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 § 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.
1. Claim 18 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.
Regarding claim 18, the phrase "including" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. It is also unclear if all of the recited elements are being implemented at the same time such that the predistortion model is implemented on FPGAs, ASICs and DSPs. See MPEP § 2173.05(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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
2. Claims 20-26 are rejected under 35 U.S.C. 102(a) (2) as being anticipated by Jung et al (US 2022/0385317).
Regarding claim 20, Jung discloses a transmission system (Figures 1 and 11) comprising;
A deployed transmitter including a linearizer and power amplifier wherein the deployed transmitter is deployed in an operational configuration in an operational environment (Figures 1 and 11. Method and device for predistortion of signals. Paragraph 0027: an output signal of the amplifier 120 may be linearly output for the input signal.), and
A processor (Figure 3) configured with:
An input interface to input digitized linearized signals (Figure 11: the interface of 1110 receives the input signals from the digital precoder and the environmental information signals.), the linearizer signals comprising:
Information carrying signals and operational conditions parameters signals other than the information carrying signal representing metrics affecting transfer characteristics of the deployed transmitter over an entirety of the deployed transmitter operating range (Figure 11: the interface of 1110 receives the input signals from the digital precoder and the environmental information signals. Paragraph 0019: for achieving the technical objectives, a method of processing an input signal of an amplifier includes obtaining a pre-distorter configured to predistort an input signal of the amplifier using a pretrained neural network model to pre-distort the input signal of the amplifier based on signals input to and output from the amplifier obtained while the amplifier operates in a plurality of different environments.); and
A digital model of the transmitter, for processing the input digitized linearizer signals and for outputting digital model output signals (Paragraph 0005: However, an output signal with a different degree of distortion is output from the amplifier each time the environmental information is changed, so coefficients or a neural network model used in the pre-distorter needs to be updated. Hence, a problem of having to perform an operation to update the pre-distorter whenever the environmental information is changed. Paragraph 0019: for achieving the technical objectives, a method of processing an input signal of an amplifier includes obtaining a pre-distorter configured to predistort an input signal of the amplifier using a pretrained neural network model to pre-distort the input signal of the amplifier based on signals input to and output from the amplifier obtained while the amplifier operates in a plurality of different environments. Paragraph 0029: the predistorter may process the input signal by using a pretrained neural network model. The neural network model used in the predistorter 110 may be model trained in advance based on a plurality of pieces of environmental information available for the amplifier. Figure 6 shows indirect learning for training of the predistortion model where a plurality of environmental attributes and a sum of the PA outputs are used to train the model. Figure 7 shows a direct learning for training of the neural network model where the DPD receives a plurality of environmental attributes and a sum of the PA outputs to train the model.).
Regarding claim 21, Jung discloses said digital model of the transmitter being trained using said digital model signals and output signals of the deployed transmitter (Paragraph 0019: for achieving the technical objectives, a method of processing an input signal of an amplifier includes obtaining a pre-distorter configured to predistort an input signal of the amplifier using a pretrained neural network model to pre-distort the input signal of the amplifier based on signals input to and output from the amplifier obtained while the amplifier operates in a plurality of different environments. In addition, figure 6 shows the outputs of the power amplifiers being input to the DPD.).
Regarding claim 22, Jung discloses wherein the processor is further configured to continually dynamically update parameters of the digital model based on variations in a state of the deployed transmitter operating conditions and signal types and to further update the model parameters based on sensor information from the deployed environment (Paragraph 0019: for achieving the technical objectives, a method of processing an input signal of an amplifier includes obtaining a pre-distorter configured to predistort an input signal of the amplifier using a pretrained neural network model to pre-distort the input signal of the amplifier based on signals input to and output from the amplifier obtained while the amplifier operates in a plurality of different environments.).
