DETAILED ACTION
This Office Action is in response to Amendment filed 29 July 2026.
Claims 1-8 and 10-21 are pending. Claim 9 has been cancelled. The pending claims have been considered and examined.
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 § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 7, 8, 10-18 and 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Park et al., U. S. Patent App. Pub. 2024/0419522, hereinafter referred to as “Park”, in view of Manuell et al., U.S. 2005/0086543, hereinafter referred to as “Manuell” and in view of Phadke et al., U.S. Patent 10,904,276. Hereinafter referred to as “Phadke”.
Referring to claim 1, Park discloses a method (See Park, paragraph 0012). - A method, comprising:
Park discloses receiving an indication of environmental condition in a data center (See Park, paragraph 0004). Park discloses receiving external and internal environment conditions (See Park, paragraph 0013). - receiving, by a device, data center data and external environmental data associated with a data center,
Park discloses training a machine learning model on component state data and environment state data (See Park, paragraphs 0013 and 0020). - training a machine learning model, with the data center data and the external environmental data;
Park discloses receiving an indication of environmental condition in a data center (See Park, paragraph 0004). - receiving, by the device, an indication of an environmental condition event associated with the data center;
Park discloses using a machine learning model that is trained to make predictions on components based on environmental data, such as temperature, humidity, etc. (See Park, paragraph 0027). Park discloses the predicted failure of a component is used to migrate virtual machines; thus, applications are allowed to remain online (See Park, paragraph 0016). - processing, by the device, the indication of the environmental condition event, with the trained machine learning model; to allow particular applications to remain online; and
Park discloses using redundant systems and determining which healthy servers have capacity for migrating (See Park, paragraphs 0002 and 0023). - determining whether to move the particular applications to a redundant location; and
Park does not disclose “determining if any applications or hardware should be shut down” and “causing, by the device, the data center to shut down one or more of the applications or the hardware.” However, Park discloses predicting a failure of a component and performing mitigating action in response (See Park, paragraphs 0012-0013).
Manuell discloses dealing with environmental events and power interruptions in data centers (See Manuell, paragraph 0002). Manuell selectively powering down one or more computer servers based on environment parameters (See Manuell, paragraph 0009).
It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to combine the processing of environment data, using a trained model, to predict issues and preform mitigating actions in a data center of Park with the selectively shutting down servers of a data center in response to environment parameters of Manuell. It would have been obvious to do because helps manage utility outages and keep critical systems operating (See Manuell, paragraph 0009).
Park does not disclose “wherein the external environmental data is received from a third party; “. However, Park discloses receiving external environment conditions such temperature and humidity (See Park, paragraph 0013).
Phadke discloses statistical anomaly detection with machine learning (See Phadke, Col. 1, lines 34-47). Phadke discloses extrinsic features including contextual information from external data sources including weather data (See Phadke, Col. 7, line 14-42). Phadke discloses training the machine learning algorithm using intrinsic and extrinsic features (See Phadke, Col. 1, line 61 to Col. 2, line 3).
It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to combine the processing of environment data, using a trained model, to predict issues and preform mitigating actions in a data center of Park and the selectively shutting down servers of a data center in response to environment parameters of Manuell with the training of the machine learning with external sourced weather data. It would have been obvious to do because provides external events or conditions from which contextual information maybe extracted, derived or correlated with respect to the reported anomaly (See Phadke, Col. 5, lines 15-39).
Referring to claim 2, Park, Manuell, and Phadke disclose all the limitations (See rejection of claim 1) including Park discloses using redundant systems and the predicted failure of a component is used to migrate virtual machines; (See Park, paragraphs 0002 and 0016). - The method of claim 1, further comprising: causing the data center to move the particular applications to the redundant location.
Referring to claim 3, Park, Manuell, and Phadke disclose all the limitations (See rejection of claim 1) including Park discloses using a machine learning model that is trained to make predictions on components based on environmental data (See Park, paragraph 0027). Manuell discloses detecting loss of primary power source (See Manuell, paragraph 0004). - The method of claim 1, further comprising: receiving another indication of a power failure event associated with the data center; processing the other indication of the power failure event, with the trained machine learning model;
Manuell selectively powering down one or more computer servers based on environment parameters and shedding load of the computers in response to remaining battery runtime, thus reducing power consumption (See Manuell, paragraphs 0009 and 0032). Park discloses the predicted failure of a component is used to migrate virtual machines; thus, applications are allowed to remain online (See Park, paragraph 0016). - determining if any other applications or other hardware should be shut down to reduce power consumption and allow the particular applications to remain online; determining whether to move the particular applications to the redundant location; and causing the data center to shut down one or more of the other applications or the other hardware.
