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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/07/2026 has been entered.
Response to Amendment/Argument
Amendment and argument filed on 01/07/2023 is considered. Claim 1, 8 and 9 are amended.
Claim rejection under 35 U.S.C 112: The amendment overcomes the rejection. Therefore, the rejection is withdrawn.
Claim rejection under 35 U.S.C 101: Applicant argument regarding monitoring of machine diagnostic by integrating the recited abstract into practical application is not persuasive as discussed below.
Applicant argues “On page 4, the Office Action rejects claims 1-18 for allegedly being directed to a judicial exception without significantly more.
Applicant submits that claims 1, 8, and 9, as amended, are patent eligible because the claims are integrated into a practical application (Step 2A, Prong 2). The claims recite a specific technical improvement to the operation and monitoring of an electrical machine, distinguishing it from an abstract mathematical calculation and mental process.
First, under Step 2A, Prong 2 of the eligibility analysis, a claim that recites a judicial exception is essentially not "directed to" that exception if the exception is integrated into a practical application. A claim is integrated into a practical application if it "improves the functioning of a computer or other technology". MPEP 2106.04(d)(1). Here, the Office Action asserts that the claim limitations merely cover mathematical concepts/mental process and that the additional elements do not reflect an improvement to technology. Applicant respectfully disagrees.
The Specification identifies a specific technical problem in the prior art: analytical thermal models (such as Lumped-Parameter Thermal Networks, LPTNs) used to monitor electrical machines rely on fixed parameters. The Specification notes that "some parameters change throughout the life of the machine due to wear or the ambient conditions" and that "inaccurate heat flow and temperature estimation is attributed to the neglection of the non-static nature of the parameters." (Spec. [0005].). Independent claims 1, 8, and 9 provides a specific technical solution to this problem: a method of continuous, iterative calibration of the thermal model during the live operation of the machine.
The claims do not merely calculate a temperature. They dynamically update the model's internal "weight parameter values" (which correspond to physical properties like thermal resistance and capacitance) based on real-time feedback from physical sensors.
By repeating steps (a)-(d) "over and over during operation," the claimed invention ensures the monitoring system remains accurate despite physical degradation or environmental changes over weeks, months, or years. (Spec. [0008].)
This is a specific improvement to the technology of electrical machine monitoring. The claimed invention transforms a static, degrading model into a dynamic, self-correcting tool that accurately reflects the physical state of the machine over its lifetime. As such, the claims are directed to an improvement in the functioning of the monitoring device itself, rendering it patent eligible.
Examiner respectfully disagrees the above argument. From the amended independent claims an improvement in the computer functioning and the technology cannot be realized. The independent claims recite a simple measurement of temperature of a motor or machine component and then estimated temperature values of the motor using a model (i.e., mathematical equation). The differences between the estimated and measured temperature values are calculated using some optimum weight parameter values. New optimum weight parameter values are updated and used as new value, then the above steps of measuring and estimating are repeated over and over by the model to monitor the machine. From the amended independent claims a repetitive calculation of values, storing and retrieving the calculated values for next cycle to monitor a machine can be understood using a basic computer used calculations.
MPEP 2106.05(d) also suggest performing repetitive calculations in Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values), storing and retrieving information in memory in Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 as a computer functions well known and conventional function which do not improve functioning of a computer. In regard to monitoring a machine using thermal model with the new initial parameter, does not provide any detail of how just repetitive mathematical calculations of weight values and monitoring of machine with new weighted value in a thermal model reflect improvement in the technology. Further search and consideration found prior arts Yuan et al “A simplified thermal model and online temperature estimation method of permanent magnet synchronous motors,” Applied Sciences, vol. 9, no. 15, p. 3158, Aug. 2019, doi: 10.3390/app9153158.” and Wang et al US 20200341062 A1 teaching the amended claims. Please see in the respective claim rejections below. Therefore, the present invention cannot be viewed to shown an improvement to technology. Courts have also suggested Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48 may not be sufficient to show an improvement to technology.
