, as in claim 1
Step 2A Prong 1: The claim recites no further judicial exception(s)
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the further additional element(s)
wherein the machine learning model is trained at least in part by: generating a set of words by transforming at least one of: (i) one or more portions of the information corresponding to the one or more user interactions into a natural language format and (ii) one or more portions of the configuration information into a natural language format; and processing the set of words to generate a corresponding set of embeddings, wherein each embedding encodes one or more features of a given word in the set of words: Obtaining at least one machine learning model is still mere data reception and is insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B: The further additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
wherein the machine learning model is trained at least in part by: generating a set of words by transforming at least one of: (i) one or more portions of the information corresponding to the one or more user interactions into a natural language format and (ii) one or more portions of the configuration information into a natural language format; and processing the set of words to generate a corresponding set of embeddings, wherein each embedding encodes one or more features of a given word in the set of words: Obtaining at least one machine learning model is still an instance of retrieving data from memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.)

Claim 4
Step 1: The claim recites a process, as in claim 1
Step 2A Prong 1: The claim recites no further judicial exception(s)
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the further additional element(s)
wherein the at least one machine learning model comprises at least one of: a transformer-based model, a long short-term memory model, and a recurrent neural network model: Generating a label with the machine learning model is still mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).
Step 2B: The further additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
wherein the at least one machine learning model comprises at least one of: a transformer-based model, a long short-term memory model, and a recurrent neural network model: Generating a label with the machine learning model is still mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).

Claim 5
Step 1: The claim recites a process, as in claim 1
Step 2A Prong 1: The claim recites the following further judicial exception(s)
assigning the at least one additional label to the at least one additional computer infrastructure element in response to one or more inputs provided by the user: This can be performed as a mental process. One can merely decide on a label for the infrastructure element in response to user inputs.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the further additional element(s)
outputting the at least one additional label to a user: This is mere output of data and amounts to insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B: The further additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
outputting the at least one additional label to a user: This is an instance of storing data in memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.).

Claim 6
Step 1: The claim recites a process, as in claim 5
Step 2A Prong 1: The claim recites the following further judicial exception(s)
wherein the one or more inputs comprise one or more edits to the additional label, and wherein the assigning comprises: updating the at least one additional label based on the one or more edits; and assigning the updated at least one additional label to the at least one additional computer infrastructure element: Assigning the label to the infrastructure element can still be performed as a mental process. One can merely decide a label based on the user edits and mentally assign it to the element.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the additional element(s)
Step 2B: The additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)

Claim 7
Step 1: The claim recites a process, as in claim 5
Step 2A Prong 1: The claim recites no further judicial exception(s)
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the further additional element(s)
wherein the outputting is performed in response to detecting, within a particular time period, at least one of: a threshold number of interactions with the additional computer infrastructure element by the user; a threshold number of times the user interacted with the additional computer infrastructure element; and a threshold number of actions performed by the user related to the additional computer infrastructure element: Outputting the at least one additional label is still mere data output, and is insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B: The further additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
wherein the outputting is performed in response to detecting, within a particular time period, at least one of: a threshold number of interactions with the additional computer infrastructure element by the user; a threshold number of times the user interacted with the additional computer infrastructure element; and a threshold number of actions performed by the user related to the additional computer infrastructure element: Outputting the at least one additional label is an instance of storing data in memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.).

Claim 8
Step 1: The claim recites a process, as in claim 1
Step 2A Prong 1: The claim recites no further judicial exception(s)
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the further additional element(s)
wherein the at least one machine learning model is retrained in response to at least one of a change to at least one label that is currently assigned to a given one of the computer infrastructure elements and a new label being assigned to at least one of the plurality of computer infrastructure elements: Obtaining at least one machine learning model is still mere data reception and is insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B: The further additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
wherein the at least one machine learning model is retrained in response to at least one of a change to at least one label that is currently assigned to a given one of the computer infrastructure elements and a new label being assigned to at least one of the plurality of computer infrastructure elements: Obtaining at least one machine learning model is still an instance of retrieving data from memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.)

Claim 10
Step 1: The claim recites a process, as in claim 1
Step 2A Prong 1: The claim recites no further judicial exception(s)
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the further additional element(s)
wherein the plurality of computer infrastructure elements corresponds to at least one datacenter and comprises: a hardware infrastructure element deployed at the at least one datacenter: This is mere instruction to apply the judicial exception(s) with generic computing hardware (MPEP 2106.05(f)).
Step 2B: The further additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
wherein the plurality of computer infrastructure elements corresponds to at least one datacenter and comprises: a hardware infrastructure element deployed at the at least one datacenter: This is mere instruction to apply the judicial exception(s) with generic computing hardware (MPEP 2106.05(f)).

Claim 11
Step 1: The claim recites a process, as in claim 1
Step 2A Prong 1: The claim recites no further judicial exception(s)
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the further additional element(s)
providing a dashboard related to the plurality of computer infrastructure elements, wherein the dashboard is configured to at least one of:
display computer infrastructure element information corresponding to one or more of the plurality of computer infrastructure elements based at least in part on one or more labels generated using the at least one machine learning model: This is mere instruction to display information based on the generated labels in a generic manner (MPEP 2106.05(f)).
initiate one or more tasks corresponding to one or more of the plurality of computer infrastructure elements based at least in part on one or more labels generated using the at least one machine learning model: This is mere instruction to initiate a task based on the generated labels in a generic manner (MPEP 2106.05(f)).
Step 2B: The further additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
providing a dashboard related to the plurality of computer infrastructure elements, wherein the dashboard is configured to at least one of:
display computer infrastructure element information corresponding to one or more of the plurality of computer infrastructure elements based at least in part on one or more labels generated using the at least one machine learning model: This is mere instruction to display information based on the generated labels in a generic manner (MPEP 2106.05(f)).
initiate one or more tasks corresponding to one or more of the plurality of computer infrastructure elements based at least in part on one or more labels generated using the at least one machine learning model: This is mere instruction to initiate a task based on the generated labels in a generic manner (MPEP 2106.05(f)).

Claim 12
Step 1: The claim recites a process, as in claim 1
Step 2A Prong 1: The claim recites no further judicial exception(s)
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the further additional element(s)
wherein the information corresponding to the one or more user interactions comprises at least one of: a type of interaction with a given one of the plurality of computer infrastructure elements; a number of interactions with a given one of the plurality of computer infrastructure elements; an amount of time interacting with a given one of the plurality of computer infrastructure elements; and one or more preferences associated with at least one user performing the one or more user interactions: Obtaining at least one machine learning model still amounts to mere reception of data, and is insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B: The further additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
wherein the information corresponding to the one or more user interactions comprises at least one of: a type of interaction with a given one of the plurality of computer infrastructure elements; a number of interactions with a given one of the plurality of computer infrastructure elements; an amount of time interacting with a given one of the plurality of computer infrastructure elements; and one or more preferences associated with at least one user performing the one or more user interactions: Obtaining at least one machine learning model is still an instance of retrieving information from memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.).

Claim 13
Step 1: The claim recites a process, as in claim 1
Step 2A Prong 1: The claim recites no further judicial exception(s)
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the further additional element(s)
wherein the configuration information associated with a given one of the plurality of computer infrastructure elements comprises at least one of: an identifier for the given computer infrastructure element; a type of the given computer infrastructure element; a type of deployment of the given computer infrastructure element; a geographical location of the given computer infrastructure element; and at least one existing label assigned to the given computer infrastructure element: Obtaining at least one machine learning model still amounts to mere reception of data, and is insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B: The further additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
wherein the configuration information associated with a given one of the plurality of computer infrastructure elements comprises at least one of: an identifier for the given computer infrastructure element; a type of the given computer infrastructure element; a type of deployment of the given computer infrastructure element; a geographical location of the given computer infrastructure element; and at least one existing label assigned to the given computer infrastructure element: Obtaining at least one machine learning model is still an instance of retrieving information from memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.).

