DETAILED ACTION
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 . This action is made final.
Claims 1-20 filed on 06/12/2026 have been reviewed and considered by this office action.
Claims 1-20 have been amended.
Drawings
The drawings filed on 06/12/2026 have been reviewed and are considered acceptable.
Response to Arguments
Applicant’s amended claims, filed 06/12/2026, have overcome the rejections under 35 U.S.C. § 103. Therefore, the rejections have been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Spitaels.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3, 8, 10, 15, and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Spitaels et al. (US 2006/0121421 A1).
Regarding claim 1, Spitaels discloses a method for verifying electrical power distribution system suitability, the method comprising:
obtaining power consumption characteristics from a computer server ([0053]: “The memory 205 also contains profiles of common networking equipment (e.g., a volume of air produced by a specific piece of IT equipment, power consumed by a specific piece of IT equipment, and/or fan control algorithms)”; [0062]: “As shown in FIG. 18, a user may choose specific pieces and quantities of equipment to simulate, such as five Dell® PowerEdge™ 2850 servers”);
generating a test power load based on the power consumption characteristics ([0062]: “The controller sets the heater and/or fan speed to simulate the combined heat output, current draw, and/or power draw of the selected pieces of equipment in block 295”), wherein the test power load is a physical entity different from the computer server that simulates a power supply ([0008]: “the invention provides an IT equipment simulator for simulating at least one piece of IT equipment, the simulator including a housing sized to fit in a standard IT equipment rack, the housing is configured to provide an airflow characteristic substantially equal to the IT equipment under simulation, a removable modular variable electric load, a fan disposed in the housing to produce airflow such that air flows into the housing, absorbs heat from the load, and flows out of the housing, a communication input, a controller coupled to the communication input, the fan, and the removable electric load, the controller being configured to adjust a volume of airflow produced by the fan and power consumed by the load, and a memory coupled to the controller, where the housing is substantially free of further IT equipment”);
identifying a power source and applying the test power load to the power source, wherein the test power load is plugged into the power source ([0038]: " A 208V/60 Hz power connection (not shown) is provided to each of the equipment racks 10. The power connection provides power to the equipment 15 and possibly to the equipment rack 10 itself"; [0045]: "The power connections 110 are the primary inputs for power to the simulator 50, and are configured to connect to the same type of power supply as the networking equipment being simulated"; [0068]: “The operator connects the simulator(s) to a power supply in block 320 and sets the power level”);
determining a connection suitability for the power source based on a response of the power source to the test power load ([0051]: “The microcontroller 200 also monitors the amount of electrical current and/or power being used by the heating elements 140, as indicated by the current sensors 190, and regulates the current drivers 185 to help ensure a substantially constant desired load is placed on an electrical system under test”; [0052]: " A power source 206 is monitored by a sensor 207 to determine operating characteristics such as input voltage, input frequency, power draw, temperature rise, current draw, power draw, power factor, etc."; [0068]: "After the simulation begins in block 330, the operator monitors the power system and/or cooling system to determine operating factors such as efficiency and capacity"); and
displaying the connection suitability for the power source on a device ([0046]: "The status lights 120 are neon indicators (although other types of indicators may be used such as LEDs) that change color and/or state (e.g., between solid and flashing) as a function of the status of the corresponding power connection 110"; [0063]: "an operator can monitor real-time operational data ... via a remote access device 260"; [0064]: "display an aggregated tabular status view of the detected loads"; [0064]: "A PowerView® control unit is a compact control panel and display that provides controlling, monitoring, and configuring a connected device").
Regarding claim 3, Spitaels discloses the method of claim 1.
Spitaels further discloses further comprising: identifying a second power source and applying the test power load to the second power source ([0044]: “While three power connections 110, two network connections 115, and three status lights 120 are shown, other quantities and types of these items may be used”; [0045]: " Each of the power connections 110 shown in FIG. 4 may be active (for example, each of the power connections 110 may draw 2 kW from separate circuits) or may be non-functional and/or cosmetic connectors");
determining that the power source and the second power source are not connected based on a second response of the second power source to the test power load and the response of the power source to the test power load ([0052]: "A power source 206 is monitored by a sensor 207 to determine operating characteristics such as input voltage, input frequency, power draw, temperature rise, current draw, power draw, power factor, etc."); and
updating the connection suitability for the power source, wherein the power source is not suitable for connection ([0046]: "if there is no power being supplied to the corresponding power connection 110, then the status light 120 does not illuminate... The status lights 120 may also indicate a fault condition, such as low-voltage, by e.g., repetitively flashing”).
