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 .
This is in response to the application filed 1/14/2025.
Information Disclosure Statement
The Information disclosure statement filed 1/14/2025 has been considered.
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.
Claim(s) 1-18 and 21 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kristensen et al., Advanced Machine Learning Techniques for Self-Interference Cancellation in Full-Duplex Radios.
Regarding claim 1, Kristensen et al., shows a method for passive intermodulation, PIM, removal in a network node, the network node comprising a transmit radio chain, a receive radio chain, and an antenna system , the method being performed by a controller , the method comprising: transmitting a transmit signal via the transmit radio chain and over-the- air from the antenna system; generating a predicted PIM signal for the transmit signal using a non-linear machine learning model by transforming the transmit signal to a signal feature representation composed of delay-aligned discrete-time samples and at least one discrete-time phase offset (Table I. and II.), wherein the non-linear machine learning model is of an architecture that uses a first neural network column for the delay-aligned discrete-time samples and a second neural network column for the at least one discrete-time phase offset, and wherein the predicted PIM signal is obtained as output from the non-linear machine learning model when the signal feature representation is fed as input to the non-linear machine learning model (Section I. and II.; CVNNs used to reduce SI); receiving a receive signal over-the-air at the antenna system and via the receive radio chain(Fig. 1); and removing PIM from the receive signal by subtracting the predicted PIM signal from the receive signal (Fig. 1, 2, Table I. and II; Abstract; i.e. Self Interference can be caused by PIM and the cancellation using CVNN to reduce SI with fewer operations and parameters).
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b. Regarding claim 2, Kristensen et al., shows the method according to claim 1, wherein the transmit signal is transmitted with a first center frequency and the receive signal is received with a second center frequency, and wherein the at least one discrete-time phase offset is a function of a difference between the first center frequency and the second center frequency (Sections I. and II.).
c. Regarding claim 3, Kristensen et al., shows the method according to claim 1, wherein the signal feature representation further is composed of any, or any combination, of: absolute value of the transmit signals, partial non-linear terms created from the transmit signal, statistics of N previous delay-aligned discrete-time samples, a weighted linear combination of the N previous delay-aligned discrete-time samples (Fig. 2; Sections II.A and II.B) .
d. Regarding claim 4, Kristensen et al., shows the method according to claim 1, wherein the predicted PIM signal is defined by the output from the non-linear machine learning model as transformed via a weighted linear combination (Fig. 2 and sections II.A and II.B).
e. Regarding claim 5, Kristensen et al., shows the method according to claim 1, wherein, in the signal feature representation, each of the delay-aligned discrete-time samples comprises a first real component and a first imaginary component, wherein the first real component and the first imaginary component for each of the delay-aligned discrete-time samples are concatenated into a respective first one-dimensional tensor, and each of the at least one discrete-time phase offset comprises a second real component and a second imaginary component, wherein the second real component and the second imaginary component for each of the at least one discrete-time phase offset are concatenated into a respective second one-dimensional tensor (Fig. 2; Tables I. and II.; Section III. B; All models are implemented using TensorFlow).
f. Regarding claim 6, Kristensen et al., shows the method according to claim 1, wherein the signal feature representation comprises discrete-time phase offsets as compressed (Section II. and III; Table I. and II.).
g. Regarding claim 7, Kristensen et al., shows the method according to claim 1, wherein the signal feature representation comprises discrete-time phase offsets for just one single sampling instant (Fig. 4 and 5; Table I. and II.; Section II. And III.).
h. Regarding claim 8, Kristensen et al., shows the method according to claim 1, wherein the signal feature representation comprises less than all delay-aligned discrete-time samples, and wherein which of all delay-aligned discrete-time samples that are included in the signal feature representation is determined using a feature attribution procedure (Fig. 4 and 5; Table I. and II.; Section II. And III.).
i. Regarding claim 9, Kristensen et al., shows the method according to claim 1, wherein the first neural network column comprises a first fully-connected layer and the second neural network column comprises a second fully-connected layer, wherein the non-linear machine learning model further comprises a common fully-connected layer, and wherein the second neural network column is merged with the first neural network column at the common fully-connected layer (Section II.B).
j. Regarding claim 10, Kristensen et al., shows the method according to claim 1, wherein the non- linear machine learning model comprises a set of model parameters, and wherein the set of model parameters are estimated as part of training the non-linear machine learning model by minimizing mean squared error between the predicted PIM signal and labelled data taken from a supervised learning dataset (Section III. B and IV. B.)
k. Regarding claim 11, Kristensen et al., shows the method according to claim 1, wherein the set of model parameters are estimated for different PIM sources (Abstract and Introduction; Section II and III).
