DETAILED ACTION
This Office action is in response to the original application filed on 12/29/2023. Claims 1-17 are
pending in the application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor
to file provisions of the AIA .
Claim Objections
3. Claim 1 is objected to because of the following informalities:
Claim 1 in line 6-7, “to at least perform:” should be replaced by “to perform at least the following:”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.— The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 5 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 5, the limitation “between the iterations” in line 3 of the claim lacks antecedent basis, which renders the claim indefinite. It is unclear as to whether this claim should depend from claim 2 (rather than claim 1) in order to have proper antecedent basis. For the purpose of examination, “between the iterations" is interpreted as being “between iterations ".
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 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-11 and 13-17 are rejected under 35 U.S.C. 103 as being unpatentable over BEHBOODI et al.
(US 2022/0123966 Al, hereinafter “Behboodi”) in view of VARATKAR et al. (US 2018/0123615 Al, hereinafter “Varatkar”).
Regarding claim 1, Behboodi discloses:
A radio receiver device, comprising (The transmission may be received at the receiver, Behboodi: Fig. 7B, [0089]):
at least one processor (A receive processor may process (e.g., demodulate and decode), Behboodi: [0045]); and
at least one non-transitory memory storing instructions that when executed with the at least one processor, cause the radio receiver device to at least perform (a non-transitory computer-readable medium with non-transitory program code. instructions stored in the memory. The program code is executed by a processor and includes program code, Behboodi: [0009]-[0010], [0117]):
wherein the determining of the log-likelihood ratios is performed with applying an iterative neural network to a frequency domain representation of the received radio signal over a transmission time interval (a tractable model may be represented by a neural network. the tractable model is differentiable. one or more parameters may be learned via backpropagation. a log-likelihood ratio (LLR) for transmitted bits may be determined based on a conditional probability of a channel. The LLR may be used for decoding, such as LDPC decoding. detection refers to finding the transmitted symbols, such as multiple-input multiple-output (MIMO) detection. The transmission may be received at the receiver as a receiver waveform, such as an OFDM waveform. the receiver may perform a fast Fourier transformation (FFT) at an FFT component to obtain the signal, Behboodi: [0028]-[0029], [0089]),
the iterative neural network comprising a single processing block iteratively executable to process the frequency domain representation of the received radio signal (The processing of each layer of a convolutional network may be considered a spatially invariant template or basis projection. the channel input x may be a sample of the channel input x, such as a sample of a received orthogonal frequency division (OFDM) waveform, Behboodi: [0067]-[0069], [0081]),
Behboodi does not explicitly disclose:
receiving a radio signal comprising information bits; and
determining log-likelihood ratios of the information bits,
the iterative neural network configured to output estimates of the log-likelihood ratios based on the processing results of the single processing block.
However, in the same field of endeavor, Varatkar teaches:
receiving a radio signal comprising information bits (Wireless node may be in communication with antennas, where antennas transmit information to wireless node over forward link. LDPC codes can be represented by bipartite graphs, wherein a set of variable nodes corresponds to bits of a code word (e.g., information bits or systematic bits), Varatkar: [0043], [0065]); and
determining log-likelihood ratios of the information bits (A decoder may then be used to decode m-bit information strings from a bitstream that has been encoded using a coding scheme. each variable node may initially be provided with a "soft bit" that indicates an estimate of the associated bit's value as determined by observations from the communications channel. The "soft bit" may be represented by a log-likelihood ratio (LLR), Varatkar: [0085]),
the iterative neural network configured to output estimates of the log-likelihood ratios based on the processing results of the single processing block (a permutation network to route LLRs (e.g., bit LLRs and a posteriori LLRs) between the memories, and the data path processors. If the signal is indicative of the a posteriori LLR value having a value within the first range of values, the mux is configured to on its output, output a signal indicative of the actual a posteriori LLR value (optionally coupled to a multiplier or bit-shifter if the quantization is a multiple other than 1) from the corresponding input, Varatkar: [0091], [0104]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Behboodi in view of Varatkar in order to further modify receiving a radio signal comprising information bits, and determining log-likelihood ratios of the information bits, and configuring the iterative neural network as to output estimates of the log-likelihood ratios based on the processing results of the single processing block from the teachings of Varatkar.
One of ordinary skill in the art would have been motivated because if the signal is indicative of the a posteriori LLR value having a value within the second range of values, the mux is configured to on its output, output a signal indicative of the corresponding quantized value for the a posteriori LLR value from the corresponding input (Varatkar: [0104]).
