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 .
DETAILED ACTION
Claims 1-19 are pending in this action.
Information Disclosure Statement
The information disclosure statement (IDS) was not submitted for consideration.
Claim Objections
Claim 17 is objected to because of the following informalities:
Claim 17 recites “the method of claim 1”. However, claim 1 recites “a system for feedback…”. It does not mention about “A method…” at all. However, claim 4 recites “A method for decoding…”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In analyzing under step 1, is the claim to a process, machine manufacture or composition of matter? Yes.
In analyzing under step 2A Prong One, Does the claim recite an abstract idea law of nature or natural phenomenon? Yes.
The claim(s) 1 recite(s) the abstract limitations such as “…receive at least one downlink transmission that requires acknowledgement; … receive the Uplink Control Information (UCI) carried by the PUCCH Format 0” is a process that, under its broadest reasonable interpretation, covers performance of the limitation under mental processes but for the recitation of generic computer processor such as “at least one User Equipment (UE) configured …a base station… the base station comprising a multi-label neural network classifier” (see claim 1), “the base station” (see claim 2) and layers (see claim 3)
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components and software module, then it falls within the “Mental Processes” grouping of abstract ideas.
The mental process with generic processor can receive an acknowledgement message or an UCI information input
The claim(s) 4 recite(s) the abstract limitations such as “providing a multi-label neural network classifier …generating a dataset…; initializing the decoding model; training the decoding model; testing the decoding model; receiving the input PUCCH Format 0 signal and associated metadata of a number of users NUE; feeding the signal to the decoding model; predicting NUE phase rotation values corresponding to at least some; and obtaining α values” is a process that, under its broadest reasonable interpretation, covers performance of the limitation under mental processes.
The mental process can (1) providing a classifier to serve as a format, (2) generating a dataset, (3) initializing the decoding model, (4) training the decoding model, (5) testing the decoding model, (6) receiving a signal, (7) input the signal into a model, (8) predicting a values and (9) obtaining a values.
Dependent claims 5-19 recite further abstract limitations
Claims 5-6 recites a method of receiving data such as sequence inputs, metadata and values. The mental process with generic computer can receive data such as sequence inputs, metadata and values.
Claim 7 recites a method of “collecting and storing waveform”. The mental process can collect and store waveform data.
Claims 10 and 11 recites a method “…summation of waveforms…” which is based on mathematical process.
Accordingly, the claim recites an abstract limitation.
This judicial exception is not integrated into a practical application under Step 2A Prong 2. The recited steps of " receive at least one downlink transmission that requires acknowledgement; … receive the Uplink Control Information (UCI) carried by the PUCCH Format 0" are extra-solution activity to the judicial exception, and hence these features are not indicative of integration into a practical application.
In analyzing under step 2A Prong Two, Does the claim recite additional elements that integrate the judicial exception into a practical application? NO.
This judicial exception is not integrated into a practical application because the claims recite a generic processor such as “at least one User Equipment (UE) configured …a base station… the base station comprising a multi-label neural network classifier” (see claim 1) for receiving acknowledge and information. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because a generic processor and software module which are high level for receiving acknowledge and information. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In analyzing under step 2B, does the claim recite additional elements that amount to significantly more than the judicial exception? NO
Claims 1-3 do not recite any additional elements except a generic processor for receiving acknowledge and information. Accordingly, the additional generic elements do not amount to significantly more than the judicial exception because a generic processor and software module which are high level of generality performing code generation
The claim is directed to an abstract idea.
Claim Rejections - 35 USC § 112
Claims 1 are 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.
Claim 1 recite a limitation “at least one User Equipment (UE) configured to receive at least one downlink transmission that requires acknowledgement”
The recited limitation such as “that requires acknowledgment” renders this limitation indefinite because it is unclear whether the UE is receiving an acknowledgement response or when the UE is to receive an acknowledgement response or where to receive acknowledgement response.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hao et al. (US 2025/0,088,232)
As per claim 1:
Hao discloses:
A system for feedback signaling in wireless communication, comprising:
(Hao, Fig. 7B, User Equipment, 510, Base Station Decode Neural Network)
at least one User Equipment (UE) configured to receive at least one downlink transmission that requires acknowledgement; and
(Hao, Fig. 7B, User Equipment, 510, Base Station Decode Neural Network)
(Hao, [0319] ….PUCCH carries uplink control information (UCI) , such as scheduling requests, a channel quality indicator (CQI), a precoding matrix indicator (PMI), a rank indicator (RI), and HARQ ACK/NACK feedback)
a base station, configured to receive the Uplink Control Information (UCI) carried by the PUCCH Format 0 for decoding,
(Hao, Fig. 7B, User Equipment, 510, Base Station Decode Neural Network)
(Hao, [0319] ….PUCCH carries uplink control information (UCI) , such as scheduling requests, a channel quality indicator (CQI), a precoding matrix indicator (PMI), a rank indicator (RI), and HARQ ACK/NACK feedback)
the base station comprising a multi-label neural network classifier.
