DETAILED ACTION
This Action is responsive to Claims filed 02/27/2026.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/16/2026 was filed after the mailing date of the Non-Final Office Action on 12/02/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of the Claims
Claims 1 and 6-13 have been amended. Claim 4 is canceled. Claims 1-3 and 5-13 are currently pending.
Response to Amendment
The amendments to Claims 9-11 and 13 have overcome the Objections to informalities.
Response to Arguments
The cancellation of Claim 4 has rendered the 35 U.S.C. 112(b) antecedent basis Rejection of Claim 4 moot.
Applicant’s arguments, see Pages 8-9, filed 02/27/2026, with respect to Claims 9, 11, and 13 have been fully considered and are persuasive. The 35 U.S.C. 101 Rejection of Claims 9, 11, and 13 has been withdrawn.
Applicant’s arguments, see Pages 9-11, filed 02/27/2026, with respect to the 35 U.S.C. 103 Rejection(s) of Claims 1-13 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Interpretation
The Examiner notes the newly amended independent Claim 12 contains contingent limitations within a method claim (“…when the input data is acquired…”). Per MPEP 2111.04(II) “The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.” If the Applicant intends the contingent limitation to be required, further amendment is necessary. The Examiner also notes, however, that amending Claim 12 in this fashion renders it functionally identical to newly amended independent Claim 10, also a method claim, requiring an inference step.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-3 and 6-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qi et al. (CN 111458142 A), hereinafter Qi; Chen et al. (CN 107526853 B), hereinafter Chen; and Plazenet et al. (A Comprehensive Study on Shaft Voltages and Bearing Currents in Rotating Machines, 2018), hereinafter Plazenet.
In regards to claim 1: The present invention claims: “A machine learning apparatus for generating a learning model to be used in a sliding-surface diagnosis apparatus for diagnosing a condition of sliding surfaces of a fixed-side sliding member and a rotation-side sliding member, comprising:” Qi teaches “The invention claims a sliding bearing fault diagnosis method based on generating antagonistic network and convolutional neural network,” (Page 2).
“a machine learning section configured to input the one or plural sets of learning data to the learning model to cause the learning model to learn a correlation between the input data and diagnostic information of the sliding surfaces; and a learned-model memory configured to store the learning model that has learned by the machine learning section.” Qi teaches “constructing a sliding bearing convolutional neural network model, and taking the final sample set as the input of the sliding bearing convolutional neural network model; corresponding fault state label as the desired output of the sliding bearing convolutional neural network model; training the sliding bearing convolutional neural network;” (Page 3). Qi also teaches an output step (Page 8), which the examiner feels reads sufficiently on the inherency of a storage medium to store the model, given its broad recitation in the claim.
While Qi teaches “collecting the vibration signal and bearing bush temperature of the sliding bearing under different fault conditions and different fault conditions, and pre-processing it, so as to obtain the initial data set formed by the vibration signal and the corresponding bearing bush temperature under the condition of no fault and multiple fault conditions;” (Page 2) Qi fails to explicitly teach all of the data type enumerated in:
“a learning-data memory configured to store one or plural sets of learning data including input data containing at least data on motor current value in a predetermined period supplied to a motor which is a driving source for the rotation-side sliding member, data on contact electric resistance between the fixed-side sliding member and the rotation-side sliding member in the predetermined period, and data on acceleration occurring in a direction perpendicular to the sliding surfaces of the fixed-side sliding member and the rotation-side sliding member in the predetermined period;” However, Chen, in a similar field of endeavor of motor fault detection, teaches “The test table comprises a driving end bearing box fan end bearing; the sensor is respectively installed on the driving end of the motor shell and the position of the fan end 12 o clock. the vibration signal is collected by the DAT recorder of 16 channel;” (Page 13), and “The embodiment adopts three-axis sensor to obtain the vibration data of the bearing in the X/Y/Z three directions; the sampling frequency is 5120Hz” (Page 14; mapping to the collection of acceleration data of a bearing in all three directions, necessarily including perpendicular). The combination of Qi and Chen fails to teach the “contact electrical resistance” limitation; however, Plazenet, in a similar field of endeavor, teaches the monitoring of shaft/bearing faults due to shaft voltage and currents (Section II, specifically Subsection A for the phenomenon, Subsection C for measurement methods, and Subsection D for failures due to said voltage and currents. See also “EDM currents arise at an established film of lubricant in the bearing and a high bearing voltage. These currents are a function of the bearing capacitance, the lubricant thickness and the bearing voltage prior to a breakdown. Furthermore, these parameters are interrelated to the bearing temperature, the motor speed and the bearing load [40], [41]. Circulating currents appear mainly at low speed and high bearing temperature, a configuration in which the oil film is thin enough to enable an ohmic contact [contact resistance] between the raceways and the rolling elements [39]. For some operating conditions, the bearings randomly alternate between capacitive and ohmic mode as discussed in [42].” (Page 3751).