Regarding claim 2, Behboodi in view of Varatkar teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Behboodi does not explicitly disclose:
The radio receiver device according to claim 1, wherein the iterative neural network further comprises a detection block executable after an executed iteration of the single processing block and
wherein the instructions, when executed with the at least one processor, cause the radio receiver device to provide the estimates of the log-likelihood ratios based on the processing results of the single processing block.
However, in the same field of endeavor, Varatkar teaches:
wherein the iterative neural network further comprises a detection block executable after an executed iteration of the single processing block (The wireless device may also include a signal detector that may be used in an effort to detect and quantify the level of signals received by the transceiver. The signal detector may detect such signals as total energy, energy per subcarrier per symbol, power spectral density and other signals, Varatkar: [0061]) and
wherein the instructions, when executed with the at least one processor, cause the radio receiver device to provide the estimates of the log-likelihood ratios based on the processing results of the single processing block (A decoder may then be used to decode m-bit information strings from a bitstream that has been encoded using a coding scheme. each variable node may initially be provided with a "soft bit" that indicates an estimate of the associated bit's value as determined by observations from the communications channel. The "soft bit" may be represented by a log-likelihood ratio (LLR), Varatkar: [0079], [0085]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Behboodi in view of Varatkar in order to further modify the iterative neural network that comprises a detection block executable after an executed iteration of the single processing block, and the instructions causing the radio receiver device to provide the estimates of the log-likelihood ratios based on the processing results of the single processing block from the teachings of Varatkar.
One of ordinary skill in the art would have been motivated because one area for improvements is the area of encoding/ decoding, applicable to NR. For example, techniques for high performance LDPC codes for NR are desirable (Varatkar: [0013]).
Regarding claim 3, Behboodi in view of Varatkar teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Behboodi further discloses:
The radio receiver device according to claim 1, wherein the single processing block comprises at least two deep residual learning blocks, a deep residual learning block comprising at least two convolutional layers (A deep learning architecture may learn a hierarchy of features. the first layer may learn to recognize spectral power in specific frequencies. The second layer may learn to recognize combinations of features. a convolutional layer may apply convolutional kernels (not shown) to the image to generate a first set of feature maps. the convolutional kernel for the convolutional layer may be a 5x5 kernel that generates 28x28 feature maps, Behboodi: [0054], [0060]).
Regarding claim 4, Behboodi in view of Varatkar teaches all the claimed limitations as set forth in the rejection of claim 2 above.
Behboodi further discloses:
The radio receiver device according to claim 2. wherein the detection block comprises a 1x1 convolutional layer (detection refers to finding the transmitted symbols, such as multiple-input multiple-output (MIMO) detection. a convolutional layer may apply convolutional kernels (not shown) to the image to generate a first set of feature maps. the convolutional kernel for the convolutional layer may be a 5x5 kernel that generates 28x28 feature maps, Behboodi: [0029], [0060], [0080]).
Regarding claim 5, Behboodi in view of Varatkar teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Behboodi further discloses:
The radio receiver device according to claims 1, wherein the single processing block is configured to share its weights between the iterations (the gradient may correspond directly to the value of a weight connecting an activated neuron in the penultimate layer and a neuron in the output layer. The deep convolutional network may include multiple different types of layers based on connectivity and weight sharing, Behboodi: [0064], [0071]).
Regarding claim 6, Behboodi in view of Varatkar teaches all the claimed limitations as set forth in the rejection of claim 2 above.
Behboodi does not explicitly disclose:
The radio receiver device according to claim 2, wherein an input to a next iteration of the single processing block comprises an output of the detection block and an output of an immediately previous iteration of the single processing block.
However, in the same field of endeavor, Varatkar teaches:
wherein an input to a next iteration of the single processing block comprises an output of the detection block and an output of an immediately previous iteration of the single processing block (The output of the mux may be coupled to the metric storage to store the quantized value. if the signal is indicative of the a posteriori LLR value having a value within the second range of values, the mux is configured to on its output, output a signal indicative of the corresponding quantized value for a posteriori LLR value from the corresponding input, Varatkar: [0104]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Behboodi in view of Varatkar in order to further modify an input to a next iteration of the single processing block that comprises an output of the detection block and an output of an immediately previous iteration of the single processing block from the teachings of Varatkar.
