DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
The current amendment(s) made by Applicant to claims 14-20 to obviate the claim objection(s) presented in the previous Office Action are proper and have been entered. These objections have been withdrawn.
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, 6, 9, 12, 14, 15, and 18 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zhang et al. (U.S. 2024/0073826) (hereinafter “Zhang”). Zhang teaches all of the limitations of the specified claims with the reasoning that follows.
Regarding claim 1, “a method of a first communication node, comprising: transmitting, to a second communication node, an initiation signal indicating to initiate a collection procedure of training data for a neural network” is anticipated by a network device 502 (first communication node) that transmits signaling that is received by one or more communication devices such as UE(s) 504 (second communication node), where the signaling 510 (initiation signal) indicates a start of training (collection procedure of training data) to the UE(s) 504 as shown in step 510 of Figure 5 and spoken of on page 12, paragraph [0139]; and where training for a neural network model is supported as spoken of on page 9, paragraph [0105].
“Transmitting, to the second communication node, an information signal including network parameters of the first communication node” is anticipated by the network device 502 that transmits one or more reference signals (information signal) to the UE(s) 504 for training sample collection as shown in step 512 of Figure 5 and spoken of on page 12, paragraph [0140]; where the one or more reference signal(s) may include path loss information (network parameters) as spoken of on page 9, paragraph [0108].
“Receiving, from the second communication node, the training data in response to the information signal” is anticipated by the UE(s) 504 that receive the reference signal(s), perform measurements, and transmit measurement results (training data) to the network device as shown in step 514, 516 of Figure 5 and spoken of on page 12, paragraph [0140].
“Training the neural network using the training data” is anticipated by the network device 502 that receives the measurement results and develops the reference power control model (trains neural network) as shown in step 518 of Figure 5 and spoken of on page 12, paragraph [0140].
“Determining optimal network parameters using the trained neural network” is anticipated by the UE(s) 504 that determines that a particular uplink channel is to be transmitted, and uses the power control reference model and channel-specific parameters (using the trained neural network) to determine power for that channel as shown in step 524 of Figure 5 and spoken of on page 12, paragraph [0142]; where obtaining base or reference transmit power according to the reference power control model may involve selecting between values of large time scale parameters related to current position or time of transmission (determining optimal network parameters) as spoken of on pages 12-13, paragraph [0143].
“Performing communication with the second communication node using the optimal network parameters” is anticipated by the transmission of an uplink channel (communication) at the determined transmit power (using optimal network parameters) between UE(s) 504 and network device 502 as shown in step 530 of Figure 5 and spoken of on page 13, paragraph [0146].
Lastly, “wherein the network parameters include at least one of a distance between the first communication node and another communication node, a location of the first communication node, an antenna angle of the first communication node, a number of sectors of the first communication node, a beam set of the first communication node, or a beamforming vector of the first communication node, and the information signal is a reference signal or a control signal” is anticipated by the obtaining of base or reference transmit power according to the reference power control model may involve selecting between values of large time scale parameters related to current position (location of communication node) or time of transmission (determining optimal network parameters) as spoken of on pages 12-13, paragraph [0143].
Regarding claim 2, “wherein the first communication node is a terrestrial base station, and the second communication node is a non-terrestrial terminal” is anticipated by the communication system 100 of Figure 2 including terrestrial transmit and receive points 170a, 170b (terrestrial base station) as well as electronic devices 110a, 110d (non-terrestrial terminal) coupled to non-terrestrial transmit and receive point 172 as spoken of on pages 2-3, paragraph [0026].
Regarding claim 6, “wherein the training data includes at least one of performance metrics for the network parameters, unmanned aerial vehicle (UAV) characteristic parameters, or environmental characteristic parameters, the UAV characteristic parameters are parameters indicating characteristics of the second communication node that is a UAV, and the environmental characteristic parameters are parameters that affect communication between the first communication node and the second communication node in addition to the network parameters and the UAV characteristic parameters” is anticipated by the UE(s) 504 that receive the reference signal(s), perform measurements, and transmit measurement results (training data) to the network device as shown in step 514, 516 of Figure 5 and spoken of on page 12, paragraph [0140]; where the UE(s) 504 measures path loss (performance metric) based on a received reference signal from network device 502 as spoken of on page 9, paragraph [0100].