Regarding claim 8, Spitaels discloses a computer system for verifying electrical power distribution system suitability, the computer system comprising:
a processor set ([0053]: “The microcontroller 200 operates in accordance with instructions stored in the memory 205 (or an internal memory contained within the microcontroller 200)”);
one or more computer readable storage media ([0053]: “The memory 205 is standard RAM, or other storage medium (e.g., Flash ROM, hard drive, tape, CD-ROM, etc.), and provides operational memory to the microcontroller 200”); and
program instructions stored on the one or more computer readable storage media to cause the processor set to perform operations ([0053]: “The memory 205 stores software code and/or data that the controller 200 reads while executing a testing routine”) comprising:
obtaining power consumption characteristics from a computer server ([0053]: “The memory 205 also contains profiles of common networking equipment (e.g., a volume of air produced by a specific piece of IT equipment, power consumed by a specific piece of IT equipment, and/or fan control algorithms)”; [0062]: “As shown in FIG. 18, a user may choose specific pieces and quantities of equipment to simulate, such as five Dell® PowerEdge™ 2850 servers”);
generating a test power load based on the power consumption characteristics ([0062]: “The controller sets the heater and/or fan speed to simulate the combined heat output, current draw, and/or power draw of the selected pieces of equipment in block 295”), wherein the test power load is a physical entity different from the computer server that simulates a power supply ([0008]: “the invention provides an IT equipment simulator for simulating at least one piece of IT equipment, the simulator including a housing sized to fit in a standard IT equipment rack, the housing is configured to provide an airflow characteristic substantially equal to the IT equipment under simulation, a removable modular variable electric load, a fan disposed in the housing to produce airflow such that air flows into the housing, absorbs heat from the load, and flows out of the housing, a communication input, a controller coupled to the communication input, the fan, and the removable electric load, the controller being configured to adjust a volume of airflow produced by the fan and power consumed by the load, and a memory coupled to the controller, where the housing is substantially free of further IT equipment”);
identifying a power source and applying the test power load to the power source, wherein the test power load is plugged into the power source ([0038]: " A 208V/60 Hz power connection (not shown) is provided to each of the equipment racks 10. The power connection provides power to the equipment 15 and possibly to the equipment rack 10 itself"; [0045]: "The power connections 110 are the primary inputs for power to the simulator 50, and are configured to connect to the same type of power supply as the networking equipment being simulated"; [0068]: “The operator connects the simulator(s) to a power supply in block 320 and sets the power level”);
determining a connection suitability for the power source based on a response of the power source to the test power load ([0051]: “The microcontroller 200 also monitors the amount of electrical current and/or power being used by the heating elements 140, as indicated by the current sensors 190, and regulates the current drivers 185 to help ensure a substantially constant desired load is placed on an electrical system under test”; [0052]: " A power source 206 is monitored by a sensor 207 to determine operating characteristics such as input voltage, input frequency, power draw, temperature rise, current draw, power draw, power factor, etc."; [0068]: "After the simulation begins in block 330, the operator monitors the power system and/or cooling system to determine operating factors such as efficiency and capacity"); and
displaying the connection suitability for the power source on a device ([0046]: "The status lights 120 are neon indicators (although other types of indicators may be used such as LEDs) that change color and/or state (e.g., between solid and flashing) as a function of the status of the corresponding power connection 110"; [0063]: "an operator can monitor real-time operational data ... via a remote access device 260"; [0064]: "display an aggregated tabular status view of the detected loads"; [0064]: "A PowerView® control unit is a compact control panel and display that provides controlling, monitoring, and configuring a connected device").
Regarding claim 10, Spitaels discloses the computer system of claim 8.
Spitaels further discloses wherein the operations further comprise identifying a second power source and applying the test power load to the second power source ([0044]: “While three power connections 110, two network connections 115, and three status lights 120 are shown, other quantities and types of these items may be used”; [0045]: " Each of the power connections 110 shown in FIG. 4 may be active (for example, each of the power connections 110 may draw 2 kW from separate circuits) or may be non-functional and/or cosmetic connectors");
determining that the power source and the second power source are not connected based on a second response of the second power source to the test power load and the response of the power source to the test power load ([0052]: "A power source 206 is monitored by a sensor 207 to determine operating characteristics such as input voltage, input frequency, power draw, temperature rise, current draw, power draw, power factor, etc."); and
updating the connection suitability for the power source, wherein the power source is not suitable for connection ([0046]: "if there is no power being supplied to the corresponding power connection 110, then the status light 120 does not illuminate... The status lights 120 may also indicate a fault condition, such as low-voltage, by e.g., repetitively flashing”).
