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
1. Claim 49 is objected to because of the following informalities: Line 2 recites the acronyms “HAD” and MIMO”. The meaning of the acronyms should be recited in the claims. For example, the term “hybrid analog-digital” (HAD) would clarify the “HAD” term. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
2. Claim 43 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 43 recites the limitation "the plurality of amplifier signals" in line 2. There is insufficient antecedent basis for this limitation in the claim. This limitation is introduced in claim 42.
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.
3. Claims 35, 36, 40 and 44-47 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhou et al (US 2014/0254716)
Regarding claims 35 and 45, Zhou discloses a DPD module and a method of performing digital predistortion, DPD, to provide transmit signal wherein the transmit signal is for deriving one or more amplifier signals (Figure 1: composite DPD 104 provides a transmit signal to the power amplifier 110 to be transmitted.), for driving one or more power amplifiers, wherein the one or more power amplifiers are associated with a respective one or more antenna elements (Figure 1. The output of the power amplifier 112 will be transmitted. Paragraph 0009. Narrowband transmit signal. Wideband transmission signal.), the method comprising:
receiving a first signal (Figures 1, 2, 3, 6A, 6B, 6C: the signals received on line 102 are the input signal.);
inputting the first signal into a combination model, wherein the combination model comprises a machine learning, ML, model and a memory polynomial, MP, model, and the MP model is configured to receive an output of the ML model or the ML model is configured to receive an output of the MP model (Figure 2: input signal 102 is input to the composite digital predistorter 104 comprising the memory polynomial (MP) digital predistorter 202 and the look-up table (ML) digital predistorter 204.);
outputting the transmit signal from the combination model (Figures 1, 2 and 3: output signal 106.);
receiving a feedback signal based on an output of the one or more power amplifiers (Figures 1: feedback signal 112.);
during a first time period performing training of the ML model using the feedback signal and the first signal (Paragraph 0023: in some embodiments, the overall function of training component 114 is to compare a delayed digital input signal 102 of composite DPD 104 with a demodulated digital form of the RF amplifier output signal (line 112) for determining coefficients of the look-up table DPD and the memory polynomial DPD to match the characteristics of a specific power amplifier 110. The comparison is made in the DPD training block 402 and results of the comparison are used to determine the coefficients of the LUT DPD and of the low order memory polynomial for MP DPD. The training calibrates the sub-system located between the input signal 102 and the amplified RF output signal 112 by determining DPD coefficients that improve the linearity of the sub-system and reduce its memory effects.);
during the first time period disabling training of the MP model (paragraph 0030: During a first training session 502, multiplexers 306, 310 of figure 3 are controlled to bypass the two predistorters 304 and 308 and to select the input signal 102 as the composite DPD output on line 106 as shown in figure 6A. During the first training session, a narrowband signal is applied at input 102 and coefficients for the LUT DPD 308 are computed to create an inverse of the memoryless saturation compression characteristics of power amplifier 110.);
during a second time period performing training of the MP model using the feedback signal and the first signal (Paragraph 0023: in some embodiments, the overall function of training component 114 is to compare a delayed digital input signal 102 of composite DPD 104 with a demodulated digital form of the RF amplifier output signal (line 112) for determining coefficients of the look-up table DPD and the memory polynomial DPD to match the characteristics of a specific power amplifier 110. The comparison is made in the DPD training block 402 and results of the comparison are used to determine the coefficients of the LUT DPD and of the low order memory polynomial for MP DPD. The training calibrates the sub-system located between the input signal 102 and the amplified RF output signal 112 by determining DPD coefficients that improve the linearity of the sub-system and reduce its memory effects.); and
during the second time period disabling training of the ML model (Paragraph 0032: During the second training session 504, multiplexers 306 and 310 are controlled to bypass the MP DPD 304 but to include the trained LUT DPD 308 in the input path, as illustrated in figure 6B. A wideband input signal is applied to input 12 and is predistorted by trained LUT DPD 308 to provide a composite DPD output at line 106. During the second training session, the trained memoryless DPD 308, block 108 and PA 110 are considered together as a target PA system for the memory based MP DPD training. Such a target PA system will generally have less nonlinearity than typical PAs because of the applied LUT DPD correction. A wideband input signal may be used to train and compute coefficients for the MP DPD 304 during the second training session.).