Regarding claim 9, “a method of a first communication node, comprising: receiving, from a second communication node, an initiation signal indicating to initiate a collection procedure of training data of a neural network” is anticipated by a network device 502 (communication node) that transmits signaling that is received by one or more communication devices such as UE(s) 504 (communication node), where the signaling 510 (initiation signal) indicates a start of training (collection procedure of training data) to the UE(s) 504 as shown in step 510 of Figure 5 and spoken of on page 12, paragraph [0139]; where training for a neural network model is supported as spoken of on page 9, paragraph [0105]; and where a base station or another device (e.g. a UE or other secondary device) in a network may initiate training or optimization by transmitting a request (initiation signal) or command to an AI agent or module to train and provide optimized power control parameters as spoken of on page 9, paragraph [0107].
“In response to the initiation signal, transmitting, to the second communication node, an information signal including network parameters of the first communication node” is anticipated by the network device 502 that transmits one or more reference signals (information signal) to the UE(s) 504 for training sample collection as shown in step 512 of Figure 5 and spoken of on page 12, paragraph [0140]; where the one or more reference signal(s) may include path loss information (network parameters) as spoken of on page 9, paragraph [0108].
“Receiving, from the second communication node, the training data in response to the information signal” is anticipated by the UE(s) 504 that receive the reference signal(s), perform measurements, and transmit measurement results (training data) to the network device as shown in step 514, 516 of Figure 5 and spoken of on page 12, paragraph [0140].
“Training the neural network using the training data” is anticipated by the network device 502 that receives the measurement results and develops the reference power control model (trains neural network) as shown in step 518 of Figure 5 and spoken of on page 12, paragraph [0140].
“Determining optimal network parameters using the trained neural network” is anticipated by the UE(s) 504 that determines that a particular uplink channel is to be transmitted, and uses the power control reference model and channel-specific parameters (using the trained neural network) to determine power for that channel as shown in step 524 of Figure 5 and spoken of on page 12, paragraph [0142]; where obtaining base or reference transmit power according to the reference power control model may involve selecting between values of large time scale parameters related to current position or time of transmission (determining optimal network parameters) as spoken of on pages 12-13, paragraph [0143].
“Performing communication with the second communication node using the optimal network parameters” is anticipated by the transmission of an uplink channel (communication) at the determined transmit power (using optimal network parameters) between UE(s) 504 and network device 502 as shown in step 530 of Figure 5 and spoken of on page 13, paragraph [0146].
Lastly, “wherein the network parameters include at least one of a distance between the first communication node and another communication node, a location of the first communication node, an antenna angle of the first communication node, a number of sectors of the first communication node, a beam set of the first communication node, or a beamforming vector of the first communication node, and the information signal is a reference signal or a control signal” is anticipated by the obtaining of base or reference transmit power according to the reference power control model may involve selecting between values of large time scale parameters related to current position (location of communication node) or time of transmission (determining optimal network parameters) as spoken of on pages 12-13, paragraph [0143].
Regarding claim 12, “wherein the training data includes at least one of performance metrics for the network parameters, unmanned aerial vehicle (UAV) characteristic parameters, or environmental characteristic parameters, the UAV characteristic parameters are parameters indicating characteristics of the second communication node that is a UAV, and the environmental characteristic parameters are parameters that affect communication between the first communication node and the second communication node in addition to the network parameters and the UAV characteristic parameters” is anticipated by the UE(s) 504 that receive the reference signal(s), perform measurements, and transmit measurement results (training data) to the network device as shown in step 514, 516 of Figure 5 and spoken of on page 12, paragraph [0140]; where the UE(s) 504 measures path loss (performance metric) based on a received reference signal from network device 502 as spoken of on page 9, paragraph [0100].
Regarding claim 14, “a method of a second communication node, comprising: transmitting, to a first communication node, an initiation signal indicating to initiate a collection procedure of training data for a neural network” is anticipated by a network device 502 (communication node) that transmits signaling that is received by one or more communication devices such as UE(s) 504 (communication node), where the signaling 510 (initiation signal) indicates a start of training (collection procedure of training data) to the UE(s) 504 as shown in step 510 of Figure 5 and spoken of on page 12, paragraph [0139]; where training for a neural network model is supported as spoken of on page 9, paragraph [0105]; and where a base station or another device (e.g. a UE or other secondary device) in a network may initiate training or optimization by transmitting a request (initiation signal) or command to an AI agent or module to train and provide optimized power control parameters as spoken of on page 9, paragraph [0107].