Claim 14
Step 1: The claim recites “A non-transitory processor-readable storage medium”, and is therefore directed to the statutory category of article of manufacture
Step 2A Prong 1: The claim recites the following judicial exception(s)
to generate, using the at least one machine learning model, at least one additional label for at least one additional computer infrastructure element using information corresponding to one or more user interactions with the at least one additional computer infrastructure element and configuration information associated with the at least one additional computer infrastructure element: This can be performed as a mental process. One can merely imagine a label for the infrastructure element related to user interactions and configuration info related to it.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the following additional element(s)
A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device: This is mere instruction to execute the judicial exception with generic computing hardware (MPEP 2106.05(f)).
to obtain at least one machine learning model, wherein the at least one machine learning model is trained, using a set of training data, to generate one or more labels for one or more of a plurality of computer infrastructure elements, and wherein the training data is based at least in part on information corresponding to one or more user interactions with at least a portion of the plurality of computer infrastructure elements and configuration information associated with at least a portion of the plurality of computer infrastructure elements: This constitutes mere data reception and is insignificant extra-solution activity (MPEP 2106.05(g)).
to generate, using the at least one machine learning model, at least one additional label for at least one additional computer infrastructure element using information corresponding to one or more user interactions with the at least one additional computer infrastructure element and configuration information associated with the at least one additional computer infrastructure element: This is mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).
to perform one or more automated actions related to the at least one additional computer infrastructure element based at least in part on the at least one additional label, wherein the at least one additional computer infrastructure element comprises at least one software infrastructure element, and wherein at least one of the one or more automated actions comprises at least one maintenance operation performed on the at least one software infrastructure element, the at least one maintenance operation comprising at least one of: updating executable code of the at least one software infrastructure element from a first version to a second version; restoring the at least one software infrastructure element to a prior state; and rebooting the at least one software infrastructure element: This is mere instruction to perform a maintenance operation based on a judicial exception in a generic manner (MPEP 2106.05(f)).
Step 2B: The following additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device: This is mere instruction to execute the judicial exception with generic computing hardware (MPEP 2106.05(f)).
to obtain at least one machine learning model, wherein the at least one machine learning model is trained, using a set of training data, to generate one or more labels for one or more of a plurality of computer infrastructure elements, and wherein the training data is based at least in part on information corresponding to one or more user interactions with at least a portion of the plurality of computer infrastructure elements and configuration information associated with at least a portion of the plurality of computer infrastructure elements: This is an instance of retrieving data from memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.)
to generate, using the at least one machine learning model, at least one additional label for at least one additional computer infrastructure element using information corresponding to one or more user interactions with the at least one additional computer infrastructure element and configuration information associated with the at least one additional computer infrastructure element: This is mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).
to perform one or more automated actions related to the at least one additional computer infrastructure element based at least in part on the at least one additional label, wherein the at least one additional computer infrastructure element comprises at least one software infrastructure element, and wherein at least one of the one or more automated actions comprises at least one maintenance operation performed on the at least one software infrastructure element, the at least one maintenance operation comprising at least one of: updating executable code of the at least one software infrastructure element from a first version to a second version; restoring the at least one software infrastructure element to a prior state; and rebooting the at least one software infrastructure element: This is mere instruction to perform a maintenance operation based on a judicial exception in a generic manner (MPEP 2106.05(f)).

Claims 15-17
Step 1: Claims 15-17 recite an article of manufacture, as in claim 14.
Step 2A Prong 1: Claims 15-17 recite the same judicial exception(s) as claims 2-4, respectively.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through any additional elements. The analysis of claims 15-17 at this step mirrors that of claims 2-4, respectively, with the exception that claims 15-17 are directed to “A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device”, performing operations mirroring those of claims 2-4. This is a mere instruction to apply the exceptions using generic computer equipment (MPEP 2106.05(f)).
Step 2B: The additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s). The analysis of claims 15-17 at this step mirrors that of claims 2-4, with the exception that claims 15-17 are directed to “A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device”, performing operations mirroring those of claims 2-4. This is mere instruction to apply the exceptions using generic computer equipment (MPEP 2106.05(f)).

Claim 18
Step 1: The claim recites “An apparatus”, and is therefore directed to the statutory category of machine
Step 2A Prong 1: The claim recites the following judicial exception(s)
to generate, using the at least one machine learning model, at least one additional label for at least one additional computer infrastructure element using information corresponding to one or more user interactions with the at least one additional computer infrastructure element and configuration information associated with the at least one additional computer infrastructure element
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the following additional element(s)
An apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured: This is mere instruction to execute the judicial exception with generic computer hardware (MPEP 2106.05(f)).
to obtain at least one machine learning model, wherein the at least one machine learning model is trained, using a set of training data, to generate one or more labels for one or more of a plurality of computer infrastructure elements, and wherein the training data is based at least in part on information corresponding to one or more user interactions with at least a portion of the plurality of computer infrastructure elements and configuration information associated with at least a portion of the plurality of computer infrastructure elements: This constitutes mere data reception and is insignificant extra-solution activity (MPEP 2106.05(g)).
to generate, using the at least one machine learning model, at least one additional label for at least one additional computer infrastructure element using information corresponding to one or more user interactions with the at least one additional computer infrastructure element and configuration information associated with the at least one additional computer infrastructure element: This is mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).
to perform one or more automated actions related to the at least one additional computer infrastructure element based at least in part on the at least one additional label, wherein the at least one additional computer infrastructure element comprises at least one software infrastructure element, and wherein at least one of the one or more automated actions comprises at least one maintenance operation performed on the at least one software infrastructure element, the at least one maintenance operation comprising at least one of: updating executable code of the at least one software infrastructure element from a first version to a second version; restoring the at least one software infrastructure element to a prior state; and rebooting the at least one software infrastructure element: This is mere instruction to perform a maintenance operation based on a judicial exception in a generic manner (MPEP 2106.05(f)).
Step 2B: The following additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
An apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured: This is mere instruction to execute the judicial exception with generic computer hardware (MPEP 2106.05(f)).
to obtain at least one machine learning model, wherein the at least one machine learning model is trained, using a set of training data, to generate one or more labels for one or more of a plurality of computer infrastructure elements, and wherein the training data is based at least in part on information corresponding to one or more user interactions with at least a portion of the plurality of computer infrastructure elements and configuration information associated with at least a portion of the plurality of computer infrastructure elements: This is an instance of retrieving data from memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.)
to generate, using the at least one machine learning model, at least one additional label for at least one additional computer infrastructure element using information corresponding to one or more user interactions with the at least one additional computer infrastructure element and configuration information associated with the at least one additional computer infrastructure element: This is mere instruction to apply a judicial exception with a generic data structure (MPEP 2106.05(f)).
to perform one or more automated actions related to the at least one additional computer infrastructure element based at least in part on the at least one additional label, wherein the at least one additional computer infrastructure element comprises at least one software infrastructure element, and wherein at least one of the one or more automated actions comprises at least one maintenance operation performed on the at least one software infrastructure element, the at least one maintenance operation comprising at least one of: updating executable code of the at least one software infrastructure element from a first version to a second version; restoring the at least one software infrastructure element to a prior state; and rebooting the at least one software infrastructure element: This is mere instruction to perform a maintenance operation based on a judicial exception in a generic manner (MPEP 2106.05(f)).

Claims 19-20
Step 1: Claims 19-20 recite a machine, as in claim 18.
Step 2A Prong 1: Claims 19-20 recite the same judicial exception(s) as claims 2-3, respectively.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through any additional elements. The analysis of claims 19-20 at this step mirrors that of claims 2-3, respectively, with the exception that claims 19-20 are directed to “An apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured”, performing operations mirroring those of claims 2-3. This is a mere instruction to apply the exceptions using generic computer equipment (MPEP 2106.05(f)).
Step 2B: The additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s). The analysis of claims 19-20 at this step mirrors that of claims 2-3, with the exception that claims 19-20 are directed to “An apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured”, performing operations mirroring those of claims 2-3. This is mere instruction to apply the exceptions using generic computer equipment (MPEP 2106.05(f)).

