DETAILED ACTION
After considering Applicant’s Remarks submitted on 11/10/2025 Examiner has withdrawn the 101 rejection due to Examiner agreeing with Applicant that the claims, when viewed under their broadest reasonable interpretation do not recite an abstract idea at step 2A, Prong One.
Response to Arguments
103
Applicant argues that the prior art of Grimaldi in view of Rawat does not teach the amended claim limitations since Grimaldi does not teach the claim limitation of associate input waveforms as metadata, while the device is in a training mode, to input parameters that describe conditions that generated the waveforms. See pg., 8 of Applicant’s Remarks submitted on 11/10/2025(arguing that “Grimaldi does not associate input waveforms with the conditions that
generated those waveforms for training...Grimaldi is silent regarding producing a waveform because it is also silent regarding associating waveforms with the conditions that generated those waveforms”). Applicant then argues that because Grimaldi does not teach the above claim limitation, there exists no motivation to combine the prior art of Rawat since Rawat is silent regarding producing predictive waveforms. Id. at pg., 9.
Respectfully, Examiner disagrees. As a preliminary matter, in response to Applicant's arguments against the references individually, one cannot show non-obviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986)(Emphasis added). In this case, the prior art of Rawat teaches the claim limitation of produce a predictive waveform, and not Grimaldi as Applicant’s Remarks on page 8 assert. See pg., 8 of Applicant’s Remarks submitted on 11/10/2025(stating that “Grimaldi is silent regarding producing a waveform”)(Emphasis added). Furthermore, it is noted that the features upon which applicant relies upon in the Remarks (i.e., neural network does not ultimately receive those conditions as user input to predict an associated waveform that would be created by those conditions) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Unlike what Applicant asserts in the Remarks, Grimaldi does teach the above claim limitation of associate input waveforms as metadata, while the device is in a training mode, to input parameters that describe conditions that generated the waveforms. As detailed by pages 39-43 of Grimaldi, “[i]n order to accelerate the training procedure for a large training set, the neural network learning procedure is performed by...software...[which is] able to simulate three layer neural networks: the input layer, the hidden layer and the output layer.” “The ANN is trained to classify a mono-harmonic sinusoidal signals into a number of classes, each one characterized by a range of frequency and amplitude values. The output expected is represented by a binary word with four bits... [i]n order to maintain compatibility with the AU, the sample period of 125μsec was used. Every input vector consists of 20 real signal samples generated over a period.” Id. Accordingly, Grimaldi teaches the claim limitation of associate input waveforms as metadata[the sample period of 125μsec was used. Every input vector consists of 20 real signal samples generated over a period], while the device is in a training mode[The ANN is trained], to input parameters that describe conditions that generated the waveforms[classify a mono-harmonic sinusoidal signals into a number of classes, each one characterized by a range of frequency and amplitude values].1
Because Grimaldi teaches the above claim limitation, it follows that Grimaldi does provide the requisite motivation to modify the teachings of Grimaldi in view of Rawat since Rawat teaches on pages 170-172 the claim limitation of produce a predictive waveform. See the Current Office Action for the detailed teaching. Accordingly, the 103 rejection is not withdrawn.
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 Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) are:
a machine learning system configured to associate waveforms as metadata to parameters that describe conditions that generated the waveforms in Claim 1;
the parameter generator is configured to sweep through multiple values of one or more parameters to generate the sets of parameters in Claim 4.
Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 1-5, 8-14, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Grimaldi, Domenico et al., "A DSP BASED NEURAL APPARATUS." Advances in Intelligent Systems 41 (1997)(“Grimaldi”) in view of Rawat, Meenakshi et al., "A mutual distortion and impairment compensator for wideband direct-conversion transmitters using neural networks." IEEE Transactions on Broadcasting 58.2 (2012)(“Rawat”).