(Hao, Fig. 7B, User Equipment, 510, Base Station Decode Neural Network)
As per claim 2:
Hao further discloses:
wherein the base station is configured for downlink transmission which is to be received by a UE.
(Hao, Fig. 7B, User Equipment, 510, Base Station Decode Neural Network)
(Hao, Fig. 7B, BS is for downlink transmission from UE and to receive feedback M from UE)
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.
Claim(s) 3-6, 8-9, 12-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hao et al. (US 2025/0,088,232), in view of Nakagiri (US 2009/0,168,913)
As per claim 3:
Hao further discloses:
the multi-label neural network classifier comprising:
an input layer containing inputs and an additional optional input for metadata; one or more dense layers, containing
(Hao, [0082]… neural network 600 may be an example of a machine learning module (e.g., the machine learning module 510) used at the UE to determine a channel estimation…The neural network 600 may use various operation layers during classification 604…)
(Hao, [0151] … the adaptive learning (e.g., used by the training system 1730) is performed using a neural network. Neural networks may be designed with a variety of connectivity patterns. … information is passed from lower to higher layers, with each neuron in a given layer communicating to neurons in higher layers… built up in successive layers of a feed-forward network. … the output from a neuron in a given layer may be communicated to another neuron in the same layer. …connection from a neuron in a given layer to a neuron in a lower layer is called a feedback (or top-down) connection. A network with many feedback connections may be helpful when the recognition of a high-level concept may aid in discriminating the particular low-level features of an input)
(Hao, [0031], [0041], [0073] phase control)
(Hao, [0313] phase tracking RS (PT-RS))
(Hao, [0144]…at various rotations of the UE)
(Hao, [0156]…speed, rotation)
Hao does not disclose:
Phase rotation.
Nakagiri discloses:
Phase rotation.
(Nakagiri, Abstract, …adjusted a phase rotation amount for each of N data symbols… carry out inverse Fourier transform on the data symbols having undergone the phase adjustment … a phase calculating part configured to determine the phase rotation amount with the use of a complex neural network. …phase calculating part has N neurons …update the phase rotation amount according to a value of an objective function calculated from the complex output signal)
It would have been obvious before the effective filing date of the claimed to a person having ordinary skill in the art to incorporate Nakagiri’s phase rotation into the system in order improve the transmission.
(Nakagiri, Abstract, …adjusted a phase rotation amount for each of N data symbols… carry out inverse Fourier transform on the data symbols having undergone the phase adjustment … a phase calculating part configured to determine the phase rotation amount with the use of a complex neural network. …phase calculating part has N neurons …update the phase rotation amount according to a value of an objective function calculated from the complex output signal)
As per claim 4:
Hao discloses:
A method for decoding UCI wireless communication, comprising:
(Hao, Fig. 7B, User Equipment, 510, Base Station Decode Neural Network)
providing a multi-label neural network classifier to serve as a generalized PUCCH Format 0 decoder; generating a dataset for training the decoding model; initializing the decoding model;
(Hao, [0319] ….PUCCH carries uplink control information (UCI) , such as scheduling requests, a channel quality indicator (CQI), a precoding matrix indicator (PMI), a rank indicator (RI), and HARQ ACK/NACK feedback)
training the decoding model; testing the decoding model;
(Hao, [0052],…Base station 180 and UE 104 may then perform beam training to determine the best receive and transmit directions for each of base station 180)
(Hao, [0144] The training repository 1715 may include training data obtained before and/or after deployment of the node 1720. The node 1720 may be trained in a simulated communication environment (e.g., in field testing, drive testing, etc.) prior to deployment of the node 1720. For example, various CSI and/or channel estimations (e.g., CQI, PMI, RSRP, SINR, etc.) can be tested in various scenarios)
receiving the input PUCCH Format 0 signal and associated metadata of a number of users N.sub.UE;
(Hao, [0319] ….PUCCH carries uplink control information (UCI) , such as scheduling requests, a channel quality indicator (CQI), a precoding matrix indicator (PMI), a rank indicator (RI), and HARQ ACK/NACK feedback)
feeding the signal to the decoding model;
(Hao, [0319] ….PUCCH carries uplink control information (UCI) , such as scheduling requests, a channel quality indicator (CQI), a precoding matrix indicator (PMI), a rank indicator (RI), and HARQ ACK/NACK feedback)
predicting N.sub.UE
(Hao, [0031], [0041], [0073] phase control)
(Hao, [0313] phase tracking RS (PT-RS))
(Hao, [0144]…at various rotations of the UE)
(Hao, [0156]…speed, rotation)
Hao does not disclose:
Phase rotation.