Regarding claim 23, Jung discloses wherein the digital model is configured to predict behavior of the deployed transmitter and provide control and update of parameters of the deployed transmitter (Paragraph 0029: the predistorter may process the input signal by using a pretrained neural network model. The neural network model used in the predistorter 110 may be model trained in advance based on a plurality of pieces of environmental information available for the amplifier. Figure 6 shows indirect learning for training of the predistortion model where a plurality of environmental attributes and a sum of the PA outputs are used to train the model. Figure 7 shows a direct learning for training of the neural network model where the DPD receives a plurality of environmental attributes and a sum of the PA outputs to train the model.)..
Regarding claim 24, Jung discloses wherein the digital model is configured to predict behavior of the deployed transmitter and to provide control and update of the operating conditions parameter signals for linear operation of the transmitter (Paragraph 0029: the predistorter may process the input signal by using a pretrained neural network model. The neural network model used in the predistorter 110 may be model trained in advance based on a plurality of pieces of environmental information available for the amplifier. Figure 6 shows indirect learning for training of the predistortion model where a plurality of environmental attributes and a sum of the PA outputs are used to train the model. Figure 7 shows a direct learning for training of the neural network model where the DPD receives a plurality of environmental attributes and a sum of the PA outputs to train the model.)..
Regarding claim 25, Jung discloses wherein the processor is further configured to provide quasi- real-time training of the deployed transmitter linearizer (Paragraph 0019: for achieving the technical objectives, a method of processing an input signal of an amplifier includes obtaining a pre-distorter configured to predistort an input signal of the amplifier using a pretrained neural network model to pre-distort the input signal of the amplifier based on signals input to and output from the amplifier obtained while the amplifier operates in a plurality of different environments. This training is quasi real time training when there is delay in the training.).
Regarding claim 26, Jung discloses wherein the processor is further configured to provide real-time training of the deployed transmitter linearizer (Paragraph 0019: for achieving the technical objectives, a method of processing an input signal of an amplifier includes obtaining a pre-distorter configured to predistort an input signal of the amplifier using a pretrained neural network model to pre-distort the input signal of the amplifier based on signals input to and output from the amplifier obtained while the amplifier operates in a plurality of different environments.).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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3. Claims 1-3, 12-18 and 20-26 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 11, 13, 15, 16, 18 and 20-21 of U.S. Patent No. 12,341,544. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference discloses additional limitations.
Regarding claim 1, the reference discloses a linearizer for a transmitter comprising the input interface, data conditioning circuit and predistortion actuator as stated in claim 1. The reference includes additional limitations that are not recited in the instant claim 1. The more specific anticipates the broader.
Regarding claims 2 and 3, the reference discloses the specific functions, coefficients, parameters and operators in claim 11 that can be used are selected based on the architecture of the transmitter.
Regarding claim 12, the reference discloses the predistortion model coefficients are based on the architecture of the transmitter as described in claim 1 and target linearization performance since the coefficients are based on the training of the predistortion actuator. The predistortion actuator will be trained until the predistortion reached a desirable performance.
Regarding claim 13, the amount of training is inherently one or more iterations.
Regarding claims 14 and 15, the reference discloses the coefficients are derived by applying direct or indirect learning architecture as stated in claim 16.
Regarding claims 16 and 17, the reference discloses the model is used for the entire operating range of the transmitter as stated in claim 1. The operating range has been extended to its maximum.
Regarding claim 18, the reference discloses the predistortion model is implementing on processing devices and processing systems such as the system of claim 1.
Regarding claim 20, the reference discloses a transmission system comprising a transmitter and processor as stated in claim 13. Though Claim 13 recites a method, the transmission system comprising the transmitter (as stated in claim 13), a linearizer comprising the circuits including an interface for inputting signals, a means for applying a preconditioning operation and a circuit for utilizing a predistortion model (as stated in claims 13 and 18) and an amplifier (as stated in claim 20) are disclosed by the reference. The processor is the processing means including the input interface and the data condition circuit and the predistortion actuator circuit. The deployed transmitter is described in claim 13 and in claims 15, 19 and 20. Claim 13 discloses the signals comprise information carrying signals and operation condition parameter signals. The metrics affecting transfer characteristics are determined by comparing a set of samples of the preconditioned signals and the transmitter output signals. Additional information is recited in claim 18. Claim 13 provides additional limitations that are not required by the transmission system. The more specific anticipates the broader.