Regarding claim 15, Spitaels discloses a computer program product for verifying electrical power distribution system suitability, the computer program product comprising:
one or more computer readable storage media ([0053]: “The memory 205 is standard RAM, or other storage medium (e.g., Flash ROM, hard drive, tape, CD-ROM, etc.), and provides operational memory to the microcontroller 200”); and
program instructions stored on the one or more computer readable storage media to perform operations ([0053]: “The memory 205 stores software code and/or data that the controller 200 reads while executing a testing routine”) comprising:
obtaining power consumption characteristics from a computer server ([0053]: “The memory 205 also contains profiles of common networking equipment (e.g., a volume of air produced by a specific piece of IT equipment, power consumed by a specific piece of IT equipment, and/or fan control algorithms)”; [0062]: “As shown in FIG. 18, a user may choose specific pieces and quantities of equipment to simulate, such as five Dell® PowerEdge™ 2850 servers”);
generating a test power load based on the power consumption characteristics ([0062]: “The controller sets the heater and/or fan speed to simulate the combined heat output, current draw, and/or power draw of the selected pieces of equipment in block 295”), wherein the test power load is a physical entity different from the computer server that simulates a power supply ([0008]: “the invention provides an IT equipment simulator for simulating at least one piece of IT equipment, the simulator including a housing sized to fit in a standard IT equipment rack, the housing is configured to provide an airflow characteristic substantially equal to the IT equipment under simulation, a removable modular variable electric load, a fan disposed in the housing to produce airflow such that air flows into the housing, absorbs heat from the load, and flows out of the housing, a communication input, a controller coupled to the communication input, the fan, and the removable electric load, the controller being configured to adjust a volume of airflow produced by the fan and power consumed by the load, and a memory coupled to the controller, where the housing is substantially free of further IT equipment”);
identifying a power source and applying the test power load to the power source, wherein the test power load is plugged into the power source ([0038]: " A 208V/60 Hz power connection (not shown) is provided to each of the equipment racks 10. The power connection provides power to the equipment 15 and possibly to the equipment rack 10 itself"; [0045]: "The power connections 110 are the primary inputs for power to the simulator 50, and are configured to connect to the same type of power supply as the networking equipment being simulated"; [0068]: “The operator connects the simulator(s) to a power supply in block 320 and sets the power level”);
determining a connection suitability for the power source based on a response of the power source to the test power load ([0051]: “The microcontroller 200 also monitors the amount of electrical current and/or power being used by the heating elements 140, as indicated by the current sensors 190, and regulates the current drivers 185 to help ensure a substantially constant desired load is placed on an electrical system under test”; [0052]: " A power source 206 is monitored by a sensor 207 to determine operating characteristics such as input voltage, input frequency, power draw, temperature rise, current draw, power draw, power factor, etc."; [0068]: "After the simulation begins in block 330, the operator monitors the power system and/or cooling system to determine operating factors such as efficiency and capacity"); and
displaying the connection suitability for the power source on a device ([0046]: "The status lights 120 are neon indicators (although other types of indicators may be used such as LEDs) that change color and/or state (e.g., between solid and flashing) as a function of the status of the corresponding power connection 110"; [0063]: "an operator can monitor real-time operational data ... via a remote access device 260"; [0064]: "display an aggregated tabular status view of the detected loads"; [0064]: "A PowerView® control unit is a compact control panel and display that provides controlling, monitoring, and configuring a connected device").
Regarding claim 17, Spitaels discloses the computer program product of claim 15.