Applicant argues “Second, the Office Action asserts that obtaining temperature measurements is mere data gathering and that updating/monitoring is insignificant extra-solution activity. Applicant submits that this analysis disconnects the steps from their technical context.
Step a) - regarding obtaining temperature measurement values - is not generic data gathering. The claims require collecting temperature data from a "plurality of locations of the electrical machine." This physical data is the necessary input for the specific technical goal of recalibrating the model. In Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016), the claims in question were ineligible because they analyzed generic data to present abstract results. Here, the data is specific to the physical machine and is used to modify the operation of the thermal model (by updating its weights).
Step d) and the step of "repeating" require updating the optimal weight parameter values as new initial values and repeating the process, which do not merely constitute insignificant "extra-solution activity"; it is the solution. The claimed invention is the continuous loop of updating these physical parameters. Without this step, the technical problem (model drift over time) remains unsolved.
Claims 1, 8, and 9 have also been amended to positively recite a step of "monitoring." With the amendment, claims 1, 8, and 9 now explicitly require "monitoring the electrical machine using the thermal model with the new initial parameter values." This step confirms that the mathematical output (the new weights) is immediately applied to a practical, physical task: monitoring the machine's condition. This integrates the abstract idea into the practical application of machine diagnostics. (See Spec. [0013].)”
Examiner respectfully agrees. Temperature measured values are considered to be required steps in thermal model, to compare the estimated and the measured temperature values (i.e., for mathematical calculation of updating weight values of a thermal model). Therefore, measuring temperature using a temperature sensor is a routine data gathering step. The step of "repeating" require updating the optimal weight parameter values as new initial values and repeating the process for analysis of data, constitute insignificant "extra-solution activity", that recalculates or repeats the method steps (i.e., gathering data and calculations) without providing any meaningful outcome or result for the evaluation of machine performance. Similarly, the step of monitoring machine using a thermal model with new initial parameter value without any meaningful result or outcome is considered to be mental step (i.e., observing the machine using a model). There are not any meaningful additional element present in the independent claims, therefore, the claims as whole do not integrate the recited judicial exception into a practical application.
Applicant argues “Third, even if the claim were considered to be directed to an abstract idea under Step 2A (which Applicant does not concede), it remains eligible under Step 2B because it includes an inventive concept.
The combination of steps recited in claims 1, 8, and 9 are not "well-understood, routine, and conventional." As noted in the Background section of the Specification, prior art methods (e.g., Sciascera et al.) involved a one-time tuning procedure during the design or commissioning phase.
In contrast, claims 1, 8, and 9 require performing the minimization and updating of weight parameters "over and over during operation of the electrical machine." (See Spec. [0008].) This continuous, in-service re-optimization loop is a novel technical process. It is unconventional to re-solve the thermal model optimization problem repeatedly during the active life of the machine to account for wear and aging. This specific limitation transforms the claim from a general mathematical calculation into a specific, inventive monitoring technique that overcomes the deficiencies of conventional static models.
For the foregoing reasons, claims 1, 8, and 9, as well as all claims dependent thereof, are directed to paten-eligible subject matter. Applicant thereof respectfully requests withdrawal of the § 101 rejection.”
Examiner respectfully disagrees because the applicant recited claim limitations are determined to be well-understood, known in the field of monitoring machine, therefore, is not considered to be novel. Please see the addressed claims below in the prior art rejection. The independent claims as presented for examination do not integrate the recited abstract idea into practical application as there are not any meaningful additional element and the claim elements or limitations are well known in the field of art.
Therefore, the independent claims are not patent eligible. Similarly, the dependent claims are not patent eligible.
Claim rejection under 35 U.S.C 103: Applicant argument regarding prior art rejection is considered to be persuasive. However, further search and consideration finds prior arts Yuan et al “A simplified thermal model and online temperature estimation method of permanent magnet synchronous motors,” Applied Sciences, vol. 9, no. 15, p. 3158, Aug. 2019, doi: 10.3390/app9153158.” and Wang et al US 20200341062 A1 teaching the amended claims. The independent claims are addressed below using new prior art.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18 are rejected under 35 U.S.C 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) without significantly more.
