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 .
Rejections under 35 USC 102 and 103
Applicant’s amendments filed 06/17/2026 with respect to Claim(s) 1-20 have been fully considered but they are not persuasive.
As to applicant(s) argument of [A] “Ukegawa Does Not Disclose a "Thermal Model" of an Electrical Machine… In the present application, thermal resistances, thermal capacitances, and heat sources are parameters internal to a physics-based thermal model (an equivalent circuit) that is used to estimate temperature distributions in the electrical machine.”, the Examiner respectfully disagrees. Ukegawa teaches thermal parameters (150 includes usage parameters drain-to-source voltage, drain-to-source resistance, temperature, thermal resistance, gate-leakage current; fig. 1, para. 49) of a thermal model (160 includes RUL or anomaly PLS-CUSUM models) of an electrical machine (210/200). Regarding BRI interpretation, a machine model that utilizes temperature as a parameter is considered a thermal model. In response to applicant' s argument that the references fail to show certain features of applicant's invention, it is noted that the features upon which applicant relies (i.e., applicant' s argument of “physics-based thermal model to estimate temperature distributions”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
As to applicant(s) argument of [B] “Ukegawa Does Not Disclose "Updating Thermal Parameter Values of the Thermal Model", the Examiner respectfully disagrees. Ukegawa teaches acquiring thermal parameter values and training the mode (para. 5 - the system acquires usage information and trains the various models about the healthy operation of the device; fig. 4).
As to applicant(s) argument of [C] “Ukegawa Does Not Disclose a Degradation Model That Takes Past Behavior of the Thermal Parameters Into Account”, the Examiner respectfully disagrees. Ukegawa teaches SOH model based on self-organizing map (SOM) which determines the SoH of the device for time points in a time-series. and interacting multiple models(IMM) algorithm adapted to constant degradation, linear degradation, exponential degradation, etc.
Based on the arguments presented above, Examiner strongly believes Ukegawa alone or in combination with others meets the current limitations for Claim(s) 1-20.
For further details see the rejections/objections for Claim(s) 1-20 herein.
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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-6, 8-10 and 12-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ukegawa et al. (US 20240210936; hereinafter Ukegawa).
Regarding claim 1, Ukegawa discloses in figure(s) 1-8 a method of monitoring thermal parameters of a thermal model of an electrical machine, the method comprising:
a) updating thermal parameter values of the thermal model based on temperature measurements of the electrical machine (para. 5 - usage information and trains the various models about the healthy operation of the device. The usage information can include various operating parameters, such as voltages, temperatures, etc.; step 410 in fig. 4),
b) estimating future values of the thermal parameters based on the updated thermal parameter values by means of a degradation model (paras. 54-55 - prediction module 120 uses the RUL model to determine the SoH for the device then uses the determined SoH values for the current and prior timepoints as inputs to interacting multiple models (IMMs) that are extended Kalman filters configured to model degradation differently for different parts of the life of the device for constant degradation, linear degradation, nonlinear degradation, and so on predicting future SoH values out to a prediction horizon, which may be dynamically adapted according to values of the SoH or may be predefined. prediction module 120 generates the SoH at each future time point; clm. 6 - self-organizing map SOM determines a state of health (SOH) for the least one feature and the IMMs predict future values of the SOH at future time points according to the usage information), which takes past behavior of the thermal parameters into account (para. 6 - determine when features (i.e., selected usage information) about the operation of the device vary outside of an operating range for the device, thereby indicating the onset of an anomaly. The anomaly may be indicative of degradation in the health of the device. The anomaly model itself may be a partial-least squares with cumulative sum (PLS-CUSUM) algorithm that operates to identify whether the observed usage information correlates with known patterns for the device; steps 430,440), and
c) determining at least one of a future degradation or a remaining useful life of the electrical machine based on the future values (para. 5 - occurrence of anomalous conditions can be identified along with a remaining useful life (RUL) of the device; step 450).
wherein the thermal parameters include at least one of thermal resistances (para. 49 - signals embodying the usage information 150 from one or more sensors associated with the device include time-series data and indicate aspects, such as drain-to-source voltage, drain-to-source resistance, temperature, thermal resistance, gate-leakage current, and so on), thermal capacitances, and heat sources.