Claim 21
Step 1: The claim recites a machine, as in claim 18
Step 2A Prong 1: The claim recites no further judicial exception(s)
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the further additional element(s)
wherein the at least one machine learning model comprises at least one of: a transformer-based model, a long short-term memory model, and a recurrent neural network model: Generating an additional label using the machine learning model is still mere instruction to apply a judicial exception with a generic data structure in a generic manner (MPEP 2106.05(f)).
Step 2B: The further additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
wherein the at least one machine learning model comprises at least one of: a transformer-based model, a long short-term memory model, and a recurrent neural network model: Generating an additional label using the machine learning model is still mere instruction to apply a judicial exception with a generic data structure in a generic manner (MPEP 2106.05(f)).

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 nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.

Claims 1-6 and 12-21 are rejected under 35 U.S.C. 103 as being unpatentable over Iyer et al. (Mixed Initiative Approach for Reliable Tagging of Maintenance Records with Machine Learning, published 2022, ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2022), hereafter referred to as Iyer, in view of Ali et al. (METHOD AND SYSTEM FOR AUTOMATION TOOL SET FOR SERVER MAINTENANCE ACTIONS, published 11/9/2021, US 11,169,815 B2), hereafter referred to as Ali.

Regarding claim 1, Iyer discloses [a] computer-implemented method comprising:
obtaining at least one machine learning model, wherein the at least one machine learning model is trained, using a set of training data, to generate one or more labels for one or more of a plurality of computer infrastructure elements:
“Free-form text-based maintenance and service records related to industrial assets (infrastructure elements) capture the observations and actions of service engineers and are a crucial resource for assessing system-level asset health. To facilitate tracking of historical asset health issues, these records are categorized using tags (labels) from a predefined taxonomy” (Iyer, page 1, left column, paragraph 1).
“a supervised learning approach can be implemented to automate the tagging (label[ing]) process, using Deep Learning based language models like BERT (machine learning model)” (Iyer, page 2, right column, paragraph 1)
“Figure 1 shows the overall workflow based on our approach. For the baseline model, historical semi-structured records, along with tags that were assigned to them, are provided as training data to a BERT-based classification model for supervised learning of tags (labels)” (Iyer, page 3, left column, paragraph 1)
“Aircraft maintenance and service records are critical for maintaining airworthiness of an aircraft; they carry details related to the repair performed on the aircraft” (Iyer, page 3, left column, paragraph 2). These records contain information about hardware, falling within the examples given of “computer infrastructure element[s]” in page 3, paragraph 4 of the instant specification.
…wherein the training data is based at least in part on information corresponding to one or more user interactions with at least a portion of the plurality of computer infrastructure elements and configuration information associated with at least a portion of the plurality of computer infrastructure elements:
“These records capture the observations and actions of service engineers (user[s]) and are a crucial resource for assessing system-level health of an asset and for inferring reliability issues arising from those. Typically, in these records, free text is used to describe observed issues and relevant corrective actions that were performed in response - Figure 2 shows snapshots of 2 such examples of service records” (Iyer, page 3, left column, paragraph 2)

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“Two examples of maintenance/service records with red boxes indicating the ATA Chapters (tag) assigned to each” (Iyer, page 4, left column, Figure 2). These records show user interactions with infrastructure elements (investigation, cleaning, replacement) and configuration information associated with infrastructure elements (leaking gasket, gear not extending, broken shaft, part time since overhaul).
generating, using the at least one machine learning model, at least one additional label for at least one additional computer infrastructure element using information corresponding to one or more user interactions with the at least one additional computer infrastructure element and configuration information associated with the at least one additional computer infrastructure element: “Figure 1 shows the overall workflow based on our approach. For the baseline model, historical semi-structured records (records corresponding to at least one additional computer infrastructure element), along with tags that were assigned to them, are provided as training data to a BERT-based classification model for supervised learning of tags (at least one additional label)” (Iyer, page 3, left column, paragraph 1). As discussed for the previous limitation, user interaction information and configuration information can be found in the records.
performing one or more automated actions related to the at least one additional computer infrastructure element based at least in part on the at least one additional label, wherein the at least one additional computer infrastructure element comprises at least one software infrastructure element, and wherein at least one of the one or more automated actions comprises at least one maintenance operation for the at least one software infrastructure element: “the less reliable tag (additional label) assignments from the classifier are routed to the human expert for tag classification … Since the fraction of tag assignments that get routed via full automation are a good measure of efficiency of the mixed initiative system, we track this number as a metric and we call it coverage.” (Iyer, page 6, left column, paragraph 1)
Iyer relates to using machine learning to automatically tag language associated with computer infrastructure and is analogous to the claimed invention.
While Iyer fails to disclose the further limitations of the claim, Ali discloses a method comprising:
performing one or more automated actions related to the at least one additional computer infrastructure element based at least in part on the at least one additional label, wherein the at least one additional computer infrastructure element comprises at least one software infrastructure element: “an automation tool set may be integrated to distribute workflows among tiers of systems or services (computer infrastructure), and enact automation for such systems. The automation tool set may integrate with a self-service portal where an administrator may define, specify, and distribute workloads for the maintenance action. Also in a further example, the automation tool set may manage a specific group (additional label) of servers has a reboot order/dependency. Also in a further example, maintenance actions (automated actions) taken with the automation tool set may be deployed first with the lower test environment followed by production environment” (Ali, [0041])
… and wherein at least one of the one or more automated actions comprises at least one maintenance operation performed on the at least one software infrastructure element, the at least one maintenance operation comprising at least one of: updating executable code of the at least one software infrastructure element from a first version to a second version; restoring the at least one software infrastructure element to a prior state; and rebooting the at least one software infrastructure element:
“The present disclosure provides examples of an automation tool set which may be utilized for implementing maintenance actions in various forms of computing systems. Such maintenance actions may include, but are not limited to, performing reboots or restarts of computing systems and associated software after software updates have been installed” (Ali, [0014])
“FIG. 2 illustrates a flowchart of an example use of an example automation tool set configuration for a software update process (e.g., for an operating system update, performed with the automation tool set 120). A software update install is performed on the computing system (operation 210), which results in a notification (operation 220) of a pending restart (reboot) needed to apply the software update” (Ali, [0035])
wherein the method is performed by at least one processing device comprising a processor coupled to a memory: “Machine (e.g., computer system) 900 may include a hardware processor 902 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 904 and a static memory 906, some or all of which may communicate with each other via an interlink (e.g., bus) 908” (Ali, [0072]); “The storage device 916 may include a machine readable medium 922 that is non-transitory on which is stored one or more sets of data structures or instructions 924 (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein” (Ali, [0073])
Ali relates to automated maintenance of software infrastructure and is analogous to the claimed invention. Iyer teaches a method of automatically tagging and managing infrastructure records. The claimed invention improves upon Iyer’s method by performing automated software infrastructure maintenance based on tags. Ali teaches a method of automatically updating and rebooting software based on groups (tags) of associated computer infrastructure. A person of ordinary skill in the art would have recognized that performing automated software updates based on infrastructure groupings would lead to the predictable result of automated selective maintenance on software infrastructure, and would improve the efficiency of the known device by automating different updates across different groups of software infrastructure with different requirements (MPEP 2143 I. (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results).
Additionally, the claimed invention improves upon this method by storing it in the form of instructions on computer hardware. Ali teaches hardware able to run methods of automatically tagging infrastructure information, applicable to Iyer. A person of ordinary skill in the art would have recognized that storing Iyer’s method as computer instructions on Ali’s hardware would lead to the predictable result of the method being executable by a computing system, and would improve the known device by allowing it to be performed with real data and real computer infrastructure (MPEP 2143 I. (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results).

Regarding claim 2, the rejection of claim 1 in view of Iyer and Ali is incorporated. Iyer further discloses a method, wherein the set of training data comprises a set of existing labels associated with one or more of the plurality of computer infrastructure elements, and wherein the at least one additional label is different than each of the existing labels: “For the baseline model, historical semi-structured records, along with tags (existing labels) that were assigned to them, are provided as training data to a BERT-based classification model for supervised learning of tags (additional label[s])” (Iyer, page 3, left column, paragraph 1). Tags assigned before the machine learning process are ‘existing labels’, while tags generated by the machine learning model are ‘additional labels’.