Nakagiri discloses:
Phase rotation.
(Nakagiri, Abstract, …adjusted a phase rotation amount for each of N data symbols… carry out inverse Fourier transform on the data symbols having undergone the phase adjustment … a phase calculating part configured to determine the phase rotation amount with the use of a complex neural network. …phase calculating part has N neurons …update the phase rotation amount according to a value of an objective function calculated from the complex output signal)
It would have been obvious before the effective filing date of the claimed to a person having ordinary skill in the art to incorporate Nakagiri’s phase rotation into the system in order improve the transmission.
(Nakagiri, Abstract, …adjusted a phase rotation amount for each of N data symbols… carry out inverse Fourier transform on the data symbols having undergone the phase adjustment … a phase calculating part configured to determine the phase rotation amount with the use of a complex neural network. …phase calculating part has N neurons …update the phase rotation amount according to a value of an objective function calculated from the complex output signal)
As per claim 5:
Hao-Nakagiri further discloses:
wherein providing the multi-label neural network classifier comprises providing: an input layer containing 12 or more neurons configured to receive 12 or more real or complex sequence inputs or 24 neurons configured to receive 24 real sequence inputs and an additional optional input for metadata; one or more dense layers, containing 64 or more neurons; and an output layer containing
(Hao, [0082]… neural network 600 may be an example of a machine learning module (e.g., the machine learning module 510) used at the UE to determine a channel estimation…The neural network 600 may use various operation layers during classification 604…)
(Hao, [0151] … the adaptive learning (e.g., used by the training system 1730) is performed using a neural network. Neural networks may be designed with a variety of connectivity patterns. … information is passed from lower to higher layers, with each neuron in a given layer communicating to neurons in higher layers… built up in successive layers of a feed-forward network. … the output from a neuron in a given layer may be communicated to another neuron in the same layer. …connection from a neuron in a given layer to a neuron in a lower layer is called a feedback (or top-down) connection. A network with many feedback connections may be helpful when the recognition of a high-level concept may aid in discriminating the particular low-level features of an input)
(Hao, [0031], [0041], [0073] phase control)
(Hao, [0313] phase tracking RS (PT-RS))
(Hao, [0144]…at various rotations of the UE)
(Hao, [0156]…speed, rotation)
Hao does not disclose:
Phase rotation.
Nakagiri discloses:
Phase rotation.
(Nakagiri, Abstract, …adjusted a phase rotation amount for each of N data symbols… carry out inverse Fourier transform on the data symbols having undergone the phase adjustment … a phase calculating part configured to determine the phase rotation amount with the use of a complex neural network. …phase calculating part has N neurons …update the phase rotation amount according to a value of an objective function calculated from the complex output signal)
It would have been obvious before the effective filing date of the claimed to a person having ordinary skill in the art to incorporate Nakagiri’s phase rotation into the system in order improve the transmission.