Regarding claim 1, Grimaldi teaches a device for generating waveforms, comprising:
a machine learning system configured to associate input waveforms as metadata, while the device is in a training mode, to input parameters that describe conditions that generated the waveforms(Grimaldi, pgs. 39-43, see also fig. 1[a machine learning system configured to], “In order to accelerate the training procedure for a large training set, the neural network learning procedure is performed by...software...[which is] able to simulate three layer neural networks: the input layer, the hidden layer and the output layer...[t]he ANN is trained to classify a mono-harmonic sinusoidal signals into a number of classes, each one characterized by a range of frequency and amplitude values. The output expected is represented by a binary word with four bits... [i]n order to maintain compatibility with the AU, the sample period of 125μsec was used. Every input vector consists of 20 real signal samples generated over a period.”);
a user interface configured to allow a user to provide one or more user inputs; one or more processors configured to execute code that causes the one or more processors
to: receive one or more inputs through the user interface, the one or more user inputs at least including one or more parameters(Grimaldi, pgs. 39-43, see also fig. 1, “The hardware tool utilized to implement the neural network is a board, ISA-BUS compatible, on which the TMS320C30, by Texas Instruments…[t]he board architecture is shown in Fig. 1… [t]he processor is programmable by means of a high-level language… [t]he data Acquisition Unit (AU) is equipped with an Analog-to-Digital Converter (ADC) with 14 bits and a maximum sampling frequency of 19.2kHz. In order to accelerate the training procedure for a large training set, the neural network learning procedure is performed by a software program[] which runs on the Personal Computer (PC)[ the one or more user inputs at least including one or more parameters]. This program[] is able to simulate three layer neural networks: the input layer, the hidden layer and the output layer…[t]he neural measurement apparatus utilizes two program[s] written in C-language with some subroutines in Assembly Language for the communications between the DSP and the PC[a user interface configured to allow a user to provide one or more user inputs; one or more processors configured to execute code that causes the one or more processors to: receive one or more inputs through the user interface].”); and
apply the machine learning system, in a runtime mode, to the received one or more parameters(Grimaldi, pgs. 39-43, see also fig. 1, tab.1, and tab. 2 “The ANN utilised in the measurement apparatus is trained to classify the actual frequency combination in the sum of two harmonic signals with a constant amplitude used in the DTMF phone selection signal into a number of classes each one characterized by a combination of two frequency values[to the received one or more parameters]... The metrological characterization of the proposed neural measurement apparatus was carried out by means of real signals acquired from external sources in two modalities (i) noise-free harmonic signals and (ii) signals affected by noise[apply the machine learning system, in a runtime mode].”);
by the machine learning system(Grimaldi, pgs. 39-43, see also fig. 1, tab.1, and tab. 2 “To create a training set, the digital signals obtained in the Matlab environment, varying frequency and amplitude values by steps of 5% and 10% in order to enhance the learning tolerance range was used. In order to maintain compatibility with the AU, the sample period of 125μsec was used. Every input vector consists of 20 real signal samples generated over a period. The neural network architecture incorporates 20 input neurons and one bias node, and four neurons of the output layer[by the machine learning system]…[t]he ANN utilised in the measurement apparatus is trained to classify the actual frequency combination in the sum of two harmonic signals with a constant amplitude used in the DTMF phone selection signal into a number of classes each one characterized by a combination of two frequency values[by the machine learning system]. Each combination corresponds to one code number according to AT&T standards… and represents one tone. The output expected is represented by a binary word with four bits.”);
based on the one or more the one or more parameters(Grimaldi, pgs. 39-43, see also fig. 1, tab.1, and tab. 2 “To create a training set, the digital signals obtained in the Matlab environment, varying frequency and amplitude values by steps of 5% and 10%[ based on the one or more the one or more parameters] in order to enhance the learning tolerance range was used. In order to maintain compatibility with the AU, the sample period of 125μsec was used. Every input vector consists of 20 real signal samples generated over a period. The neural network architecture incorporates 20 input neurons and one bias node, and four neurons of the output layer…[t]he ANN utilised in the measurement apparatus is trained to classify the actual frequency combination in the sum of two harmonic signals with a constant amplitude used in the DTMF phone selection signal into a number of classes each one characterized by a combination of two frequency values[based on the one or more the one or more parameters]. Each combination corresponds to one code number according to AT&T standards… and represents one tone. The output expected is represented by a binary word with four bits.”).
While Grimaldi teaches by the machine learning system and based on the one or more the one or more parameters, Grimaldi does not teach: produce, a predictive waveform; and output the produced waveform.
