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 .
Information Disclosure Statement
The information disclosure statement filed April 21, 2025 fails to comply with the provisions of 37 CFR 1.98(a)(4) because it lacks the appropriate size fee assertion. It has been placed in the application file, but the information referred to therein has not been considered as to the merits.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The disclosure is objected to because of the following informalities: Applicant’s disclosure on p. 1, [0001] recites “…U.S. Pat. Appl. No. 18/121,397, filed on March 14, 2023; which is a Continuation of…” where it should instead recite “…U.S. Pat. Appl. No. 18/121,397, filed on March 14, 2023, now U.S. Pat. No. 12,206,535; which is a Continuation of…”
Appropriate correction is required.
The disclosure is objected to because of the following informalities: According to MPEP 608.01(m), the present Office practice is to insist that each claim must be the object of a sentence starting with “I (or we) claim,” “The invention claimed is” (or the equivalent). Thus, the heading simply stating “CLAIMS” is not sufficient.
Appropriate correction is required.
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-3, 5-11, 13-18, and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, 8, 10, 11, 15, 16, and 19 of U.S. Patent No. 12,206,535 in view of Ahmad (see citation below).
As per Claim 1, the limitations of patent Claim 1 cover the limitations of Claim 1, as shown in the table below.
However, the patent claims do not recite wherein the ANN performs the combined plurality of signal-processing operations, comprising bits-to-symbol mapping. However, Ahmad teaches wherein the machine learning (p. 445, 1st paragraph; p. 476, 4th paragraph) performs the combined plurality of signal-processing operations, comprising bits-to-symbol mapping (Fig. 13.10, p. 462). Since patent Claim 1 recites the ANN performs the combined plurality of signal-processing operations, this teaching of bits-to-symbol mapping from Ahmad can be implemented into the ANN of patent Claim 1 so that the ANN performs the combined plurality of signal-processing operations, comprising bits-to-symbol mapping.
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 patent claims so that the ANN performs the combined plurality of signal-processing operations, comprising bits-to-symbol mapping because Ahmad suggests that it is a common practice in communication systems to map data to discrete constellations useful for transmitting over a channel (p. 461, last paragraph).
As per Claims 2-3, the limitations of patent Claim 1 covers the limitations of each of Claims 2-3. As per Claims 5-7, the limitations of patent Claims 3, 10, and 11 covers the limitations of Claims 5-7 respectively.
As per Claim 8, the limitations of patent Claim 8 cover the limitations of Claim 8.
However, the patent claims do not recite wherein the ANN is configured to perform the combined plurality of signal-processing operations, comprising bits-to-symbol mapping. However, Ahmad teaches this limitation, as discussed in the rejection for Claim 1.
As per Claim 9, the limitations of patent Claim 10 covers the limitations of Claim 9. As per Claims 10-11, the limitations of patent Claim 8 covers the limitations of each of Claims 10-11. As per Claims 13-14, the limitations of patent Claims 3 and 11 covers the limitations of Claims 13-14 respectively.
As per Claim 15, the limitations of patent Claim 15 cover the limitations of Claim 15.
However, the patent claims do not recite wherein the ANN is configured to perform the combined plurality of signal-processing operations, comprising bits-to-symbol mapping. However, Ahmad teaches this limitation, as discussed in the rejection for Claim 1.
As per Claim 16, the limitations of patent Claim 16 covers the limitations of Claim 16. As per Claims 17-18, the limitations of patent Claim 15 covers the limitations of each of Claims 17-18. As per Claim 20, the limitations of patent Claim 19 covers the limitations of Claim 20.
19/002,115
Claim 1
2
3
5
6
7
8
9
10
11
13
14
15
12,206,535
Claim 1
1
1
3
10
11
8
10
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3
11
15
19/002,115
16
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12,206,535
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19
19/002,115 (Claim 1)
12,206,535 (Claim 1)
A method, comprising:
A method, comprising:
generating training data comprising sets of input data bits and corresponding output discrete-time Orthogonal Frequency Division Multiplexing (OFDM) sequences;
generating a training data set comprising a plurality of input data symbol sequences and a corresponding plurality of sets of data-modulated Orthogonal Frequency Division Multiplexing (OFDM) tones;
provisioning an artificial neural network (ANN) to take in a set of input data bits and generate a corresponding output discrete-time OFDM sequence therefrom;
provisioning an artificial neural network (ANN) to take in an input data symbol sequence and generate a corresponding set of data-modulated OFDM tones therefrom;
wherein the ANN performs a combined plurality of signal-processing operations, comprising bits-to-symbol mapping and OFDM modulation;
wherein the ANN is configured to combine a plurality of signal-processing operations, comprising OFDM modulation
bits-to-symbol mapping taught by Ahmad
training the ANN using the training data; and
training the ANN using the training data set; and
configuring the ANN to generate new discrete-time OFDM sequences from new input data bits.
configuring the ANN to generate new sets of data-modulated OFDM tones from new input data symbol sequences.