Spitaels further discloses wherein the operations further comprise: identifying a second power source and applying the test power load to the second power source ([0044]: “While three power connections 110, two network connections 115, and three status lights 120 are shown, other quantities and types of these items may be used”; [0045]: " Each of the power connections 110 shown in FIG. 4 may be active (for example, each of the power connections 110 may draw 2 kW from separate circuits) or may be non-functional and/or cosmetic connectors");
determining that the power source and the second power source are not connected based on a second response of the second power source to the test power load and the response of the power source to the test power load ([0052]: "A power source 206 is monitored by a sensor 207 to determine operating characteristics such as input voltage, input frequency, power draw, temperature rise, current draw, power draw, power factor, etc."); and
updating the connection suitability for the power source, wherein the power source is not suitable for connection ([0046]: "if there is no power being supplied to the corresponding power connection 110, then the status light 120 does not illuminate... The status lights 120 may also indicate a fault condition, such as low-voltage, by e.g., repetitively flashing”).
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.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Spitaels et al. (US 2006/0121421 A1), in view of Straub (US 2018/0252777 A1).
Regarding claim 2, Spitaels teaches the method of claim 1.
While Spitaels teaches a status indication of connection suitability ([0046]: "The status lights 120 may also indicate a fault condition, such as low-voltage"), Spitaels does not explicitly teach “transmitting a notification of the connection suitability for the power source to a user.”
Straub further teaches further comprising transmitting a notification of the connection suitability for the power source to a user ([0038]: “If the first power supply is unable to power the load under these test conditions, the controller hardware provides notification to a respective target to log the failure and go forward with replacing and/or fixing the first power supply (or replacing the whole failing redundant power supply)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the method of Spitaels to incorporate the teachings of Straub so as to include transmitting a notification of the connection suitability for the power source to a user. Doing so would allow a user to be notified of anomalous conditions with the aim of allowing an operator to take corrective action (Straub, “[0007-0010]: “Unknown to the system or a respective controller overseeing the system, one of the large aluminum electrolytic capacitors in the output filter of the power supply may lose most of its capacity and thus cannot support 100% of the load without introducing significant ripple... Thus, even though a pair of redundant power supplies is collectively able to power a respective load, there is no indication that either of the power supplies will be able to successfully power the load while the other power supply is deactivated... If the first power supply is unable to individually power the load without power supplied by the second power supply, the controller hardware provides notification to a respective target recipient to go forward with replacing and/or fixing the first power supply”).
Regarding claim 9, Spitaels teaches the computer system of claim 8.
While Spitaels teaches a status indication of connection suitability ([0046]: "The status lights 120 may also indicate a fault condition, such as low-voltage"), Spitaels does not explicitly teach “transmitting a notification of the connection suitability for the power source to a user.”
Straub further teaches wherein the operations further comprise: transmitting a notification of the connection suitability for the power source to a user ([0038]: “If the first power supply is unable to power the load under these test conditions, the controller hardware provides notification to a respective target to log the failure and go forward with replacing and/or fixing the first power supply (or replacing the whole failing redundant power supply)”).
The reasons to combine Straub into Spitaels are the same as articulated in claim 2 above.
Regarding claim 16, Spitaels teaches the computer program product of claim 15.
While Spitaels teaches a status indication of connection suitability ([0046]: "The status lights 120 may also indicate a fault condition, such as low-voltage"), Spitaels does not explicitly teach “transmitting a notification of the connection suitability for the power source to a user.”
Straub further teaches wherein the operations further comprise: transmitting a notification of the connection suitability for the power source to a user ([0038]: “If the first power supply is unable to power the load under these test conditions, the controller hardware provides notification to a respective target to log the failure and go forward with replacing and/or fixing the first power supply (or replacing the whole failing redundant power supply)”).
The reasons to combine Straub into Spitaels are the same as articulated in claim 2 above.
Claims 4-6, 11-13, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Spitaels et al. (US 2006/0121421 A1), in view of Suryanarayana et al. (US 2023/0229220 A1).
Regarding claim 4, Spitaels teaches the method of claim 1.
Spitaels does not explicitly teach “wherein the generating the test power load uses a machine learning model that predicts electrical characteristics of a power supply based on known information about the power supply.”
Suryanarayana further teaches wherein the generating the test power load uses a machine learning model that predicts electrical characteristics of the power supply based on known information about the power supply ([0004]: “A first aspect of the present disclosure provides a method for predicting power converter health. The method includes… inputting, by the system, the first set of system measurements into a first machine learning algorithm to generate expected failure precursor measurement information; inputting, by the system, the expected failure precursor measurement information and the second set of failure precursor measurements into a second machine learning algorithm to generate component failure prediction information; and performing, by the system, one or more actions based on the generated component failure prediction information”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the method of Spitaels to incorporate the teachings of Suryanarayana so as to include the generating the test power load using a machine learning model that predicts electrical characteristics of a power supply based on known information about the power supply. Doing so would allow the condition of power system components to be determined with the aim of performing predictive maintenance (Suryanarayana, [0003]: “there remains a technical need to predict with high accuracy the health status and remaining useful lifetime of the components within a power converter so as to be able to perform predictive maintenance”).
