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 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.
This action is in response to application 18793098 filed 8/2/24.
Claim(s) 1-20 is/are presented for examination.
Claim Objections
Claim(s) 19 is/are unclear to the examiner; what does it mean by stating “generating an angle of arrival (AoA) estimation model via deep-learning, by using, as input, a plurality of pieces of reference signal received power (RSRP) pattern training data for the plurality of candidate beams and using, as output, an AoA distribution corresponding to each of the plurality of pieces of RSRP pattern training data”? the claim languages are not very clear exactly what’s going on, to use the AoA estimation model as input and RSRP pattern training data as output? Or vice versa? Please clarify
Claim Rejections - 35 USC § 102
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) 1-5, 7-14 & 16-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Bonfante, U.S. Pub/Patent No. US 2026/0135608 A1.
As to claim 1, Bonfante teaches an electronic device comprising:
at least one memory configured to store computer-readable instructions;
a communication interface comprising an antenna array, the antenna array being configured to form a plurality of candidate beams (Bonfante, page 7, paragraph 80; i.e., [0080] the antenna weights to be applied to the antenna system to form a beam in the direction of the estimated AoA. the UE 512 can select the bean from the beam codebook that has an AoA that most closely matches the AoA estimated or predicted by the ML model 520 ( e.g., UE 512 selects 1 of the M beams in codebook that is closest to the AoA output by ML model 520)); and
at least one processor operatively connected to the communication interface and the at least one memory, wherein the at least one processor is configured to execute the computer-readable instructions to:
generate a plurality of pieces of reference signal received power (RSRP) pattern data based on an RSRP measured in each of the plurality of candidate beams with respect to a signal received from an external device (Bonfante, page 3, paragraph 31; i.e., [0031] the node generating a sequence of beams across a range of directions or possibly covering all directions. After measuring the UE performs signal measurement (e.g., the UE measuring reference signal receive power (RSRP)));
estimate an angle of arrival (AoA) distribution for each of the plurality of pieces of RSRP pattern data, by applying each of the plurality of pieces of RSRP pattern data to a neural network that is trained based on a deep-learning algorithm (Bonfante, page 5, paragraph 64; i.e., [0064] the gNB may train a ML model, to be used on or by one or more UEs. Training input data: RSRP measurements of N best SSB ( or wide) beams measured by UE at Pl, and position information of UE. The training of the ML model may use a supervised learning approach: input data (RSRP measurements of N best SSB or wide beams, and UE position infonnation) and labels (as outputs being trained), that represent value of a variable, to train the ML model parameters. Labels- represent indices of the best gNB transmit (TX) beam and estimated AoA); and
perform a beam management for a wireless communication with the external device, based on the estimated AoA distribution (Bonfante, page 6, paragraph 78; i.e., [0078] AoA that most closely matches the estimated AoA of the beam output from (or predicted by) the ML model 520. There may be a different UE receive narrow beam selected or determined by the UE 512 (e.g., based on the codebook) for each of the AoAs output or predicted by the ML model 520. Thus, for example, UE 512 may select the beam of the codebook based on the minimum (or least) angular separation between the predicted Ao).
As to claim 2, Bonfante teaches the electronic device as recited in claim 1, wherein the neural network is trained, via the deep-learning algorithm, to estimate an AoA distribution corresponding to each of a plurality of pieces of RSRP pattern training data with respect to the plurality of candidate beams, by using the plurality of pieces of RSRP pattern training data as input data (Bonfante, page 3, paragraph 31; page 5, paragraph 64; i.e., [0031] the node generating a sequence of beams across a range of directions or possibly covering all directions. After measuring the UE performs signal measurement (e.g., the UE measuring reference signal receive power (RSRP)); [0064] the gNB may train a ML model, to be used on or by one or more UEs. Training input data: RSRP measurements of N best SSB ( or wide) beams measured by UE at Pl, and position information of UE. The training of the ML model may use a supervised learning approach: input data (RSRP measurements of N best SSB or wide beams, and UE position infonnation) and labels (as outputs being trained), that represent value of a variable, to train the ML model parameters. Labels- represent indices of the best gNB transmit (TX) beam and estimated AoA).