Referring to claim 4, Park, Manuell, and Phadke disclose all the limitations (See rejection of claim 1) including Park and Manuell disclose all the limitations (See rejection of claim 3) including Park discloses using redundant systems and the predicted failure of a component is used to migrate virtual machines; (See Park, paragraphs 0002 and 0016). - The method of claim 3, further comprising: causing the data center to move the particular applications to the redundant location.
Referring to claim 7, Park, Manuell and Phadke disclose all the limitations (See rejection of claim 1) including Park discloses continuous monitoring of environmental conditions (See Park, paragraph 0039). Park discloses receiving an indication of environmental condition in a data center (See Park, paragraph 0004). - The method of claim 1, further comprising: receiving another indication of an application that has been compromised in the data center;
Park discloses using a machine learning model that is trained to make predictions on components based on environmental data, such as temperature, humidity, etc. (See Park, paragraph 0027). Park discloses using redundant systems and the predicted failure of a component is used to migrate virtual machines; (See Park, paragraph 0002 and 0016). - processing the other indication of the application that has been compromised, with the trained machine learning model, to identify a particular redundant location for executing the application; and causing the data center to move the application to the particular redundant location
Referring to claim 8, Park discloses a system (See Park, paragraph 0012). - A device, comprising:
Park discloses the system includes a processor (See Park, paragraph 0041). - one or more processors configured to:
Park discloses receiving an indication of environmental condition in a data center (See Park, paragraph 0004). Park discloses receiving external and internal environment conditions (See Park, paragraph 0013). - receive data center data and external environmental data associated with a data center,
Park discloses training a machine learning model on component state data and environment state data (See Park, paragraphs 0013 and 0020). - train a machine learning model, with the data center data and the external environmental data;
Park discloses receiving an indication of environmental condition in a data center (See Park, paragraph 0004). - receive an indication of an event associated with the data center, wherein the event is an environmental condition event or a power failure event associated with the data center;
Park discloses using a machine learning model that is trained to make predictions on components based on environmental data, such as temperature, humidity, etc. (See Park, paragraph 0027). Park discloses the predicted failure of a component is used to migrate virtual machines; thus, applications are allowed to remain online (See Park, paragraph 0016). - process the event, with the trained machine learning model, to allow particular applications to remain online;
Park discloses using redundant systems and determining which healthy servers have capacity for migrating (See Park, paragraphs 0002 and 0023). - determine whether to move the particular applications to a redundant location; and
Park does not disclose “determine if any applications or hardware should be shut down” and “cause the data center to shut down one or more of the applications or the hardware”. However, Park discloses predicting a failure of a component and performing mitigating action in response (See Park, paragraphs 0012-0013).
Manuell discloses dealing with environmental events and power interruptions in data centers (See Manuell, paragraph 0002). Manuell selectively powering down one or more computer servers based on environment parameters (See Manuell, paragraph 0009). Manuell discloses detecting loss of primary power source (See Manuell, paragraph 0004).
It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to combine the processing of environment data, using a trained model, to predict issues and preform mitigating actions in a data center of Park with the selectively shutting down servers of a data center in response to environment parameters of Manuell. It would have been obvious to do because helps manage utility outages and keep critical systems operating (See Manuell, paragraph 0009).
Park does not disclose “wherein the external environmental data is received from a third party; “. However, Park discloses receiving external environment conditions such temperature and humidity (See Park, paragraph 0013).
Phadke discloses statistical anomaly detection with machine learning (See Phadke, Col. 1, lines 34-47). Phadke discloses extrinsic features including contextual information from external data sources including weather data (See Phadke, Col. 7, line 14-42). Phadke discloses training the machine learning algorithm using intrinsic and extrinsic features (See Phadke, Col. 1, line 61 to Col. 2, line 3).
It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to combine the processing of environment data, using a trained model, to predict issues and preform mitigating actions in a data center of Park and the selectively shutting down servers of a data center in response to environment parameters of Manuell with the training of the machine learning with external sourced weather data. It would have been obvious to do because provides external events or conditions from which contextual information maybe extracted, derived or correlated with respect to the reported anomaly (See Phadke, Col. 5, lines 15-39).