One of ordinary skill in the art would have been motivated because one area for improvements is the area of encoding/ decoding, applicable to NR. For example, techniques for high performance LDPC codes for NR are desirable (Varatkar: [0013]).
Regarding claim 7, Behboodi in view of Varatkar teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Behboodi further discloses:
The radio receiver device according to claim 1, wherein the instructions, when executed with the at least one processor, cause the radio receiver device to perform executing iterations of the single processing block until a predefined stopping condition is satisfied (This approximation method may be referred to as stochastic gradient descent. Stochastic gradient descent may be repeated until the achievable error rate of the entire system has stopped decreasing or until the error rate has reached a target level, Behboodi: [0065]).
Regarding claim 8, Behboodi in view of Varatkar teaches all the claimed limitations as set forth in the rejection of claim 7 above.
Behboodi further discloses:
The radio receiver device according to claim 7, wherein the stopping condition comprises a required probability of success of a reference process (a log-likelihood ratio (LLR) for transmitted bits may be determined based on a conditional probability of a channel. The LLR may be used for decoding, such as LDPC decoding, Behboodi: [0029]-[0030], [0078]-[0080]).
Regarding claim 9, Behboodi in view of Varatkar teaches all the claimed limitations as set forth in the rejection of claim 7 above.
Behboodi does not explicitly disclose:
The radio receiver device according to claim 7, wherein the received information bits comprise low-density parity-check encoded information bits, and
the instructions, when executed with the at least one processor, cause the radio receiver device to perform providing the determined log-likelihood ratios to low-density parity-check decoding.
However, in the same field of endeavor, Varatkar teaches:
wherein the received information bits comprise low-density parity-check encoded information bits (RF modem that may be configured to receive and decode a wirelessly transmitted signal including an encoded message (e.g., a message encoded using a LDPC code, Varatkar: Fig. 8, [0084]-[0085]), and
the instructions, when executed with the at least one processor, cause the radio receiver device to perform providing the determined log-likelihood ratios to low-density parity-check decoding (The instructions in the memory may be executable to implement to allow a UE to decode low density parity check (LDPC) codes including non-linear log-likelihood ratio quantization techniques, Varatkar: [0059], [0063]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Behboodi in view of Varatkar in order to further modify the received information bits that comprise low-density parity-check encoded information bits, and the instructions that cause the radio receiver device to perform providing the determined log-likelihood ratios to low-density parity-check decoding from the teachings of Varatkar.
One of ordinary skill in the art would have been motivated because one area for improvements is the area of encoding/ decoding, applicable to NR. For example, techniques for high performance LDPC codes for NR are desirable (Varatkar: [0013]).
Regarding claim 10, Behboodi in view of Varatkar teaches all the claimed limitations as set forth in the rejection of claim 9 above.
Behboodi further discloses:
The radio receiver device according to claim 9, wherein the stopping condition comprises a required probability of success of the low-density parity-check decoding (a log-likelihood ratio (LLR) for transmitted bits may be determined based on a conditional probability of a channel. The LLR may be used for decoding, such as LDPC decoding, Behboodi: [0029]-[0030], [0078]-[0080]).
Regarding claim 11, Behboodi in view of Varatkar teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Behboodi further discloses:
The radio receiver device according to claim 1, wherein the instructions, when executed with the at least one processor, cause the radio receiver device to perform training the single processing block with applying a loss after an executed iteration (during training, a loss function determines a loss based on the mean μ, the variance a, and L random samples of the latent representation z. Behboodi: [0083]-[0084]).
Regarding claim 13, Behboodi in view of Varatkar teaches all the claimed limitations as set forth in the rejection of claim 9 above.
Behboodi further discloses:
The radio receiver device according to claim 9, wherein the instructions, when executed with the at least one processor, cause the radio receiver device to perform training the stopping condition to the single processing block based on success of the low-density parity-check decoding (a log-likelihood ratio (LLR) for transmitted bits may be determined based on a conditional probability of a channel. The LLR may be used for decoding, such as LDPC decoding, Behboodi: [0029]-[0030], [0078]-[0080]).
Regarding claim 14, Behboodi in view of Varatkar teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Behboodi further discloses:
The radio receiver device according to claim 1, wherein the received radio signal comprises an orthogonal frequency division multiplexing radio signal (Each demodulator may further process the input samples (e.g., for OFDM and/or the like) to obtain received symbols, Behboodi: [0045], [0081]).