Regarding claim 3, the rejection of claim 1 in view of Iyer and Ali is incorporated. Iyer further discloses a method, wherein the machine learning model is trained at least in part by:
generating a set of words by transforming at least one of: (i) one or more portions of the information corresponding to the one or more user interactions into a natural language format and (ii) one or more portions of the configuration information into a natural language format:
“For BM1, the service records are first tokenized (natural language format), using the tokenizer provided with the BERT distribution, and fed to the encoder of the pre-trained BERT model” (Iyer, page 6, right column, paragraph 2)
processing the set of words to generate a corresponding set of embeddings, wherein each embedding encodes one or more features of a given word in the set of words.

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“The BERT-tiny architecture with elements of the epistemic framework to generate reliability assessments for tag predictions. Boxes a, b and c indicate the primary elements of the architecture - a is the pretrained BERT model, b includes additions to perform classifications from the embedding of the pretrained model, and c shows extensions that make use of the overall architecture to generate epistemic reliability assessments for individual tag predictions by the model” (Iyer, page 4, right column, Figure 3). As seen in figure 3, representative embeddings corresponding to each token are generated by the BERT-tiny model. Thus, each embedding corresponds to the word(s) from the corresponding token.

Regarding claim 4, the rejection of claim 1 in view of Iyer and Ali is incorporated. Iyer further discloses a method, wherein the at least one machine learning model comprises at least one of: a transformer-based model, a long short-term memory model, and a recurrent neural network model:
“We leverage the BERT pre-trained model as made available by Google (Devlin, Chang, Lee, & Toutanova, 2018). BERT makes use of an encoder-decoder architecture that contains Transformers” (Iyer, page 3, right column, paragraph 2)
“As a first step of our approach, we develop and show outcomes from the application of one such model for the tag assignment task. Typically, an unsupervised language model is trained using a large corpus of data and then fine-tuned on the downstream task. Multiple instances of such language models exist in literature including ELMo (Embeddings from Language Models)” (Iyer, page 3, right column, paragraph 1). ELMo models are LSTM recurrent networks, as made clear by Peters et al. (Deep contextualized word representations, published 2018, arXiv:1802.05365v2): “We use vectors derived from a bidirectional LSTM that is trained with a coupled language model (LM) objective on a large text corpus. For this reason, we call them ELMo (Embeddings from Language Models) representations.” (Peters, page 1, left column, paragraph 3).

Regarding claim 5, the rejection of claim 1 in view of Iyer and Ali is incorporated. Iyer further discloses a method, comprising:
outputting the at least one additional label to a user: “the less reliable tag assignments (additional label[s]) from the classifier are routed to the human expert (user) for tag classification” (Iyer, page 6, left column, paragraph 1)
assigning the at least one additional label to the at least one additional computer infrastructure element in response to one or more inputs provided by the user: “our approach also identifies service records where the potential for a model, even with high statistical accuracy, to assign an incorrect tag is high, thereby warranting the need for intervention by a human expert to resolve the case and assign the right tag” (Iyer, page 3, left column, paragraph 2). The tag is updated by a human expert and assigned to the record corresponding to computer infrastructure element.

Regarding claim 6, the rejection of claim 5 in view of Iyer and Ali is incorporated. Iyer further discloses a method,
wherein the one or more inputs comprise one or more edits to the additional label, and wherein the assigning comprises: updating the at least one additional label based on the one or more edits; and assigning the updated at least one additional label to the at least one additional computer infrastructure element: “our approach also identifies service records where the potential for a model, even with high statistical accuracy, to assign an incorrect tag is high, thereby warranting the need for intervention by a human expert to resolve the case and assign the right tag” (Iyer, page 3, left column, paragraph 2). The tag is updated by a human expert and assigned to the record corresponding to computer infrastructure element.

Regarding claim 12, the rejection of claim 1 in view of Iyer and Ali is incorporated. Iyer further discloses a method, wherein the information corresponding to the one or more user interactions comprises at least one of: a type of interaction with a given one of the plurality of computer infrastructure elements; a number of interactions with a given one of the plurality of computer infrastructure elements; an amount of time interacting with a given one of the plurality of computer infrastructure elements; one or more preferences associated with at least one user performing the one or more user interactions:
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(Iyer, page 4, left column, Figure 2). These records comprise an investigation of an element (type of interaction), two reports (number of interactions), and users choosing to replace parts or disassemble elements (one or more preferences).

Regarding claim 13, the rejection of claim 1 in view of Iyer and Ali is incorporated. Iyer further discloses a method, wherein the configuration information associated with a given one of the plurality of computer infrastructure elements comprises at least one of: an identifier for the given computer infrastructure element; a type of the given computer infrastructure element; a type of deployment of the given computer infrastructure element; a geographical location of the given computer infrastructure element; and at least one existing label assigned to the given computer infrastructure element:
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(Iyer, page 4, left column, Figure 2). These records specify the names of the element types (identifier / type), deploying an element to perform a leak check (type of deployment), and existing ATA labels (at least one existing label).

Regarding claim 14, Iyer discloses a method:
to obtain at least one machine learning model, wherein the at least one machine learning model is trained, using a set of training data, to generate one or more labels for one or more of a plurality of computer infrastructure elements:
“Free-form text-based maintenance and service records related to industrial assets (infrastructure elements) capture the observations and actions of service engineers and are a crucial resource for assessing system-level asset health. To facilitate tracking of historical asset health issues, these records are categorized using tags (labels) from a predefined taxonomy” (Iyer, page 1, left column, paragraph 1).
“a supervised learning approach can be implemented to automate the tagging (label[ing]) process, using Deep Learning based language models like BERT (machine learning model)” (Iyer, page 2, right column, paragraph 1)
“Figure 1 shows the overall workflow based on our approach. For the baseline model, historical semi-structured records, along with tags that were assigned to them, are provided as training data to a BERT-based classification model for supervised learning of tags (labels)” (Iyer, page 3, left column, paragraph 1)
“Aircraft maintenance and service records are critical for maintaining airworthiness of an aircraft; they carry details related to the repair performed on the aircraft” (Iyer, page 3, left column, paragraph 2). These records contain information about hardware, falling within the examples given of “computer infrastructure element[s]” in page 3, paragraph 4 of the instant specification.
…wherein the training data is based at least in part on information corresponding to one or more user interactions with at least a portion of the plurality of computer infrastructure elements and configuration information associated with at least a portion of the plurality of computer infrastructure elements:
“These records capture the observations and actions of service engineers (user[s]) and are a crucial resource for assessing system-level health of an asset and for inferring reliability issues arising from those. Typically, in these records, free text is used to describe observed issues and relevant corrective actions that were performed in response - Figure 2 shows snapshots of 2 such examples of service records” (Iyer, page 3, left column, paragraph 2)

