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 responsive to the Remark filed on 8/12/26.
Claim(s) 1, 4-5, 7, 9-10, 13-14, 16, 18-20 was/were amended. Claim(s) 3, 8, 12 & 17 is/are canceled.
Claim(s) 1-2, 4-7, 9-11, 13-16 & 18-20 is/are presented for examination.
Claim Objections
Claim(s) 1, 10 & 19 is/are unclear to the examiner; what does it mean by stating “select at least two candidate AoA classes based on the AoA distribution”? the claim languages are not very clear how does the system determine a condition to select the two candidate A0A? at what probability to select the AoA? 50%? 60%? What is the triggering condition? Please clarify
Claim(s) 1, 10 & 19 is/are unclear to the examiner; what does it mean by stating “wherein, in combining the beams corresponding to the at least two candidate AoA classes, beam gains are differently assigned to the beams corresponding to the at least two candidate AoA classes based on matching probabilities between corresponding pieces of RSRP pattern data and the at least two candidate AoA classes”? the claim languages are not very clear what is a reason of combining the two candidate AoA with the beam? Or the matching probabilities corresponding of the RSRP pattern and the two AoA? What exactly is the claim invention? To do what? To select the AoA? Please clarify
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) 1-5, 7-14 & 16-20 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 Li, US 2025/0088879 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; page 10, paragraph 87; 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);
select at least two candidate AoA classes based on the 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);
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),
But Bonfante failed to teach the claim limitation 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; in performing the beam management, the at least one processor is configured to execute the computer-readable instructions to: combine beams corresponding to the at least two candidate AoA classes and select, as a target beam, a beam generated by combining the beams; and wherein, in combining the beams corresponding to the at least two candidate AoA classes, beam gains are differently assigned to the beams corresponding to the at least two candidate AoA classes based on matching probabilities between corresponding pieces of RSRP pattern data and the at least two candidate AoA classes.
However, Li teaches the limitation 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 (Li, page 10, paragraph 87; page 11, paragraph 89; page 14, paragraph 121; i.e., [0087] a probability of beam failure associated with the second reference signal. The probability threshold may also have a value between O and 1. The UE 115 may compare the probability value to the probability threshold. [0089] a plurality of first reference signals associated with the first serving cell based on a plurality of first machine learning models. The UE 115 may determine a probability of the beam failure associated with the second reference signal. A probability of beam failure associated with the second reference signal. The UE 115 may determine a beam failure associated with the second reference signal based on the probability being higher than a probability threshold. The probability threshold may be preconfigured in the UE 115; [0121] the UE measuring at least one of a power delay profile (PDP) associated with the first reference signal or an angle of arrival (AOA) associated with the first reference signal. The UE may measure the PDP associated with the first reference signal using any suitable method. For example, the reference signal received power
(RSRP)); in performing the beam management, the at least one processor is configured to execute the computer-readable instructions to: combine beams corresponding to the at least two candidate AoA classes and select, as a target beam, a beam generated by combining the beams (Li, page 16, paragraph 130; i.e., [0130] the AOA
associated with the multiple paths of the first reference signal as determined at action 820, and/or the BLER of a hypothetical PDCCH associated with the first reference
signal. The output of the machine learning model may be a probability of beam failure associated with the second reference signal. The probability of beam failure associated
with the second reference signal may have a value between 0 and 1. The probability threshold may also have a value between O and 1. The UE may compare the probability value to the probability threshold. If the probability value is higher than the probability threshold, the UE determines a beam failure associated with the second reference signal. The UE may report the beam failure to the network unit as a binary indicator (e.g., beam failure or no beam failure)); and wherein, in combining the beams corresponding to the at least two candidate AoA classes, beam gains are differently assigned to the beams corresponding to the at least two candidate AoA classes based on matching probabilities between corresponding pieces of RSRP pattern data and the at least two candidate AoA classes (Li, page 16, paragraph 130; i.e., [0130] the AOA associated with the multiple paths of the first reference signal as determined at action 820, and/or the BLER of a hypothetical PDCCH associated with the first reference signal. The output of the machine learning model may be a probability of beam failure associated with the second reference signal. The probability of beam failure associated with the second reference signal may have a value between 0 and 1. The probability threshold may also have a value between O and 1. The UE may compare the probability value to the probability threshold. If the probability value is higher than the probability threshold, the UE determines a beam failure associated with the second reference signal. The UE may report the beam failure to the network unit as a binary indicator (e.g., beam failure or no beam failure)).
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 PSD from Li for transmission power from Bonfante to supporting communications for multiple communication devices, which may be otherwise known as user equipment (UE) (Li, page 1, paragraph 2).
As to claim 2, Bonfante-Li 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. 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 4, Bonfante-Li teaches the electronic device as recited in claim 1, wherein, in performing the beam management, the at least one processor is further 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
As to claim 5, Bonfante-Li teaches the electronic device as recited in claim 1, wherein 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).
As to claim 7, Bonfante-Li 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 the 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 (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),
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 9, Bonfante-Li 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:
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).
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(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 Li, US 2025/0088879 A1, and further in view of Tian, U.S. Patent/Pub. No. US 2025/0324315 A1.
As to claim 6, Bonfante-Li teaches the electronic device as recited in claim 5. But Bonfante-Li 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-Li 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.
Response to Arguments
Applicant's arguments with respect to claim(s) 1-2, 4-7, 9-11, 13-16 & 18-20 have been considered but are moot in view of the new ground(s) of rejection.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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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