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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
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, 2 and 11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Luo (US 2022/0052885). a) Regarding claim 1, Luo discloses a wireless communication device (wireless transmitter shown in Fig. 11) comprising:
first processing circuitry (1118, 1120, and 1180 in Fig. 11) configured to generate a calibration signal by performing linearity calibration on a first input signal using a linearity calibration model, the linearity calibration model being based on a first neural network (Fig. 5A); and
a power amplifier (1140 in Fig. 11) configured to generate a calibrated output signal by amplifying the calibration signal based on an amplification coefficient,
wherein the linearity calibration model comprises:
a first output layer (508 in Fig. 5A) configured to generate a first IQ compensation value and a second IQ compensation value by performing IQ mismatch compensation on the first input signal, the first IQ compensation value corresponding to a real part of the calibrated output signal, and the second IQ compensation value corresponding to an imaginary part of the calibrated output signal (Pub [0066], to compensate for I/Q imbalance; Fig. 12, Pub [0123], DPM has input I and Q with B’(n) to produce I and Q compensation output), and
second processing circuitry (1120 in Fig. 11) configured to generate a first predistortion value and a second pre-distortion value by performing predistortion on the first input signal, the first pre-distortion value corresponding to the real part of the calibrated output signal, and the second pre-distortion value corresponding to the imaginary part of the calibrated output signal (Pub [0097]; in view of Fig. 11 and 12 the DPD determines the predistortion values for I and Q path based on the input I and Q and recurrent neural network), and
the first processing circuitry is configured to generate the calibration signal based on the first IQ compensation value, the second IQ compensation value, the first pre-distortion value and the second pre-distortion value (Fig. 11; Pub [0097]). b) Regarding claim 11, Luo discloses an operating method of a wireless communication device (wireless transmitter shown in Fig. 11), the method comprising:
generating an IQ compensation value by performing IQ mismatch compensation on a first input signal using a linearity calibration model, the linearity calibration model being based on a neural network (1118, 1120, and 1180 in Fig. 11; Fig. 5; Pub [0066], to compensate for I/Q imbalance; Fig. 12, Pub [0123], DPM has input I and Q with B’(n) to produce I and Q compensation output);
generating a pre-distortion value by performing pre-distortion on the first input signal using the linearity calibration model (DPD in Fig. 11; Pub [0097]);
generating a first calibration signal based on the IQ compensation value and the pre-distortion value (output of mixer 1137 in Fig. 11);
generating a calibrated first output signal by amplifying the first calibration signal based on an amplification coefficient (1140 in Fig. 11); and
training the linearity calibration model based on the first input signal and the calibrated first output signal (feedback signal to recurrent neural network shown in Fig. 11; Fig. 5A; Pub [0063-0066]). c) Regarding claim 2, Luo discloses wherein the first processing circuitry is configured to update parameters of the linearity calibration model based on the first input signal and the calibrated output signal (feedback signal shown in Fig. 11; Pub [0022]).
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 6-8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Luo (US 2022/0052885) in view of Saikrishna et al (US 2023/0138959). a) Regarding claim 16, Luo discloses an operating method of a wireless communication device (wireless transmitter shown in Fig. 11), the method comprising:
generating an IQ compensation value by performing IQ mismatch compensation on a first input signal using a linearity calibration model, the linearity calibration model being based on a first neural network (1118, 1120, and 1180 in Fig. 11; Fig. 5; Pub [0066], to compensate for I/Q imbalance; Fig. 12, Pub [0123], DPM has input I and Q with B’(n) to produce I and Q compensation output);
generating a pre-distortion value by performing pre-distortion on the first input signal using the linearity calibration model (DPD in Fig. 11; Pub [0097]);
generating a first calibration signal based on the IQ compensation value and the pre-distortion value (output of mixer 1137 in Fig. 11);
generating a calibrated first output signal by amplifying the first calibration signal based on an amplification coefficient (1140 in Fig. 11).
Luo discloses I/Q imbalance compensation with a neural network, but did not explicitly teach training a power amplifier estimation model based on the first input signal and a first output signal to obtain a trained power amplifier estimation model, the power amplifier estimation model being based on a second neural network, and the first output signal being obtained by amplifying the first input signal based on the amplification coefficient.
However, Saikrishna et al disclose controlling of non-linearity of a power amplifier using a neural network (Fig. 5 and 7). The machine learning of memory polynomial enables highly efficient operation and cost reduction. Therefore, it is obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the I/Q imbalance compensation with a (first) neural network of Luo with the controlling non-linearity of a power amplifier using a (second) neural network of Saikrishna et al. By doing so, efficiently and effetely compensate I/Q imbalance and power amplifier. b) Regarding claim 6, Luo discloses I/Q imbalance compensation with a neural network, but did not explicitly teach a power amplifier estimation model based on a second neural network, wherein the first processing circuitry is configured to update parameters of the power amplifier estimation model based on the first input signal and a first output signal, the first output signal being obtained by amplifying the first input signal based on the amplification coefficient.
However, Saikrishna et al disclose controlling of non-linearity of a power amplifier using a neural network (Fig. 5 and 7). The machine learning of memory polynomial enables highly efficient operation and cost reduction. Therefore, it is obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the I/Q imbalance compensation with a (first) neural network of Luo with the controlling non-linearity of a power amplifier using a (second) neural network of Saikrishna et al. By doing so, efficiently and effetely compensate I/Q imbalance and power amplifier. c) Regarding claim 7, Saikrishna et al disclose wherein the power amplifier estimation model is configured to generate an estimated output signal based on the calibration signal (Fig. 5); and the first processing circuitry is configured to update parameters of the linearity calibration model based on the first input signal and the estimated output signal (Luo, Fig. 11). d) Regarding claim 8, Saikrishna et al disclose wherein the first processing circuitry is configured to: calculate an error vector magnitude (EVM) based on the first input signal and an estimated output signal; calculate an adjacent channel leakage ratio (ACLR) based on the estimated output signal; and update parameters of the linearity calibration model based on at least one of the EVM or the ACLR (Pub [0040]). e) Regarding claim 14, Luo discloses training the linearity calibration model such that a difference between the first input signal and the calibrated first output signal is minimized (Fig. 5 and 11; Pub [0066], compensate I/Q imbalance and noise). Luo did not explicitly teach training the linearity calibration model further based on at least one of an error vector magnitude (EVM) or an adjacent channel leakage ratio (ACLR), the EVM being calculated based on the first input signal and the calibrated first output signal, and the ACLR being calculated based on the calibrated first output signal.
However, Saikrishna et al disclose trained neural network based DPD monitors EVM parameters and ACLR parameters (Pub [0040]). EVM and ACLR are critical performance metrics in wireless communications. Therefore, it is obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the I/Q imbalance compensation with a neural network of Luo with the training DPD with neural network of Saikrishna et al. By doing so, optimize linearization performance in DPD.
Allowable Subject Matter
Claims 3-5, 9-10, 12, 13, 15 and 17-20 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 2021/0391832 Barbu et al disclose machine learning based DPD. US 2021/0203369 Kasargod et al disclose DPD and calibration.
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.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chieh Fan can be reached on 571-272-3042. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). January 15, 2026
/EVA Y PUENTE/ Primary Examiner, Art Unit 2632