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 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.
Claims 1-4, 7, 10-14, 16, 19 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nguyen et al (US 2021/0231701) (IDS). a) Regarding claim 1, Nguyen et al disclose a method of equalization, the method comprising:
obtaining, from a non-volatile memory, measured sensor parameters of a microelectromechanical systems (MEMS) sensor (110) that is coupled to a sensor signal processor (120 in Fig. 1; Pub [0023-0025]);
determining, based on the measured sensor parameters, one or more equalization filter coefficients for an equalizer of a digital signal processor (411 in Fig. 4; Pub [0030-0031], [0036-0037]);
obtaining, from the sensor signal processor, sensor data indicating a sensor output signal of the MEMS sensor (412 in Fig. 4; Pub [0038]); and
equalizing, using the equalization filter of the digital signal processor, the sensor data (130 in Fig. 1; 414 and 416 in Fig. 4; Pub [0039-0040]). b) Regarding claim 11, Nguyen et al disclose a microelectromechanical systems (MEMS) signal processing system comprising:
a MEMS sensor (110 in Fig. 1; Pub [0023]);
a sensor signal processor (120, 130, 140, 150 and 160 in Fig. 1) coupled to the MEMS sensor and configured to output sensor data indicating a sensor output signal of the MEMS sensor, wherein the sensor signal processor comprises a non-volatile memory (140) storing measured sensor parameters of the MEMS sensor (Pub [0036]); and
a digital signal processor (130 in Fig. 1) configured to receive the sensor data and the measured sensor parameters from the sensor signal processor, the digital signal processor further configured to determine one or more equalization filter coefficients based on the measured sensor parameters and to equalize the sensor data based on the one or more equalization filter coefficients (Pub [0031]; 414 and 416 in Fig. 4). c) Regarding claim 20, Nguyen et al disclose a microelectromechanical systems (MEMS) module comprising:
a MEMS sensor (110 in Fig. 1; Pub [0023]); and
a sensor signal processor (120, 130, 140, 150 and 160 in Fig. 1) coupled to the MEMS sensor and configured to output sensor data indicating a sensor output signal of the MEMS sensor, wherein the sensor signal processor comprises a non-volatile memory storing measured sensor parameters of the MEMS sensor (Pub [0036]),
wherein the sensor signal processor is further configured to provide the sensor data and the measured sensor parameters to a digital signal processor (130 in Fig. 1; Pub [0031]) that equalizes the sensor data based on the measured sensor parameters (Fig. 4). d) Regarding claims 2 and 12, Nguyen et al disclose wherein the measured sensor parameters include a resonant frequency and a quality-factor of the MEMS sensor (Pub [0036]). e) Regarding claims 3 and 13, Nguyen et al disclose wherein the measured sensor parameters further include a measured gain of a signal channel of the sensor signal processor (Fig. 2; Pub [0028], magnitude is closely related to channel gain, channel gain is often expressed as magnitude squared). f) Regarding claims 4 and 14, Nguyen et al disclose wherein the measured sensor parameters include at least one equalization filter coefficient (Fig. 4; Pub [0037]). g) Regarding claims 7 and 16, Nguyen et al disclose further comprising decimating the sensor data prior to equalizing the sensor data (Pub [0045]). h) Regarding claims 10 and 19, Nguyen et al disclose wherein the MEMS sensor comprises a MEMS accelerometer (Pub [0023]).
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.
Claims 5, 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Nguyen et al (US 2021/0231701) (IDS) in view of Pan et al (US 2015/0362362). a) Regarding claims 5 and 15, Nguyen et al disclose MEMS sensor and digital equalization. Nguyen et al did not explicitly teach wherein the sensor data is in a pulse density modulation (PDM) format.
However, Pan et al disclose MEMS sensors and control interfaces and comprising a pulse density modulation output (Fig. 3 and Fig. 4; Pub [0055]). Pulse density modulation (PDM) is a well known technique to represent analog signal with a binary signal in MEMS sensors. Therefore, it is obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the PDM teaching of Pan et al with the MEMS sensor of Nguyen et al. By doing so, facilitate signal format conversion and produce desirable output of a system.
b) Regarding claim 6, Nguyen et al disclose wherein the non-volatile memory is on the sensor signal processor (140 in Fig. 1), but did not explicitly teach the method further comprising obtaining the measured sensor parameters using an interface of the sensor signal processor.
However, Pan et al disclose MEMS sensors and control interfaces (1204 in Fig. 12; Pub [0088-0089]). The interface component can facilitate signal processing. Therefore, it is obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the interface component teaching of Pan et al with the MEMS sensor of Nguyen et al. By doing so, facilitate signal processing and functionality.
Allowable Subject Matter
Claims 8, 9, 17 and 18 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. US 9,291,636 Otis et al disclose acceleration sensor. US 2022/0216829 Petrovic et al disclose adaptive filter with acceleration.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Eva Y Puente whose telephone number is 571-272-3049. The examiner can normally be reached on M-F, 7:30 AM to 5:00 PM.
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June 10, 2026
/EVA Y PUENTE/ Primary Examiner, Art Unit 2632
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