Specifically, claim 1 recites:
A method of monitoring an electrical machine, wherein the method comprises:
a) obtaining, via a plurality of sensors, temperature measurement values of the temperature at a plurality of locations of the electrical machine,
b) obtaining estimated temperatures at said plurality of locations given by a thermal model of the electrical machine, the thermal model initial weight parameter values,
c) minimizing a difference between the temperature measurement values and the estimated temperatures by finding optimal weight parameter values,
d) storing the initial weight parameter values to thereby obtain a storage of used weight parameter values, and updating the optimal weight parameter values as new initial weight parameter values, and
repeating steps a)-d) over and over during operation of the electrical machine.
monitoring the electrical machine using the thermal model with the new initial parameter values.
The claim limitations in the abstract idea have been highlighted in bold above.
Under the step 1 of the eligibility analysis, it is determined whether the claims are drawn to a statutory category by considering whether the claimed subject matter fall within the four statutory categories of patentable subject matter identified by 35 U.S.C 101: process, machine, manufacture, or composition of matter. The above claim is considered to be in the statutory category of (process).
Under the step 2A, prong one, it is considered whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into groupings of subject matter when recited as such in a claim limitation, that cover mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental process – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, a step of “b) obtaining estimated temperatures at said plurality of locations given by a thermal model of the electrical machine, the thermal model initial weight parameter values (considered to be a mathematical relationships or step to obtain estimated temperatures using thermal model),
c) minimizing a difference between the temperature measurement values and the estimated temperatures by finding optimal weight parameter values (considered to be a mathematical relationships),
monitoring the electrical machine using the thermal model with the new initial parameter values (considered to be a mental step i.e., observing, evaluating machine using thermal model),
These mental process or mathematical relationships steps represent that, under the broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind.
Similar limitations comprise the abstract ideas of the independent claims 8 and 9.
Next, under the step 2A, prong two, it is considered whether the claim that recites a judicial exception is integrated into a practical application.
In this step, it is evaluated whether the claim recites meaningful additional elements that integrate the exception into a practical application of that exception.
In claim 1, the additional element in the preamble of “A method of monitoring an electric machine” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. The additional elements/steps “obtaining via a plurality of sensors, temperature measurement …” is also recited in generality which seem to merely be gathering data and not really performing any kind of inventive step to provide any meaningful additional element. Also, it represents an extra-solution activity to the judicial exception. The additional elements “d) storing the initial weight parameter values…, and
repeating steps a)-d) over and over during …” are also recited to be generality which is insignificant extra solution activity. All uses of judicial exception require it.
In claim 8, the additional element is a non- transitory computer readable medium is recited in generality and represent extra solution activity to the judicial exception. The additional element in the preamble of “A non-transitory computer …” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. The additional elements/steps “obtaining via a plurality of sensors, temperature measurement …” is also recited in generality which seem to merely be gathering data and not really performing any kind of inventive step to provide any meaningful additional element. Also, it represents an extra-solution activity to the judicial exception. The additional elements “d) storing the initial weight parameter values…, and repeating steps a)-d) over and over during …” are also recited to be generality which is insignificant extra solution activity. All uses of judicial exception require it.
In claim 9, the additional elements are a storage medium comprising computer code and processing circuitry are recited in generality and represent extra- solution activity to the judicial exception. The additional element in the preamble of “A monitoring device for monitoring an electric machine…” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. The additional elements/steps “obtaining, via a plurality of sensors, temperature measurement …” is also recited in generality which seem to merely be gathering data and not really performing any kind of inventive step to provide any meaningful additional element. Also, it represents an extra-solution activity to the judicial exception. The additional elements “d) storing the initial weight parameter values…, and
repeating steps a)-d) over and over during …” are also recited to be generality which is insignificant extra solution activity. All uses of judicial exception require it.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the step 2B.
