DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, anycorrection of the statutory basis for the rejection will not be considered a new ground ofrejection if the prior art relied upon, and the rationale supporting the rejection, would bethe same under either status.
Claim Rejections - 35 USC § 103
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 non-obviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1 and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Matsuda et al. (US 2023/0370901 A1) in view of Kaya et al. (EP 3,826,415 A1).
Regarding claim 1, Matsuda et al. teach a radio receiver device, comprising: at least one processor); at least one memory including computer program code; and at least one receive antenna; the at least one memory and the computer program code configured to, with the at least one processor, cause the radio receiver device at least to perform (Figs. 4, 6, 10 [0102, 0104, 0109], base station 20 includes a radio communication unit 21, a storage unit 22, and a control unit 23. The control unit 23 is a controller that controls each unit of the base station 20. The control unit 23 is realized, for example, by a processor such as a central processing unit (CPU) or a micro processing unit (MPU). For example, the control unit 23 is realized by the processor executing various programs stored in the storage device inside the base station 20 with a random access memory (RAM). Furthermore, the antenna 213 may include a plurality of antenna elements (for example, a plurality of patch antennas). In this case, the radio communication unit 21 may be configured to be beam formable),
Matsuda et al. teach receiving over a physical random-access channel, PRACH, via one or more of the at least one receive antenna, at least one uplink, UL, synchronization signal, each of the at least one UL synchronization signal comprising a PRACH preamble, the PRACH preamble comprising a set of at least one instance of a preamble sequence (Figs. 4, 6 and 9-10, [0204, 0215], the random access procedure is also used for the purpose of “scheduling request” for making a resource request for uplink data transmission and a “timing advance adjustment” for adjusting uplink synchronization. The terminal device 40 randomly selects a preamble sequence to be used from a plurality of predetermined preamble sequences. Then, the terminal device 40 transmits a message (Message 1: Random Access Preamble) including the selected preamble sequence to the connection destination base station 20 (Step S201). The random access preamble is transmitted by the PRACH),
Matsuda et al. teach extracting the set of the at least one instance of the preamble sequence; and processing the extracted set of the at least one instance of the preamble sequence (Figs. 4, 6 and 10, [0216], when receiving the random access preamble, the control unit 23 of the base station 20 transmits a random access response (Message 2) to the random access preamble to the terminal device 40. This random access response is transmitted using, for example, the PDSCH. The terminal device 40 receives the random access response (Message 2) transmitted from the base station 20 (Step S202). The random access response includes one or a plurality of random access preambles that can be received by the base station 20 and a resource (hereinafter, referred to as an uplink grant) of an up link (UL) corresponding to the random access preamble. In addition, the random response includes a temporary cell radio network temporary identifier (TC-RNTI) which is an identifier unique to the terminal device 40 temporarily assigned to the terminal device 40 by the base station 20),
Matsuda et al. teach wherein the processing of the extracted set of the at least one instance of the preamble sequence comprises applying a neural network, NN, to the extracted set of the at least one instance of the preamble sequence, the NN comprising at least one of a fully connected layer, a recurrent neural network layer, or a convolutional neural network layer, and the NN being executable to (Figs. 4, 6 and 10-11, [0224, 0308, 0310], The control unit 23 of the base station 20 receives the random access preamble (Message 1) from the terminal device 40. Then, the control unit 23 transmits a random access response (Message 2) to the random access preamble to the terminal device 40 (Step S303). The random access response includes, for example, information about the uplink grant corresponding to the received random access preamble. The communication device can indicate the height of degree of adaptability to the signal processing scheme to another communication device by using the numerical information (for example, levels 0, 1, 2,...). For example, the terminal device 40 may express a signal processing scheme that can be supported by the terminal device 40 by the numerical information. Then, the terminal device 40 may indicate this numerical information to the base station 20 as its own capability information. The neural network model is, for example, a model in a form called a convolution neural network (CNN), a recurrent neural network (RNN), or a long short-term memory (LSTM)),
Matsuda et al. teach determine at least one of a physical root sequence index, an associated cyclic shift value, a timing offset value, or any combination thereof, for at least one instance of the preamble sequence in the extracted set of the at least one instance of the preamble sequence (Figs. 4, 6 and 10-12, [0244], where the transmission resource of the preamble is determined, the transmission resource of the PUSCH that can be unique or a plurality of candidates is determined. As an example, the time offset and the frequency offset between the preamble of the PRACH occasion and the PUSCH occasion are defined by one value. As another example, the time offset and the frequency offset between the preamble of the PRACH occasion and the PUSCH occasion are set to different values for each preamble. The value of the offset may be determined by a specification, or may be quasi-statically set by the base station 20. As an example of the values of the time offset and the frequency offset, for example, it is defined by a predetermined frequency),
Matsuda et al. teach and output at least one of the determined at least one of the physical root sequence index, the associated cyclic shift value, the timing offset value, or the any combination thereof, for the at least one instance of the preamble sequence in the extracted set of the at least one instance of the preamble sequence (Figs. 4, 6 and 10-12, [0309, 0311], an output layer, including a plurality of nodes, and the nodes are connected via edges. The hidden layer includes layers called a convolution layer and a pooling layer. In the convolution layer, filtering is performed by a convolution operation, and data called a feature map is extracted. In the pooling layer, information about the feature map output from the convolutional layer is compressed, and down-sampling is performed. The CNN is used, for example, for image recognition. Information about each pixel, which is also referred to as a pixel of an image, is input to an input layer, and information related to the image recognized as an output layer can be obtained. The transmission resource of the preamble is determined, the transmission resource of the PUSCH that can be unique or a plurality of candidates is determined. As an example, the time offset and the frequency offset between the preamble of the PRACH occasion and the PUSCH occasion are defined by one value).