Regarding claims 21-24, the reference discloses the limitations since the metrics affecting transfer characteristics are determined by comparing a set of samples of the preconditioned signals and the transmitter output signals and the comparison is used to generate a set of coefficients for said predistortion model in the recited method for linearizing a transmitter as stated in claims 13 and 18.
Regarding claims 25 and 26, the reference discloses the training of the transmitter. The transmitter will be deployed for use in a communication system. The processor will provide quasi- real time or real time training since, during the training, as inputs change, the system will adapt to the changing conditions. Claim 18 provides this configuring to produce predistorted signals in response to signals representing parameters affecting transfer characteristics of the transmitter over the operating range of the transmitter. These characteristics change over time according to the operating conditions. The training will occur over this time.
4. Claims 4-6, 10, 11 and 19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 11, 13, 15, 16, 18 and 20-21 of U.S. Patent No. 12,341,544 in view of Lv et al (US 2023/0006741). Although the claims at issue are not identical, they are not patentably distinct from each other because the reference discloses additional limitations.
Regarding claim 4, the reference discloses the linearizer as stated above. The reference does not disclose the predistortion model being memoryless.
Lv discloses the predistortion system described in the abstract. Figure 10A shows the predistortion method comprising a static DPD and a dynamic DPD. These are two different types and categories of DPD models. Paragraph 0112 discloses when a memoryless model of the PA is coupled to the operating temperature of the PA, a model set related to the temperature is obtained. By utilizing these different methods of digital predistortion, channel conditions and operating conditions can be addressed separately, reducing the complexity of each of the models and improving the efficiency and effectiveness of the transmitter. 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 Lv into the transmitter of the reference.
Regarding claim 5, the reference discloses the linearizer as stated above. The reference does not explicitly disclose the predistortion model compensating for memory effects.
Lv discloses the predistortion system described in the abstract. Figure 10A shows the predistortion method comprising a static DPD and a dynamic DPD. These are two different types and categories of DPD models. Paragraph 0036 discloses the present algorithm is any one of a memory polynomial MP a normalized polynomial GMP etc. These algorithms will compensate for memory effects. By utilizing these different methods of digital predistortion, channel conditions and operating conditions can be addressed separately, reducing the complexity of each of the models and improving the efficiency and effectiveness of the transmitter. 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 Lv into the transmitter of the reference.
Regarding claim 6, the reference discloses the linearizer as stated above. The reference does not disclose wherein a nonlinearity order and memory depth of the prediction model being based in the architecture of the transmitter and a target linearization performance.
Lv discloses the predistortion system described in the abstract. Figure 10A shows the predistortion method comprising a static DPD and a dynamic DPD. These are two different types and categories of DPD models. The model is based on the architecture of the transmitter and the desired performance of the transmitter since the DPD transmitter is designed to meet a level of desired performance. Paragraph 0133 discloses the memory depth and the input signal as the model will comprise a nonlinearity order. By utilizing these different methods of digital predistortion, channel conditions and operating conditions can be addressed separately, reducing the complexity of each of the models and improving the efficiency and effectiveness of the transmitter. 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 Lv into the transmitter of the reference.
Regarding claims 10 and 11, the reference discloses the linearizer as stated above. The reference does not disclose the predistortion model being a distributed model including at least two or more interconnected models.
Lv discloses the predistortion system described in the abstract. Figure 10A shows the predistortion method comprising a static DPD and a dynamic DPD. These are two different types and categories of DPD models. By utilizing these different methods of digital predistortion, channel conditions and operating conditions can be addressed separately, reducing the complexity of each of the models and improving the efficiency and effectiveness of the transmitter. 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 Lv into the transmitter of the reference.
Regarding claim 19, the reference discloses the linearizer as stated above. The reference does not disclose the predistortion model is continuously updated to adapt to variations in a state of the transmitter, operating conditions and signal types based on sensing supplementary information when deployed in real-field conditions.