As to claim 3, Bonfante teaches the electronic device as recited in claim 1, wherein the AoA distribution, output from the neural network, comprises a distribution of matching probabilities between each of the plurality of pieces of RSRP pattern data and each of AoA classes (Bonfante, page 9, paragraph 107-110; i.e., [0107] From the ML model output, the UE or gNB may use the predictions of the 2nd, 3rd and Kth beam and AoA pairs that have probabilities; [0109] associated estimated (or predicted) AoAs. 1) the gNB configures the UE to measure (e.g., measure RSRP of signals received via these beams) a set of narrow Tx beams specified in the list of the 2-elements vectors. 2) the UE computes or determines a list of predicted Rx beams from the list of predicted AoAs; [0110] generate a report like the one shown in Table 2, where each CSI-RS may be measured considering the T (transmit) beam and the Rx (receive) beam selected at the UE side based on the predicted AoA. a list of CSI-RS measurements suggested by the ML model output. For each CSI-RS, the predictions from ML model may. ) The list of measured RSRPs may then used by the UE to find the best Rx beam).
As to claim 4, Bonfante teaches the electronic device as recited in claim 1, wherein, in performing the beam management, the at least one processor is configured to execute the computer-readable instructions to:
identify whether a dominant AoA class exists in the AoA distribution, the dominant AoA class having a highest matching probability with a corresponding piece of RSRP pattern data (Bonfante, page 9, paragraph 107-110; i.e., [0107] From the ML model output, the UE or gNB may use the predictions of the 2nd, 3rd and Kth beam and AoA pairs that have probabilities; [0109] associated estimated (or predicted) AoAs. 1) the gNB configures the UE to measure (e.g., measure RSRP of signals received via these beams) a set of narrow Tx beams specified in the list of the 2-elements vectors. 2) the UE computes or determines a list of predicted Rx beams from the list of predicted AoAs; [0110] generate a report like the one shown in Table 2, where each CSI-RS may be measured considering the T (transmit) beam and the Rx (receive) beam selected at the UE side based on the predicted AoA. a list of CSI-RS measurements suggested by the ML model output. For each CSI-RS, the predictions from ML model may. ) The list of measured RSRPs may then used by the UE to find the best Rx beam);
select, as a target beam, a beam corresponding to the dominant AoA class, based on identifying that the dominant AoA class exists (Bonfante, page 9, paragraph 107-110; i.e., [0107] From the ML model output, the UE or gNB may use the predictions of the 2nd, 3rd and Kth beam and AoA pairs that have probabilities; [0109] associated estimated (or predicted) AoAs. 1) the gNB configures the UE to measure (e.g., measure RSRP of signals received via these beams) a set of narrow Tx beams specified in the list of the 2-elements vectors. 2) the UE computes or determines a list of predicted Rx beams from the list of predicted AoAs; [0110] generate a report like the one shown in Table 2, where each CSI-RS may be measured considering the T (transmit) beam and the Rx (receive) beam selected at the UE side based on the predicted AoA. a list of CSI-RS measurements suggested by the ML model output. For each CSI-RS, the predictions from ML model may. ) The list of measured RSRPs may then used by the UE to find the best Rx beam); and
perform the wireless communication with the external device using the target beam (Bonfante, page 6, paragraph 78; i.e., [0078] AoA that most closely matches the estimated AoA of the beam output from (or predicted by) the ML model 520. There may be a different UE receive narrow beam selected or determined by the UE 512 (e.g., based on the codebook) for each of the AoAs output or predicted by the ML model 520. Thus, for example, UE 512 may select the beam of the codebook based on the minimum (or least) angular separation between the predicted Ao).