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.
Claim(s) 1, 2, 6, 8-10, and 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Commons (US009015093B1) and Ahmad (see citation below).
As per Claim 1, Commons teaches a method, comprising: generating training data comprising sets of input data bits and corresponding output discrete-time Orthogonal Frequency Division Multiplexing (OFDM) sequences; provisioning an artificial neural network (ANN) to take in a set of input data bits and generate a corresponding output discrete-time OFDM sequence therefrom (neural network can be used to control a WiFi router, these routers typically use orthogonal frequency-division multiplexing (OFDM) technology for high data rate wireless transmissions, use of neural networks as a tool for MIMO-OFDM channel estimation and compensation, col. 50, line 53-col. 51, line 6; artificial neural networks (ANNs), col. 2, lines 47-54). Commons describes “OFDM…The technology however imposes a challenge due to the increase complexity of channel equalization. Wireless channels are multipath fading channels, causing deformation in the signal. To remove the effect (imposed by channel) from received signal, the receiver needs to have knowledge of CIR (Channel impulse response) that is usually provided by a separate channel estimator. One of the many goals of this invention the use of the inventive hierarchical stacked neural networks disclosed herein as a tool for MIMO-OFDM channel estimation and compensation” (col. 50, line 61-col. 51, line 6). Thus, the data (data bits) input into the OFDM channel is input into the neural network. Then the neural network uses those data bits for OFDM channel estimation and compensation, and the estimated and compensated OFDM channel data is the OFDM sequences. Thus, Commons teaches training data comprising sets of input data bits and corresponding output OFDM sequences. Commons teaches that the neural network is an ANN (col. 2, lines 47-54). Thus, Commons teaches that the data (input data bits) input into the OFDM channel is input into the ANN. Then the ANN uses those input data bits for OFDM channel estimation and compensation (col. 50, line 61-col. 51, line 6; col. 2, lines 47-54), and the estimated and compensated OFDM channel is the discrete-time OFDM sequence. Thus, ANN converts the input data bits to a discrete-time OFDM sequence. Commons teaches wherein the ANN performs a combined plurality of signal-processing operations, comprising OFDM modulation (col. 21, lines 49-55). Commons describes “neural network technology data compression and error correction technologies, which are useful in wireless networking…Artificial Neural Networks…Data compression techniques exploit the redundancy that naturally exists in most data for efficient…transmission purposes. Here, a data set is encoded…lossy compression…The compression of images can be posed as an optimization problem where, ideally, the encoding and decoding is done in a way that optimizes the quality of the decoded data” (col. 51, lines 47-62). Thus, Commons teaches that the ANN is compressing for transmission in a transmitter. Thus, since the ANN outputs the OFDM data (col. 50, line 61-col. 51, line 6; col. 2, lines 47-54), this means that the ANN performs OFDM modulation. Commons teaches training the ANN using the training data set (training begins with the first neural network and ends with the last neural network, col. 25, lines 23-32; col. 50, line 53-col. 51, line 6); and configuring the ANN to generate new discrete-time OFDM sequences from new input data bits (col. 50, line 53-col. 51, line 6).
However, Commons does not teach wherein the ANN performs the combined plurality of signal-processing operations, comprising bits-to-symbol mapping. However, Ahmad teaches wherein the machine learning (p. 445, 1st paragraph; p. 476, 4th paragraph) performs the combined plurality of signal-processing operations, comprising bits-to-symbol mapping (Fig. 13.10, p. 462). Since Commons teaches the ANN performs the combined plurality of signal-processing operations (col. 21, lines 49-55), this teaching of bits-to-symbol mapping from Ahmad can be implemented into the ANN of Commons so that the ANN performs the combined plurality of signal-processing operations, comprising bits-to-symbol mapping.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Commons so that the ANN performs the combined plurality of signal-processing operations, comprising bits-to-symbol mapping because Ahmad suggests that it is a common practice in communication systems to map data to discrete constellations useful for transmitting over a channel (p. 461, last paragraph).