Regarding claims 36 and 46, Zhou discloses training the MP model and the ML model using the feedback signal by performing periodic and alternate updates to the MP model and the ML model (Paragraph 0023: in some embodiments, the overall function of training component 114 is to compare a delayed digital input signal 102 of composite DPD 104 with a demodulated digital form of the RF amplifier output signal (line 112) for determining coefficients of the look-up table DPD and the memory polynomial DPD to match the characteristics of a specific power amplifier 110. The comparison is made in the DPD training block 402 and results of the comparison are used to determine the coefficients of the LUT DPD and of the low order memory polynomial for MP DPD. The training calibrates the sub-system located between the input signal 102 and the amplified RF output signal 112 by determining DPD coefficients that improve the linearity of the sub-system and reduce its memory effects. This training takes place periodically when required in the system.).
Regarding claim 40, Zhou discloses wherein the first signal comprises online training data (Paragraph 0023: in some embodiments, the overall function of training component 114 is to compare a delayed digital input signal 102 of composite DPD 104 with a demodulated digital form of the RF amplifier output signal (line 112) for determining coefficients of the look-up table DPD and the memory polynomial DPD to match the characteristics of a specific power amplifier 110. The comparison is made in the DPD training block 402 and results of the comparison are used to determine the coefficients of the LUT DPD and of the low order memory polynomial for MP DPD. The training calibrates the sub-system located between the input signal 102 and the amplified RF output signal 112 by determining DPD coefficients that improve the linearity of the sub-system and reduce its memory effects. During the training, training data is used.).
Regarding claim 44, Zhou discloses wherein the transmit signal is for deriving one amplifier signal (abstract: a power amplifier system including a composite DPD ensuring optimized linearity for the power amplifier is described.).
Regarding claim 47, Zhou discloses a DPD module for training a combination module to perform digital predistortion, DPD, on a first signal (Figures 1, 2, 3, 6A, 6B, 6C: the signals received on line 102 are the input signal.) input into the combination model to provide a transmit signal wherein the transmit is for deriving one or more amplifier signals for driving one or more power amplifiers (Figure 1: composite DPD 104 provides a transmit signal to the power amplifier 110 to be transmitted.), wherein the one or more power amplifiers are associated with a respective one or more antenna elements (Figure 1. The output of the power amplifier 112 will be transmitted. Paragraph 0009. Narrowband transmit signal. Wideband transmission signal.), wherein the combination module comprises a machine learning, ML, model and a memory polynomial, MP, model, and the MP model is configured to receive an output of the ML model or the ML model is configured to receive an output of the MP model (Figure 2: input signal 102 is input to the composite digital predistorter 104 comprising the memory polynomial (MP) digital predistorter 202 and the look-up table (ML) digital predistorter 204.), the DPD module comprising processing circuitry configured to:
receive a feedback signal based on an output of the one or more power amplifiers (Figures 1: feedback signal 112.);
train the ML model during a first time period using the feedback signal and the first signal (Paragraph 0023: in some embodiments, the overall function of training component 114 is to compare a delayed digital input signal 102 of composite DPD 104 with a demodulated digital form of the RF amplifier output signal (line 112) for determining coefficients of the look-up table DPD and the memory polynomial DPD to match the characteristics of a specific power amplifier 110. The comparison is made in the DPD training block 402 and results of the comparison are used to determine the coefficients of the LUT DPD and of the low order memory polynomial for MP DPD. The training calibrates the sub-system located between the input signal 102 and the amplified RF output signal 112 by determining DPD coefficients that improve the linearity of the sub-system and reduce its memory effects.);
disable training of the MP model during the first time period (paragraph 0030: During a first training session 502, multiplexers 306, 310 of figure 3 are controlled to bypass the two predistorters 304 and 308 and to select the input signal 102 as the composite DPD output on line 106 as shown in figure 6A. During the first training session, a narrowband signal is applied at input 102 and coefficients for the LUT DPD 308 are computed to create an inverse of the memoryless saturation compression characteristics of power amplifier 110.);
train the MP model during a second time period using the feedback signal and the first signal (Paragraph 0023: in some embodiments, the overall function of training component 114 is to compare a delayed digital input signal 102 of composite DPD 104 with a demodulated digital form of the RF amplifier output signal (line 112) for determining coefficients of the look-up table DPD and the memory polynomial DPD to match the characteristics of a specific power amplifier 110. The comparison is made in the DPD training block 402 and results of the comparison are used to determine the coefficients of the LUT DPD and of the low order memory polynomial for MP DPD. The training calibrates the sub-system located between the input signal 102 and the amplified RF output signal 112 by determining DPD coefficients that improve the linearity of the sub-system and reduce its memory effects.); and