(Nakagiri, Abstract, …adjusted a phase rotation amount for each of N data symbols… carry out inverse Fourier transform on the data symbols having undergone the phase adjustment … a phase calculating part configured to determine the phase rotation amount with the use of a complex neural network. …phase calculating part has N neurons …update the phase rotation amount according to a value of an objective function calculated from the complex output signal)
As per claim 6:
Hao-Nakagiri further discloses:
wherein providing the multi-label neural network classifier comprises providing: an input layer containing 25 neurons configured to receive 24 real sequence inputs and the metadata as 25.sup.th input; three dense layers, each containing 256 neurons; and an output layer containing 12 neurons configured to receive up to 12
(Hao, [0082]… neural network 600 may be an example of a machine learning module (e.g., the machine learning module 510) used at the UE to determine a channel estimation…The neural network 600 may use various operation layers during classification 604…)
(Hao, [0151] … the adaptive learning (e.g., used by the training system 1730) is performed using a neural network. Neural networks may be designed with a variety of connectivity patterns. … information is passed from lower to higher layers, with each neuron in a given layer communicating to neurons in higher layers… built up in successive layers of a feed-forward network. … the output from a neuron in a given layer may be communicated to another neuron in the same layer. …connection from a neuron in a given layer to a neuron in a lower layer is called a feedback (or top-down) connection. A network with many feedback connections may be helpful when the recognition of a high-level concept may aid in discriminating the particular low-level features of an input)
(Hao, [0031], [0041], [0073] phase control)
(Hao, [0313] phase tracking RS (PT-RS))
(Hao, [0144]…at various rotations of the UE)
(Hao, [0156]…speed, rotation)
As per claim 8:
Hao-Nakagiri further discloses:
wherein training the multi-label neural network classifier comprises running multiple epochs of training wherein each epoch is 1 pass of the entire training dataset through the decoding model.
(Hao, [0052],…Base station 180 and UE 104 may then perform beam training to determine the best receive and transmit directions for each of base station 180)
(Hao, [0144] The training repository 1715 may include training data obtained before and/or after deployment of the node 1720. The node 1720 may be trained in a simulated communication environment (e.g., in field testing, drive testing, etc.) prior to deployment of the node 1720. For example, various CSI and/or channel estimations (e.g., CQI, PMI, RSRP, SINR, etc.) can be tested in various scenarios)
As per claim 9:
Hao-Nakagiri further discloses:
wherein training the multi-label neural network classifier comprises running 150 or more epochs of training wherein each epoch is 1 pass of the entire training dataset through the decoding model.
(Hao, [0052],…Base station 180 and UE 104 may then perform beam training to determine the best receive and transmit directions for each of base station 180)
(Hao, [0144] The training repository 1715 may include training data obtained before and/or after deployment of the node 1720. The node 1720 may be trained in a simulated communication environment (e.g., in field testing, drive testing, etc.) prior to deployment of the node 1720. For example, various CSI and/or channel estimations (e.g., CQI, PMI, RSRP, SINR, etc.) can be tested in various scenarios)
As per claim 12:
Hao-Nakagiri further discloses:
comprising performing the training on a same hardware on which the method is deployed, or on a different system.
(Hao, [0052],…Base station 180 and UE 104 may then perform beam training to determine the best receive and transmit directions for each of base station 180)
(Hao, [0144] The training repository 1715 may include training data obtained before and/or after deployment of the node 1720. The node 1720 may be trained in a simulated communication environment (e.g., in field testing, drive testing, etc.) prior to deployment of the node 1720. For example, various CSI and/or channel estimations (e.g., CQI, PMI, RSRP, SINR, etc.) can be tested in various scenarios)
As per claim 13:
Hao-Nakagiri further discloses:
wherein testing the decoding model includes providing upper bound value of the number of multiplexed users as the metadata input which is offset from the true value.
(Hao, [0052],…Base station 180 and UE 104 may then perform beam training to determine the best receive and transmit directions for each of base station 180)
(Hao, [0144] The training repository 1715 may include training data obtained before and/or after deployment of the node 1720. The node 1720 may be trained in a simulated communication environment (e.g., in field testing, drive testing, etc.) prior to deployment of the node 1720. For example, various CSI and/or channel estimations (e.g., CQI, PMI, RSRP, SINR, etc.) can be tested in various scenarios)
As per claim 14:
Hao-Nakagiri further discloses:
:
comprising pre-processing the signal prior to feeding.