Matsuda et al. is teaching of an access point receiving, from a terminal device, PRACH preamble for processing to obtain uplink synchronization. Matsuda et al., however, fail to expressly teach of extracting a preamble sequence and processing it by applying a neural network. (Emphasis added).
Regarding claim 1, Kaya et al. teach extracting the set of the at least one instance of the preamble sequence; and processing the extracted set of the at least one instance of the preamble sequence, wherein the processing of the extracted set of the at least one instance of the preamble sequence comprises applying a neural network (Figs. 3 and 6, [0069, 0083], the access node attempts detection (i.e. extracting) of the random access preamble in the received signal. Upon detecting the preamble in the received signal, the access node may label the received signal with information indicating that the received signal comprises the random access preamble before applying the received signal as the training input. The label may comprise at least one of the following information elements: indication of the presence of a random access preamble in the received signal, an index of the random access preamble comprised in the received signal, and an index and a cyclic shift of a logical root sequence of the random access preamble comprised in the received signal. The random access preamble detection process, as the training input the random access preambles acquired, uses a neural network. The neural network may be a convolutional neural network or a deep neural network, or a combination thereof. Operation of the neural network may be logically separated into two steps: training and inference. In the training phase, the training input is acquired, comprising the input sequence(s) and label(s), as described above. An input sequence may include an in-phase (1) and a quadrature (Q) component of the received signal. Thus, a received signal of 4095 samples may be represented by a 4095x2 matrix. The labels may include the information element(s) described above. Once the neural network has been trained, a signal received from the idle terminal device 102 may be processed with the trained neural network in order to predict the presence of the random access preamble and/or the time of arrival of the random access preamble).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Matsuda et al. by incorporating the features as taught by Kaya et al. in order to provide a more effective and efficient system that is capable of extracting the set of the at least one instance of the preamble sequence; and processing the extracted set of the at least one instance of the preamble sequence, wherein the processing of the extracted set of the at least one instance of the preamble sequence comprises applying a neural network. The motivation is to support an improved method of detecting a preamble at an access node (see [0001]).
