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 non-final.
Claims 1-20 filed on 11/06/2023 have been reviewed and considered by this office action.
Information Disclosure Statement
The information disclosure statement filed on 11/06/2023 has been reviewed and considered by this office action.
Drawings
The drawings are objected to because “HOT PHYSICAL MACHINE SET 142” in Fig. 1 should read “HOST PHYSICAL MACHINE SET 142.”
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The specification filed on 11/06/2023 has been reviewed and is considered acceptable.
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 1-3, 8-10, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Straub (US 2018/0252777 A1), in view of Nguyen et al. (US 9,973,006 B1).
Regarding claim 1, Straub teaches a computer-implemented method for verifying electrical power distribution system suitability, the computer-implemented method comprising: obtaining power consumption characteristics from a computer server ([0044]: “Status information received from the power supply manager 150 can include any suitable information such as the temperature of each power supply, magnitude of voltage V1, magnitude of voltage V2, current supplied by each of the power supplies to the circuit load 125, etc.”);
generating a test power load based on the power consumption characteristics ([0098]: “execution of the software instructions (such as software instructions associated with PST CODE #1) ensure that the server resource 195-1 consumes power above a threshold value (such as any suitable value between 50-100% of maximum possible power consumption by the server resource 195-1)”);
identifying a power source and applying the test power load to the power source ([0102]: “the controller 140 notifies the server management resource 150-1 to disable the second power supply 102-1 from delivering power to the first server resource 195-1. This ensures that only power supply 101-1 powers the respective server resource 195-1”);
determining a connection suitability for the power source based on a response of the power source to the test power load ([0115-0116]: “If there is a detected failure… the controller 140 provides notification of the failure… Possible reporting of failures can include i) a detected inability of the first power supply 101-1 to power the first server resource 195-1 while the second power supply 102-1 is disabled from powering the first server resource 195-1”).
While Straub teaches notifying a user of connection suitability ([0054]: “in the event that the controller 140 receives feedback from the power supply manager 150 of one or more failure conditions associated with the power supply 101 powering circuit load 125, the controller 140 is operable to break out of a test loop and provide appropriate notification such that a respective technician or administrator is notified to replace the failing redundant power supply 120 with a non-failing redundant power supply”), Straub does not explicitly teach “displaying the connection suitability for the power source on a device.”
Nguyen teaches displaying the connection suitability for the power source on a device (Col. 12, Lines 36-44: “if the primary power system must be coupled to the first power input, but the ATS determines that it is coupled to the second power input, then the ATS may change an LED from green to yellow or display a message to the technician (e.g., by displaying on a display device associated with the ATS or by sending an SMS message to a mobile device of the technician) indicating that the wires connected to the power inputs on the ATS need to be swapped”).
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 Straub to incorporate the teachings of Nguyen so as to include displaying the connection suitability for the power source on a device. Doing so would allow a user to be alerted to a connection suitability with the aim of taking appropriate corrective actions if needed (Nguyen, Col. 4, Lines 17-24: “The handheld device can also output information (e.g., via one or more LED lights, information on a display of the handheld device, paper output, one or more electronic communications sent to a telephone or other device, etc.) to the technician indicating whether a single power system or separate power systems are coupled to the power inputs, to instruct the technician to correct any problems or otherwise initiate corrective actions”).
Regarding claim 2, Straub in view of Nguyen teaches the computer-implemented method of claim 1.
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)”).
Regarding claim 3, Straub in view of Nguyen teaches the computer-implemented method of claim 1.
Straub further teaches further comprising: identifying a second power source and applying the test power load to the second power source ([0012]: “In the second portion of the power supply test mode, the controller hardware activates the second power supply to power the load again and selects the second power supply for testing”);
updating the connection suitability for the power source, wherein the power source is not suitable for connection ([0010]: “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”).
While Straub teaches determining that the first power source is disabled ([0012]: “The controller hardware then tests an ability of the second power supply to power the load while the first power supply is disabled from powering the load”), Straub does not explicitly teach “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.”