“In response to the initiation signal, receiving, from the first communication node, an information signal including network parameters of the first communication node” is anticipated by the network device 502 that transmits one or more reference signals (information signal) to the UE(s) 504 for training sample collection as shown in step 512 of Figure 5 and spoken of on page 12, paragraph [0140]; where the one or more reference signal(s) may include path loss information (network parameters) as spoken of on page 9, paragraph [0108].
“Generating the training data based on the information signal” and “training the neural network using the training data” is anticipated by the UE(s) 504 that receive the reference signal(s), perform measurements, and transmit measurement results (training using training data) to the network device as shown in step 514, 516 of Figure 5 and spoken of on page 12, paragraph [0140].
“Determining optimal network parameters using the trained neural network” is anticipated by the UE(s) 504 that determines that a particular uplink channel is to be transmitted, and uses the power control reference model and channel-specific parameters (using the trained neural network) to determine power for that channel as shown in step 524 of Figure 5 and spoken of on page 12, paragraph [0142]; where obtaining base or reference transmit power according to the reference power control model may involve selecting between values of large time scale parameters related to current position or time of transmission (determining optimal network parameters) as spoken of on pages 12-13, paragraph [0143].
“Transmitting information on the optimal network parameters to the first communication terminal” is anticipated by the transmission of an uplink channel (communication) at the determined transmit power (using optimal network parameters) between UE(s) 504 and network device 502 as shown in step 530 of Figure 5 and spoken of on page 13, paragraph [0146]; where the uplink channel transmission may include various channel specific parameters (information on optimal network parameters) as spoken of on page 13, paragraphs [0149]-[0150].
Lastly, “wherein the network parameters include at least one of a distance between the first communication node and another communication node, a location of the first communication node, an antenna angle of the first communication node, a number of sectors of the first communication node, a beam set of the first communication node, or a beamforming vector of the first communication node, and the information signal is a reference signal or a control signal” is anticipated by the obtaining of base or reference transmit power according to the reference power control model may involve selecting between values of large time scale parameters related to current position (location of communication node) or time of transmission (determining optimal network parameters) as spoken of on pages 12-13, paragraph [0143].
Regarding claim 15, “wherein the first communication node is a terrestrial base station, and the second communication node is a non-terrestrial terminal” is anticipated by the communication system 100 of Figure 2 including terrestrial transmit and receive points 170a, 170b (terrestrial base station) as well as electronic devices 110a, 110d (non-terrestrial terminal) coupled to non-terrestrial transmit and receive point 172 as spoken of on pages 2-3, paragraph [0026].
Regarding claim 18, “wherein the training data includes at least one of performance metrics for the network parameters, unmanned aerial vehicle (UAV) characteristic parameters, or environmental characteristic parameters, the UAV characteristic parameters are parameters indicating characteristics of the second communication node that is a UAV, and the environmental characteristic parameters are parameters that affect communication between the first communication node and the second communication node in addition to the network parameters and the UAV characteristic parameters” is anticipated by the UE(s) 504 that receive the reference signal(s), perform measurements, and transmit measurement results (training data) to the network device as shown in step 514, 516 of Figure 5 and spoken of on page 12, paragraph [0140]; where the UE(s) 504 measures path loss (performance metric) based on a received reference signal from network device 502 as spoken of on page 9, paragraph [0100].
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.
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) 3, 4, 10, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Lu et al. (U.S. 2022/0129753) (hereinafter “Lu”).
Regarding claim 3, Zhang teaches claim 1 as described above. While Zhang also teaches where AI/ML communication may enable utilization of AI/ML capability with learning, prediction, and decision making to solve complicated optimization problems with better strategy and optimal solution as spoken of on page 5, paragraph [0051], Zhang does not explicitly teach “wherein when a prediction accuracy of the neural network does not satisfy determination criteria, at least one of the initiation signal or the information signal is transmitted to the second communication node”.
However, Lu teaches a pre-training method for a neural network model where a loss value of an initial neural network model is generated according to a prediction result, where the loss value indicates the prediction accuracy of the initial neural network model, and when the loss value indicates that the prediction accuracy of the initial neural network model is low (does not satisfy determination criteria), the model can continue to be pre-trained (initiation/information signal transmitted) to improve the prediction effect of the model as spoken of on page 4, paragraph [0072].