Lv discloses the predistortion system described in the abstract. Figure 10A shows the predistortion method comprising a static DPD and a dynamic DPD. These are two different types and categories of DPD models. The static DPD is updated according to the temperature of the PA. The dynamic DPD is updated according to the input signal from the static DPD and the feedback signal shown in figure 10B. By utilizing these different methods of digital predistortion, channel conditions and operating conditions can be addressed separately, reducing the complexity of each of the models and improving the efficiency and effectiveness of the transmitter. 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 Lv into the transmitter of the reference.
5. Claims 7-9 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 11, 13, 15, 16, 18 and 20-21 of U.S. Patent No. 12,341,544 in view of Hausmair et al (US 2018/0167092). Although the claims at issue are not identical, they are not patentably distinct from each other because the reference discloses additional limitations.
Regarding claim 7, the reference discloses the linearizer as stated above. The reference does not disclose said predistortion model compensating for crosstalk in a MIMO transmitter.
Hausmair discloses the DPD system described in the abstract. Hausmair discloses a radio node herein may comprise a radio node operating in one or more frequencies or frequency bands (paragraph 0045). Paragraph 0061 discloses to reduce crosstalk introduced before the amplifier in a multi-antenna system, multi-antenna system DPDs, often referred to as MIMO-DPDs can be applied. Paragraph 0065 also discloses the inventor have realized that the antenna crosstalk and mismatches at the antenna ports can be compensated for in a much simpler manner. Utilizing the multiple antennas can increase the capacity of the communication system. By eliminating or compensating for mismatches and errors in the communication system, the communication system can operate more efficiently and effectively. 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 teachings of Hausmair into the linearizer of the reference.
Regarding claim 8, the reference discloses the linearizer as stated above. Claim 3 recites the model is one of a plurality of predistortion models. Each of these models would be selected and applied to the linearizer when a condition dictating its selection is met. That predistortion model with its corresponding predistortion coefficients would be applied to the linearizer. The reference does not disclose said predistortion model being selected to compensate for cross-modulation and intra-band distortion between multiple bands in a multiband transmitter.
Hausmair discloses the DPD system described in the abstract. Hausmair discloses a radio node herein may comprise a radio node operating in one or more frequencies or frequency bands (paragraph 0045). Paragraph 0061 discloses to reduce crosstalk introduced before the amplifier in a multi-antenna system, multi-antenna system DPDs, often referred to as MIMO-DPDs can be applied. Paragraph 0065 also discloses the inventor have realized that the antenna crosstalk and mismatches at the antenna ports can be compensated for in a much simpler manner. This crosstalk would include the distortion between the one or more frequencies or frequency bands. Utilizing the multiple antennas can increase the capacity of the communication system. By eliminating or compensating for mismatches and errors in the communication system, the communication system can operate more efficiently and effectively. 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 teachings of Hausmair into the linearizer of the reference.
Regarding claim 9, the combination discloses wherein signals applied to the multiband transmitter are harmonically related since when the power amplifier operates in a non-linear region, other frequency components are created such as harmonics and intermodulation products which fall outside the allocated frequency range of that signal. Each signal in each the multiband would experience this. Therefore, the signals would be harmonically related since these harmonics would appear in each of the frequency bands.
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lin et al (US 2022/0345091) discloses the method of DPD optimization shown in figure 5. Paragraph 0075 discloses the established model exactly conforms to the pre-distortion processing range required by the actual signal. Since the distortion types contained in a fixed DPD model are fixed, during the processing of the multiband signal, if the contained types are not enough, a model deficiency will be caused. Therefore, the fixed DPD is fixed and will have fixed coefficients. Changes will not trigger changes in the predistortion model coefficients of the model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEVIN M. BURD whose telephone number is (571)272-3008. The examiner can normally be reached 9:30 - 5:00.
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/KEVIN M BURD/Primary Examiner, Art Unit 2632 8/13/2025
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