Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1 and 15 are objected to because of the following informalities:
In claim 1, line 8, it appears Applicant intended “diagnosing health condition” to read --diagnosing a health condition--
In claim 15, line 9, it appears Applicant intended “diagnosing health condition” to read --diagnosing a health condition--
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the preprocessed parameter data" in line 4. There is insufficient antecedent basis for this limitation in the claim. Claim(s) 2-14 depend(s) from claim(s) 1, (respectively,) fail(s) to cure said indefiniteness issues, and is/are thereby similarly rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
Claim 15 recites the limitation "the preprocessed parameter data" in line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 7, 12, and 14-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhu et al. (US PGPub. No. 2021/0286995).
Regarding claim 1, Zhu discloses a health diagnosis method for motor bearings (¶0057) of an electric vehicle (¶0003), the health diagnosis method comprising:
collecting parameter data related to the motor bearings (¶0058);
preprocessing the parameter data to generate the preprocessed parameter data (¶0059, ¶0063);
performing a feature extraction processing on the preprocessed parameter data to obtain one or more features (¶0060, ¶0064); and
inputting the one or more obtained features into an abnormality detection model, and diagnosing health condition of the motor bearings based on an output of the abnormality detection model (¶0061-0062, ¶0065);
wherein the parameter data includes at least one of current data associated with a motor of the electric vehicle and rotor rotation data associated with a rotor of the motor (¶0058, ¶0077-0087).
Regarding claim 2, Zhu discloses the health diagnosis method according to claim 1, further comprising training the abnormality detection model (¶0066), wherein the training comprises:
obtaining parameter data related to the motor bearings from a training data set (¶0067);
preprocessing the parameter data to generate the preprocessed parameter data (¶0068, ¶0072);
performing a feature extraction processing on the preprocessed parameter data to obtain one or more features (¶0069); and
training the abnormality detection model by taking at least one of the one or more features as an input to the abnormality detection model and taking data corresponding to the health condition of the motor bearings as an output of the abnormality detection model (¶0070-0071).
Regarding claim 3, Zhu discloses the health diagnosis method according to claim 1, wherein the rotor rotation data includes speed information of rotation and angular position information of rotation of the rotor (¶0087).
Regarding claim 7, Zhu discloses the health diagnosis method according to claim 1, wherein the feature extraction processing comprises extracting at least one of the following features based on the preprocessed parameter data: kurtosis, skewness, root mean square, peak-to-peak value (¶0015, ¶0023), variance, impulse factor and power spectral density.
Regarding claim 12, Zhu discloses the health diagnosis method according to claim 3, wherein the feature extraction processing comprises extracting at least one of the following features based on the preprocessed parameter data: kurtosis, skewness, root mean square, peak-to-peak value (¶0015, ¶0023), variance, impulse factor and power spectral density.
Regarding claim 14, Zhu discloses a computer-readable storage medium comprising instructions that are executed by a computer to realize the health diagnosis method according to claim 1. While Zhu appears to be silent on the use of processors and associated memory for executing the described processing, Examiner understands these elements to be required for carrying out Zhu’s disclosed invention. Furthermore, Zhu’s description of the prior art (¶0006, ¶0008) includes discussion of controllers and chips that form the basis of the prior art on which Zhu is improving.
Regarding claim 15, Zhu discloses a health diagnosis system for motor bearings (¶0057) of an electric vehicle (¶0003), the health diagnosis system comprising a memory and a processor coupled to the memory;
wherein the processor is configured to perform the following steps:
receiving parameter data related to the motor bearings collected by sensors (¶0058);
preprocessing the parameter data to generate the preprocessed parameter data (¶0059, ¶0063);
performing a feature extraction processing on the preprocessed parameter data to obtain one or more features (¶0060, ¶0064); and
inputting the one or more obtained features into an abnormality detection model, and diagnosing health condition of the motor bearings based on an output of the abnormality detection model (¶0061-0062, ¶0065), wherein the parameter data includes at least one of current data associated with a motor of the electric vehicle and rotor rotation data associated with a rotor of the motor (¶0058, ¶0077-0087).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu as applied to claim 1 above, and further in view of Cha et al. (US PGPub. No. 2020/0169198).
Regarding claim 4, Zhu discloses the health diagnosis method according to claim 1 (Zhu ¶0003, ¶0057-0065), wherein the current data is obtained by a current sensor [Zhu 1] (Zhu ¶0058), but appears to be silent on the method wherein the rotor rotation data is obtained by a resolver.
Cha, however, teaches a motor control device including a resolver [Cha 200] mounted on a shaft [Cha 143] of a motor [Cha 140] for generating rotor rotation data (Cha Figure 2; ¶0097). It would have been obvious to one having ordinary skill in the art before the effective filing date to have modified Zhu in view of Cha. One having ordinary skill in the art before the effective filing date would have been motivated to have modified Zhu, and would have had a reasonable expectation of success therein, to include the method wherein the rotor rotation data is obtained by a resolver, as doing so was a known sensor type for collecting rotor rotation data, as recognized by Cha (Cha ¶0097).
Regarding claim 9, Zhu in view of Cha teaches the health diagnosis method according to claim 3 (Zhu ¶0003, ¶0057-0065, ¶0087), wherein the current data is obtained by a current sensor [Zhu 1] (Zhu ¶0058) and the rotor rotation data is obtained by a resolver [Cha 200] (Cha ¶0097), as previously modified, and with the same motivation as applied in regard to claim(s) 4, above.
Claim(s) 5 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Cha as applied to claims 4 and 9 above, and further in view of Wang et al. (US PGPub. No. 2020/0348207).