Referring to claim 10, Park, Manuell, and Phadke disclose all the limitations (See rejection of claim 8) including Park discloses utilizing environment condition data, telemetry data, capacity of servers, and real time temperatures (See Park, paragraphs 0014, 0023, and 0035). - The device of claim 8, wherein the data center data includes data identifying one or more of environmental conditions, server conditions, server capacities, power consumption, heat output, security components, particular application utilization, redundant location capacity, or network performance key performance indicators associated with the data center.
Referring to claim 11, Park, Manuell, and Phadke disclose all the limitations (See rejection of claim 8) including Manuell discloses a shutdown starting at level 5 and proceeding to all levels down to level 1 of equipment based on environmental conditions including temperature (See Manuell, Fig. 4; paragraph 0032). - The device of claim 8, wherein the one or more processors are further configured to: initiate a shutdown sequence for the data center based on the indication of the environmental condition event.
Referring to claim 12, Park, Manuell, and Phadke disclose all the limitations (See rejection of claim 11) including Manuell discloses a shutdown starting at level 5 and proceeding to all levels down to level 1 of equipment, thus with each piece of equipment shut down, reducing power consumption (See Manuell, Fig. 4; paragraph 0032). Park discloses the predicted failure of a component is used to migrate virtual machines (See Park, paragraph 0016). - The device of claim 11, wherein the shutdown sequence causes the data center to shut down particular components, migrate particular traffic to backup locations, and reduce power consumption.
Referring to claim 13, Park, Manuell, and Phadke disclose all the limitations (See rejection of claim 11) including Manuell selectively powering down one or more computer servers (See Manuell, paragraph 0009). Manuell discloses a shutdown starting at level 5 and proceeding to all levels down to level 1 of equipment, thus with each piece of equipment shut down, reducing power consumption (See Manuell, Fig. 4; paragraph 0032). Park discloses the predicted failure of a component is used to migrate virtual machines (See Park, paragraph 0016). - The device of claim 11, wherein the one or more processors are further configured to: identify particular components of the data center to shut down during the shutdown sequence; identify particular traffic to migrate to backup locations during the shutdown sequence; and identify one or more servers to deactivate during the shutdown sequence to reduce power consumption.
Referring to claim 14, Park, Manuell, and Phadke disclose all the limitations (See rejection of claim 11) including Manuell discloses notifying text pagers of IT of load shedding category level, which is part of the shutdown sequence (See Manuell, Fig. 4; paragraphs 0032-0033). The device of claim 11, wherein the one or more processors are further configured to: notify a system administrator about initiation of the shutdown sequence for the data center.
Referring to claim 15, Park discloses a computer-readable medium with computer executable instructions (See Park, paragraph 0042). Park discloses a system includes a processor (See Park, paragraph 0041). - A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to:
Park discloses receiving an indication of environmental condition in a data center (See Park, paragraph 0004). Park discloses receiving external and internal environment conditions (See Park, paragraph 0013). - receive data center data associated with a data center; receive external environmental data associated with the data center;
Park discloses training a machine learning model on component state data and environment state data (See Park, paragraph 0020). - train a machine learning model, with the data center data and the external environmental data;
Park discloses receiving an indication of environmental condition in a data center (See Park, paragraph 0004). - receive an indication of an environmental condition event associated with the data center;
Park discloses using a machine learning model that is trained to make predictions on components based on environmental data, such as temperature, humidity, etc. (See Park, paragraph 0027). Park discloses the predicted failure of a component is used to migrate virtual machines; thus, applications are allowed to remain online (See Park, paragraph 0016). - process the indication of the environmental condition event, with the trained machine learning model, to allow particular applications to remain online and
Park discloses using redundant systems and determining which healthy servers have capacity for migrating (See Park, paragraphs 0002 and 0023). - determine whether to move the particular applications to a redundant location; and
Park does not disclose “determine if any applications or hardware to shut down” and “cause the data center to shut down one or more of the applications or the hardware”. However, Park discloses predicting a failure of a component and performing mitigating action in response (See Park, paragraphs 0012-0013).
Manuell discloses dealing with environmental events and power interruptions in data centers (See Manuell, paragraph 0002). Manuell selectively powering down one or more computer servers based on environment parameters (See Manuell, paragraph 0009).