Regarding claim 5, Spitaels teaches the method of claim 1.
Spitaels does not explicitly teach “generating a normal response for the power source to the test power load using a machine learning model that predicts electrical characteristics of a power distribution system in response to a power load.”
Suryanarayana further teaches further comprising: generating a normal response for the power source to the test power load using a machine learning model that predicts electrical characteristics of a power distribution system in response to a power load ([0050]: “during normal operating conditions (e.g., when the power converter system 104 is operating normally), the first set of measurements may affect the second set of measurements… The control system 110 may input the ambient temperature and/or other sensor measurements (e.g., other temperature measurements or voltage/current measurements) into the first ML/AI model to generate expected failure precursor parameter measurements (e.g., an expected Tj for the semiconductor device). The expected measurement may indicate an expected value the measurement (e.g., the Tj) should be given the ambient temperature, the input/output voltage, the input/output current, and/or other measurements”).
The reasons to combine Suryanarayana into Spitaels are the same as articulated in claim 4 above.
Regarding claim 6, Spitaels in view of Suryanarayana teaches the method of claim 5.
Spitaels does not explicitly teach “detecting that the normal response for the power source to the test power load does not match the response of the power source to the test power load; and updating the connection suitability for the power source, wherein the power source is not suitable for connection.”
Suryanarayana further teaches further comprising: detecting that the normal response for the power source to the test power load does not match the response of the power source to the test power load ([0049]: “the control system 110 may obtain two sets of measurements—a first set of measurements (e.g., measured voltages or current at the input, output, or DC bus of the power converter system 104) and a second set of measurements (e.g., a semiconductor junction temperature Tj). The control system 110 uses two ML/AI models (e.g., two neural networks (NN)) to determine anomalies within the power converter system 104. For instance, the control system 110 may use the first ML/AI model to determine expected measurements. Then, the control system 110 may use the second ML/AI model to compare the expected measurements with the actual measurements (e.g., the second set of measurements) to determine whether there are any component anomalies within the power converter system 104”); and
updating the connection suitability for the power source, wherein the power source is not suitable for connection ([0018]: “the one or more actions based on the generated component failure prediction information comprises triggering an action to modify a mode of operation of the power converter”).
Regarding claim 11, Spitaels teaches the computer system of claim 8.
Spitaels does not explicitly teach “wherein the generating the test power load uses a machine learning model that predicts electrical characteristics of a power supply based on known information about the power supply.”
Suryanarayana further teaches wherein the generating the test power load uses a machine learning model that predicts electrical characteristics of the power supply based on known information about the power supply ([0004]: “A first aspect of the present disclosure provides a method for predicting power converter health. The method includes… inputting, by the system, the first set of system measurements into a first machine learning algorithm to generate expected failure precursor measurement information; inputting, by the system, the expected failure precursor measurement information and the second set of failure precursor measurements into a second machine learning algorithm to generate component failure prediction information; and performing, by the system, one or more actions based on the generated component failure prediction information”).
The reasons to combine Suryanarayana into Spitaels are the same as articulated in claim 4 above.
Regarding claim 12, Spitaels teaches the computer system of claim 8.
Spitaels does not explicitly teach “generating a normal response for the power source to the test power load using a machine learning model that predicts electrical characteristics of a power distribution system in response to a power load.”
Suryanarayana further teaches wherein the operations further comprise: generating a normal response for the power source to the test power load using a machine learning model that predicts electrical characteristics of a power distribution system in response to a power load ([0050]: “during normal operating conditions (e.g., when the power converter system 104 is operating normally), the first set of measurements may affect the second set of measurements… The control system 110 may input the ambient temperature and/or other sensor measurements (e.g., other temperature measurements or voltage/current measurements) into the first ML/AI model to generate expected failure precursor parameter measurements (e.g., an expected Tj for the semiconductor device). The expected measurement may indicate an expected value the measurement (e.g., the Tj) should be given the ambient temperature, the input/output voltage, the input/output current, and/or other measurements”).
The reasons to combine Suryanarayana into Spitaels are the same as articulated in claim 4 above.