(Hao, [0052],…Base station 180 and UE 104 may then perform beam training to determine the best receive and transmit directions for each of base station 180)
(Hao, [0144] The training repository 1715 may include training data obtained before and/or after deployment of the node 1720. The node 1720 may be trained in a simulated communication environment (e.g., in field testing, drive testing, etc.) prior to deployment of the node 1720. For example, various CSI and/or channel estimations (e.g., CQI, PMI, RSRP, SINR, etc.) can be tested in various scenarios)
As per claim 15:
Hao-Nakagiri further discloses:
wherein predicting comprises determining the N.sub.UE phase rotation values α.sub.0, α.sub.1 . . . αN.sub.UE−1 applied to the base sequence for classification.
(Hao, [0052],…Base station 180 and UE 104 may then perform beam training to determine the best receive and transmit directions for each of base station 180)
(Hao, [0144] The training repository 1715 may include training data obtained before and/or after deployment of the node 1720. The node 1720 may be trained in a simulated communication environment (e.g., in field testing, drive testing, etc.) prior to deployment of the node 1720. For example, various CSI and/or channel estimations (e.g., CQI, PMI, RSRP, SINR, etc.) can be tested in various scenarios)
As per claim 16:
Hao-Nakagiri further discloses:
wherein the obtaining includes obtaining either a single α value, multiple α values, or zero α values.
(Hao, [0052],…Base station 180 and UE 104 may then perform beam training to determine the best receive and transmit directions for each of base station 180)
(Hao, [0144] The training repository 1715 may include training data obtained before and/or after deployment of the node 1720. The node 1720 may be trained in a simulated communication environment (e.g., in field testing, drive testing, etc.) prior to deployment of the node 1720. For example, various CSI and/or channel estimations (e.g., CQI, PMI, RSRP, SINR, etc.) can be tested in various scenarios)
As per claim 17:
Hao-Nakagiri further discloses:
wherein receiving at the base station includes: Hybrid Automatic Repeat Request (HARQ) acknowledgements for prior downlink transmissions; Scheduling Request (SR) for the subsequent allocation of uplink transmission resources; and Channel State Information (CSI) reports including channel quality metrics that facilitate link adaptation, precoding, and downlink resource allocation.
(Hao, [0319] ….PUCCH carries uplink control information (UCI) , such as scheduling requests, a channel quality indicator (CQI), a precoding matrix indicator (PMI), a rank indicator (RI), and HARQ ACK/NACK feedback)
Hao, [0052],…Base station 180 and UE 104 may then perform beam training to determine the best receive and transmit directions for each of base station 180)
(Hao, [0144] The training repository 1715 may include training data obtained before and/or after deployment of the node 1720. The node 1720 may be trained in a simulated communication environment (e.g., in field testing, drive testing, etc.) prior to deployment of the node 1720. For example, various CSI and/or channel estimations (e.g., CQI, PMI, RSRP, SINR, etc.) can be tested in various scenarios)
As per claim 18:
Hao-Nakagiri further discloses:
generating the dataset for training includes operating the transmitter or the receiver or both in a specific data collection mode to generate specific training sequences.
(Hao, [0052],…Base station 180 and UE 104 may then perform beam training to determine the best receive and transmit directions for each of base station 180)
(Hao, [0144] The training repository 1715 may include training data obtained before and/or after deployment of the node 1720. The node 1720 may be trained in a simulated communication environment (e.g., in field testing, drive testing, etc.) prior to deployment of the node 1720. For example, various CSI and/or channel estimations (e.g., CQI, PMI, RSRP, SINR, etc.) can be tested in various scenarios)
As per claim 19:
Hao-Nakagiri further discloses:
wherein the preprocessing comprises using one of Fourier Transform, correlation, scaling or absolute value squared to the input signal.
(Hao, [0052],…Base station 180 and UE 104 may then perform beam training to determine the best receive and transmit directions for each of base station 180)
(Hao, [0144] The training repository 1715 may include training data obtained before and/or after deployment of the node 1720. The node 1720 may be trained in a simulated communication environment (e.g., in field testing, drive testing, etc.) prior to deployment of the node 1720. For example, various CSI and/or channel estimations (e.g., CQI, PMI, RSRP, SINR, etc.) can be tested in various scenarios)
Claim(s) 7, 10 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hao et al. (US 2025/0,088,232), in view of Nakagiri (US 2009/0,168,913), in view of Rundo et al. (US 2021/0,232,901)
As per claim 7:
Hao-Nakagiri further discloses:
wherein generating the dataset for training includes one or both of: collecting and storing waveform samples generated in a simulation software environment; or collecting and storing real time received signal waveform samples from a live communication link.