l. Regarding claim 12, Kristensen et al., shows the method according to claim 11, wherein each of the different PIM sources represents a respective testcase, and wherein each testcase corresponds to a respective PIM source configuration (Section II. And III.).
m. Regarding claim 13, Kristensen et al., shows the method according to claim 1,wherein the non- linear machine learning model is trained with a first supervised learning dataset that is common for all the different PIM sources and a separate respective supervised learning dataset per each of the different PIM sources (Section II. and III.; Table I. and II.).
n. Regarding claim 14, Kristensen et al., shows the method according to claim 13, wherein the non-linear machine learning model is trained with the separate respective supervised learning dataset per each of the different PIM sources either using a dedicated per-testcase dataset of transmit signals or using transmit signals transmitted towards user equipment during live operation of the network node (Section II. and III.; Table I. and II.).
o. Regarding claim 15, Kristensen et al., shows the method according to claim 1,wherein the set of model parameters define a set of non-linear basis functions in the non-linear machine learning model (Section II. And III.; Table I. and II.).
p. Regarding claim 16, Kristensen et al., shows the method according to claim 1, wherein the PIM is caused by a PIM source external to the network node (Abstract and Introduction ; Fig. 1).
q. Regarding claim 17, Kristensen et al., shows the method according to claim 1, wherein the PIM is caused by an electric component of the transmit radio chain (Abstract and Introduction; Fig. 1).
r. Regarding claim 18, Kristensen et al., shows a controller for passive intermodulation, PIM, removal in a network node, the network node comprising a transmit radio chain, a receive radio chain, and an antenna system, the controller comprising processing circuitry, the processing circuitry being configured to cause the controller to: transmit a transmit signal via the transmit radio chain and over-the-air from the antenna system; generate a predicted PIM signal for the transmit signal using a non-linear machine learning model by transforming the transmit signal to a signal feature representation composed of delay-aligned discrete-time samples and at least one discrete-time phase offset, wherein the non-linear machine learning model is of an architecture that uses a first neural network column for the delay-aligned discrete-time samples and a second neural network column for the at least one discrete-time phase offset, and wherein the predicted PIM signal is obtained as output from the non-linear machine learning model when the signal feature representation is fed as input to the non-linear machine learning model; receive a receive signal over-the-air at the antenna system and via the receive radio chain; and remove PIM from the receive signal by subtracting the predicted PIM signal from the receive signal (Fig. 1, 2, Table I. and II; Abstract; i.e. Self Interference can be caused by PIM and the cancellation using CVNN to reduce SI with fewer operations and parameters’ Section I. and II.).
s. Regarding claim 21, Kristensen et al., shows a computer program for passive intermodulation, PIM, removal in a network node, the network node comprising a transmit radio chain, a receive radio chain, and an antenna system, the computer program comprising computer code which, when run on processing circuitry of a controller, causes the controller to: transmit a transmit signal via the transmit radio chain and over-the-air from the antenna system; generate a predicted PIM signal for the transmit signal using a non-linear machine learning model by transforming the transmit signal to a signal feature representation composed of delay-aligned discrete-time samples and at least one discrete-time phase offset, wherein the non-linear machine learning model is of an architecture that uses a first neural network column for the delay-aligned discrete-time samples and a second neural network column for the at least one discrete-time phase offset, and wherein the predicted PIM signal is obtained as output from the non-linear machine learning model when the signal feature representation is fed as input to the non-linear machine learning model; receive a receive signal over-the-air at the antenna system and via the receive radio chain; and remove PIM from the receive signal by subtracting the predicted PIM signal from the receive signal (Fig. 1, 2, Table I. and II; Abstract; i.e. Self Interference can be caused by PIM and the cancellation using CVNN to reduce SI with fewer operations and parameters’ Section I. and II.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
a. O’shea et al., US 10,581,469 B1, show a machine learning based nonlinear pre-distortion system (Fig. 1; Column 5-8). O’shea helps to further clarify the distortion and interference that the systems consider when using machine learning systems to train for prediction and control transmission. The disclosure of O’shea et al., further contributes to the prior known application of Machine learning processes for communication systems and model optimization for signal interference, power, and efficiency.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEAHVOSH J NIKMANESH whose telephone number is (571)270-5549. The examiner can normally be reached M-F 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Yuwen Pan can be reached at (571)272-7855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Seahvosh Nikmanesh/ Examiner, Art Unit 2649
/YUWEN PAN/ Supervisory Patent Examiner, Art Unit 2649
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