It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to combine the processing of environment data, using a trained model, to predict issues and preform mitigating actions in a data center of Park with the selectively shutting down servers of a data center in response to environment parameters of Manuell. It would have been obvious to do because helps manage utility outages and keep critical systems operating (See Manuell, paragraph 0009).
Park does not disclose “external environmental data associated with the data center from a third party; “. However, Park discloses receiving external environment conditions such temperature and humidity (See Park, paragraph 0013).
Phadke discloses statistical anomaly detection with machine learning (See Phadke, Col. 1, lines 34-47). Phadke discloses extrinsic features including contextual information from external data sources including weather data (See Phadke, Col. 7, line 14-42). Phadke discloses training the machine learning algorithm using intrinsic and extrinsic features (See Phadke, Col. 1, line 61 to Col. 2, line 3).
It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to combine the processing of environment data, using a trained model, to predict issues and preform mitigating actions in a data center of Park and the selectively shutting down servers of a data center in response to environment parameters of Manuell with the training of the machine learning with external sourced weather data. It would have been obvious to do because provides external events or conditions from which contextual information maybe extracted, derived or correlated with respect to the reported anomaly (See Phadke, Col. 5, lines 15-39).
Referring to claim 16, Park, Manuell, and Phadke disclose all the limitations (See rejection of claim 15) including Park discloses using redundant systems and the predicted failure of a component is used to migrate virtual machines; (See Park, paragraphs 0002 and 0016). - The non-transitory computer-readable medium of claim 15, wherein the one or more instructions further cause the device to: cause the data center to move the particular applications to the redundant location.
Referring to claim 17, Park, Manuell, and Phadke disclose all the limitations (See rejection of claim 15) including Park discloses using a machine learning model that is trained to make predictions on components based on environmental data (See Park, paragraph 0027). Manuell discloses detecting loss of primary power source (See Manuell, paragraph 0004). - The non-transitory computer-readable medium of claim 15, wherein the one or more instructions further cause the device to: receive another indication of a power failure event associated with the data center; process the other indication of the power failure event, with the trained machine learning model,
Manuell selectively powering down one or more computer servers based on environment parameters and shedding load of the computers in response to remaining battery runtime, thus reducing power consumption (See Manuell, paragraphs 0009 and 0032). Park discloses the predicted failure of a component is used to migrate virtual machines; thus, applications are allowed to remain online (See Park, paragraph 0016). - determine if any other applications or other hardware should be shut down to reduce power consumption and allow the particular applications to remain online determine whether to move the particular application to the redundant location; and cause the data center to shut down one or more of the other applications or the other hardware.
Referring to claim 18, Park, Manuell, and Phadke disclose all the limitations (See rejection of claim 17) including Park discloses using redundant systems and the predicted failure of a component is used to migrate virtual machines; (See Park, paragraphs 0002 and 0016). - The non-transitory computer-readable medium of claim 17, wherein the one or more instructions further cause the device to: cause the data center to move the particular applications to the redundant location.
Referring to claim 20, Park, Manuell, and Phadke disclose all the limitations (See rejection of claim 15) including Park discloses continuous monitoring of environmental conditions (See Park, paragraph 0039). Park discloses receiving an indication of environmental condition in a data center (See Park, paragraph 0004). - The non-transitory computer-readable medium of claim 15, wherein the one or more instructions further cause the device to: receive another indication of an application that has been compromised in the data center;
Park discloses using a machine learning model that is trained to make predictions on components based on environmental data, such as temperature, humidity, etc. (See Park, paragraph 0027). Park discloses using redundant systems and the predicted failure of a component is used to migrate virtual machines; (See Park, paragraph 0002 and 0016). - process the other indication of the application that has been compromised, with the trained machine learning model, to identify a particular redundant location for executing the application; and cause the data center to move the application to the particular redundant location.
Referring to claim 21, Park, Manuell, and Phadke disclose all the limitations (See rejection of claim 15) including Park discloses utilizing environment condition data, telemetry data, capacity of servers, and real time temperatures (See Park, paragraphs 0014, 0023, and 0035). - The non-transitory computer-readable medium of claim 15, wherein the data center data includes data identifying one or more of environmental conditions, server conditions, server capacities, power consumption, heat output, security components, particular application utilization, redundant location capacity, or network performance key performance indicators associated with the data center.