However, as evidenced by the prior art of record, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (step 2B analysis). Please below in the claim rejection, where prior art is applied to address the limitations Yuan et al.
Considering the claim as a whole, one of ordinary skill in the art would not know the practical application of the present invention since the claims do not apply or use the judicial exception in some meaningful way.
The independent claims 1 and 9, therefore, are not patent eligible.
With regards to the dependent claims, the claims 2-7,16, 17 and 9-, 15 and 18 comprise the analogous subject matter and also comprise additional features/steps which are the part of an expanded abstract idea of the independent claim 1 (additionally comprising mathematical relationship/mental process steps) and, therefore, the dependent claims are not eligible without additional elements that reflect a practical application and qualified for significantly more for substantially similar reason as discussed with regards to claim 1 and 9.
Claim Rejections - 35 USC § 102
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.
(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, 7, 8, 9, 15, 17, is/are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Yuan et al “A simplified thermal model and online temperature estimation method of permanent magnet synchronous motors,” Applied Sciences, vol. 9, no. 15, p. 3158, Aug. 2019, doi: 10.3390/app9153158.” herein after “Yuan”
Regarding claim 1 Yuan teaches a method of monitoring an electrical machine, wherein the method comprises:
obtaining, via a plurality of sensors, temperature measurement values of the temperature at a plurality of locations of the electrical machine (Page 12 of 18, 6.1, third paragraph line 3, the sensors located in the middle of the stator slot and winding end were applied to measure the stator temperature.),
obtaining estimated temperatures at said plurality of locations given by a thermal model of the electrical machine, the thermal model including initial weight parameter values (page 9 of 18, 5. Temperature online estimation. the temperature of the motor can be estimated online. When the parameters in the state equation and losses of the thermal model can be determined, motor temperatures can be estimated online. page 10 of 18 5.2. Online Temperature Estimation Based on Kalman Filter Algorithm…The filter needs to be initialized before making an estimation. The initial values of the state variables , x and error covariance, p of the prior estimation during each period are calculated as follows.),
Herein examiner views the temperatures at the plurality of location of motor is calculated by using a thermal model from online estimation (i.e., uses Kalman algorithm). The thermal model uses an initial values of the state variables (as initial weight parameter value)
c) minimizing a difference between the temperature measurement values and the estimated temperatures by finding optimal weight parameter values (page 10 of 18 5.2. Online Temperature Estimation Based on Kalman Filter Algorithm.
Page 11 of 18. (c)K(k) is the filter gain matrix ,which is the optimal estimated coefficient matrix of the priori estimation and measurements. It can be expressed as follows:…
Equation (30) Where wS(k) ,wR(k) ,wE(k) represent the error between the measured temperature and the estimated temperature from the state equation of the stator, rotor, and end cap at time k, which can be obtained according to the previous experimental data in Section 5.1.),
Herein examine views the Kalman filtering algorithm perform a minimization of error between the estimated and the measured temperature value by finding an optimum filter gain matrix (i.e., weight value).
storing the initial weight parameter values to thereby obtain a storage of used weight parameter values, and updating the optimal weight parameter values as new initial weight parameter values (page 11 of 18, (d) The prior estimation is corrected based on the filter gain matrix. (e) the error covariance of the prior estimation is adjusted for the next calculation.)
Herein examine views the Kalman filtering algorithm performs an update or adjusted of error covariance matrix (i.e., to update optimal weight parameter value) based on filter gain matrix (which is viewed as the updated value is stored or saved where the initial value saved for reproducibility, and be used for next cycle). The updated weight value is viewed as a new initial weight value for the next iteration or cycle for monitoring of machine performance.
repeating steps a)-d) over and over during operation of the electrical machine (page 11 of 18. In each cycle, according to the state equation and measurement equation of the system, the temperature of each part of the motor can be estimated online by applying the Kalman filter algorithm.),
Examiner views Kalman filtering as a recursive, closed loop system or iterative which repeats the above steps a-d over and over during the operation of the motor.
monitoring the electrical machine using the thermal model with the new initial parameter value (page 15 of 18, line 5. Thus, by establishing a lumped parameter network thermal model of the motor, the change in motor temperature can be accurately predicted.