Given the above references, it would have been obvious to someone of ordinary skill in the art, before the effective filing date of the invention, to apply the continuation of training in relation to measured prediction accuracy as taught in Lu to the system of Zhang in order to increase the overall accuracy of the training model, thereby improving the quality of service experienced by the user(s) during communication as spoken of on page 4, paragraph [0072] of Lu.
Regarding claim 4, Zhang teaches claim 1 as described above. While Zhang also teaches where AI/ML communication may enable utilization of AI/ML capability with learning, prediction, and decision making to solve complicated optimization problems with better strategy and optimal solution as spoken of on page 5, paragraph [0051], Zhang does not explicitly teach “wherein when a prediction accuracy of the trained neural network satisfies determination criteria, a transmission periodicity of the initiation signal increases, and when the prediction accuracy of the trained neural network does not satisfy the determination criteria, the transmission periodicity of the initiation signal decreases.
However, Lu teaches a pre-training method for a neural network model where a loss value of an initial neural network model is generated according to a prediction result, where the loss value indicates the prediction accuracy of the initial neural network model, and when the loss value indicates that the prediction accuracy of the initial neural network model is low (does not satisfy determination criteria), the model can continue to be pre-trained (initiation signal transmission) to improve the prediction effect of the model, while continued pre-training is not needed if the loss value indicates a high prediction accuracy (satisfies determination criteria) as the prediction is correct, and where the neural network model can be pre-trained in a cyclic manner (transmission periodicity) to improve the prediction as spoken of on page 4, paragraphs [0069] and [0072].
Given the above references, it would have been obvious to someone of ordinary skill in the art, before the effective filing date of the invention, to apply the continuation of training in relation to measured prediction accuracy as taught in Lu to the system of Zhang in order to increase the overall accuracy of the training model, thereby improving the quality of service experienced by the user(s) during communication as spoken of on page 4, paragraph [0072] of Lu.
Regarding claim 10, Zhang teaches claim 9 as described above. While Zhang also teaches where AI/ML communication may enable utilization of AI/ML capability with learning, prediction, and decision making to solve complicated optimization problems with better strategy and optimal solution as spoken of on page 5, paragraph [0051], Zhang does not explicitly teach “wherein when a prediction accuracy of the trained neural network satisfies determination criteria, information on an increased transmission periodicity of the initiation signal is transmitted to the second communication node, and when the prediction accuracy of the trained neural network does not satisfy the determination criteria, information on a decreased transmission periodicity of the initiation signal is transmitted to the second communication node”.
However, Lu teaches a pre-training method for a neural network model where a loss value of an initial neural network model is generated according to a prediction result, where the loss value indicates the prediction accuracy of the initial neural network model, and when the loss value indicates that the prediction accuracy of the initial neural network model is low (does not satisfy determination criteria), the model can continue to be pre-trained (initiation signal transmission) to improve the prediction effect of the model, while continued pre-training is not needed if the loss value indicates a high prediction accuracy (satisfies determination criteria) as the prediction is correct, and where the neural network model can be pre-trained in a cyclic manner (transmission periodicity) to improve the prediction as spoken of on page 4, paragraphs [0069] and [0072].
Given the above references, it would have been obvious to someone of ordinary skill in the art, before the effective filing date of the invention, to apply the continuation of training in relation to measured prediction accuracy as taught in Lu to the system of Zhang in order to increase the overall accuracy of the training model, thereby improving the quality of service experienced by the user(s) during communication as spoken of on page 4, paragraph [0072] of Lu.
Regarding claim 16, Zhang teaches claim 14 as described above. While Zhang also teaches where AI/ML communication may enable utilization of AI/ML capability with learning, prediction, and decision making to solve complicated optimization problems with better strategy and optimal solution as spoken of on page 5, paragraph [0051], Zhang does not explicitly teach “wherein when a prediction accuracy of the trained neural network satisfies determination criteria, a transmission periodicity of the initiation signal increases, and when the prediction accuracy of the trained neural network does not satisfy the determination criteria, the transmission periodicity of the initiation signal decreases.
However, Lu teaches a pre-training method for a neural network model where a loss value of an initial neural network model is generated according to a prediction result, where the loss value indicates the prediction accuracy of the initial neural network model, and when the loss value indicates that the prediction accuracy of the initial neural network model is low (does not satisfy determination criteria), the model can continue to be pre-trained (initiation signal transmission) to improve the prediction effect of the model, while continued pre-training is not needed if the loss value indicates a high prediction accuracy (satisfies determination criteria) as the prediction is correct, and where the neural network model can be pre-trained in a cyclic manner (transmission periodicity) to improve the prediction as spoken of on page 4, paragraphs [0069] and [0072].