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“Two examples of maintenance/service records with red boxes indicating the ATA Chapters (tag) assigned to each” (Iyer, page 4, left column, Figure 2). These records show user interactions with infrastructure elements (investigation, cleaning, replacement) and configuration information associated with infrastructure elements (leaking gasket, gear not extending, broken shaft, part time since overhaul).
to generate, using the at least one machine learning model, at least one additional label for at least one additional computer infrastructure element using information corresponding to one or more user interactions with the at least one additional computer infrastructure element and configuration information associated with the at least one additional computer infrastructure element: “Figure 1 shows the overall workflow based on our approach. For the baseline model, historical semi-structured records (records corresponding to at least one additional computer infrastructure element), along with tags that were assigned to them, are provided as training data to a BERT-based classification model for supervised learning of tags (at least one additional label)” (Iyer, page 3, left column, paragraph 1). As discussed for the previous limitation, user interaction information and configuration information can be found in the records.
to perform one or more automated actions related to the at least one additional computer infrastructure element based at least in part on the at least one additional label, wherein the at least one additional computer infrastructure element comprises at least one software component, and wherein at least one of the one or more automated actions comprises a maintenance operation for the at least one software component: “the less reliable tag (additional label) assignments from the classifier are routed to the human expert for tag classification … Since the fraction of tag assignments that get routed via full automation are a good measure of efficiency of the mixed initiative system, we track this number as a metric and we call it coverage.” (Iyer, page 6, left column, paragraph 1)
Iyer relates to using machine learning to automatically tag language associated with computer infrastructure and is analogous to the claimed invention.
While Iyer fails to disclose the further limitations of the claim, Ali discloses [a] non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device: “Machine (e.g., computer system) 900 may include a hardware processor 902 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 904 and a static memory 906, some or all of which may communicate with each other via an interlink (e.g., bus) 908” (Ali, [0072]); “The storage device 916 may include a machine readable medium 922 that is non-transitory on which is stored one or more sets of data structures or instructions 924 (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein” (Ali, [0073])
…causes the at least one processing device:
to perform one or more automated actions related to the at least one additional computer infrastructure element based at least in part on the at least one additional label, wherein the at least one additional computer infrastructure element comprises at least one software infrastructure element: “an automation tool set may be integrated to distribute workflows among tiers of systems or services (computer infrastructure), and enact automation for such systems. The automation tool set may integrate with a self-service portal where an administrator may define, specify, and distribute workloads for the maintenance action. Also in a further example, the automation tool set may manage a specific group (additional label) of servers has a reboot order/dependency. Also in a further example, maintenance actions (automated actions) taken with the automation tool set may be deployed first with the lower test environment followed by production environment” (Ali, [0041])
… and wherein at least one of the one or more automated actions comprises at least one maintenance operation performed on the at least one software infrastructure element, the at least one maintenance operation comprising at least one of: updating executable code of the at least one software infrastructure element from a first version to a second version; restoring the at least one software infrastructure element to a prior state; and rebooting the at least one software infrastructure element:
“The present disclosure provides examples of an automation tool set which may be utilized for implementing maintenance actions in various forms of computing systems. Such maintenance actions may include, but are not limited to, performing reboots or restarts of computing systems and associated software after software updates have been installed” (Ali, [0014])
“FIG. 2 illustrates a flowchart of an example use of an example automation tool set configuration for a software update process (e.g., for an operating system update, performed with the automation tool set 120). A software update install is performed on the computing system (operation 210), which results in a notification (operation 220) of a pending restart (reboot) needed to apply the software update” (Ali, [0035])
wherein the method is performed by at least one processing device comprising a processor coupled to a memory: “Machine (e.g., computer system) 900 may include a hardware processor 902 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 904 and a static memory 906, some or all of which may communicate with each other via an interlink (e.g., bus) 908” (Ali, [0072]); “The storage device 916 may include a machine readable medium 922 that is non-transitory on which is stored one or more sets of data structures or instructions 924 (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein” (Ali, [0073])
Ali relates to automated maintenance of software infrastructure and is analogous to the claimed invention. Iyer teaches a method of automatically tagging and managing infrastructure records. The claimed invention improves upon Iyer’s method by performing automated software infrastructure maintenance based on tags. Ali teaches a method of automatically updating and rebooting software based on groups (tags) of associated computer infrastructure. A person of ordinary skill in the art would have recognized that performing automated software updates based on infrastructure groupings would lead to the predictable result of automated selective maintenance on software infrastructure, and would improve the efficiency of the known device by automating different updates across different groups of software infrastructure with different requirements (MPEP 2143 I. (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results).
Additionally, the claimed invention improves upon this method by storing it in the form of instructions on computer hardware. Ali teaches hardware able to run methods of automatically tagging infrastructure information, applicable to Iyer. A person of ordinary skill in the art would have recognized that storing Iyer’s method as computer instructions on Ali’s hardware would lead to the predictable result of the method being executable by a computing system, and would improve the known device by allowing it to be performed with real data and real computer infrastructure (MPEP 2143 I. (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results).

The analysis of claims 15-17 mirrors that of claims 2-4, with the exception that claims 15-17 are directed to additional generic computer hardware which executes the methods of claims 2-4. This generic hardware is taught by Ali, as discussed regarding claim 14. Thus, claims 15-17 are rejected under the same rationales used for claims 2-4, respectively.

Regarding claim 18, Iyer discloses an apparatus, able to:
to obtain at least one machine learning model, wherein the at least one machine learning model is trained, using a set of training data, to generate one or more labels for one or more of a plurality of computer infrastructure elements:
“Free-form text-based maintenance and service records related to industrial assets (infrastructure elements) capture the observations and actions of service engineers and are a crucial resource for assessing system-level asset health. To facilitate tracking of historical asset health issues, these records are categorized using tags (labels) from a predefined taxonomy” (Iyer, page 1, left column, paragraph 1).
“a supervised learning approach can be implemented to automate the tagging (label[ing]) process, using Deep Learning based language models like BERT (machine learning model)” (Iyer, page 2, right column, paragraph 1)
“Figure 1 shows the overall workflow based on our approach. For the baseline model, historical semi-structured records, along with tags that were assigned to them, are provided as training data to a BERT-based classification model for supervised learning of tags (labels)” (Iyer, page 3, left column, paragraph 1)
“Aircraft maintenance and service records are critical for maintaining airworthiness of an aircraft; they carry details related to the repair performed on the aircraft” (Iyer, page 3, left column, paragraph 2). These records contain information about hardware, falling within the examples given of “computer infrastructure element[s]” in page 3, paragraph 4 of the instant specification.
…wherein the training data is based at least in part on information corresponding to one or more user interactions with at least a portion of the plurality of computer infrastructure elements and configuration information associated with at least a portion of the plurality of computer infrastructure elements:
“These records capture the observations and actions of service engineers (user[s]) and are a crucial resource for assessing system-level health of an asset and for inferring reliability issues arising from those. Typically, in these records, free text is used to describe observed issues and relevant corrective actions that were performed in response - Figure 2 shows snapshots of 2 such examples of service records” (Iyer, page 3, left column, paragraph 2)