However, Rawat teaches:
produce, [by the machine learning system], a waveform [based on the one or more
parameters](Rawat, pgs. 170-172, see also figs. 2, 3, and 4, “Once the weights are adjusted to this inverse behavior, the trained model is used as a digital processing element before the PA; and, the inputs are fed to this model. The output from this element is predistorted and available as a baseband signal
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[produce, a predictive waveform]to compensate for the distortion in the Tx, which is further fed to the transmitter modulator.”);2
and output the produced waveform(Rawat, pgs. 174-175, see also figs. 5, 6, and 7, “Once the weights are extracted, the original baseband signals are processed through the model… [f]ig. 7 shows a constellation diagram for…the linearized signal after RVFTDNN-based linearization. It is evident that the linearization control circuit was able to remove most of the distortion, as the output signal was very clean[and output the produced waveform].” ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Grimaldi with the teachings of Rawat the motivation to do so would be to use a neural network to learn the “parameters” that impair waveforms so as to compensate for them(Rawat, pgs., 168-169, “This paper focuses on a topology of batch-mode backpropagation-based feedforward NNs that taps and magnifies the adaptive properties of multilayer perceptrons, which have been shown to adapt to all the linear imperfections (i.e., gain/phase errors/ripples) and provide solutions for problems caused by
nonlinearity (i.e., IMD) to finally mitigate all the imperfections in the transmitter system in one step.”).
Regarding claim 2, Grimaldi in view of Rawat teaches the device for generating waveforms according to claim 1, further comprising, in the training mode, a test automation system to send sets of parameters to the device under test, acquire resulting waveforms from the device under test, and send the sets of parameters and the resulting waveforms to the machine learning system as training input(Rawat, pgs. 171-174, see also figs. 4, 5, and tab. 2, “Fig. 4 shows a generic functional block diagram of the characterization scheme used for data acquisition…[t]he vector signal generator (VSG)[ESG4438C] itself acts as the part of the linear transmitter that performs digital modulation, digital-to-analog conversion, and frequency up-conversion, eventually feeding the RF signal to the PA input. For capturing the output waveform of the PA, the output signal is first attenuated and then fed to a vector signal analyser[E4440A][ a test automation system to send sets of parameters to the device under test, acquire resulting waveforms from the device under test, and]…[t]he vector signal analyser (VSA) functions as a receiver…[t]he resulting output data
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are then downloaded to a computer to compare with the input data, using MATLAB (Mathworks Inc.) software along with Agilent’s ADS software to determine the instantaneous amplitude and phase variation characteristics (i.e., AM/AM and AM/PM) as a function of the input power of the device under test (DUT). Using the captured output data and already available input data, models are trained in a computer to learn the inverse behavior of the transmitter system[in a training mode, send the sets of parameters and the resulting waveforms to the machine learning system as training input].” ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Grimaldi with the above teachings of Rawat for the same rationale stated at Claim 1.
Regarding claim 3, teaches Grimaldi in view of Rawat teaches the device for generating waveforms according to claim 2, in which the test automation system includes a parameter generator and a test and measurement instrument to acquire waveforms from the device under test(Rawat, pgs. 171-174, see also figs. 4, 5, and tab. 2, “Fig. 4 shows a generic functional block diagram of the characterization scheme used for data acquisition…[t]he vector signal generator (VSG)[ESG4438C] itself acts as the part of the linear transmitter that performs digital modulation, digital-to-analog conversion, and frequency up-conversion, eventually feeding the RF signal to the PA input[in which the test automation system includes a parameter generator]. For capturing the output waveform of the PA, the output signal is first attenuated and then fed to a vector signal analyser[E4440A]… [t]he vector signal analyser (VSA) functions as a receiver[and a test and measurement instrument to acquire waveforms from the device under test]”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Grimaldi with the above teachings of Rawat for the same rationale stated at Claim 1.
Regarding claim 4, Grimaldi in view of Rawat teaches the device for generating waveforms according to claim 3, in which the parameter generator is configured to sweep through multiple values of one or more parameters to generate the sets of parameters(Rawat, pgs. 171-174, see also figs. 4, 5, and tab. 2, “The transmitter system has a 10-W LDMOS class-AB type PA with a small-signal gain of 42 dB and a saturation power
of 40 dBm. The DUT is driven with an OFDM-based 16-QAM (quadrature amplitude modulation) modulated WiMAX signal with a 10-MHz bandwidth and a PAPR of 9.5 dB. These signals are modulated at a carrier frequency of 1.96 GHz for the class-AB PA. Data were sampled at a frequency of 92.16 MHz and collected over an interval of 2 milliseconds. Eight thousand (8k) data of a WiMAX signal were used… [t]he dataset for training the models was selected to contain the minimum and maximum input voltage level data points to expose the NN to the complete range of input signal states[in which the parameter generator is configured to sweep through multiple values of one or more parameters to generate the sets of parameters]”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Grimaldi with the above teachings of Rawat for the same rationale stated at Claim 1.