Claim(s) 5-6 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Park and Manuell and Phadke as applied to claims 1 and 15 above, and further in view of Chan et al., U.S. patent App. Pub. 2009/0183016, hereinafter referred to as “Chan”.
Referring to claim 5, Park, Manuell, and Phadke disclose all the limitations (See rejection of claim 1) including Park discloses receiving an indication of environmental condition in a data center (See Park, paragraph 0004). Park discloses using a machine learning model that is trained to make predictions on components based on environmental data, such as temperature, humidity, etc. (See Park, paragraph 0027). Manuell discloses dealing with environmental events and power interruptions in data centers (See Manuell, paragraph 0002). Manuell selectively powering down one or more computer servers based on environment parameters and shedding load of the computers, thus reducing power consumption (See Manuell, paragraphs 0009 and 0032). - The method of claim 1, further comprising: receiving new data center data associated with the data center; processing the new data center data, with the trained machine learning model, to identify redundant applications and hardware to shut down to reduce power consumption; and causing the data center to shut down the redundant applications and hardware.
Park, Manuell, and Phadke do not disclose “during low traffic periods”. However, Park does disclose a desire for the data center to be less costly to operate (See, Park, paragraph 0016).
Chan discloses a data center and managing power consumption (See Chan, paragraph 0005). Chan discloses during periods of low traffic turning systems of and redirecting traffic to a subset of available servers (See Chan, paragraph 0005).
It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to combine the processing of environment data, using a trained model, to predict issues and preform mitigating actions in a data center of Park, the selectively shutting down servers of a data center in response to environment parameters of Manuel, and the external sourced weather data of Phadke with the shutting down servers during periods of low traffic of Chan. It would have been obvious to do because it is a common approach to save power of a data center thus save on the cost of data center power consumption (See Chan, paragraph 0005).
Referring to claim 6, Park, Manuell, Phadke, and Chan disclose all the limitations (See rejection of claim 5) including Park discloses utilizing environment condition data, telemetry data, capacity of servers, and real time temperatures (See Park, paragraphs 0014, 0023, and 0035). - The method of claim 5, wherein the data center data includes data identifying one or more of environmental conditions, server conditions, server capacities, power consumption, heat output, security components, particular application utilization, redundant location capacity, or network performance key performance indicators associated with the data center.
Referring to claim 19, Park, Manuell, and Phadke disclose all the limitations (See rejection of claim 15) including Park discloses receiving an indication of environmental condition in a data center (See Park, paragraph 0004). Park discloses using a machine learning model that is trained to make predictions on components based on environmental data, such as temperature, humidity, etc. (See Park, paragraph 0027). Manuell discloses dealing with environmental events and power interruptions in data centers (See Manuell, paragraph 0002). Manuell selectively powering down one or more computer servers based on environment parameters and shedding load of the computers, thus reducing power consumption (See Manuell, paragraphs 0009 and 0032). - The non-transitory computer-readable medium of claim 15, wherein the one or more instructions further cause the device to: receive new data center data associated with the data center; process the new data center data, with the trained machine learning model, to identify redundant applications and hardware to shut down to reduce power consumption; and cause the data center to shut down the redundant applications and hardware.
Park, Manuell, and Phadke do not disclose “during low traffic periods”. However, Park does disclose a desire for the data center to be less costly to operate (See, Park, paragraph 0016).
Chan discloses a data center and managing power consumption (See Chan, paragraph 0005). Chan discloses during periods of low traffic turning systems of and redirecting traffic to a subset of available servers (See Chan, paragraph 0005).
It would have been obvious to one of ordinary skill in the art at the time of filing of the invention to combine the processing of environment data, using a trained model, to predict issues and preform mitigating actions in a data center of Park, the selectively shutting down servers of a data center in response to environment parameters of Manuel, and the external sourced weather data of Phadke with the shutting down servers during periods of low traffic of Chan. It would have been obvious to do because it is a common approach to save power of a data center thus save on the cost of data center power consumption (See Chan, paragraph 0005).
Response to Arguments
Applicant’s arguments, see pages 10-11 of remarks, filed 29 July 2026, with respect to the rejection(s) of claim(s) 1-8 and 10-20 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of new found prior art, see above rejections.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH D MANOSKEY whose telephone number is (571)272-3648. The examiner can normally be reached M-F 7:30am to 3:30pm.
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/JOSEPH D MANOSKEY/Primary Examiner, Art Unit 2113 September 23, 2026
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