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“Two examples of maintenance/service records with red boxes indicating the ATA Chapters (tag) assigned to each” (Iyer, page 4, left column, Figure 2). These records show user interactions with infrastructure elements (investigation, cleaning, replacement) and configuration information associated with infrastructure elements (leaking gasket, gear not extending, broken shaft, part time since overhaul).
to generate, using the at least one machine learning model, at least one additional label for at least one additional computer infrastructure element using information corresponding to one or more user interactions with the at least one additional computer infrastructure element and configuration information associated with the at least one additional computer infrastructure element: “Figure 1 shows the overall workflow based on our approach. For the baseline model, historical semi-structured records (records corresponding to at least one additional computer infrastructure element), along with tags that were assigned to them, are provided as training data to a BERT-based classification model for supervised learning of tags (at least one additional label)” (Iyer, page 3, left column, paragraph 1). As discussed for the previous limitation, user interaction information and configuration information can be found in the records.
to perform one or more automated actions related to the at least one additional computer infrastructure element based at least in part on the at least one additional label, wherein the at least one additional computer infrastructure element comprises at least one software component, and wherein at least one of the one or more automated actions comprises a maintenance operation for the at least one software component: “the less reliable tag (additional label) assignments from the classifier are routed to the human expert for tag classification … Since the fraction of tag assignments that get routed via full automation are a good measure of efficiency of the mixed initiative system, we track this number as a metric and we call it coverage.” (Iyer, page 6, left column, paragraph 1)
Iyer relates to using machine learning to automatically tag language associated with computer infrastructure and is analogous to the claimed invention.
While Iyer fails to disclose the further limitations of the claim, Ali discloses [a]n apparatus comprising: at least one processing device comprising a processor coupled to a memory: “Machine (e.g., computer system) 900 may include a hardware processor 902 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 904 and a static memory 906, some or all of which may communicate with each other via an interlink (e.g., bus) 908” (Ali, [0072]); “The storage device 916 may include a machine readable medium 922 that is non-transitory on which is stored one or more sets of data structures or instructions 924 (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein” (Ali, [0073])
… the at least one processing device being configured:
to perform one or more automated actions related to the at least one additional computer infrastructure element based at least in part on the at least one additional label, wherein the at least one additional computer infrastructure element comprises at least one software infrastructure element: “an automation tool set may be integrated to distribute workflows among tiers of systems or services (computer infrastructure), and enact automation for such systems. The automation tool set may integrate with a self-service portal where an administrator may define, specify, and distribute workloads for the maintenance action. Also in a further example, the automation tool set may manage a specific group (additional label) of servers has a reboot order/dependency. Also in a further example, maintenance actions (automated actions) taken with the automation tool set may be deployed first with the lower test environment followed by production environment” (Ali, [0041])
… and wherein at least one of the one or more automated actions comprises at least one maintenance operation performed on the at least one software infrastructure element, the at least one maintenance operation comprising at least one of: updating executable code of the at least one software infrastructure element from a first version to a second version; restoring the at least one software infrastructure element to a prior state; and rebooting the at least one software infrastructure element:
“The present disclosure provides examples of an automation tool set which may be utilized for implementing maintenance actions in various forms of computing systems. Such maintenance actions may include, but are not limited to, performing reboots or restarts of computing systems and associated software after software updates have been installed” (Ali, [0014])
“FIG. 2 illustrates a flowchart of an example use of an example automation tool set configuration for a software update process (e.g., for an operating system update, performed with the automation tool set 120). A software update install is performed on the computing system (operation 210), which results in a notification (operation 220) of a pending restart (reboot) needed to apply the software update” (Ali, [0035])
Ali relates to automated maintenance of software infrastructure and is analogous to the claimed invention. Iyer teaches a method of automatically tagging and managing infrastructure records. The claimed invention improves upon Iyer’s method by performing automated software infrastructure maintenance based on tags. Ali teaches a method of automatically updating and rebooting software based on groups (tags) of associated computer infrastructure. A person of ordinary skill in the art would have recognized that performing automated software updates based on infrastructure groupings would lead to the predictable result of automated selective maintenance on software infrastructure, and would improve the efficiency of the known device by automating different updates across different groups of software infrastructure with different requirements (MPEP 2143 I. (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results).
Additionally, the claimed invention improves upon this method by storing it in the form of instructions on computer hardware. Ali teaches hardware able to run methods of automatically tagging infrastructure information, applicable to Iyer. A person of ordinary skill in the art would have recognized that storing Iyer’s method as computer instructions on Ali’s hardware would lead to the predictable result of the method being executable by a computing system, and would improve the known device by allowing it to be performed with real data and real computer infrastructure (MPEP 2143 I. (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results).

The analysis of claims 19-20 mirrors that of claims 2-3, with the exception that claims 19-20 are directed to additional generic computer hardware which executes the methods of claims 2-3. This generic hardware is taught by Ali, as discussed regarding claim 18. Thus, claims 19-20 are rejected under the same rationales used for claims 2-3, respectively.

Regarding claim 21, the rejection of claim 18 in view of Iyer and Ali is incorporated. Iyer further discloses an apparatus, wherein the at least one machine learning model comprises at least one of: a transformer-based model, a long short-term memory model, and a recurrent neural network model:
“a supervised learning approach can be implemented to automate the tagging process, using Deep Learning based language models like BERT (machine learning model)” (Iyer, page 2, right column, paragraph 1)
“BERT makes use of an encoder-decoder architecture that contains Transformers” (Iyer, page 3, right column, paragraph 2)

Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Iyer et al. (Mixed Initiative Approach for Reliable Tagging of Maintenance Records with Machine Learning, published 2022, ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2022), hereafter referred to as Iyer, in view of Ali et al. (METHOD AND SYSTEM FOR AUTOMATION TOOL SET FOR SERVER MAINTENANCE ACTIONS, published 11/9/2021, US 11,169,815 B2), hereafter referred to as Ali, and further in view of Wark (Method And System For Identifying Data And Users Of Interest From Patterns Of User Interaction With Existing Data, published 1/9/2014, US 20140012870 A1).

Regarding claim 7, the rejection of claim 5 in view of Iyer and Ali is incorporated. Wark, in combination with Iyer, discloses a method wherein the outputting is performed in response to detecting, within a particular time period, at least one of: a threshold number of interactions with the additional computer infrastructure element by the user; a threshold number of times the user interacted with the additional computer infrastructure element; and a threshold number of actions performed by the user related to the additional computer infrastructure element: “expert analysts or users may be identifiable, through using the recommendation system to identify users' who have had a certain amount of interaction (threshold number of interactions / actions) with certain types of data elements. One example where this may be useful is in the financial industry, which encompasses thousands of analysts, and where it may be difficult to locate which analyst has the desired familiarity (interaction) with certain data sets. Likewise, similar scenarios arise in the military, research labs, and other industries having numerous analysts. In scientific research, if a particular user often performs a similar synthesis or analysis on certain types of data elements, such as looking for particular markers among mouse data, that information may be used to identify that user as one familiar with that type of marker.” (Wark, [0040]). Output in Iyer’s method is performed in response to the assignment of experts (decisions are output to experts for label review). With Wark’s method, those experts can be selected based on a threshold of (inter)actions.
Wark relates to identifying users relevant to a particular interaction type and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Iyer and Ali to automatically determine expert users based on a threshold number of interactions with relevant data types, as disclosed by Wark. Wark’s method enables quick and automatic determination of experts, even in fields where there may be thousands of similar users that would be hard to categorize manually. See Wark, [0040].

Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Iyer et al. (Mixed Initiative Approach for Reliable Tagging of Maintenance Records with Machine Learning, published 2022, ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2022), hereafter referred to as Iyer, in view of Ali et al. (METHOD AND SYSTEM FOR AUTOMATION TOOL SET FOR SERVER MAINTENANCE ACTIONS, published 11/9/2021, US 11,169,815 B2), hereafter referred to as Ali, and further in view of Saetia et al. (Data-driven Approach to Equipment Taxonomy Classification, published 2019, ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2019), hereafter referred to as Saetia.

Regarding claim 8, the rejection of claim 1 in view of Iyer and Ali is incorporated. Saetia, in combination with Iyer, discloses a method, wherein the at least one machine learning model is retrained in response to at least one of a change to at least one label that is currently assigned to a given one of the computer infrastructure elements and a new label being assigned to at least one of the plurality of computer infrastructure elements: “To measure performance of the compatibility check process (Figure 3), 600 unlabeled descriptions with the lowest compatibility scores were reviewed and labeled (new label[s]) by an SME. The classification model (machine learning model) was retrained with the additional 600 newly labeled samples. The number of equipment descriptions auto-labeled by the compatibility and prediction score criteria rose to 33%.” (Saetia, page 10, right column, paragraph 1). When applied to Iyer, this method can be used to label unlabeled records associated with computer infrastructure elements and retrain the model with them.
Saetia relates to using machine learning to automatically tag infrastructure and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Iyer and Ali to label unlabeled data and use it to retrain the model, as disclosed by Saetia. Doing so would enable the use of otherwise unusable unlabeled data in the training set, which may contain words and infrastructure not present in the labeled data. When Saetia incorporated this retraining, the model’s automatic labeling ability increased significantly. See Saetia, page 10, left column, paragraph 2 to page 10, right column, paragraph 1.

Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Iyer et al. (Mixed Initiative Approach for Reliable Tagging of Maintenance Records with Machine Learning, published 2022, ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2022), hereafter referred to as Iyer, in view of Ali et al. (METHOD AND SYSTEM FOR AUTOMATION TOOL SET FOR SERVER MAINTENANCE ACTIONS, published 11/9/2021, US 11,169,815 B2), hereafter referred to as Ali, and further in view of Stenström et al. (Natural language processing of maintenance records data, published 2015, International Journal of COMADEM - April 2015), hereafter referred to as Stenstrom.