Regarding claim 5, Zhu in view of Cha teaches the health diagnosis method according to claim 4 (Zhu ¶0003, ¶0057-0065, ¶0087; Cha ¶0097), wherein the resolver [Cha 200] is installed on a motor shaft [Cha 143] of the motor [Cha 140], but appears to be silent on the method wherein the current sensor is arranged to be electrically coupled to a stator in the motor.
Wang, however, teaches a method for estimating bearing fault severity, including current sensors [Wang 105a-c] for measuring stator current of the motor [Wang 101] (Wang ¶0037, ¶0051). It would have been obvious to one having ordinary skill in the art before the effective filing date to have modified Zhu in view of Cha in view of Wang. One having ordinary skill in the art before the effective filing date would have been motivated to have modified Zhu in view of Cha, and would have had a reasonable expectation of success therein, to include the method wherein the current sensor is arranged to be electrically coupled to a stator in the motor, as doing so was a known way of collecting motor current data, as recognized by Wang (Wang ¶0037, ¶0051).
Regarding claim 10, Zhu in view of Cha in view of Wang teaches the health diagnosis method according to claim 9 (Zhu ¶0003, ¶0057-0065, ¶0087; Cha ¶0097), wherein the current sensor [Wang 105a-c] is arranged to be electrically coupled to a stator in the motor [Wang 101] (Wang ¶0037, ¶0051), and the resolver [Cha 200] is installed on a motor shaft [Cha 143] of the motor [Cha 140], as previously modified, and with the same motivation as applied in regard to claim(s) 5, above.
Claim(s) 6 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu as applied to claims 1 and 3 above, and further in view of Wang et al. (US PGPub. No. 2020/0348207).
Regarding claim 6, Zhu discloses the health diagnosis method according to claim 1 (Zhu ¶0003, ¶0057-0065), but appears to be silent on the method further wherein the preprocessing the parameter data comprises performing a band-pass filtering processing on the parameter data.
Wang, however, teaches a method for estimating bearing fault severity, including band-pass filtering measured motor parameters as part of a preprocessing step (Wang ¶0083). It would have been obvious to one having ordinary skill in the art before the effective filing date to have modified Zhu in view of Wang. One having ordinary skill in the art before the effective filing date would have been motivated to have modified Zhu, and would have had a reasonable expectation of success therein, to include the method further wherein the preprocessing the parameter data comprises performing a band-pass filtering processing on the parameter data, as doing so [was a known way of isolating signal of interest from noise, as recognized by Wang (Wang ¶0083).
Regarding claim 11, Zhu in view of Wang teachews the health diagnosis method according to claim 3 (Zhu ¶0003, ¶0057-0065, ¶0087), wherein the preprocessing the parameter data comprises performing a band-pass filtering processing on the parameter data (Wang ¶0083), as previously modified, and with the same motivation as applied in regard to claim(s) 6, above.
Claim(s) 8 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu as applied to claims 1 and 3 above, and further in view of Lebacher et al. (US PGPub. No. 2025/0258061).
Regarding claim 8, Zhu discloses the health diagnosis method according to claim 1 (Zhu ¶0003, ¶0057-0065), but appears to be silent on the method further wherein the diagnosing the health condition of the motor bearings based on the output of the abnormality detection model comprises: comparing an output value of the abnormality detection model with a threshold; diagnosing the health condition of the motor bearings as healthy if the output value is less than or equal to the threshold; or diagnosing the health condition of the motor bearings as unhealthy if the output value is greater than the threshold; wherein the output value of the abnormality detection model is an abnormality probability value of the motor bearings.
Lebacher, however, teaches a method for providing an explainable fault information of a bearing using a machine learning model, wherein outputs of the model represent respective probabilities for the presence of a defect (Lebacher ¶0076). Examiner notes that choosing the highest probability related to fault status represents a comparison to at least one lower probability fault status (i.e. threshold) output from the model. It would have been obvious to one having ordinary skill in the art before the effective filing date to have modified Zhu in view of Lebacher. One having ordinary skill in the art before the effective filing date would have been motivated to have modified Zhu, and would have had a reasonable expectation of success therein, to include the method further wherein the diagnosing the health condition of the motor bearings based on the output of the abnormality detection model comprises: comparing an output value of the abnormality detection model with a threshold; diagnosing the health condition of the motor bearings as healthy if the output value is less than or equal to the threshold; or diagnosing the health condition of the motor bearings as unhealthy if the output value is greater than the threshold; wherein the output value of the abnormality detection model is an abnormality probability value of the motor bearings, as doing so was a known way of identifying a most likely fault status from a machine learning model, as recognized by Lebacher (Lebacher ¶0076).
Regarding claim 13, Zhu discloses the health diagnosis method according to claim 3 (Zhu ¶0003, ¶0057-0065, ¶0087), wherein the diagnosing the health condition of the motor bearings based on the output of the abnormality detection model comprises: comparing an output value of the abnormality detection model with a threshold; diagnosing the health condition of the motor bearings as healthy if the output value is less than or equal to the threshold; or diagnosing the health condition of the motor bearings as unhealthy if the output value is greater than the threshold; wherein the output value of the abnormality detection model is an abnormality probability value of the motor bearings (Lebacher ¶0076), as previously modified, and with the same motivation as applied in regard to claim(s) 6, above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL V KERRIGAN whose telephone number is (571)272-8552, the examiner can normally be reached Monday-Friday 9:30am-8:00pm.
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/MICHAEL V KERRIGAN/Primary Examiner, Art Unit 3664