Regarding claim 2, Ukegawa discloses in figure(s) 1-8 the method as claimed in claim 1, wherein the degradation model takes current electrical machine conditions (para. 9 - one or more processors to acquire usage information about operation of an electronic device) into account when estimating the future values of the thermal parameters.
Regarding claim 3, Ukegawa discloses in figure(s) 1-8 the method as claimed in claim 2, wherein the current electrical machine conditions include at least one of ambient temperature, load speed, and internal temperatures (para. 29 - usage information 150 includes operating temperatures) of the electrical machine.
Regarding claim 10, Ukegawa discloses in figure(s) 1-8 the method as claimed in claim 9, wherein the mitigating action comprises generating an alarm, presenting a recommendation to an operator (para. 8 - provide information to a driver in order to account for the RUL of the device and avoid unexpected failure), and/or controlling the electrical machine.
Regarding claim 12, Ukegawa discloses in figure(s) 1-8 the method as claimed claim 9, comprising: after performing the mitigating action, repeating steps a) to c) (para. 53 - correlation system 100 is iterative to repeat analysis of the device over and over in order to detect when the device begins to degrade).
Regarding claim 13, Ukegawa discloses in figure(s) 1-8 A non-transitory storage medium comprising a computer program having computer code (para. 70 - computer program products) which when executed by processing circuity of a control system causes the control system to perform a method of monitoring thermal parameters of a thermal model of an electrical machine, the method including:
a) updating thermal parameter values of the thermal model based on temperature measurements of the electrical machine (para. 5 - usage information and trains the various models about the healthy operation of the device. The usage information can include various operating parameters, such as voltages, temperatures, etc.; step 410 in fig. 4),
b) estimating future values of the thermal parameters based on the updated thermal parameter values by means of a degradation model (paras. 54-55 - prediction module 120 uses the RUL model to determine the SoH for the device then uses the determined SoH values for the current and prior timepoints as inputs to interacting multiple models (IMMs) that are extended Kalman filters configured to model degradation differently for different parts of the life of the device for constant degradation, linear degradation, nonlinear degradation, and so on predicting future SoH values out to a prediction horizon, which may be dynamically adapted according to values of the SoH or may be predefined. prediction module 120 generates the SoH at each future time point; clm. 6 - self-organizing map SOM determines a state of health (SOH) for the least one feature and the IMMs predict future values of the SOH at future time points according to the usage information), which takes past behavior of the thermal parameters into account (para. 6 - determine when features (i.e., selected usage information) about the operation of the device vary outside of an operating range for the device, thereby indicating the onset of an anomaly. The anomaly may be indicative of degradation in the health of the device. The anomaly model itself may be a partial-least squares with cumulative sum (PLS-CUSUM) algorithm that operates to identify whether the observed usage information correlates with known patterns for the device; steps 430,440), and
c) determining at least one of a future degradation or a remaining useful life of the electrical machine based on the future values (para. 5 - occurrence of anomalous conditions can be identified along with a remaining useful life (RUL) of the device; step 450).
wherein the thermal parameters include at least one of thermal resistances (para. 49 - signals embodying the usage information 150 from one or more sensors associated with the device include time-series data and indicate aspects, such as drain-to-source voltage, drain-to-source resistance, temperature, thermal resistance, gate-leakage current, and so on), thermal capacitances, and heat sources.