Regarding claim 10, the rejection of claim 1 in view of Iyer and Ali is incorporated. While Iyer and Ali fail to disclose the further limitations of the claim, Stenstrom discloses a method,
wherein the plurality of computer infrastructure elements corresponds to at least one datacenter: “The data used in this study was provided by Trafikverket (Swedish Transport Admini-stration)” (Stenstrom, page 3, right column, paragraph 5). Both the location managed by Trafikverket storing the data and the location that received the data for this study can be considered datacenters.
and further comprises: a hardware infrastructure element deployed at the at least one datacenter: “The text entry fields of the 10 958 records is found to contain 69 382 words in total” (Stenstrom, page 4, right column, paragraph 4); “Another type is “freeze”, which occurred 144 times. Freeze is referring to computer freeze/hang” (Stenstrom, page 5, left column, paragraph 4). A computer freeze is a problem with the software infrastructure being run on the hardware infrastructure of the computer.
Stenstrom relates to using machine learning to analyze infrastructure and is analogous to the claimed invention. Iyer teaches a method of automatically tagging infrastructure records. Stenstrom teaches a method of retrieving software / hardware computer infrastructure records from a data center. It would have been obvious to one of ordinary skill in the art to combine these methods by retrieving software / hardware computer infrastructure from a data center for automatic tagging by Iyer’s method. This would achieve the predictable result of tagged software / hardware computer infrastructure records, with Iyer and Stenstrom’s methods performing the same together as they did separately. (MPEP 2143 I. (A) Combining prior art elements according to known methods to yield predictable results).

Regarding claim 11, the rejection of claim 1 in view of Iyer and Ali is incorporated. Stenstrom, in combination with Iyer, discloses a method of providing a dashboard related to the plurality of computer infrastructure elements, wherein the dashboard is configured to at least one of:
display computer infrastructure element information corresponding to one or more of the plurality of computer infrastructure elements based at least in part on one or more labels generated using the at least one machine learning model: “The data of preventive and corrective maintenance work is commonly called maintenance records (computer infrastructure element information), reports or work orders, and follows a set template and procedure for registration and closure, through a graphical user interface (GUI) (dashboard). Maintenance records contain a number of fields/boxes (labels), such as: record identification number; asset information regarding system, subsystem and components; maintenance activity; failure cause; and remedy. However, the content depends if it is corrective or preventive maintenance records. The records fields within a GUI comprise of drop-down lists, list boxes, check boxes and text entry fields” (Stenstrom, page 2, left column, paragraph 1). Stenstrom’s method in combination with Iyer can be used to display record information, including tags automatically applied with Iyer’s method.
initiate one or more tasks corresponding to one or more of the plurality of computer infrastructure elements based at least in part on one or more labels generated using the at least one machine learning model
Stenstrom relates to using machine learning to analyze infrastructure and is analogous to the claimed invention. Iyer teaches a method of automatically tagging infrastructure records. The claimed invention improves upon this method by displaying automatically tagged records on a dashboard. Stenstrom teaches a method of displaying record information on a dashboard GUI, applicable to the combination of Iyer and Ali. A person of ordinary skill in the art would have recognized that presenting records tagged with Iyer’s method on a GUI dashboard would lead to the predictable result of displaying automatically generated tags, and would improve the known device by making tags generated through Iyer’s method readable / accessible by users (MPEP 2143 I. (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results).

Response to Arguments
The following responses address arguments and remarks made in the instant remarks dated 05/18/2026.

101 Rejections
On pages 13-14 of the instant remarks, the Applicant argues that claim 1 does not recite mental processes:
“The August 4, 2025 USPTO Memorandum Reminders on Evaluating Subject Matter
Eligibility of Claims Under 35 USC §JOI (hereinafter "USPTO Memo") confirms that a claim
does not recite a mental process when it contains limitations that "cannot practically be performed
in the human mind" (USPTO Memo, page 2). A human cannot mentally update the executable
code of a software infrastructure element from one version to another, mentally restore a software
infrastructure element to a prior state, or mentally reboot a software infrastructure element. These
operations are inherently performed by and on computer systems. They are not mental steps that a
human could perform with pen and paper.

In addition, claim 1 recites obtaining at least one machine learning model that is trained,
using a set of training data, where the training data is based on user-interaction information and
configuration information associated with computer infrastructure elements, and generating, using
the at least one machine learning model, at least one additional label. Training and applying a
machine learning model on user-interaction and configuration data drawn from a plurality of
computer infrastructure elements is not a mental process but a process that includes computational
processing of feature representations through a learned model. Accordingly, the amended
independent claims do not recite a mental process and, thus, are eligible under Step 2A, Prong
One.”
Regarding the Applicant’s arguments above, the Examiner respectfully disagrees. For 2A Prong One analysis for U.S.C. 101 eligibility analysis, each limitation of the claim is evaluated individually for recitation of abstract ideas, not merely the claim as a whole, as noted by MPEP 2106.04(a): Examiners should determine whether a claim recites an abstract idea by (1) identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and (2) determining whether the identified limitations(s) fall within at least one of the groupings of abstract ideas listed above. One limitation not being mentally performable does not preclude other limitations from reciting abstract ideas.
With that in mind, the Examiner respectfully disagrees that claim 1, as amended, recites no mental processes. As stated in MPEP 2106.04(a)(2)(III), The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 … Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer- implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
Claim 1, as amended, recites limitations amounting to mental processes performed on generic data structures. Generic data structures amount to generic computer components and are insufficient to render a mentally performable task non-abstract. For example, claim 1’s second limitation “generating, using the at least one machine learning model, at least one additional label for at least one additional computer infrastructure element using information corresponding to one or more user interactions with the at least one additional computer infrastructure element and configuration information associated with the at least one additional computer infrastructure element”, recites a mental process of generating an additional label performed by a machine learning model, a generic data structure insufficient to render the limitation non-abstract.
The Examiner asserts that claim 1, as amended, recites mental processes, and maintains its rejection on the basis of the Alice/Mayo tests performed (See 101 rejections).

On pages 14-15 of the instant remarks, the Applicant argues that recited judicial exceptions are integrated into a practical application through improvement to technology:
“The amended claims also integrate any alleged abstract idea into a practical application by
providing a specific improvement to the functioning of a computer system and computer
infrastructure management. The specification explains that conventional approaches to
characterizing and maintaining computer infrastructure elements rely on manual grouping and
labeling, which is time-consuming and can lead to inconsistencies regarding how a particular
computer infrastructure element is assigned, negatively impacting security, usability, efficiency,
and availability (see, e.g., specification, page 1, lines 11-14, and page 3, lines 8-15). The amended
claims address these technical problems by training a machine learning model on user-interaction
and configuration data, generating a label for a software infrastructure element using the trained
model, and automatically performing a maintenance operation on that software infrastructure
element based on the generated label, where the maintenance operation includes updating
executable code, restoring to a prior state, or rebooting. These features correspond to a specific
technical improvement to how computer infrastructure is managed and can mitigate the
inconsistencies and inefficiencies of prior manual approaches described in the specification (see,
e.g., specification, page 2, lines 1-7).”
In response to the Applicant’s assertion that the recited judicial exception(s) of the claimed invention are practically integrated through improvement to technology, the Examiner respectfully disagrees. The Examiner notes that practical integration through improvement only comes through improvements to the functioning of a technology or technical field, as noted by MPEP 2106.04(d)(I): Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include: … An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a). Such technical improvements can similarly indicate a limitation amounts to significantly more than the claim’s recited judicial exceptions, as noted in MPEP 2106.05(I)(A): Limitations that the courts have found to qualify as "significantly more" when recited in a claim with a judicial exception include: i. Improvements to the functioning of a computer, e.g., a modification of conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, as discussed in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258-59, 113 USPQ2d 1097, 1106-07 (Fed. Cir. 2014) (see MPEP § 2106.05(a)).
Improving a manual human-performable process by automating it does not constitute improvement to a technology or technical field, and is insufficient to show an improvement in computer-functionality, as noted in MPEP 2106.05(a)(I): Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality: … iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential).
The problem described by the Applicant, manual grouping and labeling of computer infrastructure components, is that of a human-performable process and does not constitute a problem in a technology or technical field. Thus, improvements to this problem represented by the claimed invention are insufficient to practically integrate the recited judicial exception(s). No rejections are withdrawn on this basis.