As to claim 5, Bonfante teaches the electronic device as recited in claim 1, wherein, in performing the beam management, the at least one processor is configured to execute the computer-readable instructions to:
select at least two candidate AoA classes based on the AoA distribution (Bonfante, page 7, paragraph 83; i.e., [0083] These two beams form a beam pair that may be used for data transmission between the gNB and UE (for uplink and/or downlink transmissions));
perform a beam training based on beams corresponding to the at least two candidate AoA classes, and select a target beam based on a result of the beam training (Bonfante, page 8, paragraph 91-92; i.e., [0091] the AoAs of the K best beams obtained from ML-model output. This message may include a list of K AoAs, i.e., [AoA C1l , AoA C2l, ... , AoACK)]; [0092] The UE adapts the UE receive narrow beam direction according to the AoA value in the sequence of the K AoAs values contained in the ML model (or according to the codebook beam that most closely matches the Ao A values sent to the UE). the UE 512 identifies the best UE receive narrow beam from RSRP measurements and reports the best beam indexes to the gNB 410, and identifies the best gNB narrow beam from RSRP measurements); and
perform the wireless communication with the external device using the target beam (Bonfante, page 8, paragraph 93; i.e., [0093] AoA that most closely matches the estimated AoA of the beam output from (or predicted by) the ML model 520. There may be a different UE receive narrow beam selected or determined by the UE 512 (e.g., based on the codebook) for each of the AoAs output or predicted by the ML model 520. Thus, for example, UE 512 may select the beam of the codebook based on the minimum (or least) angular separation between the predicted Ao).
As to claim 7, Bonfante teaches the electronic device as recited in claim 5, wherein, in selecting the target beam based on the result of the beam training, the at least one processor is configured to execute the computer-readable instructions to:
measure at least one channel indicator for channels based on beams corresponding to the at least two candidate AoA classes (Bonfante, page 7, paragraph 80 & 83; i.e., ., [0080] the antenna weights to be applied to the antenna system to form a beam in the direction of the estimated AoA. the UE 512 can select the bean from the beam codebook that has an AoA that most closely matches the AoA estimated or predicted by the ML model 520 ( e.g., UE 512 selects 1 of the M beams in codebook that is closest to the AoA output by ML model 520); [0083] These two beams form a beam pair that may be used for data transmission between the gNB and UE (for uplink and/or downlink transmissions)); and
select the target beam based on the at least one channel indicator,
wherein the at least one channel indicator comprises at least one of a RSRP, a signal-to-noise ratio (SNR), and a reference signal received quality (RSRQ) of a signal passing through the channels (Bonfante, page 8, paragraph 91-92; i.e., [0091] the AoAs of the K best beams obtained from ML-model output. This message may include a list of K AoAs, i.e., [AoA C1l , AoA C2l, ... , AoACK)]; [0092] The UE adapts the UE receive narrow beam direction according to the AoA value in the sequence of the K AoAs values contained in the ML model (or according to the codebook beam that most closely matches the Ao A values sent to the UE). the UE 512 identifies the best UE receive narrow beam from RSRP measurements and reports the best beam indexes to the gNB 410, and identifies the best gNB narrow beam from RSRP measurements).
As to claim 8, Bonfante teaches the electronic device as recited in claim 5, wherein the at least one processor is further configured to execute the computer-readable instructions to select, as the target beam, a beam generated by combining beams corresponding to the at least two candidate AoA classes together (Bonfante, page 7, paragraph 80 & 83; i.e., ., [0080] the antenna weights to be applied to the antenna system to form a beam in the direction of the estimated AoA. the UE 512 can select the bean from the beam codebook that has an AoA that most closely matches the AoA estimated or predicted by the ML model 520 ( e.g., UE 512 selects 1 of the M beams in codebook that is closest to the AoA output by ML model 520); [0083] These two beams form a beam pair that may be used for data transmission between the gNB and UE (for uplink and/or downlink transmissions)).