Nguyen further teaches 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; (Col. 2, Lines 56-67: “The ATS determines (e.g., by detecting or measuring) the electrical characteristics of the power system coupled to a first input on the ATS and the electrical characteristics of the power system coupled to a second input on the ATS. The ATS then compares the electrical characteristics of the two power systems. If the electrical characteristics are substantially similar, then a single power system is determined to be coupled to both power inputs on the ATS. But if the electrical characteristics are significantly different (e.g., the difference in the electrical characteristics is above a defined threshold), the power system coupled to one power input on the ATS is determined to be different from the power system coupled to the other power input on the ATS”)
Regarding claim 8, Straub teaches a computer system for verifying electrical power distribution system suitability, the computer system comprising: one or more processors ([0016]: “one or more computerized devices or processors can be programmed and/or configured to operate as explained herein to carry out the different embodiments as described herein”), one or more computer-readable memories, and one or more computer-readable storage media ([0022]: “the system, method, apparatus, instructions on computer readable storage media, etc., as discussed herein also can be embodied strictly as a software program, firmware, as a hybrid of software, hardware and/or firmware, or as hardware alone such as within a processor (hardware or software), or within an operating system or a within a software application”);
program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, to obtain power consumption characteristics from a computer server ([0044]: “Status information received from the power supply manager 150 can include any suitable information such as the temperature of each power supply, magnitude of voltage V1, magnitude of voltage V2, current supplied by each of the power supplies to the circuit load 125, etc.”);
program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, to generate a test power load based on the power consumption characteristics ([0098]: “execution of the software instructions (such as software instructions associated with PST CODE #1) ensure that the server resource 195-1 consumes power above a threshold value (such as any suitable value between 50-100% of maximum possible power consumption by the server resource 195-1)”);
program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, to identify a power source and apply the test power load to the power source ([0102]: “the controller 140 notifies the server management resource 150-1 to disable the second power supply 102-1 from delivering power to the first server resource 195-1. This ensures that only power supply 101-1 powers the respective server resource 195-1”);
program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, to determine a connection suitability for the power source based on a response of the power source to the test power load ([0115-0116]: “If there is a detected failure… the controller 140 provides notification of the failure… Possible reporting of failures can include i) a detected inability of the first power supply 101-1 to power the first server resource 195-1 while the second power supply 102-1 is disabled from powering the first server resource 195-1”).
While Straub teaches notifying a user of connection suitability ([0054]: “in the event that the controller 140 receives feedback from the power supply manager 150 of one or more failure conditions associated with the power supply 101 powering circuit load 125, the controller 140 is operable to break out of a test loop and provide appropriate notification such that a respective technician or administrator is notified to replace the failing redundant power supply 120 with a non-failing redundant power supply”), Straub does not explicitly teach “displaying the connection suitability for the power source on a device.”
Nguyen teaches program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, to display the connection suitability for the power source on a device (Col. 12, Lines 36-44: “if the primary power system must be coupled to the first power input, but the ATS determines that it is coupled to the second power input, then the ATS may change an LED from green to yellow or display a message to the technician (e.g., by displaying on a display device associated with the ATS or by sending an SMS message to a mobile device of the technician) indicating that the wires connected to the power inputs on the ATS need to be swapped”).
The reasons to combine Nguyen into Straub are the same as articulated in claim 1 above.
Regarding claim 9, Straub in view of Nguyen teaches the computer system of claim 8.
Straub further teaches further comprising: program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, to transmit 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)”).
Regarding claim 10, Straub in view of Nguyen teaches the computer system of claim 8.
Straub further teaches further comprising: program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, to identify a second power source and applying the test power load to the second power source ([0012]: “In the second portion of the power supply test mode, the controller hardware activates the second power supply to power the load again and selects the second power supply for testing”);
program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, to update the connection suitability for the power source, wherein the power source is not suitable for connection ([0010]: “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”).
While Straub teaches determining that the first power source is disabled ([0012]: “The controller hardware then tests an ability of the second power supply to power the load while the first power supply is disabled from powering the load”), Straub does not explicitly teach “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.”