On pages 15-16 of the instant remarks, the Applicant argues that the claimed invention represents an unconventional combination of elements and thus amounts to significantly more than its recited judicial exceptions:
“The amended independent claims recite an unconventional ordered combination of
features, which includes: (i) obtaining a machine learning model specifically trained on a
combination of user-interaction information and configuration information associated with a
plurality of computer infrastructure elements; (ii) generating a label for an additional computerinfrastructure element based on these specific data parameters; and (iii) automatically initiating a
structural, state-changing maintenance operation (updating executable code, restoring, or
rebooting) based on the generated label

None of the references cited by the Office Action in the §101 rejection (i.e., Gare!, Cheng,
or Wei) demonstrate that it was well-understood, routine, or conventional to automatically execute
these specific software-altering operations dynamically in response to a label generated by a
machine learning model trained on user-interaction and configuration data. The combination of
claimed features transforms the operations into a specific, unconventional process of computer
infrastructure management by automating infrastructure changes based on predictive, non-manual
tagging”
Regarding the Applicant’s arguments above, the Examiner respectfully disagrees. Unconventional arrangements of conventional elements are not sufficient to amount to significantly more than the recited judicial exceptions if these arrangements are generic, as noted in MPEP 2106.05(d)(I)(3): in BASCOM, even though the court found that all of the additional elements in the claim recited generic computer network or Internet components, the elements in combination amounted to significantly more because of the non-conventional and non-generic arrangement that provided a technical improvement in the art.
Regarding limitation 1 of claim 1, “obtaining at least one machine learning model, wherein the at least one machine learning model is trained, using a set of training data, to generate one or more labels for one or more of a plurality of computer infrastructure elements, and wherein the training data is based at least in part on information corresponding to one or more user interactions with at least a portion of the plurality of computer infrastructure elements and configuration information associated with at least a portion of the plurality of computer infrastructure elements”, the means by which user interaction information and configuration information are used to form a training set or how this is used to train the machine learning model are recited at a high level of generality.
Similarly, in limitation 2 of claim 1, “generating, using the at least one machine learning model, at least one additional label for at least one additional computer infrastructure element using information corresponding to one or more user interactions with the at least one additional computer infrastructure element and configuration information associated with the at least one additional computer infrastructure element”, the means by which the machine learning model generates a label based on the user and configuration information is completely generic.
The third limitation, “performing one or more automated actions related to the at least one additional computer infrastructure element based at least in part on the at least one additional label, wherein the at least one additional computer infrastructure element comprises at least one software infrastructure element, and wherein at least one of the one or more automated actions comprises at least one maintenance operation performed on the at least one software infrastructure element, the at least one maintenance operation comprising at least one of: updating executable code of the at least one software infrastructure element from a first version to a second version; restoring the at least one software infrastructure element to a prior state; and rebooting the at least one software infrastructure element”, details three well-known methods of software infrastructure maintenance (updates, rollbacks, and reboots), while keeping the exact relationship between the additional label and corresponding maintenance operations highly generic.
The combination of these elements does not make clear the relationship between the training data and machine learning model training, the output label generation process, or the relationship between the output label and corresponding maintenance operations. Thus, it’s found to be generic, and insufficient for the claimed invention to amount to significantly more than its recited judicial exception.
No rejections are withdrawn on this basis.

103 Rejections
On pages 10-13 of the instant remarks, the Applicant argues that Iyer and Muppidi fail to disclose the amended independent claims:
“The Office Action concedes that "Iyer does not disclose performing maintenance
operations on software infrastructure," and instead asserts that "this deficiency is remedied by
Muppidi," (see Office Action, page 62). The Office Action relies on Muppidi as disclosing an
automated action comprising a maintenance operation performed on a software infrastructure
element. Specifically, pages 28-39 of the Office Action cite Muppidi at: (i) col. 2, lines 28-34
describing that, when metadata maps to a classification, "associating the policy with the
component may occur automatically from assigning the component to the classification"; and (ii)
Muppidi's process 900, which "begins by receiving a policy update (step 902)" and "enforces the
policy update for that service" (see col. 11, lines 30-46). However, the cited portions of Muppidi
do not disclose or suggest a maintenance operation performed on a software infrastructure element
as recited in amended claim 1
…
In contrast to Muppidi, amended claim 1 includes a maintenance operation performed on
the claimed software infrastructure element and that the maintenance operation includes updating
executable code of the software infrastructure element, restoring the software infrastructure
element to a prior state and/or rebooting the software infrastructure element. Associating a security
policy with a service is not analogous to updating the executable code of that service, restoring the
service, or rebooting the service. Muppidi's policy association is not performed on a software
infrastructure element, as recited by amended claim 1.
…
Additionally, Muppidi's "policy update" in process 900 is not an update of the software
component. The policy update merely is an update to a rule that references components of a given
classification. Muppidi explains that a "policy update may include changes to policies or policy
templates already associated with classifications" or "newly added policies or policy templates"
(see col. 11, lines 26-29). When a policy update is received, Muppidi determines whether any
services belonging to the affected classification exist within the boundary of process 900, and if
so, enforces the policy update for that service (see col. 11, lines 42-46). Enforcing an updated
security policy (e.g., a security policy requiring a different token type or authentication
mechanism) does not result in the executable code of the service being updated, the service being
restored to a prior state, or the service being rebooted. The security rules merely control access to
the service
…
Muppidi's policy-association action is not performed based on a label generated by a
machine learning model. Muppidi's classification mechanism is a rule-based metadata mapping,
where a mapping is performed between a component's metadata and a predefined classification
(see, e.g., FIGS. 6 and 8 and col. 10, lines 36-48). Muppidi does not describe or suggest either
training a machine learning model on user-interaction information and configuration information
or generating a label using such a trained model”
The Applicant’s arguments with respect to the independent claims have been considered but are moot because the new grounds of rejections for the amended limitation “performing one or more automated actions related to the at least one additional computer infrastructure element based at least in part on the at least one additional label, wherein the at least one additional computer infrastructure element comprises at least one software infrastructure element, and wherein at least one of the one or more automated actions comprises at least one maintenance operation performed on the at least one software infrastructure element, the at least one maintenance operation comprising at least one of: updating executable code of the at least one software infrastructure element from a first version to a second version; restoring the at least one software infrastructure element to a prior state; and rebooting the at least one software infrastructure element” of claim 1 and similar amended limitations in substantially similar independent claims 14 and 18 do not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
The amended independent claims are found to be obvious over Iyer in view of Ali. See the 103 rejections section for more detail.

Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Sharma et al. (MACHINE LEARNING TEST RESULT ANALYZER FOR IDENTIFYING AND TRIGGERING REMEDIAL ACTIONS, published 12/8/2022, US 20220391312 A1) discloses a method of using a machine learning model to analyze software test results and apply remedial actions in response
Canning et al. (System, Method And Program To Distribute Program Updates, published 4/6/2006, US 20060075001 A1) discloses a method of automatically updating and rebooting systems based on infrastructure grouping
Castel et al. (COMPUTER SYSTEM AND METHOD FOR MAINTENANCE MANAGEMENT INCLUDING COLLABORATION ACROSS CLIENTS, published 2014, US 2014/0164603 A1) discloses a method of automatically recommending and performing computer infrastructure maintenance based on automatically generated tags.

Any inquiry concerning this communication or earlier communications from the examiner should be directed to Aaron P Gormley whose telephone number is [phone]. The examiner can normally be reached Monday - Friday 12:00 PM - 8:00 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle T Bechtold can be reached at [phone]. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AG/Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148

Prosecution Timeline

• Jan 20, 2023: Application Filed
• Oct 28, 2025: Non-Final Rejection mailed — §101, §103, §Other
• Jan 12, 2026: Interview Requested
• Jan 27, 2026: Examiner Interview Summary
• Jan 27, 2026: Applicant Interview (Telephonic)
• Jan 28, 2026: Response Filed
• Mar 18, 2026: Final Rejection mailed — §101, §103, §Other
• May 18, 2026: Response after Non-Final Action
• Jun 18, 2026: Request for Continued Examination
• Jun 23, 2026: Response after Non-Final Action
• Jul 07, 2026: Non-Final Rejection mailed — §101, §103, §Other (current)