(Hao, [0052],…Base station 180 and UE 104 may then perform beam training to determine the best receive and transmit directions for each of base station 180)
(Hao, [0144] The training repository 1715 may include training data obtained before and/or after deployment of the node 1720. The node 1720 may be trained in a simulated communication environment (e.g., in field testing, drive testing, etc.) prior to deployment of the node 1720. For example, various CSI and/or channel estimations (e.g., CQI, PMI, RSRP, SINR, etc.) can be tested in various scenarios)
(Hao, [0081]…The machine learning module may be trained to adapt to the spatial correlation (e.g., the transmission spatial information 502) of the training channel samples.)
(Hao, [0077]…network entity (e.g., the BS 102) may transmit a certain reference signal to train the machine learning module 510 at the UE 104)
Hao-Nakagari does not mention about collecting waveforms.
Rundo discloses:
collecting waveforms.
(Rundo, [0146] an artificial neural network (briefly, ANN) processing stage 34, … to receive the set of filtered waveforms WF therefrom, the ANN processing stage 34 configured to produce or generate a first dataset of patterns P and to use them to alter the received set of waveforms WF, …classification stage 36, configured to receive a set of “real” measured waveforms or signals T1, T2, stored in a dedicated training database 35…)
It would have been obvious before the effective filing date of the claimed to a person having ordinary skill in the art to incorporate Rundo’s method of analyzing waveform into neural network training in order to alter the received set of waveforms based on collected waveforms.
(Rundo, [0146] an artificial neural network (briefly, ANN) processing stage 34, … to receive the set of filtered waveforms WF therefrom, the ANN processing stage 34 configured to produce or generate a first dataset of patterns P and to use them to alter the received set of waveforms WF, …classification stage 36, configured to receive a set of “real” measured waveforms or signals T1, T2, stored in a dedicated training database 35…)
As per claim 10:
Hao-Nakagiri further discloses:
wherein training the decoding model includes:
running multiple epochs of training wherein each epoch is 1 pass of the entire training dataset through the decoding model; using a summation of
(Hao, [0052],…Base station 180 and UE 104 may then perform beam training to determine the best receive and transmit directions for each of base station 180)
(Hao, [0144] The training repository 1715 may include training data obtained before and/or after deployment of the node 1720. The node 1720 may be trained in a simulated communication environment (e.g., in field testing, drive testing, etc.) prior to deployment of the node 1720. For example, various CSI and/or channel estimations (e.g., CQI, PMI, RSRP, SINR, etc.) can be tested in various scenarios)
Hao-Nakagari does not mention about collecting waveforms.
Rundo discloses:
collecting waveforms.
(Rundo, [0146] an artificial neural network (briefly, ANN) processing stage 34, … to receive the set of filtered waveforms WF therefrom, the ANN processing stage 34 configured to produce or generate a first dataset of patterns P and to use them to alter the received set of waveforms WF, …classification stage 36, configured to receive a set of “real” measured waveforms or signals T1, T2, stored in a dedicated training database 35…)
It would have been obvious before the effective filing date of the claimed to a person having ordinary skill in the art to incorporate Rundo’s method of analyzing waveform into neural network training in order to alter the received set of waveforms based on collected waveforms.
(Rundo, [0146] an artificial neural network (briefly, ANN) processing stage 34, … to receive the set of filtered waveforms WF therefrom, the ANN processing stage 34 configured to produce or generate a first dataset of patterns P and to use them to alter the received set of waveforms WF, …classification stage 36, configured to receive a set of “real” measured waveforms or signals T1, T2, stored in a dedicated training database 35…)
As per claim 11:
Hao-Nakagiri-Rundo further discloses:
wherein using a summation of
(Rundo, [0146] an artificial neural network (briefly, ANN) processing stage 34, … to receive the set of filtered waveforms WF therefrom, the ANN processing stage 34 configured to produce or generate a first dataset of patterns P and to use them to alter the received set of waveforms WF, …classification stage 36, configured to receive a set of “real” measured waveforms or signals T1, T2, stored in a dedicated training database 35…)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THIEN DANG NGUYEN whose telephone number is (571)272-9189. The examiner can normally be reached Monday-Friday 7 AM - 3:30 PM.
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/Thien Nguyen/Primary Examiner, Art Unit 2111
Prosecution Insights