Examiner views LPNT (using Kalman filtering as a closed loop system) which repeats the steps a-d over and over during the operation of the motor with new or updated initial parameter value and monitor any changes in the performance of machine.
Claim 8 and 9 is rejected as claim 1 above having same claim limitation/element.
Regarding claim 7 the Yuan teaches the method as claimed in 1, wherein the thermal model is a lumped-parameter thermal network, LPTN, model (page 3 of 18, 2. propose thermal model simplified Lumped Parameter thermal network model, Fig. 1)
Claim 15 and 17 is rejected as claim 7 above having same claim limitation/element.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2-4, 10-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yuan in view of in view of Sarangapani (US 6119074 A).
Regarding claim 2, the Yuan teach the method as claimed in claim 1 however the Yuan, comprising: comparing the optimal weight parameter values with the initial weight parameter values or with the used weight parameter values and detecting whether a change in electrical machine performance or an electrical machine fault has occurred based on the comparison result.
Sarangapani teaches comparing the optimal weight parameter values with the initial weight parameter values or with the used weight parameter values (col.1, line 35. A computer produces a data trend of the parameter in response to the electrical signal, calculates a confidence value of the machine parameter, the confidence value representing the degree of certainty that a failure has been detected, assigns a weight to the machine parameter, the weight representing the degree of certainty that the machine parameter is the root cause of the failure, multiplies the confidence value by the associated weight of the parameter value, compares the overall confidence value to a plurality of limit values where each limit value is associated with a particular parameter,) herein examiner views by comparing the limit values (i.e., viewed as an initial parameter ) with the confidence or degree of certainty value (i.e., viewed as optimal weighted parameter) the cause of failure of machine is determined, and detecting whether a change in electrical machine performance or an electrical machine fault has occurred based on the comparison result (and determines the cause of the failure in response to the overall confidence value corresponding to a particular set of limit values.).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Sarangapani in Yuan for the purpose of comparing a weighted parameter to an initial parameter to determine a failure cause in an electric machine.
Regarding claim 3 the combination of Yuan and Sarangapani teach the method as claimed in claim 2, Sarangapani teaches wherein the detecting involves detecting a change in electrical machine performance or an electrical machine fault in case one of the optimal weight parameter values deviates by more than a predetermined amount from its corresponding initial weight parameter value or used weight parameter value (col. 1 line 55. A computer produces a data trend of the parameter in response to the electrical signal, calculates a confidence value of the machine parameter, the confidence value representing the degree of certainty that a failure has been detected, assigns a weight to the machine parameter, the weight representing the degree of certainty that the machine parameter is the root cause of the failure, multiplies the confidence value by the associated weight of the parameter value, compares the overall confidence value to a plurality of limit values where each limit value is associated with a particular parameter, and determines the cause of the failure in response to the overall confidence value corresponding to a particular set of limit values.). herein examiner views by comparing (i.e., broadly interpreted to be more or less than a predetermined amount) each corresponding limit values (i.e., viewed as an initial parameter) with the corresponding confidence or degree of certainty value (i.e., viewed as optimal weighted parameter) the cause of failure of machine is determined.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Sarangapani in Yuan for the purpose of comparing a corresponding weighted parameter to each corresponding initial parameter to determine a failure cause in an electric machine.
Regarding claim 4 the combination of Yuan and Sarangapani teach the method as claimed in claim 3, Sarangapani teaches comprising determining a reason for the change in electrical machine performance or electrical machine fault based on the deviating optimal weight parameter value (col. 1 line 55. … compares the overall confidence value to a plurality of limit values where each limit value is associated with a particular parameter, and determines the cause of the failure in response to the overall confidence value corresponding to a particular set of limit values.)
please see claims 2 and 3 for the determination of cause of failure in response to the comparison (i.e., interpreted as deviation) of the confidence or degree of certainty value (i.e., viewed as optimal weighted parameter.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Sarangapani in Yuan for the purpose of comparing a corresponding weighted parameter to each corresponding initial parameter to determine a failure cause in an electric machine.