All Qi (Background), Chen (Background), and Plazenet () highlight the importance of monitoring for faults within sliding bearing or shaft and bearing mechanisms. And Plazenet teaches “In shaft condition monitoring, all collected raw signals are sent to a monitor for data processing, and logging. An online diagnostic is performed through fast Fourier transform (FFT) analysis based on specific frequencies tracking and associated amplitudes. However, all authors highly recommend the use of other monitoring techniques to cross results for diagnostic improvement, such as partial discharge level, stator current analysis (MCSA), vibration analysis [11], [89]. A machine learning method based on a Bayesian estimation algorithm has been developed for shaft voltage monitoring [95]. This method is very robust in detecting rotor eccentricity faults and provides good performance if the training of the classifier bases itself on accurate training data which could be difficult to obtain in practice [96].” (Page 3755). It would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing to collect known attributes commonly linked to bearing faults, such as vibration/acceleration data as taught by Chen, and lubricant/shaft capacitance/voltage/resistance as taught by Plazenet, when implementing a machine learning monitoring system in conjunction with Qi.
In regards to claim 2: The present invention claims: “wherein the learning data further includes output data containing the diagnostic information associated with the input data, the diagnostic information indicating that the condition of the sliding surfaces in the predetermined period is one of a plurality of conditions, and the machine learning section is configured to perform supervised learning to cause the learning model to learn the correlation between the input data and the output data.” Qi teaches “performing edge expansion to the data of the initial sample set, and setting the fault state label; constructing a sliding bearing convolutional neural network model, and using the data set and the corresponding fault tag for training model;” (Abstract, mapping to obtaining pertinent data and training the model on labeled (supervised) data).
In regards to claim 3: The present invention claims: “wherein the learning data includes only the input data in the predetermined period when the diagnostic information indicates that the condition of the sliding surfaces is a predetermined condition, and the machine learning section is configured to perform unsupervised learning to cause the learning model to learn the correlation between the input data and the diagnostic information indicating that the condition of the sliding surfaces is the predetermined condition.” Qi teaches “collecting the data of the current sliding bearing, constructing state matrix, and inputting to the trained neural network model, performing fault diagnosis and prediction.” (Abstract, mapping to performing an unsupervised inference on the state of a machine).
In regards to claim 6: The present invention claims: “A sliding-surface diagnosis apparatus for diagnosing a condition of sliding surfaces of a fixed-side sliding member and a rotation-side sliding member using a learning model generated by the machine learning apparatus according to claim 1, comprising: an input-data acquisition section configured to acquire input data containing data on motor current value in a predetermined period supplied to a motor which is a driving source for the rotation-side sliding member, data on contact electric resistance between the fixed- side sliding member and the rotation-side sliding member in the predetermined period, and data on acceleration occurring in a direction perpendicular to the fixed-side sliding member and the rotation- side sliding member in the predetermined period; and an inference section configured to input the input data acquired by the input-data acquisition section into the learning model and infer diagnostic information of the sliding surfaces.” The Examiner submits the limitations of Claim 6 are not patentably distinct from the limitations of Claims 1-4. See above how the combination of Qi, Chen, and Plazenet reads on a system for fault diagnosis in a similar machine environment, and how a person of ordinary skill in the art may have accounted for various types or sources of observational data pertaining to the machine environment. Both Qi and Plazenet read on a machine learning system taking in input data and outputting a fault diagnostic inference on the state of the machine.