Regarding claim 16, Matsuda et al. teach a method comprising: receiving, at a radio receiver device over a physical random-access channel, PRACH, via at least one receive antenna of the radio receiver device, at least one uplink, UL, synchronization signal, each of the at least one UL synchronization signal comprising a PRACH preamble, the PRACH preamble comprising a set of at least one instance of a preamble sequence (Figs. 4, 6, 9-10 [0102, 0104, 0109, 0204, 0215], base station 20 includes a radio communication unit 21, a storage unit 22, and a control unit 23. The control unit 23 is a controller that controls each unit of the base station 20. The control unit 23 is realized, for example, by a processor such as a central processing unit (CPU) or a micro processing unit (MPU). For example, the control unit 23 is realized by the processor executing various programs stored in the storage device inside the base station 20 with a random access memory (RAM). Furthermore, the antenna 213 may include a plurality of antenna elements (for example, a plurality of patch antennas). In this case, the radio communication unit 21 may be configured to be beam formable. The random access procedure is also used for the purpose of “scheduling request” for making a resource request for uplink data transmission and a “timing advance adjustment” for adjusting uplink synchronization. The terminal device 40 randomly selects a preamble sequence to be used from a plurality of predetermined preamble sequences. Then, the terminal device 40 transmits a message (Message 1: Random Access Preamble) including the selected preamble sequence to the connection destination base station 20 (Step S201). The random access preamble is transmitted by the PRACH),
Matsuda et al. teach extracting, by the radio receiver device, the set of the at least one instance of the preamble sequence (Figs. 4, 6 and 10, [0216], when receiving the random access preamble, the control unit 23 of the base station 20 transmits a random access response (Message 2) to the random access preamble to the terminal device 40. This random access response is transmitted using, for example, the PDSCH. The terminal device 40 receives the random access response (Message 2) transmitted from the base station 20 (Step S202). The random access response includes one or a plurality of random access preambles that can be received by the base station 20 and a resource (hereinafter, referred to as an uplink grant) of an up link (UL) corresponding to the random access preamble. In addition, the random response includes a temporary cell radio network temporary identifier (TC-RNTI) which is an identifier unique to the terminal device 40 temporarily assigned to the terminal device 40 by the base station 20),
Matsuda et al. teach applying, by the radio receiver device, a neural network, NN, to the extracted set of the at least one instance of the preamble sequence to (Figs. 4, 6 and 10-11, [0224, 0308, 0310], The control unit 23 of the base station 20 receives the random access preamble (Message 1) from the terminal device 40. Then, the control unit 23 transmits a random access response (Message 2) to the random access preamble to the terminal device 40 (Step S303). The random access response includes, for example, information about the uplink grant corresponding to the received random access preamble. The communication device can indicate the height of degree of adaptability to the signal processing scheme to another communication device by using the numerical information (for example, levels 0, 1, 2,...). For example, the terminal device 40 may express a signal processing scheme that can be supported by the terminal device 40 by the numerical information. Then, the terminal device 40 may indicate this numerical information to the base station 20 as its own capability information. The neural network model is, for example, a model in a form called a convolution neural network (CNN), a recurrent neural network (RNN), or a long short-term memory (LSTM)),
Matsuda et al. teach determine at least one of a physical root sequence index, an associated cyclic shift value, a timing offset value, or any combination thereof, for at least one instance of the preamble sequence in the extracted set of the at least one instance of the preamble sequence (Figs. 4, 6 and 10-12, [0244], where the transmission resource of the preamble is determined, the transmission resource of the PUSCH that can be unique or a plurality of candidates is determined. As an example, the time offset and the frequency offset between the preamble of the PRACH occasion and the PUSCH occasion are defined by one value. As another example, the time offset and the frequency offset between the preamble of the PRACH occasion and the PUSCH occasion are set to different values for each preamble. The value of the offset may be determined by a specification, or may be quasi-statically set by the base station 20. As an example of the values of the time offset and the frequency offset, for example, it is defined by a predetermined frequency),
Matsuda et al. teach the NN comprising at least one of a fully connected layer, a recurrent neural network layer, or a convolutional neural network layer (Figs. 4, 6 and 10-11, [0310], the neural network model is, for example, a model in a form called a convolution neural network (CNN), a recurrent neural network (RNN), or a long short-term memory (LSTM)),
Matsuda et al. teach and applying, by the radio receiver device, the NN to output at least one of the determined at least one of the physical root sequence index, the associated cyclic shift value, the timing offset value, or the any combination thereof, for the at least one instance of the preamble sequence in the extracted set of the at least one instance of the preamble sequence (Figs. 4, 6 and 10-12, [0309, 0311], an output layer, including a plurality of nodes, and the nodes are connected via edges. The hidden layer includes layers called a convolution layer and a pooling layer. In the convolution layer, filtering is performed by a convolution operation, and data called a feature map is extracted. In the pooling layer, information about the feature map output from the convolutional layer is compressed, and down-sampling is performed. The CNN is used, for example, for image recognition. Information about each pixel, which is also referred to as a pixel of an image, is input to an input layer, and information related to the image recognized as an output layer can be obtained. The transmission resource of the preamble is determined, the transmission resource of the PUSCH that can be unique or a plurality of candidates is determined. As an example, the time offset and the frequency offset between the preamble of the PRACH occasion and the PUSCH occasion are defined by one value).
Matsuda et al. is teaching of an access point receiving, from a terminal device, PRACH preamble for processing to obtain uplink synchronization. Matsuda et al., however, fail to expressly teach of extracting a preamble sequence and processing it by applying a neural network. (Emphasis added).