Regarding claim 5, Grimaldi in view of Rawat teaches the device for generating waveforms according to claim 1, in which the produced waveform is output in digital form, the device further comprising a digital-to-analog converter to convert the digital form of the produced waveform to an analog form of the produced waveform(Rawat, pgs. 171-174, see also figs. 2 and 4, “Fig. 4 shows a generic functional block diagram of the characterization scheme used for data acquisition…[t]he vector signal generator (VSG)[ESG4438C] itself acts as the part of the linear transmitter that performs digital modulation, digital-to-analog conversion, and frequency up-conversion, eventually feeding the RF signal to the PA input[the device further comprising a digital-to-analog converter to convert the digital form of the produced waveform to an analog form of the produced waveform]…[t]he resulting output data
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are then downloaded to a computer to compare with the input data, using MATLAB (Mathworks Inc.) software along with Agilent’s ADS software to determine the instantaneous amplitude and phase variation characteristics (i.e., AM/AM and AM/PM) as a function of the input power of the device under test (DUT)…the original input is applied to the model to achieve predistorted signals. These predistorted signals are then downloaded from computer to the VSG[in which the produced waveform is output in digital form]….”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Grimaldi with the above teachings of Rawat for the same rationale stated at Claim 1.
Regarding claim 8, Grimaldi in view of Rawat teaches the device for generating waveforms according to claim 1, in which the device further comprises an impairment parameter mixer for applying one or more impairments to the produced waveform(Rawat, pgs. 171-174, see also figs. 2, 4, and tab. 1, “Fig. 4 shows a generic functional block diagram of the characterization scheme used for data acquisition…[t]he vector signal generator (VSG)[ESG4438C] itself acts as the part of the linear transmitter that performs digital modulation, digital-to-analog conversion, and frequency up-conversion, eventually feeding the RF signal to the PA input…[t]able I summarizes the three cases of transmitter imperfections and the conditions undertaken for analysis in the following section. Case I is the most common case of PA nonlinearity for predistortion in the literature, which is extended to extreme impairment cases (Cases II and III), including other modulator imperfections along with the PA nonlinearity[an impairment parameter mixer for applying one or more impairments to the produced waveform].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Grimaldi with the above teachings of Rawat for the same rationale stated at Claim 1.
Regarding claim 9, Grimaldi in view of Rawat teaches the device for generating waveforms according to claim 8, in which the user interface is structured to receive an impairment selection from a user(Rawat, pgs. 171-174, see also figs. 2, 4, and tab. 1, “Fig. 4 shows a generic functional block diagram of the characterization scheme used for data acquisition…[t]he vector signal generator (VSG)[ESG4438C][ in which the user interface is structured to receive]itself acts as the part of the linear transmitter that performs digital modulation, digital-to-analog conversion, and frequency up-conversion, eventually feeding the RF signal to the PA input…[t]able I summarizes the three cases of transmitter imperfections and the conditions undertaken for analysis in the following section. Case I is the most common case of PA nonlinearity for predistortion in the literature, which is extended to extreme impairment cases (Cases II and III), including other modulator imperfections along with the PA nonlinearity[an impairment selection from a user].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Grimaldi with the above teachings of Rawat for the same rationale stated at Claim 1.
Regarding claim 10, Grimaldi in view of Rawat teaches the device for generating waveforms according to claim 1, in which the one or more processors are further structured to execute code that causes the one or more processors to train the machine learning system by creating associations in the machine learning system between the waveforms and parameters(Grimaldi, pgs. 39-43, see also fig. 1, tab.1, and tab. 2 “The board architecture is shown in Fig. 1… [t]he processor is programmable by means of a high level language…[t]he processor possesses a general purpose register file, a program[] cache, dedicated auxiliary register arithmetic units, internal dual-access memories, and one OMA channel supporting the Data Acquisition System (DAS). The machine cycle is 60 ns. The data Acquisition Unit (AU) is equipped with an Analog-to-Digital Converter (ADC) with 14 bits and a maximum sampling frequency of 19.2kHz[in which the one or more processors are further structured to execute code that causes the one or more processors to train the machine learning system]…[t]o create a training set, the digital signals obtained in the Matlab environment, varying frequency and amplitude values by steps of 5% and 10% in order to enhance the learning tolerance range was used. In order to maintain compatibility with the AU, the sample period of 125μsec was used. Every input vector consists of 20 real signal samples generated over a period. The neural network architecture incorporates 20 input neurons and one bias node, and four neurons of the output layer[by creating associations in the machine learning system between the waveforms and parameters].”).