Nguyen further teaches program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, to determine 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 (Col. 2, Lines 56-67: “The ATS determines (e.g., by detecting or measuring) the electrical characteristics of the power system coupled to a first input on the ATS and the electrical characteristics of the power system coupled to a second input on the ATS. The ATS then compares the electrical characteristics of the two power systems. If the electrical characteristics are substantially similar, then a single power system is determined to be coupled to both power inputs on the ATS. But if the electrical characteristics are significantly different (e.g., the difference in the electrical characteristics is above a defined threshold), the power system coupled to one power input on the ATS is determined to be different from the power system coupled to the other power input on the ATS”).
Regarding claim 15, Straub teaches a computer program product for verifying electrical power distribution system suitability, the computer program product comprising: one or more computer-readable storage media ([0022]: “the system, method, apparatus, instructions on computer readable storage media, etc., as discussed herein also can be embodied strictly as a software program, firmware, as a hybrid of software, hardware and/or firmware, or as hardware alone such as within a processor (hardware or software), or within an operating system or a within a software application”);
program instructions, stored on at least one of the one or more computer-readable storage media, to obtain power consumption characteristics from a computer server ([0044]: “Status information received from the power supply manager 150 can include any suitable information such as the temperature of each power supply, magnitude of voltage V1, magnitude of voltage V2, current supplied by each of the power supplies to the circuit load 125, etc.”);
program instructions, stored on at least one of the one or more computer-readable storage media, to generate a test power load based on the power consumption characteristics ([0098]: “execution of the software instructions (such as software instructions associated with PST CODE #1) ensure that the server resource 195-1 consumes power above a threshold value (such as any suitable value between 50-100% of maximum possible power consumption by the server resource 195-1)”);
program instructions, stored on at least one of the one or more computer-readable storage media, to identify a power source and apply the test power load to the power source ([0102]: “the controller 140 notifies the server management resource 150-1 to disable the second power supply 102-1 from delivering power to the first server resource 195-1. This ensures that only power supply 101-1 powers the respective server resource 195-1”);
program instructions, stored on at least one of the one or more computer-readable storage media, to determine a connection suitability for the power source based on a response of the power source to the test power load ([0115-0116]: “If there is a detected failure… the controller 140 provides notification of the failure… Possible reporting of failures can include i) a detected inability of the first power supply 101-1 to power the first server resource 195-1 while the second power supply 102-1 is disabled from powering the first server resource 195-1”).
While Straub teaches notifying a user of connection suitability ([0054]: “in the event that the controller 140 receives feedback from the power supply manager 150 of one or more failure conditions associated with the power supply 101 powering circuit load 125, the controller 140 is operable to break out of a test loop and provide appropriate notification such that a respective technician or administrator is notified to replace the failing redundant power supply 120 with a non-failing redundant power supply”), Straub does not explicitly teach “displaying the connection suitability for the power source on a device.”
Nguyen teaches program instructions, stored on at least one of the one or more computer-readable storage media, to display the connection suitability for the power source on a device (Col. 12, Lines 36-44: “if the primary power system must be coupled to the first power input, but the ATS determines that it is coupled to the second power input, then the ATS may change an LED from green to yellow or display a message to the technician (e.g., by displaying on a display device associated with the ATS or by sending an SMS message to a mobile device of the technician) indicating that the wires connected to the power inputs on the ATS need to be swapped”).
The reasons to combine Nguyen into Straub are the same as articulated in claim 1 above.
Regarding claim 16, Straub in view of Nguyen teaches the computer program product of claim 15.
Straub further teaches further comprising program instructions, stored on at least one of the one or more computer-readable storage media, to transmit 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)”).
Regarding claim 17, Straub in view of Nguyen teaches the computer program product of claim 15.
Straub further teaches further comprising: program instructions, stored on at least one of the one or more computer-readable storage media, to identify a second power source and applying the test power load to the second power source ([0012]: “In the second portion of the power supply test mode, the controller hardware activates the second power supply to power the load again and selects the second power supply for testing”);
program instructions, stored on at least one of the one or more computer-readable storage media, to update the connection suitability for the power source, wherein the power source is not suitable for connection ([0010]: “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”).
While Straub teaches determining that the first power source is disabled ([0012]: “The controller hardware then tests an ability of the second power supply to power the load while the first power supply is disabled from powering the load”), Straub does not explicitly teach “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.”