Regarding claim 14, Ukegawa discloses in figure(s) 1-8 a control system comprising:
a storage medium comprising having computer code (para. 71 - hardware and software can be a processing system with computer-usable program code), and
processing circuitry configured to execute the computer code, wherein execution of the computer code is configured to cause the control system to execute the steps of a method of monitoring thermal parameters of a thermal model of an electrical machine (clm. 1 - a memory communicably coupled to the one or more processors and storing instructions that, when executed by the one or more processors), the method including:
a) updating thermal parameter values of the thermal model based on temperature measurements of the electrical machine (para. 5 - usage information and trains the various models about the healthy operation of the device. The usage information can include various operating parameters, such as voltages, temperatures, etc.; step 410 in fig. 4),
b) estimating future values of the thermal parameters based on the updated thermal parameter values by means of a degradation model, which takes past behavior of the thermal parameters into account (para. 6 - determine when features (i.e., selected usage information) about the operation of the device vary outside of an operating range for the device, thereby indicating the onset of an anomaly. The anomaly may be indicative of degradation in the health of the device. The anomaly model itself may be a partial-least squares with cumulative sum (PLS-CUSUM) algorithm that operates to identify whether the observed usage information correlates with known patterns for the device; steps 430,440), and
c) determining at least one of a future degradation or a remaining useful life of the electrical machine based on the future values (para. 5 - occurrence of anomalous conditions can be identified along with a remaining useful life (RUL) of the device; step 450).
wherein the thermal parameters include at least one of thermal resistances (para. 49 - signals embodying the usage information 150 from one or more sensors associated with the device include time-series data and indicate aspects, such as drain-to-source voltage, drain-to-source resistance, temperature, thermal resistance, gate-leakage current, and so on), thermal capacitances, and heat sources.
Regarding claim 15, Ukegawa discloses in figure(s) 1-8 An electrical machine system comprising:
an electrical machine (para. 3 - an electronic component within a vehicle), and
a control system, which includes:
a storage medium having computer code, and
processing circuitry configured to execute the computer code (para. 71 - hardware and software can be a processing system with computer-usable program code), wherein execution of the computer code is configured to cause the control system to execute the steps of a method of monitoring thermal parameters of a thermal model of the electrical machine, the method having:
a) updating thermal parameter values of the thermal model based on temperature measurements of the electrical machine (para. 5 - usage information and trains the various models about the healthy operation of the device. The usage information can include various operating parameters, such as voltages, temperatures, etc.; step 410 in fig. 4),
b) estimating future values of the thermal parameters based on the updated thermal parameter values by means of a degradation model, which takes past behavior of the thermal parameters into account (para. 6 - determine when features (i.e., selected usage information) about the operation of the device vary outside of an operating range for the device, thereby indicating the onset of an anomaly. The anomaly may be indicative of degradation in the health of the device. The anomaly model itself may be a partial-least squares with cumulative sum (PLS-CUSUM) algorithm that operates to identify whether the observed usage information correlates with known patterns for the device; steps 430,440), and
c) determining at least one of a future degradation or a remaining useful life of the electrical machine based on the future values (para. 5 - occurrence of anomalous conditions can be identified along with a remaining useful life (RUL) of the device; step 450).
wherein the thermal parameters include at least one of thermal resistances (para. 49 - signals embodying the usage information 150 from one or more sensors associated with the device include time-series data and indicate aspects, such as drain-to-source voltage, drain-to-source resistance, temperature, thermal resistance, gate-leakage current, and so on), thermal capacitances, and heat sources.
Regarding claim(s) 4 and 16, Ukegawa discloses in figure(s) 1-8 the method as claimed in claim 1 and 2 respectively, wherein the degradation model takes past behavior of the thermal parameters of a fleet of electrical machines of the same type into account (para. 45 - models 160 can include determinations about anomalies, previous recorded values of the usage information 150; para. 58 - prediction module 120 using a distribution of threshold values from a dataset that describes the same failure mode behavior in a same class/type of devices.).