disable training of the ML model during the second time period (Paragraph 0032: During the second training session 504, multiplexers 306 and 310 are controlled to bypass the MP DPD 304 but to include the trained LUT DPD 308 in the input path, as illustrated in figure 6B. A wideband input signal is applied to input 12 and is predistorted by trained LUT DPD 308 to provide a composite DPD output at line 106. During the second training session, the trained memoryless DPD 308, block 108 and PA 110 are considered together as a target PA system for the memory based MP DPD training. Such a target PA system will generally have less nonlinearity than typical PAs because of the applied LUT DPD correction. A wideband input signal may be used to train and compute coefficients for the MP DPD 304 during the second training session.).
Claim Rejections - 35 USC § 103
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.
4. Claims 37 and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al (US 2014/0254716) in view of Lee et al (US 2023/0268943).
Regarding claim 37, Zhou discloses the method stated above. Zhou does not disclose triggering the start of the first time period in response to a request to update the ML model.
Lee discloses the transmitter shown in figure 4 comprising the predistortion circuit. Paragraphs 0143-0145 disclose the components in the communication system may request a nonlinear compensation model update and additional components of the system may receive that request. The receiving of the request will start a process of updating the model. By controlling when updates to the model will occur, the communication system will improve efficiency and effectiveness by eliminating unwanted or unnecessary updates. For these reasons, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Lee into the method of Zhou.
Regarding claim 38, Zhou discloses the method stated above. Zhou does not disclose triggering the start of the second time period in response to a request to update the MP model.
Lee discloses the transmitter shown in figure 4 comprising the predistortion circuit. Paragraphs 0143-0145 disclose the components in the communication system may request a nonlinear compensation model update and additional components of the system may receive that request. The receiving of the request will start a process of updating the model. By controlling when updates to the model will occur, the communication system will improve efficiency and effectiveness by eliminating unwanted or unnecessary updates. For these reasons, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Lee into the method of Zhou.
5. Claims 39 and 41 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al (US 2014/0254716) in view of Tan et al (US 2025/0112599).
Regarding claim 39, Zhou discloses the method stated above. Zhou does not disclose wherein the first signal comprises offline training data.
Tan discloses the predistortion system described in the abstract. Paragraph 0057 discloses there may be one or more approaches that can be applied to a known PA model for the DPD operation according to some embodiments. The UE may use a neural network to train a DPD function to perform the pre-distortion. During the offline training, the UE may use a neural network to train a DPD function to compensate for the nonlinearity of the PA model, which may be controlled by the PA parameters as inputs. Given one set of PA parameters, the offline training process may train a neural network to train a DPD function to fit the respective PA model. Because the training process may be performed offline, online training computing resources may be conserved. For this reasons, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the offline training of Tan into the method of Zhou.
Regarding claim 41, Zhou discloses the method stated above. Zhou does not disclose wherein the ML model comprises one of: a tree based ML model and neural network, NN, based ML model.
Tan discloses the predistortion system described in the abstract. Paragraph 0057 discloses there may be one or more approaches that can be applied to a known PA model for the DPD operation according to some embodiments. The UE may use a neural network to train a DPD function to perform the pre-distortion. During the offline training, the UE may use a neural network to train a DPD function to compensate for the nonlinearity of the PA model, which may be controlled by the PA parameters as inputs. Given one set of PA parameters, the offline training process may train a neural network to train a DPD function to fit the respective PA model. Because the training process may be performed offline, online training computing resources may be conserved. The use of a neural network to train the model for predistortion is well known and allows for correct training and adaptation of the predistorter to compensate for distortion in the PA. Overcoming this distortion and permitting the PA to operate in the linear region will improve the efficiency of the communication system. For these reasons, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Tan into the method of Zhou.
