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 .
Claim Objections
Claim 20 is objected to because of the following informalities: Claim 20 line 4, the phrase “date” should be changed to “data”. Appropriate correction is required.
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.
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) 1-6, 12-15 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 8,696,073 to Cetinkaya et al. in view of publication “Create Data from Random Noise with Generative Adversarial Networks” attributed to Nash (2017).
Publication date is found in US 12,475,280 under references cited.
Re-claims 1 and 12, Cetinkaya et al. teaches a device and method, comprising: a memory (as part of control unit 24) configured to store one or more instructions; and a processor (as part of the control unit 24) configured to execute the one or more instructions comprising: inputting input data (such as actual power consumption calculated from pump capacity, see at least column 3 lines 59-64), associated with one or more operations of a vehicle (such as pump operation), configured to, in response to the input data, output an estimation value (such as expected pressure, see column 5 lines 36-39) of an output variable related to an output of a hydraulic pressure generation motor configured to drive a pump configured to generate a hydraulic pressure for performing a brake; obtaining the estimation value of the output variable; and detecting whether the hydraulic pressure generation motor is in an abnormal state based on a difference between the estimation value of the output variable and an actual measurement value (as from pressure sensors in fluid pressure lines, see column 5 lines 31-36 and 39-40) of the output variable (an abnormal state is determined if a pressure difference beyond a defined threshold is present). However, Cetinkaya et al. fail to teach the control unit using an artificial neural network model, and from the artificial neural network model determining the estimation value.
Nash teaches an artificial neural network model capable of generating more realistic output data (such as an estimation value) from input data. The expectation is improved estimation values for expected pressures from pump operation parameters would improve abnormal or fault determination, when having been compared to actual pressure measurements. Therefore, as per the teachings of Nash, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have provided the control unit of Cetinkaya et al. an artificial neural network model of the type taught by Nash, thus improving the output data utilized for comparison with actual measured values, thereby increasing reliability of the abnormal state determination.
Re-claims 2 and 13, Cetinkaya et al. teach the input data, associated with the one or more operations of the vehicle, includes a wheel speed of the vehicle. A wheel speed is determined as part of the hydraulic pressure analysis.
Re-claims 3 and 14, Cetinkaya et al. teach a fluid pressure generated by the pump as the output variable.
Re-claim 4, communication between the control unit and the pump as well as various sensors is carried out on controller area network (as is common in vehicle architecture).
Re-claims 5 and 15, Nash further teaches the artificial neural network comprising a generative adversarial network including a generator that receives the input data, associated with the one or more operations of the vehicle, and generates the estimation value of the output variable and a discriminator configured to, in response to the input data, associated with the one or more operations of the vehicle, and the actual measurement value of the output variable, output an actual measurement related discrimination value (specifically the real data).
Re-claim 6, Nash further teaches the discriminator further configured to, in response to the input data, associated with the one or more operations of the vehicle, and the estimation value of the output variable, output an estimation related discrimination value. This is interpreted as the adversarial component of the discriminator, specifically the generated data.
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Re-claim 20, Cetinkaya et al. teach a non-transitory computer-readable storage medium (as part of a control unit 24) configured to store instructions (typically in the form of software) that when executed by one or more processors 24, cause the one or more processors to perform operations comprising: inputting input data (such as actual power consumption calculated from pump capacity, see at least column 3 lines 59-64), associated with one or more operations of a vehicle, and configured to, in response to the input data, output an estimation value (such as expected pressure, see column 5 lines 36-39) of an output variable related to an output of a hydraulic pressure generation motor configured to drive a pump configured to generate a hydraulic pressure for performing a brake, and obtaining the estimation value of the output variable; and detecting whether the hydraulic pressure generation motor is in an abnormal state based on a difference between the estimation value of the output variable and an actual measurement value of the output variable (as from pressure sensors in fluid pressure lines, see column 5 lines 31-36 and 39-40). However, Cetinkaya et al. fail to teach the control unit using an artificial neural network model, and from the artificial neural network model determining the estimation value.
Nash teaches an artificial neural network model capable of generating more realistic output data (such as an estimation value) from input data. The expectation is improved estimation values for expected pressures from pump operation parameters would improve abnormal or fault determination, when having been compared to actual pressure measurements. Therefore, as per the teachings of Nash, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have provided the control unit of Cetinkaya et al. an artificial neural network model of the type taught by Nash, thus improving the output data utilized for comparison with actual measured values, thereby increasing reliability of the abnormal state determination.
Allowable Subject Matter
Claims 7-11 and 16-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Shimada, Svensson, Versin, Zhao, Won and No each teach a pressure analysis. Hill teaches a generative network.
Any inquiries concerning this communication or earlier communications from the
examiner should be directed to Thomas Williams whose telephone number is 571-272-7128.
The examiner can normally be reached on Tuesday-Friday from 6:00 AM to 4:00 PM.
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supervisor, Robert Siconolfi, can be reached at 571-272-7124. The fax phone number for the
organization where this application or proceeding is assigned is 571-273-8300.
Any inquiry of a general nature or relating to the status of this application or proceeding
should be directed to the receptionist whose telephone number is 571-272-6584.
TJW /THOMAS J WILLIAMS/ Primary Examiner, Art Unit 3616
September 23, 2026
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