Regarding claim(s) 5 and 17, Ukegawa discloses in figure(s) 1-8 the method as claimed in claim 1 and 2 respectively, wherein the future degradation is an occurrence of one or more future hotspots determined by re-evaluating the thermal model (para. 3 - functioning of a device that may rely on predefined degradation rates or static mathematical models) with the future values (para. 44 - RUL model further implements the IMMs to predict future SOHs according to current and preceding SOH values out to a defined prediction of “n” timepoints into the future).
Regarding claim(s) 6 and 18, Ukegawa discloses in figure(s) 1-8 the method as claimed in claim 1 and 2 respectively, wherein the future degradation is a future defect in the electrical machine determined based on a magnitude greater than a threshold value of at least one of the future values (para. 2 - determining the RUL according to feature selection and a threshold determination).
Regarding claim(s) 8 and 19, Ukegawa discloses in figure(s) 1-8 the method as claimed in claim 1 and 2 respectively, wherein the remaining useful life is determined based on the future values and on past values of at least one of the thermal parameters (abs. - a remaining useful life of the component based on the data relative to current operating condition of the components, the data relative to historical data of the operating condition associated with the failure mode and a predicted failure mode rate.).
Regarding claim(s) 9 and 20, Ukegawa discloses in figure(s) 1-8 the method as claimed in claim 1 and 2 respectively, comprising: after step c), performing a mitigating action based on the future degradation and/or a remaining useful life (para. 8 - system functions to improve determinations about the SoH and RUL of the device and facilitate mitigation efforts), in an attempt to reduce the degradation and/or increase the remaining useful life.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 7 are rejected under 35 U.S.C. 103 as being unpatentable over Ukegawa in view of Mitchell et al. (US 20120283963).
Regarding claim 7, Ukegawa teaches in figure(s) 1-8 the method as claimed in claim 6,
Ukegawa does not teach explicitly wherein the future defect is a degradation of insulation of the electrical machine.
However, Mitchell teaches in figure(s) 1-9 wherein the future defect is a degradation of insulation (para. 27 - determining insulation performance and degradation; fig. 7B) of the electrical machine (10; fig. 1).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Ukegawa by having wherein the future defect is a degradation of insulation of the electrical machine as taught by Mitchell in order to provide simple substitution of one known element for another to obtain predictable results as evidenced by "component life curve providing an estimated remaining life for a component due to the coating depletion (oxide growth) failure mode for that component." (para. 24).
Claim(s) 11 are rejected under 35 U.S.C. 103 as being unpatentable over Ukegawa in view of Bowers et al. (US 5680025).
Regarding claim 11, Ukegawa teaches in figure(s) 1-8 the method as claimed in claim 10,
Ukegawa does not teach explicitly wherein the recommendation is to clean off dirt from the electrical machine, and the controlling involves reducing the load of the electrical machine.
However, Bowers teaches in figure(s) 1-8 wherein the recommendation is to clean off dirt from the electrical machine, and the controlling involves reducing the load of the electrical machine (col. 7 lines 20-40 :- anything that restricts the flow of air has a direct effect on the ability of the motor to transfer heat away. The most common deterrents are grime and dirt… Increasing load produces increasing current and, with excessive load, the increased current produces excessive heat… excessive loads reduces the life of the insulation system due to increased current. All of the foregoing conditions, if detected in accordance with the invention, can be remedied, thus prolonging the life of the motor; figs. 2,4).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Ukegawa by having wherein the recommendation is to clean off dirt from the electrical machine, and the controlling involves reducing the load of the electrical machine as taught by Bowers in order to provide "abnormal temperatures are indicative of various potential motor problems such as hot spots in the stator, overheating due to poor airflow" (abstract).
Conclusion
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).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AKM ZAKARIA whose telephone number is (571)270-0664. The examiner can normally be reached on 8-5 PM (PST).
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Judy Nguyen can be reached on (571) 272-2258. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/AKM ZAKARIA/Primary Examiner, Art Unit 2858
Prosecution Insights