Regarding claim 15, Behboodi in view of Varatkar teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Behboodi further discloses:
The radio receiver device according to claim 1, wherein the radio receiver device comprises a multiple-input and multiple-output capable radio receiver device (A MIMO detector may obtain received symbols from all R demodulators, perform MIMO detection on the received symbols, Behboodi: [0045] -[0046]).
Regarding claim 16, Behboodi discloses:
A method, comprising:
wherein the determining of the log-likelihood ratios is performed with applying, with the radio receiver device, an iterative neural network to a frequency domain representation of the received radio signal over a transmission time interval (a tractable model may be represented by a neural network. the tractable model is differentiable. one or more parameters may be learned via backpropagation. a log-likelihood ratio (LLR) for transmitted bits may be determined based on a conditional probability of a channel. The LLR may be used for decoding, such as LDPC decoding. detection refers to finding the transmitted symbols, such as multiple-input multiple-output (MIMO) detection. The transmission may be received at the receiver as a receiver waveform, such as an OFDM waveform. the receiver may perform a fast Fourier transformation (FFT) at an FFT component to obtain the signal, Behboodi: [0028]-[0029], [0089]),
the iterative neural network comprising a single processing block iteratively executable to process the frequency domain representation of the received radio signal (The processing of each layer of a convolutional network may be considered a spatially invariant template or basis projection. the channel input x may be a sample of the channel input x, such as a sample of a received orthogonal frequency division (OFDM) waveform. Behboodi: [0067]-[0069], [0081]), and
Behboodi does not explicitly disclose:
receiving, at a radio receiver device, a radio signal comprising information bits; and
determining, with the radio receiver device, log-likelihood ratios of the information bits,
the iterative neural network configured to output estimates of the log-likelihood ratios based on the processing results of the single processing block.
However, in the same field of endeavor, Varatkar teaches:
receiving, at a radio receiver device, a radio signal comprising information bits (Wireless node may be in communication with antennas, where antennas transmit information to wireless node over forward link. LDPC codes can be represented by bipartite graphs, wherein a set of variable nodes corresponds to bits of a code word (e.g., information bits or systematic bits), Varatkar: [0043], [0065]); and
determining, with the radio receiver device, log-likelihood ratios of the information bits (A decoder may then be used to decode m-bit information strings from a bitstream that has been encoded using a coding scheme. each variable node may initially be provided with a "soft bit" that indicates an estimate of the associated bit's value as determined by observations from the communications channel. The "soft bit" may be represented by a log-likelihood ratio (LLR). Varatkar: [0085]),
the iterative neural network configured to output estimates of the log-likelihood ratios based on the processing results of the single processing block (a permutation network to route LLRs (e.g., bit LLRs and a posteriori LLRs) between the memories, and the data path processors. If the signal is indicative of the a posteriori LLR value having a value within the first range of values, the mux is configured to on its output, output a signal indicative of the actual a posteriori LLR value (optionally coupled to a multiplier or bit-shifter if the quantization is a multiple other than 1) from the corresponding input, Varatkar: [0091], [0104]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Behboodi in view of Varatkar in order to further modify receiving a radio signal comprising information bits, and determining log-likelihood ratios of the information bits, and configuring the iterative neural network as to output estimates of the log-likelihood ratios based on the processing results of the single processing block from the teachings of Varatkar.
One of ordinary skill in the art would have been motivated because if the signal is indicative of the a posteriori LLR value having a value within the second range of values, the mux is configured to on its output, output a signal indicative of the corresponding quantized value for the a posteriori LLR value from the corresponding input (Varatkar: [0104]).