Referring to independent claim 11, it is rejected on the same basis as
independent claim 1 since they are analogous claims.
Regarding claim 12, Grimaldi in view of Rawat teaches the method for generating waveforms according to claim 11, further comprising training the machine learning system with output from a waveform simulation device(Rawat, pgs. 171-174, see also figs. 4, 5, and tab. 2, “Fig. 4 shows a generic functional block diagram of the characterization scheme used for data acquisition…[t]he vector signal generator (VSG)[ESG4438C] itself acts as the part of the linear transmitter that performs digital modulation, digital-to-analog conversion, and frequency up-conversion, eventually feeding the RF signal to the PA input. For capturing the output waveform of the PA, the output signal is first attenuated and then fed to a vector signal analyser[E4440A]…[t]he vector signal analyser (VSA) functions as a receiver…[t]he resulting output data
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are then downloaded to a computer to compare with the input data, using MATLAB (Mathworks Inc.) software along with Agilent’s ADS software to determine the instantaneous amplitude and phase variation characteristics (i.e., AM/AM and AM/PM) as a function of the input power of the device under test (DUT). Using the captured output data and already available input data, models are trained in a computer to learn the inverse behavior of the transmitter system[training the machine learning system with output from a waveform simulation device].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Grimaldi with the above teachings of Rawat for the same rationale stated at Claim 1 since Claim 11 and Claim 1 are analogous claims.
Regarding claim 13, Grimaldi in view of Rawat teaches the method for generating waveforms according to claim 11 further comprising training the machine learning system with output from a test automation system, in which the test automation system includes a parameter generator to send sets of parameters to a device under test and a test and measurement instrument to acquire waveforms from the device under test operating according to the sets of parameters(Rawat, pgs. 171-174, see also figs. 4, 5, and tab. 2, “Fig. 4 shows a generic functional block diagram of the characterization scheme used for data acquisition…[t]he vector signal generator (VSG)[ESG4438C] itself acts as the part of the linear transmitter that performs digital modulation, digital-to-analog conversion, and frequency up-conversion, eventually feeding the RF signal to the PA input[with output from a test automation system in which the test automation system includes a parameter generator to send sets of parameters to a device under test]. For capturing the output waveform of the PA, the output signal is first attenuated and then fed to a vector signal analyser[E4440A]… [t]he vector signal analyser (VSA) functions as a receiver[and a test and measurement instrument to acquire waveforms from the device under test operating according to the sets of parameters]”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Grimaldi with the above teachings of Rawat for the same rationale stated at Claim 1 since Claim 11 and Claim 1 are analogous claims.
Referring to dependent claims 14 and 17-18, they are rejected on the same basis as
dependent claims 5 and 8-9 since they are analogous claims.
Regarding claim 19, Grimaldi in view of Rawat teaches the method for generating waveforms according to claim 11, further comprising training the machine learning system by creating associations in the machine learning system between the accepted parameters and associated waveform metadata(Grimaldi, pgs. 39-43, see also fig. 1, tab.1, and tab. 2 “The board architecture is shown in Fig. 1…[t]he data Acquisition Unit (AU) is equipped with an Analog-to-Digital Converter (ADC) with 14 bits and a maximum sampling frequency of 19.2kHz…[t]o create a training set, the digital signals obtained in the Matlab environment, varying frequency and amplitude values by steps of 5% and 10% in order to enhance the learning tolerance range was used. In order to maintain compatibility with the AU, the sample period of 125μsec was used. Every input vector consists of 20 real signal samples generated over a period[training the machine learning system by creating associations in the machine learning system between the accepted parameters and associated waveform metadata].”).