Regarding claim 13, Spitaels in view of Suryanarayana teaches the computer system of claim 12.
Spitaels does not explicitly teach “wherein the operations further comprise: detecting that the normal response for the power source to the test power load does not match the response of the power source to the test power load; and updating the connection suitability for the power source, wherein the power source is not suitable for connection.”
Suryanarayana further teaches wherein the operations further comprise: detecting that the normal response for the power source to the test power load does not match the response of the power source to the test power load ([0049]: “the control system 110 may obtain two sets of measurements—a first set of measurements (e.g., measured voltages or current at the input, output, or DC bus of the power converter system 104) and a second set of measurements (e.g., a semiconductor junction temperature Tj). The control system 110 uses two ML/AI models (e.g., two neural networks (NN)) to determine anomalies within the power converter system 104. For instance, the control system 110 may use the first ML/AI model to determine expected measurements. Then, the control system 110 may use the second ML/AI model to compare the expected measurements with the actual measurements (e.g., the second set of measurements) to determine whether there are any component anomalies within the power converter system 104”); and
updating the connection suitability for the power source, wherein the power source is not suitable for connection ([0018]: “the one or more actions based on the generated component failure prediction information comprises triggering an action to modify a mode of operation of the power converter”).
Regarding claim 18, Spitaels teaches the computer program product of claim 15.
Spitaels does not explicitly teach “wherein the generating the test power load uses a machine learning model that predicts electrical characteristics of a power supply based on known information about the power supply.”
Suryanarayana further teaches wherein the generating the test power load uses a machine learning model that predicts electrical characteristics of the power supply based on known information about the power supply ([0004]: “A first aspect of the present disclosure provides a method for predicting power converter health. The method includes… inputting, by the system, the first set of system measurements into a first machine learning algorithm to generate expected failure precursor measurement information; inputting, by the system, the expected failure precursor measurement information and the second set of failure precursor measurements into a second machine learning algorithm to generate component failure prediction information; and performing, by the system, one or more actions based on the generated component failure prediction information”).
The reasons to combine Suryanarayana into Spitaels are the same as articulated in claim 4 above.
Regarding claim 19, Spitaels teaches the computer program product of claim 15.
Spitaels does not explicitly teach “generating a normal response for the power source to the test power load using a machine learning model that predicts electrical characteristics of a power distribution system in response to a power load.”
Suryanarayana further teaches wherein the operations further comprise: generating a normal response for the power source to the test power load using a machine learning model that predicts electrical characteristics of a power distribution system in response to a power load ([0050]: “during normal operating conditions (e.g., when the power converter system 104 is operating normally), the first set of measurements may affect the second set of measurements… The control system 110 may input the ambient temperature and/or other sensor measurements (e.g., other temperature measurements or voltage/current measurements) into the first ML/AI model to generate expected failure precursor parameter measurements (e.g., an expected Tj for the semiconductor device). The expected measurement may indicate an expected value the measurement (e.g., the Tj) should be given the ambient temperature, the input/output voltage, the input/output current, and/or other measurements”).
The reasons to combine Suryanarayana into Spitaels are the same as articulated in claim 4 above.
Regarding claim 20, Spitaels in view of Suryanarayana teaches the computer program product of claim 19.
Spitaels does not explicitly teach “detecting that the normal response for the power source to the test power load does not match the response of the power source to the test power load; and updating the connection suitability for the power source, wherein the power source is not suitable for connection.”
Suryanarayana further teaches wherein the operations further comprise: detecting that the normal response for the power source to the test power load does not match the response of the power source to the test power load ([0049]: “the control system 110 may obtain two sets of measurements—a first set of measurements (e.g., measured voltages or current at the input, output, or DC bus of the power converter system 104) and a second set of measurements (e.g., a semiconductor junction temperature Tj). The control system 110 uses two ML/AI models (e.g., two neural networks (NN)) to determine anomalies within the power converter system 104. For instance, the control system 110 may use the first ML/AI model to determine expected measurements. Then, the control system 110 may use the second ML/AI model to compare the expected measurements with the actual measurements (e.g., the second set of measurements) to determine whether there are any component anomalies within the power converter system 104”); and
updating the connection suitability for the power source, wherein the power source is not suitable for connection ([0018]: “the one or more actions based on the generated component failure prediction information comprises triggering an action to modify a mode of operation of the power converter”).