Regarding claim 17, Behboodi discloses:
A non-transitory program storage device readable with a radio receiver device, tangibly embodying a program of instructions executable with the radio receiver device to perform at least the following (a non-transitory computer-readable medium with non-transitory program code recorded thereon is disclosed. The program code is executed by a processor and includes program code. The program code further includes program code to perform a channel-based function, Behboodi: [0009]):
wherein the determining of the log-likelihood ratios is performed with applying an iterative neural network to a frequency domain representation of the received radio signal over a transmission time interval (a tractable model may be represented by a neural network. the tractable model is differentiable. one or more parameters may be learned via backpropagation. a log-likelihood ratio (LLR) for transmitted bits may be determined based on a conditional probability of a channel. The LLR may be used for decoding, such as LDPC decoding. detection refers to finding the transmitted symbols, such as multiple-input multiple-output (MIMO) detection. The transmission may be received at the receiver as a receiver waveform, such as an OFDM waveform. the receiver may perform a fast Fourier transformation (FFT) at an FFT component to obtain the signal Behboodi: [0028]-[0029], [0089]),
the iterative neural network comprising a single processing block iteratively executable to process the frequency domain representation of the received radio signal (The processing of each layer of a convolutional network may be considered a spatially invariant template or basis projection. the channel input x may be a sample of the channel input x, such as a sample of a received orthogonal frequency division (OFDM) waveform. Behboodi: [0067]-[0069], [0081]), and
Behboodi does not explicitly disclose:
receiving a radio signal comprising information bits; and
determining log-likelihood ratios of the information bits,
the iterative neural network configured to output estimates of the log-likelihood ratios based on the processing results of the single processing block.
However, in the same field of endeavor, Varatkar teaches:
receiving a radio signal comprising information bits (Wireless node may be in communication with antennas, where antennas transmit information to wireless node over forward link. LDPC codes can be represented by bipartite graphs, wherein a set of variable nodes corresponds to bits of a code word (e.g., information bits or systematic bits), Varatkar: [0043], [0065]); and
determining log-likelihood ratios of the information bits (A decoder may then be used to decode m-bit information strings from a bitstream that has been encoded using a coding scheme. each variable node may initially be provided with a "soft bit" that indicates an estimate of the associated bit's value as determined by observations from the communications channel. The "soft bit" may be represented by a log-likelihood ratio (LLR), Varatkar: [0085]),
the iterative neural network configured to output estimates of the log-likelihood ratios based on the processing results of the single processing block (a permutation network to route LLRs ( e.g., bit LLRs and a posteriori LLRs) between the memories, and the data path processors. If the signal is indicative of the a posteriori LLR value having a value within the first range of values, the mux is configured to on its output, output a signal indicative of the actual a posteriori LLR value (optionally coupled to a multiplier or bit-shifter if the quantization is a multiple other than 1) from the corresponding input, Varatkar: [0091], [0104]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Behboodi in view of Varatkar in order to further modify receiving a radio signal comprising information bits, and determining log-likelihood ratios of the information bits, and configuring the iterative neural network as to output estimates of the log-likelihood ratios based on the processing results of the single processing block from the teachings of Varatkar.
One of ordinary skill in the art would have been motivated because if the signal is indicative of the a posteriori LLR value having a value within the second range of values, the mux is configured to on its output, output a signal indicative of the corresponding quantized value for the a posteriori LLR value from the corresponding input (Varatkar: [0104]).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Behboodi in view of Varatkar
and in further view of AGRAWAL et al. (US 2021/0142158 Al, hereinafter “Agrawal”).
Regarding claim 12, Behboodi in view of Varatkar teaches all the claimed limitations as set forth in the rejection of claim 11 above.
Behboodi in view of Varatkar does not explicitly disclose:
The radio receiver device according to claim 11, wherein the loss comprises a sum of one or more cross-entropy losses.
However, in the same field of endeavor, Agrawal teaches:
wherein the loss comprises a sum of one or more cross-entropy losses (the loss function which is minimised to optimise trainable parameters may be a cross entropy multi-loss function, The cross entropy multi-loss function includes contributions from all even layers up to and including the layer at which the syndrome check is satisfied, Agrawal: [0151]-[0153]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Behboodi and Varatkar in view of Agrawal in order to further modify the loss that comprises a sum of one or more cross-entropy losses from the teachings of Agrawal.
One of ordinary skill in the art would have been motivated because Machine Learning based algorithms can lead to reliability (low error rate), generality, low latency (low complexity), and energy efficiency in communication system design. (Agrawal: [0275]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
References considered relevant to this application are listed in the attached “Notice of References Cited” (PTO-892).
O'Shea et al. (US 2020/0343985 Al); See Fig. 2A, [0064] -[0065], [0133].
TULLBERG et al. (US 2021/0110241 Al); See [0005], [0097]-[0099].
VANKAYALA et al. (US 2022/0278769 Al); See Fig. 3, [0105]-[0109].
Any inquiry concerning this communication or earlier communications from the examiner should be
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/S.C.L./Examiner, Art Unit 2467
/Robert C Scheibel/Primary Examiner, Art Unit 2467 January 23, 2026