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 11 is objected to because of the following informalities: The Claim states “A motor vehicle” and this limitation was previously claimed in Claim 10 and as such this appears as a typo since the claim ought to read as “The motor vehicle” per the Applicant’s claimed invention . 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.
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.
Claims 1-3 and 9-15 are rejected under 35 U.S.C. 103 as being unpatentable over Kei et al. JP 2019217867 A (herein, Kei) and in view of US 20230137357 A1 (Chopra).
Regarding Claims 1 and 12, Kei discloses, A filtering system (¶[0048] – “…the filter model may be performed by the ECU200…”),
configured to implement filtering of a raw detection signal (Sd) provided by a sensor (11) designed to be installed on board a motor vehicle (1) (FIG. 1 and ¶[0046] – “…the steering angle detected by the steering angle sensor 50 is time-series data,…”),
[AltContent: connector]characterized by comprising a neural network stage (22) configured to receive, as an input, said raw detection signal (Sa) (¶[0047] – “…The learning model applied to the filter model is a machine-learning model using a neural network such as Deep Learning (deep learning), but may be another learning model…”) and to implement a neural network architecture configured to generate, in real time and as a function of the raw detection signal (Sd), a filtered detection signal (Sd) with a zero-phase filtering (FIG. 5 and ¶[0052] – “….as shown in FIG. 5, a zero phase filter 2027c is used to generate the teacher signal. The zero phase filter 2027c is an off-line filter which can make the phase delay of the outputs zero by using not only the past values but also the future values. By utilizing the property that the phase delay of the zero phase filter 2027c can be made zero,…”).
Kei discloses the filtering system but does not disclose an automotive application.
However, Chopra teaches an automotive application (FIG. 1 and ¶[0002] – “…In automotive applications, for example, the vehicle powertrain is generally typified by a prime mover that delivers driving torque through an automatic or manually shifted power transmission to the vehicle’s final drive system (e.g., differential, axle shafts, corner modules, road wheels, etc.)….”).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the filtering system as disclosed by Kei to be part of the automotive application as taught by Chopra. Doing so, enhances the capability of the vehicle by providing the application that is enhanced by the filtering system.
Regarding Claims 2 and 13, modified Kei further discloses, wherein said neural network stage (22) is configured to further receive, as an input, one or more further parameters of the raw detection signal (Sa) calculated in real time (¶[0049] – “…learning with a teacher signal (also referred to as "supervised learning") is used. In supervised learning, the weights in the neural network are adjusted so that the output of the filter model for the input data, i.e., the input signal, matches the teacher signal….”); and wherein said neural network architecture is configured to generate said filtered detection signal (5) also as a function of said one or more further parameters (¶[0055] – “…the filter unit 2027a inputs signals such as a target turning angle to the vehicular model 2027b, and inputs a yaw rate signal output from the vehicular model 2027b to the filter model 2027a. The filter unit 2027a also inputs the inputted signal to the filter model 2027b without passing through the car model 2027a…”).
Regarding Claims 3 and 14, modified Kei further discloses, wherein
said one or more further parameters comprise one or more of:
a frequency of said raw detection signal (S); and a difference (A) between past samples of said raw detection signal (Sa) (¶[0055] – “…The input signal includes a target steered angle and a past value of the target steered angle. An example of the input signal is time-series data of the target turning angle. Therefore, the target turning angle, the past values (x1 to xn)…”); a difference (A) between past samples of said raw detection signal (Sa) (¶[0057] – “…Such a zero phase filter 2027c exhibits frequency responses of a plurality of vehicular specifications having the same phase characteristics and different gain characteristics…”).
Regarding Claims 9 and 15, modified Kei further discloses, wherein said neural network architecture is trained and validated starting from a training signal, which is filtered in post- processing by means of a zero-phase digital filter (¶[0053] – “…by using the nonlinearity of the neural network, the filter model 2027a can realize characteristics close to those of the zero phase filter 2027c in a pseudo manner even though the filter model LA is an online filter. By using the filter model 2027a learned offline using the teaching signal,…”).
Regarding Claim 10, modified Kei further discloses, a control system (10) (FIG. 2 and ECU 100 – ¶[0013] - “electronic control unit) for controlling an automotive system (14) (¶[0013] – “…vehicle steering system…”) in a motor vehicle (1) (¶[0004] – “…the vehicle…”), comprising:
at least one sensor on board the motor vehicle (1) and configured to detect a quantity of interest for the control and to generate a raw detection signal (Sd) indicative of said quantity (FIG. 1 and ¶[0046] – “…the steering angle detected by the steering angle sensor 50 is time-series data,…”); and
a processing unit (12) (¶[0014] – “….”ECU100” - a microcomputer including a processor such as a CPU ( Central Processing Unit )…”) configured to implement a control logic of said automotive system (14),
[AltContent: connector][AltContent: connector]characterized by comprising the filtering system (20) according to claim 1 (¶[0014] – “…a DSP ( Digital Signal Processor ) and a memory. Examples of memory may be volatile memories such as RAM ( Random Access Memory ) and non-volatile memories such as ROM (Read - Only Memory )….”), configured to generate, in real time and as a function of the raw detection signal (Sd), a filtered detection signal (Sa) with a zero-phase filtering; wherein said processing unit (12) is configured to implement said control logic as a function of said filtered detection signal (Sd) (FIG. 5 and ¶[0052] – “….as shown in FIG. 5, a zero phase filter 2027c is used to generate the teacher signal. The zero phase filter 2027c is an off-line filter which can make the phase delay of the outputs zero by using not only the past values but also the future values. By utilizing the property that the phase delay of the zero phase filter 2027c can be made zero,…”).
Regarding Claim 11, modified Kei discloses, a motor vehicle (1) (See above Claim Objection) comprising the control system (10) (ECU100) according to claim 10.
Allowable Subject Matter
Claims 4-8 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUIS G DEL VALLE whose telephone number is (303)297-4313. The examiner can normally be reached Monday-Friday, 0730 - 1630 MST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anne Antonucci can be reached at (313) 446-6519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LUIS G DEL VALLE/Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/Supervisory Patent Examiner, Art Unit 3666
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