Nguyen further teaches program instructions, stored on at least one of the one or more computer-readable storage media, to determine 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 (Col. 2, Lines 56-67: “The ATS determines (e.g., by detecting or measuring) the electrical characteristics of the power system coupled to a first input on the ATS and the electrical characteristics of the power system coupled to a second input on the ATS. The ATS then compares the electrical characteristics of the two power systems. If the electrical characteristics are substantially similar, then a single power system is determined to be coupled to both power inputs on the ATS. But if the electrical characteristics are significantly different (e.g., the difference in the electrical characteristics is above a defined threshold), the power system coupled to one power input on the ATS is determined to be different from the power system coupled to the other power input on the ATS”).
Claims 4-6, 11-13, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Straub (US 2018/0252777 A1), in view of Nguyen et al. (US 9,973,006 B1), and in view of Suryanarayana et al. (US 2023/0229220 A1).
Regarding claim 4, Straub in view of Nguyen teaches the computer-implemented method of claim 1.
Straub and Nguyen do 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 a 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 Straub in view of Nguyen 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, Straub in view of Nguyen teaches the computer-implemented method of claim 1.
Straub and Nguyen do 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 Straub in view of Nguyen are the same as articulated in claim 4 above.
Regarding claim 6, Straub in view of Nguyen and Suryanarayana teaches the computer-implemented method of claim 5.
Straub and Nguyen do 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, Straub in view of Nguyen teaches the computer system of claim 8.
Straub and Nguyen do 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 program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, to generate the test power load use a machine learning model that predicts electrical characteristics of a 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 Straub in view of Nguyen are the same as articulated in claim 4 above.
Regarding claim 12, Straub in view of Nguyen teaches the computer system of claim 8.
Straub and Nguyen do 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 program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, to generate 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 Straub in view of Nguyen are the same as articulated in claim 4 above.
Regarding claim 13, Straub in view of Nguyen and Suryanarayana teaches the computer system of claim 12.
Straub and Nguyen do 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: program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, to detect 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
program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, to update 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, Straub in view of Nguyen teaches the computer program product of claim 15.
Straub and Nguyen do 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 program instructions, stored on at least one of the one or more computer-readable storage media, to generate the test power load use a machine learning model that predicts electrical characteristics of a 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 Straub in view of Nguyen are the same as articulated in claim 4 above.
Regarding claim 19, Straub in view of Nguyen teaches the computer program product of claim 15.
Straub and Nguyen do 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 program instructions, stored on at least one of the one or more computer-readable storage media, to generate 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 Straub in view of Nguyen are the same as articulated in claim 4 above.
Regarding claim 20, Straub in view of Nguyen and Suryanarayana teaches the computer program product of claim 19.
Straub and Nguyen do 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: program instructions, stored on at least one of the one or more computer-readable storage media, to detect 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
program instructions, stored on at least one of the one or more computer-readable storage media, to update 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 Straub (US 2018/0252777 A1), in view of Nguyen et al. (US 9,973,006 B1), and in view of Locker et al. (US 2005/0134248 A1).
Regarding claim 7, Straub in view of Nguyen teaches the computer-implemented method of claim 1.
While Nguyen teaches monitoring user interactions and updating the test power load based on the user interactions (Col. 14, Lines 19-25: “one or more different threshold values may be utilized to determine how different the selected characteristics are from one another… thresholds may be selected by a human technician or administrator”), Staub and Nguyen do 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 Straub in view of Nguyen 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, Straub in view of Nguyen teaches the computer system of claim 8.
While Nguyen teaches monitoring user interactions and updating the test power load based on the user interactions (Col. 14, Lines 19-25: “one or more different threshold values may be utilized to determine how different the selected characteristics are from one another… thresholds may be selected by a human technician or administrator”), Staub and Nguyen do 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: program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, to display 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”);
program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, to monitor 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
program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, to update 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 Straub in view of Nguyen 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 2014/0164814 A1: Power redundancy program that determines connection suitability
US 2021/0158186 A1: Machine learning generation of test loads
US 2023/0420938 A1: Predicts expected load parameter values to calculate overload capacity and change transformer operation parameters
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/M.I.K./Examiner, Art Unit 2117
/ROBERT E FENNEMA/Supervisory Patent Examiner, Art Unit 2117