As per Claim 2, Commons teaches wherein the combined plurality of signal-processing operations comprises discrete Fourier transform spread orthogonal division multiplexing (DFT-s-OFDM), error-correction coding, or multiple-input multiple-output (MIMO) precoding (col. 21, lines 49-55). Commons describes “neural network technology data compression and error correction technologies, which are useful in wireless networking…Artificial Neural Networks…Data compression techniques exploit the redundancy that naturally exists in most data for efficient…transmission purposes. Here, a data set is encoded…lossy compression…The compression of images can be posed as an optimization problem where, ideally, the encoding and decoding is done in a way that optimizes the quality of the decoded data” (col. 51, lines 47-62). Thus, Commons teaches that the ANN is compressing for transmission in a transmitter. Thus, since the ANN outputs the OFDM data (col. 50, line 61-col. 51, line 6; col. 2, lines 47-54), this means that the ANN performs OFDM modulation. Thus, Commons teaches that the ANN is encoding the data in such a way to optimize the error correction decoding (col. 51, lines 47-62). Thus, the ANN performs error-correction coding.
As per Claim 6, Commons teaches wherein provisioning the ANN (artificial neural networks (ANNs), col. 2, lines 47-54) comprises employing a graphics processing unit (GPU) architecture (neural networks on GPUs, col. 38, lines 45-62).
As per Claim 8, Claim 8 is similar in scope to Claim 1, except that Claim 8 is directed to an apparatus, comprising: one or more processors, coupled to a memory that includes instructions to execute operations of the one or more processors, and configuring the corresponding discrete-time OFDM sequence to be a transmission signal in a wireless communication network. Commons teaches an apparatus, comprising: one or more processors, coupled to a memory that includes instructions to execute operations of the one or more processors (those techniques are performed by computer system in response to processor 404 executing one or more sequences of one or more instructions contained in main memory 406, col. 53, lines 27-30), and configuring the corresponding discrete-time OFDM sequence to be a transmission signal in a wireless communication network (routers use OFDM technology for high data rate wireless transmissions, col. 50, lines 55-57). Thus, Claim 8 is rejected under the same rationale as Claim 1.
As per Claim 9, Claim 9 is similar in scope to Claim 6, and therefore is rejected under the same rationale. As per Claim 10, Claim 10 is similar in scope to Claim 2, and therefore is rejected under the same rationale. As per Claim 15, Claim 15 is similar in scope to Claim 8, and therefore is rejected under the same rationale. As per Claims 16-17, these claims are similar in scope to Claims 1-2 respectively, and therefore are rejected under the same rationale.
Claim(s) 3, 11, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Commons (US009015093B1) and Ahmad (see citation below) in view of Xue (see citation below).
As per Claim 3, Commons and Ahmad are relied upon for the teachings as discussed above relative to Claim 1.
However, Commons and Ahmad do not teach wherein the combined plurality of signal-processing operations comprises resource mapping. However, Xue teaches wherein the combined plurality of signal-processing operations comprises resource mapping (prediction algorithm in grid resource state prediction based on neural network algorithm, p. 294, Abstract; artificial neural network, p. 294, section 2; 3. Resource prediction model base on neural network in the grid, (4) Resource state prediction module calculates the future states information for the required resource according to the prediction algorithm, and output the prediction result to resource scheduling module, (5) The resource scheduling module maps the grid resources to the application tasks according to their prediction result, p. 296-297).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Commons and Ahmad so that the combined plurality of signal-processing operations comprises resource mapping because Xue suggests that this way, the mapping and scheduling between computing applications and computing resources is done in an efficient way (p. 296-297).
As per Claims 11 and 18, these claims are each similar in scope to Claim 3, and therefore are rejected under the same rationale.
Claim(s) 4, 7, 12, 14, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Commons (US009015093B1) and Ahmad (see citation below) in view of Yigit (see citation below).
As per Claim 4, Commons and Ahmad are relied upon for the teachings as discussed above relative to Claim 1.
However, Commons and Ahmad do not teach wherein the training data reflects communication scenarios with varying channel conditions, and configuring the ANN provides for dynamically adapting the new discrete-time OFDM sequences to the varying channel conditions. However, Yigit teaches wherein the training data (construct training set in NN, set that meets the target PER for a given channel, the class i with the highest rate R is chosen, p. 392, 2nd to last paragraph) reflects communication scenarios with varying channel conditions, and configuring the ANN provides for dynamically adapting the new discrete-time OFDM sequences to the varying channel conditions (neural network framework as a machine learning technique for link adaptation based on adaptive modulation and coding in MIMO-OFDM wireless system to predict best modulation and coding scheme index under packet error rate constraints, p. 390, Abstract; adaptive modulation and coding (AMC), AMC in MIMO-OFDM selects the best modulation order and coding rate parameters based on a link quality metric obtained from the channel state information to reduce packet error rate and maximize data rate under packet error rate reliability constraint, p. 390, 1st paragraph of Introduction).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Commons and Ahmad so that the training data reflects communication scenarios with varying channel conditions, and configuring the ANN provides for dynamically adapting the new discrete-time OFDM sequences to the varying channel conditions because Yigit suggests that this reduces packet error rate and maximizes data rate under packet error rate reliability constraint (p. 390, 1st paragraph of Introduction).