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Spitaels et al. (US 2006/0121421 A1), in view of Locker et al. (US 2005/0134248 A1).
Regarding claim 7, Spitaels teaches the method of claim 1.
Spitaels does not explicitly teach “displaying a model of the test power load to a user; monitoring user interactions with the model of the test power load; and updating the test power load based on the user interactions.”
Locker further teaches further comprising: displaying a model of the test power load to a user ([0050]: “If manual mode is selected, a manual mode screen can be provided on the HMI 86 to allow the user to scroll through the metering displays, set the desired power via keypad or up/down arrow keys, and start/stop the testing. If auto mode is selected, an auto mode screen can be provided on the HMI 86 to allow the user to set a complete load profile (a kW vs. time graph) by entering an unlimited number of data points (at time X, power is Y kW). Also, the user can select the type of transition (step or ramp) between data points, and start/stop the test”);
monitoring user interactions with the model of the test power load ([0055]: “the manual mode screen allows the user to scroll through the metering displays, set the desired power via the keypad or up/down arrow keys and start or stop the testing. The user can the select the appropriate digits for the desired powered, and hit enter, and the program will show the requested and actual kilowatts being monitored. Using the arrows or keypad, the user can move the desired power value up or down manually in real-time”); and
updating the test power load based on the user interactions (FIG. 6 and [0071]: “at block 122, changes in the desired power dissipation can be received from the user, such as by using the display and user input devices. These changes are then implemented by the HMI unit by modifying the duty cycle in response to the power dissipation change, as shown at step 124, such as by providing a modified duty cycle command to the load bank unit resulting in a modified duty cycle control signal to the high speed switching electronics”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to adapt the method of Spitaels to incorporate the teachings of Locker so as to include displaying a model of the test power load to a user, monitoring user interactions with the model of the test power load, and updating the test power load based on the user interactions. Doing so would allow a test load to be updated with the aim of improving system configuration (Locker, [0005]: “conventional load banks can suffer from a number of disadvantages. First, adjusting the load that a conventional load bank places upon a connected source (e.g., a generator) can be time consuming, tedious, and inaccurate, as such changes often involve the physical insertion or extraction of one or more resistors into an existing resistor network. Hence, such load banks typically require changing a load in steps and therefore are limited as to the type of load changes that can be made during the testing procedure as well as the amount of load that can be changed. In addition, while controls can be provided for configuring the load bank to the appropriate load, there is typically little or no ability to provide other control inputs to a conventional load bank from auxiliary controls or other ancillary automation equipment”).
Regarding claim 14, Spitaels teaches the computer system of claim 8.
Spitaels does not explicitly teach “displaying a model of the test power load to a user; monitoring user interactions with the model of the test power load; and updating the test power load based on the user interactions.”
Locker further teaches wherein the operations further comprise: displaying a model of the test power load to a user ([0050]: “If manual mode is selected, a manual mode screen can be provided on the HMI 86 to allow the user to scroll through the metering displays, set the desired power via keypad or up/down arrow keys, and start/stop the testing. If auto mode is selected, an auto mode screen can be provided on the HMI 86 to allow the user to set a complete load profile (a kW vs. time graph) by entering an unlimited number of data points (at time X, power is Y kW). Also, the user can select the type of transition (step or ramp) between data points, and start/stop the test”);
monitoring user interactions with the model of the test power load ([0055]: “the manual mode screen allows the user to scroll through the metering displays, set the desired power via the keypad or up/down arrow keys and start or stop the testing. The user can the select the appropriate digits for the desired powered, and hit enter, and the program will show the requested and actual kilowatts being monitored. Using the arrows or keypad, the user can move the desired power value up or down manually in real-time”); and
updating the test power load based on the user interactions (FIG. 6 and [0071]: “at block 122, changes in the desired power dissipation can be received from the user, such as by using the display and user input devices. These changes are then implemented by the HMI unit by modifying the duty cycle in response to the power dissipation change, as shown at step 124, such as by providing a modified duty cycle command to the load bank unit resulting in a modified duty cycle control signal to the high speed switching electronics”).
The reasons to combine Locker into Spitaels are the same as articulated in claim 7 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 9,594,125 B1: Loadbank that applies a power load that simulates a power load drawn by a server rack
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.
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/M.I.K./Examiner, Art Unit 2117
/ROBERT E FENNEMA/Supervisory Patent Examiner, Art Unit 2117
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