6. Claims 42, 43 and 48-50 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al (US 2014/0254716) in view of Jung et al (US 2022/0385317).
Regarding claim 42, Zhou discloses the method stated above. Zhou does not disclose wherein the transmit signal is for deriving a plurality of amplifier signals.
Jung discloses the transmitter shown in figure 9. The transmitter comprises the digital precoder, the DPD, the analog beamformer, the plurality of power amplifiers and the plurality of antennas. Paragraph 0086 discloses a hybrid beamformer system in an embodiment is a wireless communication system that performs transmission after sequentially performing digital beamforming and analog beamforming on an input stream. Paragraph 0091 discloses the streams to be transmitted may be subjected to digital beamforming by the digital precoder and may be input to the DPD. The streams output from the DPD may be phase shifted by the analog beamformers, amplified by the plurality of PAs and wirelessly transmitted to the received through antennas. The use of a plurality of power amplifiers and antennas will allow diversity to take place and can reduce interference in the transmission, improving the quality of the signal received by the receiver. For these reasons, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Jung into the method and modules of Zhou.
Regarding claim 43, Zhou discloses the method stated above. Zhou does not disclose deriving the plurality of amplifier signals by inputting the transmit signal into an analog beamforming module.
Jung discloses the transmitter shown in figure 9. The transmitter comprises the digital precoder, the DPD, the analog beamformer, the plurality of power amplifiers and the plurality of antennas. Paragraph 0086 discloses a hybrid beamformer system in an embodiment is a wireless communication system that performs transmission after sequentially performing digital beamforming and analog beamforming on an input stream. Paragraph 0091 discloses the streams to be transmitted may be subjected to digital beamforming by the digital precoder and may be input to the DPD. The streams output from the DPD may be phase shifted by the analog beamformers, amplified by the plurality of PAs and wirelessly transmitted to the received through antennas. Beamforming will improve the communication system by directing the transmission to the proper receiver. This will improve the quality of the signal received by the receiver and improve the overall efficiency and effectiveness of the system. For these reasons, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Jung into the method and modules of Zhou.
Regarding claims 48 and 50, Jung discloses the DPD module as stated above. Zhou does not disclose a beamforming module for performing digital predistortion, DPD, the beamforming module comprising a DPD module as stated above.
Jung discloses the transmitter shown in figure 9. The transmitter comprises the digital precoder, the DPD, the analog beamformer, the plurality of power amplifiers and the plurality of antennas. Paragraph 0086 discloses a hybrid beamformer system in an embodiment is a wireless communication system that performs transmission after sequentially performing digital beamforming and analog beamforming on an input stream. Paragraph 0091 discloses the streams to be transmitted may be subjected to digital beamforming by the digital precoder and may be input to the DPD. The streams output from the DPD may be phase shifted by the analog beamformers, amplified by the plurality of PAs and wirelessly transmitted to the received through antennas. Digital beamforming will improve the communication system by directing the transmission to the proper receiver. This will improve the quality of the signal received by the receiver and improve the overall efficiency and effectiveness of the system. For these reasons, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Jung into the method and modules of Zhou.
Regarding claim 49, the combination discloses wherein the beamforming module comprises a HAD MIMO beamforming module (Jung: figure 9. Paragraph 0086: a hybrid beamformer system in an embodiment is a wireless communication system that performs transmission after sequentially performing digital beamforming and analog beamforming on an input stream.).
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ghandhi et al (US 2014/0294120) discloses a DPD module and a method of performing digital predistortion. Figure 4 shows the transmitter comprising the DPD 404, power amplifier 410 and antenna 414. Figure 5 shows the cascading DPDs comprising a first DPD (FDPD) and a second DPD (CDPD). Each of these DPDs are triggered according to an input signal and are trained according to the input signal and a feedback signal. The adaptation of each of the DPDs take place at different times.
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/KEVIN M BURD/Primary Examiner, Art Unit 2632 6/3/2026
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