Regarding claim 16, Kaya et al. teach extracting, by the radio receiver device, the set of the at least one instance of the preamble sequence; applying, by the radio receiver device, a neural network, NN, to the extracted set of the at least one instance of the preamble sequence (Figs. 3 and 6, [0069, 0083], the access node attempts detection (i.e. extracting) of the random access preamble in the received signal. Upon detecting the preamble in the received signal, the access node may label the received signal with information indicating that the received signal comprises the random access preamble before applying the received signal as the training input. The label may comprise at least one of the following information elements: indication of the presence of a random access preamble in the received signal, an index of the random access preamble comprised in the received signal, and an index and a cyclic shift of a logical root sequence of the random access preamble comprised in the received signal. The random access preamble detection process, as the training input the random access preambles acquired, uses a neural network. The neural network may be a convolutional neural network or a deep neural network, or a combination thereof. Operation of the neural network may be logically separated into two steps: training and inference. In the training phase, the training input is acquired, comprising the input sequence(s) and label(s), as described above. An input sequence may include an in-phase (1) and a quadrature (Q) component of the received signal. Thus, a received signal of 4095 samples may be represented by a 4095x2 matrix. The labels may include the information element(s) described above. Once the neural network has been trained, a signal received from the idle terminal device 102 may be processed with the trained neural network in order to predict the presence of the random access preamble and/or the time of arrival of the random access preamble).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Matsuda et al. by incorporating the features as taught by Kaya et al. in order to provide a more effective and efficient system that is capable of extracting, by the radio receiver device, the set of the at least one instance of the preamble sequence; applying, by the radio receiver device, a neural network, NN, to the extracted set of the at least one instance of the preamble sequence. The motivation is to support an improved method of detecting a preamble at an access node (see [0001]).
Regarding claim 17, Matsuda et al. teach a computer program comprising instructions for causing a radio receiver device to perform at least the following (Figs. 4, 6, 10 [0102, 0104, 0109], base station 20 includes a radio communication unit 21, a storage unit 22, and a control unit 23. The control unit 23 is a controller that controls each unit of the base station 20. The control unit 23 is realized, for example, by a processor such as a central processing unit (CPU) or a micro processing unit (MPU). For example, the control unit 23 is realized by the processor executing various programs stored in the storage device inside the base station 20 with a random access memory (RAM). Furthermore, the antenna 213 may include a plurality of antenna elements (for example, a plurality of patch antennas). In this case, the radio communication unit 21 may be configured to be beam formable),
Matsuda et al. teach receiving, over a physical random-access channel, PRACH, via at least one receive antenna of the radio receiver device, at least one uplink, UL, synchronization signal, each of the at least one UL synchronization signal comprising a PRACH preamble, the PRACH preamble comprising a set of at least one instance of a preamble sequence (Figs. 4, 6 and 9-10, [0204, 0215], the random access procedure is also used for the purpose of “scheduling request” for making a resource request for uplink data transmission and a “timing advance adjustment” for adjusting uplink synchronization. The terminal device 40 randomly selects a preamble sequence to be used from a plurality of predetermined preamble sequences. Then, the terminal device 40 transmits a message (Message 1: Random Access Preamble) including the selected preamble sequence to the connection destination base station 20 (Step S201). The random access preamble is transmitted by the PRACH),
Matsuda et al. teach extracting the set of the at least one instance of the preamble sequence (Figs. 4, 6 and 10, [0216], when receiving the random access preamble, the control unit 23 of the base station 20 transmits a random access response (Message 2) to the random access preamble to the terminal device 40. This random access response is transmitted using, for example, the PDSCH. The terminal device 40 receives the random access response (Message 2) transmitted from the base station 20 (Step S202). The random access response includes one or a plurality of random access preambles that can be received by the base station 20 and a resource (hereinafter, referred to as an uplink grant) of an up link (UL) corresponding to the random access preamble. In addition, the random response includes a temporary cell radio network temporary identifier (TC-RNTI) which is an identifier unique to the terminal device 40 temporarily assigned to the terminal device 40 by the base station 20),
Matsuda et al. teach applying a neural network, NN, to the extracted set of the at least one instance of the preamble sequence to determine at least one of a physical root sequence index, an associated cyclic shift value, a timing offset value, or any combination thereof, for at least one instance of the preamble sequence in the extracted set of the at least one instance of the preamble sequence (Figs. 4, 6 and 10-12, [0244], where the transmission resource of the preamble is determined, the transmission resource of the PUSCH that can be unique or a plurality of candidates is determined. As an example, the time offset and the frequency offset between the preamble of the PRACH occasion and the PUSCH occasion are defined by one value. As another example, the time offset and the frequency offset between the preamble of the PRACH occasion and the PUSCH occasion are set to different values for each preamble. The value of the offset may be determined by a specification, or may be quasi-statically set by the base station 20. As an example of the values of the time offset and the frequency offset, for example, it is defined by a predetermined frequency),