In regards to claim 7: The present invention claims: “An inference apparatus for use in diagnosing a condition of sliding surfaces of a fixed-side sliding member and a rotation-side sliding member, comprising: a memory; and a processor configured to perform: input-data acquisition processing of acquiring input data containing data on motor current value in a predetermined period supplied to a motor which is a driving source for the rotation-side sliding member, data on contact electric resistance between the fixed-side sliding member and the rotation-side sliding member in the predetermined period, and data on acceleration occurring in a direction perpendicular to the fixed-side sliding member and the rotation-side sliding member in the predetermined period; and inference processing of inferring diagnostic information of the sliding surfaces when the input data is acquired in the input-data acquisition processing.” The Examiner submits the limitations of Claim 7 are not patentably distinct from the limitations of Claims 1-4. See above how the combination of Qi, Chen, and Plazenet reads on a system for fault diagnosis in a similar machine environment, and how a person of ordinary skill in the art may have accounted for various types or sources of observational data pertaining to the machine environment. Both Qi and Plazenet read on a machine learning system taking in input data and outputting a fault diagnostic inference on the state of the machine.
In regards to claim 8: Claim 8 recites similar limitations to Claim 1, with the exception of “A machine learning method of causing a learning model to learn for use in a sliding-surface diagnosis apparatus for diagnosing a condition of sliding surfaces of a fixed-side sliding member and a rotation-side sliding member, comprising:”; therefore, both claims are similarly rejected.
In regards to claim 9: The present invention claims: “A non-transitory computer-readable medium storing a machine learning program…” Qi operates a machine learning method/system, and Plazenet makes reference to the use of machine learning in fault monitoring, a program to execute such operations would be inherent to their operation. The remaining limitations of the newly independent Claim 9 are similar to the limitations of Claim 8; therefore, both claims are similarly rejected.
In regards to claim 10: Claim 10 recites similar limitations to Claim 6, with the exception of “A sliding-surface diagnosis method of diagnosing a condition of sliding surfaces of a fixed-side sliding member and a rotation-side sliding member using a learning model comprising:”; therefore, both claims are similarly rejected.
In regards to claim 11: The present invention claims: “A non-transitory computer-readable medium storing a sliding-surface diagnosis program for causing a computer to perform…” Qi operates a machine learning method/system, and Plazenet makes reference to the use of machine learning in fault monitoring, a program to execute such operations would be inherent to their operation. The remaining limitations of the newly independent Claim 11 are similar to the limitations of Claim 10; therefore, both claims are similarly rejected.
In regards to claim 12: Claim 12 recites similar limitations to Claim 7, with the exception of “An inference method of inferring a condition of sliding surfaces of a fixed-side sliding member and a rotation-side sliding member, comprising:”; therefore, both claims are similarly ejected.
In regards to claim 13: The present invention claims: “A non-transitory computer-readable medium storing an inference program for causing a computer to perform…” Qi operates a machine learning method/system, and Plazenet makes reference to the use of machine learning in fault monitoring, a program to execute such operations would be inherent to their operation. The remaining limitations of the newly independent Claim 13 are similar to the limitations of Claim 12; therefore, both claims are similarly rejected.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qi, Chen, and Plazenet as applied to claim 1 above, and further in view of Poddar et al. (Detection of particle contamination in journal bearing using acoustic emission and vibration monitoring techniques, 2019), hereinafter Poddar.
In regards to claim 5: While Qi measures vibration and temperature (Abstract) and Plazenet makes reference to lubricant contamination (Pages 3755-3756), and it would be reasonable for their output to indicate faults associated with said data, the combination of Qi, Chen, and Plazenet fails to explicitly teach an output related to lubricant as recited in “wherein the diagnostic information includes at least one of diagnostic information on wear of the sliding surfaces, diagnostic information on burned state of the sliding surfaces, and diagnostic information on contamination of a lubricant for lubricating the sliding surfaces.” However, Poddar, in a similar filed of endeavor of machine fault diagnosis, teaches “The present study has been carried out to assess the case of externally ingested particle contaminants in a journal bearing lubrication using acoustic emission (AE) and vibration monitoring techniques.” (Abstract).
Poddar teaches “Oil contamination resulting from externally ingested particles is one of the major causes of journal bearing failure in industrial machinery” (Abstract). Similar to Plazenet, Poddar demonstrates lubricant contamination would have been a measure well known in the art before the Applicant’s filing, and a person of ordinary skill in the art would have known to monitor lubricant contamination, collect data regarding it, or perform an inference in a system such as a combination of Qi, Chen, and Plazenet.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRIFFIN T BEAN whose telephone number is (703)756-1473. The examiner can normally be reached M - F 7:30 - 4:30.
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/GRIFFIN TANNER BEAN/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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