As to claim 9, Bonfante teaches the electronic device as recited in claim 1, further comprising at least one sensor, wherein the at least one processor is further configured to execute the computer-readable instructions to:
select a target beam among the plurality of candidate beams based on the estimated AoA distribution (Bonfante, page 9, paragraph 107-110; i.e., [0107] From the ML model output, the UE or gNB may use the predictions of the 2nd, 3rd and Kth beam and AoA pairs that have probabilities; [0109] associated estimated (or predicted) AoAs. 1) the gNB configures the UE to measure (e.g., measure RSRP of signals received via these beams) a set of narrow Tx beams specified in the list of the 2-elements vectors. 2) the UE computes or determines a list of predicted Rx beams from the list of predicted AoAs; [0110] generate a report like the one shown in Table 2, where each CSI-RS may be measured considering the T (transmit) beam and the Rx (receive) beam selected at the UE side based on the predicted AoA. a list of CSI-RS measurements suggested by the ML model output. For each CSI-RS, the predictions from ML model may. ) The list of measured RSRPs may then used by the UE to find the best Rx beam);
receive, from the at least one sensor, sensing data related to a change in at least one of a position or an orientation of the electronic device (Bonfante, page 8, paragraph 97-98; i.e., [0097] 1) the position information of the UE, e.g., the estimated 2D (two dimensional) coordinates of the UE position (x,, y,), including the vertical location of the UE, i.e., z,; [0098] 2) the RSRP (reference signal receive power) signal measurements);
obtain first position coordinates of the electronic device before the change of the electronic device, based on AoA class information corresponding to the target beam (Bonfante, page 8, paragraph 97-98; i.e., [0097] 1) the position information of the UE, e.g., the estimated 2D (two dimensional) coordinates of the UE position (x,, y,), including the vertical location of the UE, i.e., z,; [0098] 2) the RSRP (reference signal receive power) signal measurements);
obtain, based on the sensing data and the first position coordinates, second position coordinates of the electronic device after the change of the electronic device (Bonfante, page 11, paragraph 128; i.e., [0128] selected network node transmit second beam);
generate correction information based on the first position coordinates and the second position coordinates (Bonfante, page 8, paragraph 97-98; i.e., [0097] 1) the position information of the UE, e.g., the estimated 2D (two dimensional) coordinates of the UE position (x,, y,), including the vertical location of the UE, i.e., z,; [0098] 2) the RSRP (reference signal receive power) signal measurements); and
select, as a final target beam, a beam generated by correcting the target beam based on the correction information (Bonfante, page 9, paragraph 107-110; i.e., [0107] From the ML model output, the UE or gNB may use the predictions of the 2nd, 3rd and Kth beam and AoA pairs that have probabilities; [0109] associated estimated (or predicted) AoAs. 1) the gNB configures the UE to measure (e.g., measure RSRP of signals received via these beams) a set of narrow Tx beams specified in the list of the 2-elements vectors. 2) the UE computes or determines a list of predicted Rx beams from the list of predicted AoAs; [0110] generate a report like the one shown in Table 2, where each CSI-RS may be measured considering the T (transmit) beam and the Rx (receive) beam selected at the UE side based on the predicted AoA. a list of CSI-RS measurements suggested by the ML model output. For each CSI-RS, the predictions from ML model may. ) The list of measured RSRPs may then used by the UE to find the best Rx beam).
As to claim 19, Bonfante teaches a method of operating a wireless communication system, the method comprising:
forming a plurality of candidate beams for performing a wireless communication with an external device (Bonfante, page 7, paragraph 80; i.e., [0080] the UE 512 can select the bean from the beam codebook that has an AoA that most closely matches the AoA estimated or predicted by the ML model 520 ( e.g., UE 512 selects 1 of the M beams in codebook that is closest to the AoA output by ML model 520));
generating an angle of arrival (AoA) estimation model via deep-learning, by using, as input, a plurality of pieces of reference signal received power (RSRP) pattern training data for the plurality of candidate beams and using, as output, an AoA distribution corresponding to each of the plurality of pieces of RSRP pattern training data (Bonfante, page 5, paragraph 64; i.e., [0064] the gNB may train a ML model, to be used on or by one or more UEs. Training input data: RSRP measurements of N best SSB ( or wide) beams measured by UE at Pl, and position information of UE. The training of the ML model may use a supervised learning approach: input data (RSRP measurements of N best SSB or wide beams, and UE position infonnation) and labels (as outputs being trained), that represent value of a variable, to train the ML model parameters. Labels- represent indices of the best gNB transmit (TX) beam and estimated AoA);
generating a plurality of pieces of RSRP pattern data by measuring an RSRP in each of the plurality of candidate beams with respect to a signal received from the external device (Bonfante, page 3, paragraph 31; i.e., [0031] the node generating a sequence of beams across a range of directions or possibly covering all directions. After measuring the UE performs signal measurement (e.g., the UE measuring reference signal receive power (RSRP)));
estimating an AoA distribution for each of the plurality of pieces of RSRP pattern data, by applying each of the plurality of pieces of RSRP pattern data to the AoA estimation model (Bonfante, page 5, paragraph 64; i.e., [0064] the gNB may train a ML model, to be used on or by one or more UEs. Training input data: RSRP measurements of N best SSB ( or wide) beams measured by UE at Pl, and position information of UE. The training of the ML model may use a supervised learning approach: input data (RSRP measurements of N best SSB or wide beams, and UE position infonnation) and labels (as outputs being trained), that represent value of a variable, to train the ML model parameters. Labels- represent indices of the best gNB transmit (TX) beam and estimated AoA); and
performing a beam management based on the estimated AoA distribution (Bonfante, page 6, paragraph 78; i.e., [0078] AoA that most closely matches the estimated AoA of the beam output from (or predicted by) the ML model 520. There may be a different UE receive narrow beam selected or determined by the UE 512 (e.g., based on the codebook) for each of the AoAs output or predicted by the ML model 520. Thus, for example, UE 512 may select the beam of the codebook based on the minimum (or least) angular separation between the predicted Ao).