Matsuda et al. teach the NN comprising at least one of a fully connected layer, a recurrent neural network layer, or a convolutional neural network layer (Figs. 4, 6 and 10-11, [0224, 0308, 0310], The control unit 23 of the base station 20 receives the random access preamble (Message 1) from the terminal device 40. Then, the control unit 23 transmits a random access response (Message 2) to the random access preamble to the terminal device 40 (Step S303). The random access response includes, for example, information about the uplink grant corresponding to the received random access preamble. The communication device can indicate the height of degree of adaptability to the signal processing scheme to another communication device by using the numerical information (for example, levels 0, 1, 2,...). For example, the terminal device 40 may express a signal processing scheme that can be supported by the terminal device 40 by the numerical information. Then, the terminal device 40 may indicate this numerical information to the base station 20 as its own capability information. The neural network model is, for example, a model in a form called a convolution neural network (CNN), a recurrent neural network (RNN), or a long short-term memory (LSTM)),
Matsuda et al. teach and applying the NN to output at least one of the determined at least one of the physical root sequence index, the associated cyclic shift value, the timing offset value, or the any combination thereof, for the at least one instance of the preamble sequence in the extracted set of the at least one instance of the preamble sequence (Figs. 4, 6 and 10-12, [0309, 0311], an output layer, including a plurality of nodes, and the nodes are connected via edges. The hidden layer includes layers called a convolution layer and a pooling layer. In the convolution layer, filtering is performed by a convolution operation, and data called a feature map is extracted. In the pooling layer, information about the feature map output from the convolutional layer is compressed, and down-sampling is performed. The CNN is used, for example, for image recognition. Information about each pixel, which is also referred to as a pixel of an image, is input to an input layer, and information related to the image recognized as an output layer can be obtained. The transmission resource of the preamble is determined, the transmission resource of the PUSCH that can be unique or a plurality of candidates is determined. As an example, the time offset and the frequency offset between the preamble of the PRACH occasion and the PUSCH occasion are defined by one value).
Matsuda et al. is teaching of an access point receiving, from a terminal device, PRACH preamble for processing to obtain uplink synchronization. Matsuda et al., however, fail to expressly teach of extracting a preamble sequence and processing it by applying a neural network. (Emphasis added).
Regarding claim 17, Kaya et al. teach extracting the set of the at least one instance of the preamble sequence; applying a neural network, NN, to the extracted set of the at least one instance of the preamble sequence (Figs. 3 and 6, [0069, 0083], the access node attempts detection (i.e. extracting) of the random access preamble in the received signal. Upon detecting the preamble in the received signal, the access node may label the received signal with information indicating that the received signal comprises the random access preamble before applying the received signal as the training input. The label may comprise at least one of the following information elements: indication of the presence of a random access preamble in the received signal, an index of the random access preamble comprised in the received signal, and an index and a cyclic shift of a logical root sequence of the random access preamble comprised in the received signal. The random access preamble detection process, as the training input the random access preambles acquired, uses a neural network. The neural network may be a convolutional neural network or a deep neural network, or a combination thereof. Operation of the neural network may be logically separated into two steps: training and inference. In the training phase, the training input is acquired, comprising the input sequence(s) and label(s), as described above. An input sequence may include an in-phase (1) and a quadrature (Q) component of the received signal. Thus, a received signal of 4095 samples may be represented by a 4095x2 matrix. The labels may include the information element(s) described above. Once the neural network has been trained, a signal received from the idle terminal device 102 may be processed with the trained neural network in order to predict the presence of the random access preamble and/or the time of arrival of the random access preamble).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Matsuda et al. by incorporating the features as taught by Kaya et al. in order to provide a more effective and efficient system that is capable of extracting the set of the at least one instance of the preamble sequence; applying a neural network, NN, to the extracted set of the at least one instance of the preamble sequence. The motivation is to support an improved method of detecting a preamble at an access node (see [0001]).
Claim(s) 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Matsuda et al. (US 2023/0370901 A1) in view of Kaya et al. (EP 3,826,415 A1) as applied to claim 1 above, and further in view of Namgoong et al. (US 2023/0114870 A1).
Matsuda et al. and Kaya et al. disclose the claimed limitations as described in paragraph 5 above.
Regarding claim 13, Matsuda et al. teach wherein the NN comprises at least one of a convolutional neural network, a fully connected neural network, or recurrent neural network (Fig. 2, [0310], the neural network model is, for example, a model in a form called a convolution neural network (CNN), a recurrent neural network (RNN), or a long short-term memory (LSTM)).