Claim 10 is rejected as claim 2 above having same claim limitation/element.
Claim 11 is rejected as claim 3 above having same claim limitation/element.
Claim 12 is rejected as claim 4 above having same claim limitation/element.
Claim(s) 5-6, 13, 14,16,18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yuan in view of Yang et al. (US 20200119625 A1) herein after “Yang”.
Regarding claim 5, Yuan teach the method as claimed in 1, however Yuan does not clearly teach wherein the weight parameter values are arranged in subsets forming respective correction matrices.
Yang teaches or suggest a lumped-parameter thermal network, LPTN, model and obtain wherein the weight parameter values are arranged in subsets forming respective correction matrices (para [0151] For transient LPTN model, the thermodynamic equation in Matrix form is below… para [0152] For each node, C is ρp Vp cp, [G] is thermal conductance matrix, [P] is heat loss vector, [C] is thermal capacitance matrix, [T] is temperature vector, ξ is number of node, R.sub.i,j is the thermal resistance between node i and j. ρp Vp cp are density, volume and specific heat of the node respectively.). The lumped-parameter thermal network model (LPTN) does not disclose the weighted parameter; however, examiner views the weight parameter as recited in the instant claim or in the application is utilized or chosen by the inventor to provide an importance or weight to a monitored data set by multiplying by the weighted parameters. So that the weight parameters (i.e., subset of correction matrices as identity matrices) help to understand the situations of an electric machine whether the monitored data remains stable or change in optimization process.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Yang to calculate a thermal vector by implementing a weight parameter (i.e., identity matrices in subset form) for thermal resistance, power loss and capacitance and incorporated into Yuan to arrive at the present invention.
Regarding claim 6 the combination Yuan and Yang teach The method as claimed in claim 5, Yang teaches wherein the thermal model is a matrix equation including a thermal capacitance matrix, a thermal resistance matrix, and a power loss injection vector, wherein each of the thermal capacitance matrix, the thermal resistance matrix, and the power loss injection vector is multiplied with a respective one of the correction matrices (para [0151] For transient LPTN model, the thermodynamic equation in Matrix form is below… para [0152] For each node, C is ρp Vp cp, [G] is thermal conductance matrix, [P] is heat loss vector, [C] is thermal capacitance matrix, [T] is temperature vector, ξ is number of node, R.sub.i,j is the thermal resistance between node i and j. ρp Vp cp are density, volume and specific heat of the node respectively.). The lumped-parameter thermal network model (LPTN) does not disclose the weighted parameter; however, examiner views the weight parameter as recited in the claim or in the application is utilized or chosen by the inventor to provide an importance or weight to a monitored data set by multiplying by the respective each weighted parameter. So that the weight parameters (i.e., correction matrices as identity matrices) helps to understand the situations of an electric machine whether the monitored data remains stable or change in optimization process.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Yang to monitor a temperature in a machine by implementing a weight parameter (i.e., identity matrices in subset form) for thermal resistance, power loss and capacitance and incorporated into Yuan to arrive at the present invention.
Claim 13 is rejected as claim 5 above having same claim limitation/element.
Claim 14 is rejected as claim 6 above having same claim limitation/element.
Claim 16 is rejected as claim 5 above having same claim limitation/element.
Claim 18 is rejected as claim 5 above having same claim limitation/element.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang et al US 20200341062 A1 discusses using thermal model to monitor machine.
Yeh et al. (US 20110050141 A1) discusses motor stator winding temperature estimation.
Genta et al. (US 20120290261 A1) discusses fault diagnosis in motor using thermal
model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHARAD TIMILSINA whose telephone number is (571)272-7104. The examiner can normally be reached Monday-Friday 9:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached at 571-270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHARAD TIMILSINA/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857