As per Claim 7, Commons and Ahmad do not teach wherein the training is configured to tune the ANN to improve at least one signal property of the output discrete-time OFDM sequence, the at least one signal property comprising one or more of a low multiple-input multiple-output (MIMO) condition number, a predetermined number of eigenvalues above a threshold value, low peak-to-average-power ratio (PAPR), a low bit error probability, a high bandwidth efficiency, faster processing time, or low computational complexity. However, Yigit teaches wherein the training (construct training set in NN, set that meets the target PER for a given channel, the class i with the highest rate R is chosen, p. 392, 2nd to last paragraph) is configured to tune the ANN to improve at least one signal property of the output discrete-time OFDM sequence, the at least one signal property comprising one or more of a low multiple-input multiple-output (MIMO) condition number, a predetermined number of eigenvalues above a threshold value, low peak-to-average-power ratio (PAPR), a low bit error probability, a high bandwidth efficiency, faster processing time, or low computational complexity (neural network framework as a machine learning technique for link adaptation based on adaptive modulation and coding in MIMO-OFDM wireless system to predict best modulation and coding scheme index under packet error rate constraints, p. 390, Abstract; adaptive modulation and coding (AMC), AMC in MIMO-OFDM selects the best modulation order and coding rate parameters based on a link quality metric obtained from the channel state information to reduce packet error rate and maximize data rate under packet error rate reliability constraint, p. 390, 1st paragraph of Introduction).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Commons and Ahmad so that the training is configured to tune the ANN to improve at least one signal property of the output discrete-time OFDM sequence, the at least one signal property comprising one or more of a low multiple-input multiple-output (MIMO) condition number, a predetermined number of eigenvalues above a threshold value, low peak-to-average-power ratio (PAPR), a low bit error probability, a high bandwidth efficiency, faster processing time, or low computational complexity because Yigit suggests that this way, the data will be more accurate (p. 390, 1st paragraph of Introduction).
As per Claims 12 and 14, these claims are similar in scope to Claims 4 and 7 respectively, and thus are rejected under the same rationale. As per Claims 19-20, these claims are similar in scope to Claims 4 and 7 respectively, and thus are rejected under same rationale.
Claim(s) 5 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Commons (US009015093B1) and Ahmad (see citation below) in view of Hoydis (US 20200177418A1).
As per Claim 5, Commons and Ahmad are relied upon for the teachings as discussed above relative to Claim 1.
However, Commons and Ahmad do not teach the ANN comprises a deep learning neural network, a multilayer perceptron, or a convolutional network. However, Hoydis teaches wherein the ANN comprises a deep learning neural network, a multilayer perceptron, or a convolutional network (this is useful for multi-carrier systems, such as OFDM, where neural networks could be used to generate complex symbols that are mapped to subcarriers with the help of an inverse Fourier transform, [0080], deep neural network, [0117], deep neural network, [0118]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Commons and Ahmad so that the ANN comprises a deep learning neural network, a multilayer perceptron, or a convolutional network as suggested by Hoydis. It is well-known in the art that the multiple layers in deep neural networks allow models to become more efficient at learning complex features and performing more intensive computational tasks, and execute many complex operations simultaneously.
As per Claim 13, Claim 13 is similar in scope to Claim 5, and therefore is rejected under the same rationale.
Prior Art of Record
1. Ahmad, Aitzaz; Mathematical Foundations for Signal Processing, Communications, and Networking; December 2011; CRC Press; 1st Edition; p. 443-476; https://www.taylorfrancis.com/chapters/edit/10.1201/9781351105668-13/factor-graphs-message-passing-algorithms-aitzaz-ahmad-erchin-serpedin-khalid-qaraqe
2. Xue, Shengjun; Resource State Prediction in the Grid Based on Neural Network; January 2009; 2009 Fifth International Conference on Natural Computation; p. 294-298; https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5363881
3. Yigit, Halil; Adaptation using Neural Network in Frequency Selective MIMO-OFDM Systems; June 2010; IEEE 5th International Symposium on Wireless Pervasive Computing 2010; p. 390-393; https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5483745
Conclusion
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JH
/JONI HSU/Primary Examiner, Art Unit 2611
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