Matsuda et al. and Kaya et al. do not expressly disclose the following features: regarding claim 14, wherein the radio receiver device comprises a multiple- input and multiple-output, MIMO, capable radio receiver device; regarding claim 15, wherein the radio receiver device is comprised in a network node device.
Regarding claim 14, Namgoong et al. teach wherein the radio receiver device comprises a multiple- input and multiple-output, MIMO, capable radio receiver device ( Fig. 2 and 9, [0052, 0098], the user equipment (UE) receives different sets of parameters from different sources as input to a receiver neural network. For example, the UE (e.g., using the antenna 252 (multiple antennas, fig. 2) , DEMOD/MOD 254 (multiple receivers, fig. 2), MIMO detector 256, receiver processor 258, controller/processor 280, and/or memory 282) may receive the sets of parameters. In some aspects, the different sets of parameters comprise a set of channel state information (CSI) parameters, a set of modulation and coding scheme (MCS) parameters, and a set of observed physical downlink shared channel (PDSCH) subcarriers.
Regarding claim 15, Namgoong et al. teach wherein the radio receiver device is comprised in a network node device ( Fig. 2 and 9, [0052, 0101], the user equipment (UE) transmits the multiple sets of scaled parameters to the receiver neural network (i.e. received at by the multiple antenna 234 and MIMO receivers 232 at the base station 130, fig. 2). For example, the UE (e.g., using the antenna 252 (multiple antenna 252, fig. 2), DEMOD/MOD 254, TX MIMO processor 266, transmit processor 264, controller/processor 280, and/or memory 282) may transmit the multiple sets of scaled parameters. In some aspects, the UE concatenates the multiple sets of scaled parameters prior to transmitting. At the base station 110, the uplink signals from the UE 120 and other UEs may be received by the antennas 234, processed by the demodulators 254, detected by a MIMO detector 236 if applicable, and further processed by a receive processor 238 to obtain decoded data and control information sent by the UE 120.
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Matsuda et al. with Kaya et al. by incorporating the features as taught by Namgoong et al. in order to provide a more effective and efficient system that is capable of using the radio receiver device comprises a multiple- input and multiple-output, MIMO, capable radio receiver device, and the radio receiver device is comprised in a network node device. The motivation is to support an improved method for scaling the gain of inputs to a receiver neural network that is participating in end-to-end learning of a wireless communication system detecting a preamble at an access node (see [0001]).
Claim(s) 2-3, 7 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Matsuda et al. (US 2023/0370901 A1) in view of Kaya et al. (EP 3,826,415 A1) as applied to claims 1 and 16 above, and further in view of Sengupta et al. (US 2020/0383167 A1).
Matsuda et al. and Kaya et al. disclose the claimed limitations as described in paragraph 5 above.
Regarding claim 2, Kaya et al. teach wherein the NN is executable to output at least the physical root sequence index and the associated cyclic shift value, and the at least one memory and the computer program code are further configured to, with the at least one processor, cause the radio receiver device to perform determining a logical preamble index for the at least one instance of the preamble sequence in the extracted set of the at least one instance of the preamble sequence based on the output physical root sequence index and the associated cyclic shift value (Figs. 3 and 6, [0068], the access node attempts detection of the random access preamble in the received signal. Upon detecting the preamble in the received signal, the access node may label the received signal with information indicating that the received signal comprises the random access preamble before applying the received signal as the training input. (Note: training input are fed into neural network (NN) so it can learn patterns and adjust its internal weights) The label may comprise at least one of the following information elements: indication of the presence of a random access preamble in the received signal, an index of the random access preamble comprised in the received signal, and an index and a cyclic shift of a logical root sequence of the random access preamble comprised in the received signal. In other words, in the simplest form, the label may only indicate the presence of the preamble, and the training procedure may then detect the actual preamble comprised in the received signal).