Claim(s) 10-14, 16-18 is/are directed to a method/computer readable medium claims and they do not teach or further define over the limitations recited in claim(s) 1-5, 7-9. Therefore, claim(s) 10-14, 16-18 is/are also rejected for similar reasons set forth in claim(s) 1-5, 7-9.
Claim(s) 19 & 20 is/are directed to a method/computer readable medium claims and they do not teach or further define over the limitations recited in claim(s) 1 & 3. Therefore, claim(s) 19 & 20 is/are also rejected for similar reasons set forth in claim(s) 1 & 3.
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.
Claim(s) 6 & 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bonfante, U.S. Pub/Patent No. US 2026/0135608 A1 in view of Tian, U.S. Patent/Pub. No. US 2025/0324315 A1.
As to claim 6, Bonfante teaches the electronic device as recited in claim 5. But Bonfante failed to teach the claim limitation wherein each of the at least two candidate AoA classes has a matching probability with a corresponding piece of RSRP pattern data greater than or equal to a predetermined threshold in the AoA distribution (Bonfante, page 9, paragraph 177; page 10, paragraph 186; i.e., [0177] AoA that most closely matches the estimated AoA of the beam output from (or predicted by) the ML model 520. There may be a different UE receive narrow beam selected or determined by the UE 512 (e.g., based on the codebook) for each of the AoAs output or predicted by the ML model 520. Thus, for example, UE 512 may select the beam of the codebook based on the minimum (or least) angular separation between the predicted Ao).
However, Tian teaches the limitation wherein each of the at least two candidate AoA classes has a matching probability with a corresponding piece of RSRP pattern data greater than or equal to a predetermined threshold in the AoA distribution (Tian, page 9, paragraph 177; page 10, paragraph 186; i.e., [0177] a target accuracy threshold is less than a first threshold; or that the performance of the positioning scheme based on AI and/or ML is a second performance if the probability that the positioning accuracy from the output result of the communication scheme is greater than the target accuracy threshold is greater than a second threshold; [0186] a beam selection scheme (e.g., an AI and/or ML scheme for processing beam selection) is a first performance if at least one of an RSRP difference, an RSRQ difference, an SINR difference, or a beam angle difference between the selected beam obtained from the output result of the communication scheme and the target beam is greater than a first threshold; or that the performance of the beam selection scheme is a second performance if at least one of the RSRP difference, the RSRQ difference, the SINR difference, or the beam angle difference between the selected beam obtained from the output result of the communication scheme and the target beam is less than a second threshold).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Bonfante to substitute non-terrestrial communication networks from Tian for cellular network from Bonfante to meets a performance monitoring condition (Tian, page 1, paragraph 5).
Claim(s) 15 is/are directed to a method claim and they do not teach or further define over the limitations recited in claim(s) 6. Therefore, claim(s) 15 is/are also rejected for similar reasons set forth in claim(s) 6.
Listing of Relevant Arts
Bai, U.S. Patent/Pub. No. US 20240039606 A1 discloses measuring and reporting RSRP and identifying AoA.
Ma, U.S. Patent/Pub. No. US 20220007346 A1 discloses probability threshold and target beam.
Contact Information
The present application is being examined under the pre-AIA first to invent provisions.
THUONG NGUYEN whose telephone number is (571)272-3864. The examiner can normally be reached on Monday-Friday 9:00-6:00.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Noel Beharry can be reached on 571-270-5630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THUONG NGUYEN/Primary Examiner, Art Unit 2416
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