Given the above references, it would have been obvious to someone of ordinary skill in the art, before the effective filing date of the invention, to apply the continuation of training in relation to measured prediction accuracy as taught in Lu to the system of Zhang in order to increase the overall accuracy of the training model, thereby improving the quality of service experienced by the user(s) during communication as spoken of on page 4, paragraph [0072] of Lu.
Claim(s) 7, 13, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Khapali et al. (U.S. 2021/0034960) (hereinafter “Khapali”).
Regarding claims 7, 13, and 19, Zhang teaches claims 1, 9, and 14 as described above. While Zhang also teaches where AI/ML communication may enable utilization of AI/ML capability with learning, prediction, and decision making to solve complicated optimization problems with better strategy and optimal solution as spoken of on page 5, paragraph [0051], Zhang does not explicitly teach “wherein the determining of the optimal network parameters comprises: generating a signal quality map using the trained neural network when a prediction accuracy of the trained neural network satisfies determination criteria; and determining the optimal network parameters using the signal quality map”.
However, Khapali teaches a method of intelligent retraining of deep learning models where a cognitive learning program (CLP) 150 maintains, stores, modifies, and monitors (generates) a self-learning table 126 (signal quality map) in relation to a calculated predictive accuracy of a network utilizing one or more testing and validation sets, and where responsive to a trained model, the CLP 150 conducts a plurality of tests and validation methods in order to calculate a plurality of feedback data values (determining optimal network parameters) that are subsequently added to self-learning table 126 as spoken of on pages 3-4, paragraph [0023].
Given the above references, it would have been obvious to someone of ordinary skill in the art, before the effective filing date of the invention, to apply the use of a self-learning table as taught in Khapali to the system of Zhang in order to increase the overall accuracy of the training model, thereby improving the quality of service experienced by the user(s) during communication as spoken of on pages 3-4, paragraph [0023] of Khapali.
Allowable Subject Matter
Claims 8 and 20 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.
Response to Arguments
Applicant's arguments filed 6/3/26 pertaining to the prior art rejections have been fully considered but they are not persuasive.
Regarding amended claims 1, 9, and 14, Applicant argues that Zhang does not teach “wherein the network parameters include at least one of a distance between the first communication node and another communication node, a location of the first communication node, an antenna angle of the first communication node, a number of sectors of the first communication node, a beam set of the first communication node, or a beamforming vector of the first communication node, and the information signal is a reference signal or a control signal”. Applicant further asserts that Zhang is directed to selection of power control model parameters and does not disclose or suggest network parameters including a distance between communication nodes, an antenna angle, a number of sectors, a beam set, or a beamforming vector, as specifically required by claims 1, 9, and 14.
However, the claimed “network parameters” are currently claimed in the alternative, and as such, only one of the claimed network parameters is required to meet the claim limitation.
As provided in the previous Office Action and reiterated above, Zhang teaches the network device 502 that transmits one or more reference signals (information signal) to the UE(s) 504 for training sample collection as shown in step 512 of Figure 5 and spoken of on page 12, paragraph [0140]; where the one or more reference signal(s) may include path loss information (network parameters) as spoken of on page 9, paragraph [0108].
Zhang also teaches where the obtaining of base or reference transmit power according to the reference power control model may involve selecting between values of large time scale parameters related to current position (location of communication node) or time of transmission (determining optimal network parameters) as spoken of on pages 12-13, paragraph [0143].
Zhang further states on page 9, paragraph [0108], that for training input collection, a communication device measures path loss from a reference signal (RS), reports the path loss measurement, and optionally reports its position (location of communication node). The above path loss and position information are considered “network parameters” as they are parameters related to signaling exchanged between network device 502 and UE(s) 504 in a wireless communication network.
Based on the above, it is maintained that Zhang teaches the above limitation in question.
A suggestion to obviate this particular rejection would be to amend the added list of network parameters in claims 1, 9, and 14 to not be claimed in the alternative.
Conclusion
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J. MOORE, JR., whose telephone number is (571)272-3168. The examiner can normally be reached M-F (9am-4pm).
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/MICHAEL J MOORE JR/Primary Examiner, Art Unit 2467
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