Regarding claim 7, Kaya et al. teach wherein input dimensions of the NN (305) correspond to at least one of a length of the preamble sequence, a number of radio receiver chains in the radio receiver device, a number of the instances of the preamble sequence in the set of the at least one instance of the preamble sequence, or a number of in-phase components and quadrature components, for the at least one instance of the preamble sequence in the extracted set of the at least one instance of the preamble sequence (Figs. 3 and 6, [0069, 0083], upon detecting the preamble in the received signal, the access node may label the received signal with information indicating that the received signal comprises the random access preamble before applying the received signal as the training input. The label may comprise at least one of the following information elements: indication of the presence of a random access preamble in the received signal, an index of the random access preamble comprised in the received signal, and an index and a cyclic shift of a logical root sequence of the random access preamble comprised in the received signal. Operation of the neural network may be logically separated into two steps: training and inference. In the training phase, the training input is acquired, comprising the input sequence(s) and label(s), as described above. An input sequence may include an in-phase (1) and a quadrature (Q) component of the received signal. Thus, a received signal of length 4095 samples may be represented by a 4095x2 matrix (Note: received signal sample counts as block around 4,090 measurement which represents fixed-length batch of digitized In-phase and Quadrature data and fed into a neural network).
Regarding claim 18, Kaya et al. teach wherein the NN is executable to output at least the physical root sequence index and the associated cyclic shift value, and further comprising determining a logical preamble index for the at least one instance of the preamble sequence in the extracted set of the at least one instance of the preamble sequence based on the output physical root sequence index and the associated cyclic shift value (Figs. 3 and 6, [0068], the access node attempts detection of the random access preamble in the received signal. Upon detecting the preamble in the received signal, the access node may label the received signal with information indicating that the received signal comprises the random access preamble before applying the received signal as the training input. (Note: training input are fed into neural network (NN) so it can learn patterns and adjust its internal weights) The label may comprise at least one of the following information elements: indication of the presence of a random access preamble in the received signal, an index of the random access preamble comprised in the received signal, and an index and a cyclic shift of a logical root sequence of the random access preamble comprised in the received signal. In other words, in the simplest form, the label may only indicate the presence of the preamble, and the training procedure may then detect the actual preamble comprised in the received signal).
Matsuda et al. and Kaya et al. do not expressly disclose the following features: regarding claim 3, wherein the NN is further executable to determine the physical root sequence index for the at least one instance of the preamble sequence in the extracted set of the at least one instance of the preamble sequence based on a first set of configuration information indicating a predetermined subset of applicable physical root sequence indices among which to limit the determination of the physical root sequence index; regarding claim 19, wherein the NN is further executable to determine the physical root sequence index for the at least one instance of the preamble sequence in the extracted set of the at least one instance of the preamble sequence based on a first set of configuration information indicating a predetermined subset of applicable physical root sequence indices among which to limit the determination of the physical root sequence.
Regarding claim 3, Sengupta et al. teach wherein the NN is further executable to determine the physical root sequence index for the at least one instance of the preamble sequence in the extracted set of the at least one instance of the preamble sequence based on a first set of configuration information indicating a predetermined subset of applicable physical root sequence indices among which to limit the determination of the physical root sequence index (Fig. 4, [0081-0082], Prior to initiation of the PRACH procedure, Layer 1 (L1) receives from higher layers a set of SS/PBCH block indexes and provides to higher layers a corresponding set of signal (e.g., RSRP measurements). L1 then receives a (P)RACH configuration of PRACH transmission parameters (e.g., including a PRACH preamble format, a preamble index, a preamble SCS, P.sub.PRACH,target, a corresponding RA-RNTI, and time and frequency resources for the PRACH transmission), and parameters for determining the root sequences and their cyclic shifts in the PRACH preamble sequence set, index to logical root sequence table, cyclic shift (Ncs), and set type (unrestricted, restricted set A, or restricted set B). The PDCCH ordered RACH configuration includes a Random Access Preamble index field (6 bits) indicating which RA preamble to use in case of contention-free RA procedure, or the value 000000 in case of contention-based RA procedure).
Regarding claim 19, Sengupta et al. teach , wherein the NN is further executable to determine the physical root sequence index for the at least one instance of the preamble sequence in the extracted set of the at least one instance of the preamble sequence based on a first set of configuration information indicating a predetermined subset of applicable physical root sequence indices among which to limit the determination of the physical root sequence (Fig. 4, [0081-0082], Prior to initiation of the PRACH procedure, Layer 1 (L1) receives from higher layers a set of SS/PBCH block indexes and provides to higher layers a corresponding set of signal (e.g., RSRP measurements). L1 then receives a (P)RACH configuration of PRACH transmission parameters (e.g., including a PRACH preamble format, a preamble index, a preamble SCS, P.sub.PRACH,target, a corresponding RA-RNTI, and time and frequency resources for the PRACH transmission), and parameters for determining the root sequences and their cyclic shifts in the PRACH preamble sequence set, index to logical root sequence table, cyclic shift (Ncs), and set type (unrestricted, restricted set A, or restricted set B). The PDCCH ordered RACH configuration includes a Random Access Preamble index field (6 bits) indicating which RA preamble to use in case of contention-free RA procedure, or the value 000000 in case of contention-based RA procedure).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Matsuda et al. with Kaya et al. by incorporating the features as taught by Sengupta et al. in order to provide a more effective and efficient system that is capable of determining the physical root sequence index for the at least one instance of the preamble sequence in the extracted set of the at least one instance of the preamble sequence based on a first set of configuration information indicating a predetermined subset of applicable physical root sequence indices among which to limit the determination of the physical root sequence index. The motivation is to support an improved method for beam management in cellular communication networks (see [0002]).