Claims 6-7 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Grimaldi, Domenico et al., "A DSP BASED NEURAL APPARATUS." Advances in Intelligent Systems 41 (1997)(“Grimaldi”) in view of Rawat, Meenakshi et al., "A mutual distortion and impairment compensator for wideband direct-conversion transmitters using neural networks." IEEE Transactions on Broadcasting 58.2 (2012)(“Rawat”) and in view of Yamazaki, Hiroshi, et al. "Ultra-high-speed optical transmission using digital-preprocessed analog-multiplexed DAC." Optics Communications 409 (2018)(“Yamazaki”).
Regarding claim 6, Grimaldi in view of Rawat teaches the device for generating waveforms according to claim 5, but does not teach: in which the analog form of the produced waveform is presented to an electrical-to-optical interface.
However, Yamazaki teaches:
in which the analog form of the produced waveform is presented to an electrical-to-optical interface(Yamazaki, pgs., 68-69, see also figs. 3 and 9, “With the DP-AM-DAC, we have demonstrated high-speed optical transmission experiments at data rates beyond 100 Gbit/s with simple intensity-modulated direct-detection (IMDD) configurations…[t]he basic experimental setup is shown in Fig. 9. The DSP including the preprocessor was emulated by an offline PC. Two channels of a CMOS-based arbitrary waveform generator (AWG) were used as the sub-DACs and their outputs were fed to the AMUX module[in which the analog form of the produced waveform]. An O-band (1.3 μm) externally modulated laser (EML) with a modulation bandwidth of >55 GHz was used as the optical transmitter[is presented to an electrical-to-optical interface]…[t]he optical signal was transmitted over standard single-mode fiber and received by a photodiode (PD).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Grimaldi in view of Rawat with the teachings of Yamazaki the motivation to do so would be to devise test bed systems i.e., DUT’s for DACs for implementing optical based transmission systems due to their high rate of data exchange and bandwidths(Yamazaki, pgs. 66-67, “Due to the exponential growth of data traffic, technologies to achieve higher data rates continue to be required in the field of optical fiber transmission systems. Recent advancement of optical transmission technologies has been backed by the evolution of digital signal processors (DSPs). Digital coherent transmission systems with advanced modulation, pulse-shaping, and impairment compensation functionalities have been extensively studied and developed to achieve high-speed long-haul transmission with per-channel (per-wavelength) data rates of 100 Gbit/s and beyond. In DSP-based transmitters, high-speed digital-to-analog converters (DACs) are essential for interfacing the DSPs and electro-optic (EO) devices, such as in-phase-and-quadrature modulators (IQMs), externally modulated lasers (EMLs), and directly modulated lasers (DMLs). The analog bandwidth of the DAC is one of the factors limiting the transmitter’s bandwidth because cutting-edge EO devices provide much larger bandwidths.”).
Regarding claim 7, Grimaldi in view of Rawat and in view of Yamazaki teaches the device for generating waveforms according to claim 6, in which the device further comprises a de-embed filter for applying electrical-to-optical compensation to the produced waveform prior to conversion by the digital-to-analog converter(Yamazaki, pg. 67, see also fig. 3, “In the type-I DP-AM-DAC, the AMUX is driven at
f
c
l
k
=
f
B
. With this condition, basically the fundamental and image generated in the AMUX do not significantly overlap each other. The digital preprocessor splits the target signal into low- and high frequency (LF and HF) signals…[s]pecifically, it emphasizes the HF signal by a factor of 1/r, flips it around
f
B
/
2
, and adds it to the LF signal with relative phases of 0 and 𝜋 to generate the signals input to the sub-DACs 1 and 2 , respectively. A digital equalizer (EQ) is then applied to each signal to de-embed the responses of the corresponding sub-DAC and the cable to the AMUX[a de-embed filter for applying electrical-to-optical compensation to the produced waveform prior to conversion by the digital-to-analog converter]”).
Referring to dependent claims 15-16, they are rejected on the same basis as
dependent claims 6-7 since they are analogous claims.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Adam C Standke/
Primary Examiner
Art Unit 2129
1 Examiner Remarks: the elements contained with square brackets i.e. [ ] are the teachings of Grimaldi associated with the previous claim element.
2 Examiner Remarks: The claim limitations that are not in bold and contained within square brackets i.e., [ ] are limitations taught by the prior art of Grimaldi