Claim(s) 4 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Matsuda et al. (US 2023/0370901 A1) in view of Kaya et al. (EP 3,826,415 A1) and Sengupta et al. (US 2020/0383167 A1) as applied to claims 1 and 16 above, and further in view of Shin et al. (US 2022/0116881 A1).
Matsuda et al., Kaya et al. and Sengupta et al. disclose the claimed limitations as described in paragraph 5 above. Matsuda et al., Kaya et al. and Sengupta et al. do not expressly disclose the following features: regarding claim 4, wherein the first set of configuration information comprises a first vector indicating the predetermined subset of applicable physical root sequence indices; regarding claim 20, wherein the first set of configuration information comprises a first vector indicating the predetermined subset of applicable physical root sequence indices.
Regarding claim 4, Shin et al. teach wherein the first set of configuration information comprises a first vector indicating the predetermined subset of applicable physical root sequence indices (Figs. 3 and 17, [0147, 0694, 0696], the AI server 200 may mean an apparatus which trains an artificial neural network using a machine learning algorithm or which uses a trained artificial neural network. Random access preambles are generated from Zadoff-Chu (ZC) sequences with zero correlation zone, generated from one or several root Zadoff-Chu sequences. The network configures the set of preamble sequences the UE is allowed to use. set of 64 preamble sequences in a cell may be found by including first, in the order of increasing cyclic shift, all the available cyclic shifts of a root Zadoff-Chu sequence with the logical index root Sequence Index High Speed (for Set 2, if configured) or with the logical index RACH_ROOT_SEQUENCE (for Set 1), where both root Sequence Index High Speed and RACH_ROOT_SEQUENCE are broadcasted as part of the system information. Additional preamble sequences, in case 64 preambles cannot be generated from a single root Zadoff-Chu sequence, are obtained from the root sequences with the consecutive logical indexes until all the 64 sequences are found (Note: a vector indicating a subset of root sequences in a wireless neural network refers to an indicator to pinpoint which specific Zadoff-Chu (ZC) root indices are active or allocated for detecting random access preambles)).
Regarding claim 20, Shin et al. teach wherein the first set of configuration information comprises a first vector indicating the predetermined subset of applicable physical root sequence indices (Figs. 3 and 17, [0147, 0694, 0696], the AI server 200 may mean an apparatus which trains an artificial neural network using a machine learning algorithm or which uses a trained artificial neural network. Random access preambles are generated from Zadoff-Chu (ZC) sequences with zero correlation zone, generated from one or several root Zadoff-Chu sequences. The network configures the set of preamble sequences the UE is allowed to use. set of 64 preamble sequences in a cell may be found by including first, in the order of increasing cyclic shift, all the available cyclic shifts of a root Zadoff-Chu sequence with the logical index root Sequence Index High Speed (for Set 2, if configured) or with the logical index RACH_ROOT_SEQUENCE (for Set 1), where both root Sequence Index High Speed and RACH_ROOT_SEQUENCE are broadcasted as part of the system information. Additional preamble sequences, in case 64 preambles cannot be generated from a single root Zadoff-Chu sequence, are obtained from the root sequences with the consecutive logical indexes until all the 64 sequences are found (Note: a vector indicating a subset of root sequences in a wireless neural network refers to an indicator to pinpoint which specific Zadoff-Chu (ZC) root indices are active or allocated for detecting random access preambles)).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Matsuda et al. with Kaya et al. and Sengupta et al. by incorporating the features as taught by Shin et al. in order to provide a more effective and efficient system that is capable of having the first set of configuration information comprises a first vector indicating the predetermined subset of applicable physical root sequence indices. The motivation is to support an improved method for transmitting power in a narrowband (NB) wireless communication system, and a UE (see [0001]).
Allowable Subject Matter
Claims 5, 6 and 8-12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/SYED M BOKHARI/ Examiner, Art Unit 2473
8/29/2026
/KWANG B YAO/Supervisory Patent Examiner, Art Unit 2473
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