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 .
Response to Amendment
This is in response to an amendment/response filed 3/23/2026.
Claims 2 and 12 have been cancelled.
Claim 41 has been added.
Claims 1, 3-11, 13, 15-21, and 41 are now pending.
Applicant’s amendments to the Abstract and Claims have overcome each and every objection previously set forth in the Non-Final Office Action mailed 12/23/2025.
Response to Arguments
Applicant’s arguments with respect to the independent claims (pages 13-14) in a reply filed 3/23/2026 have been considered but are moot because the arguments are based on newly changed limitations in the amendment and new ground of rejections using newly introduced references or a newly introduced portion of an existing reference are applied in the current rejection.
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) 1, 4, 6, 11, 13, 16, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. “A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks” (hereinafter “Chen”) in view of Pezeshki et al. US 20220182802 (hereinafter “Pezeshki”) and in further view of Wang et al. US 20220369332 (hereinafter “Wang”)
As to claim 1 and 21 (claim 21 is the method claim for the device in claim 1):
Chen discloses:
A user equipment in a wireless communication system, the user equipment comprising: processing circuitry configured to: (FIG. 1, Chen)
receive uplink-resource information indicating an allocated uplink resource, wherein the allocated uplink resource is allocated by the base station to a plurality of user equipment including the user equipment by minimizing an objective function that includes (“This minimization problem includes optimizing transmit power allocation as well as resource allocation for each user…”, Chen [E. Problem Formulation]) (Examiner’s Note: the minimization problem includes using the global model which is a function of K_i and the minimization problem is dependent on e_i, and P_i which are related to the relative distance)
(i) an expected error of a next global model based on the quantity information indicating the total number of training samples received from the plurality of user equipment and (Equation 2 shows that the global model is updated using K_i which is the number of samples collected by each user i, Chen) (Examiner’s Note: the global model is updated by the base station and since it uses K_i to update the globel model, it must receive this information from each user equipment)
(ii) an energy consumption cost based on the distance information indicating the distance received from the plurality of user equipment; (“Similarly, the downlink data rate achieved by the BS when transmitting the parameters of global FL model to each user i is given by…”, Chen [Section 2]) (“B_u is the bandwidth of each RB and P_i is the transmit power of user i; h_i = o_i*d_i^-2 is the channel gain between user i and the BS with d_i being the distance between user i and the BS…”, Chen [Section 2]) (Equation 11d, Chen) (Examiner’s Note: the energy consumption is a function of the transmission delay, which in turn is determined by the channel gain, which is defined by the distance)
and upload parameters of the local model to the base station via the allocated uplink resource, to cause the base station to obtain the next global model. (FIG. 2 shows this process, Chen) (Examiner’s Note: the parameters of the local model must be transmitted via the allocated uplink resource)
Chen as described above does not explicitly teach:
send, to a base station, quantity information indicating a total number of training samples used by the user equipment during a current training of a local model;
send, to the base station, distance information indicating a distance between the user equipment and the base station;
However, Pezeshki further teaches indicating training dataset size which includes:
send, to a base station, quantity information indicating a total number of training samples used by the user equipment during a current training of a local model; (“In some aspects, the UE 405 may perform as many training epochs as the UE 405 is capable of performing within the local training period. The UE 405 may transmit an indication of the number of training epochs performed by the UE 405 during the local training period. In some aspects, for example, the UE 405 may receive, from the base station 410, a training start command. The UE 405 may perform the plurality of training epochs based at least in part on the training start command. The UE 405 may perform the epochs until the UE 405 receives a stop command.”, Pezeshki [0085]) (“For example, the UE 405 may transmit an indication of at least one of a training dataset size or a training minibatch size. In embodiments, for example, the UE 405 may batch the local training dataset into batches of data to facilitate more efficient epochs. Such batches of data may be referred to as “minibatches.” The UE 405 may indicate, to the base station 110, a size of a minibatch or minibatches that are to be used for training.”, Pezeshki [0086])
Chen and Pezeshki are analogous because they pertain to training a global machine learning model.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include indicating training dataset size as described in Pezeshki into Chen. By modifying the method to include indicating training dataset size as taught by Pezeshki, the benefits of improved machine learning techniques (Pezeshki [0005] and Chen [FIG. 1]) are achieved.
The combination of Chen and Pezeshki as described above does not explicitly teach:
send, to the base station, distance information indicating a distance between the user equipment and the base station;
However, Wang further teaches sending quantity and distance information and prioritizing UE based on the information which includes:
send, to the base station, distance information indicating a distance between the user equipment and the base station; (“In an implementation, when selecting the K to-be-scheduled terminal devices from the n to-be-scheduled terminal devices, the network device may sort the n to-be-scheduled terminal devices based on at least one performance parameter in status information, and determine the K to-be-scheduled terminal devices based on the sorted n to-be-scheduled terminal devices. The at least one performance parameter may be one or more of the following performance parameters: an estimated throughput of the terminal device, an average throughput of the terminal device, a cache queue length of the terminal device, a packet delay of the terminal device, an identifier of the terminal device, a packet loss rate of the terminal device, channel quality, a historical throughput of the terminal device, and the like. For example, the network device may sort the n to-be-scheduled terminal devices in ascending order based on the average throughput of the terminal device, and then select first K terminal devices as the selected terminal devices.”, Wang [0124]) (“Alternatively, when selecting the K to-be-scheduled terminal devices from the n to-be-scheduled terminal devices, the network device may preferentially select, based on a priority of the terminal device, a terminal device with a higher priority. The priority of the terminal device is specified in a standard, determined by a service provider or a telecommunications operator, or determined based on a geographical location. For example, a terminal device close to the network device has a high priority, and a terminal device far from the network device has a low priority.”, Wang [0126]) (“For example, the network device inputs the status information s2 of the terminal device 2, the status information s5 of the terminal device 5, and status information s10 of the terminal device 10 to the scheduling model, to obtain a scheduling weight MT of the terminal device 2, a scheduling weight M5′ of the terminal device 5, and a scheduling weight M10 of the terminal device 10. The network device inputs the status information s5 of the terminal device 5, the status information s8 of the terminal device 8, and status information s11 of the terminal device 11 to the scheduling model, to obtain a scheduling weight M5″ of the terminal device 5, a scheduling weight M8′ of the terminal device 8, and a scheduling weight M11 of the terminal device 11.”, Wang [0111]) (“B6: The network device inputs M5′″ and M11′ to the decision module, to determine a terminal device corresponding to a highest scheduling weight, where it is assumed that the terminal device corresponding to a highest scheduling weight is the terminal device 5, and the terminal device 5 is the scheduled terminal device. B7: The network device allocates the available transmission resource in the communication system to the terminal device 5.”, Wang [0114-0115]) (“In a possible design, the status information includes at least one of the following performance parameters: an estimated throughput of the terminal device, an average throughput of the terminal device, a cache queue length of the terminal device, a packet delay of the terminal device, an identifier of the terminal device, a packet loss rate of the terminal device, channel quality, and a historical throughput of the terminal device.”, Wang [0017]) (Fig. 6, Wang)
Wang, Chen, and Pezeshki are analogous because they pertain to training a global machine learning model.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include sending quantity and distance information and prioritizing UE based on the information as described in Wang into Chen as modified by Pezeshki. By modifying the method to include sending quantity and distance information and prioritizing UE based on the information as taught by Wang, the benefits of improved machine learning techniques (Pezeshki [0005], Wang [0010], and Chen [FIG. 1]) are achieved.
As to claim 4:
Chen as described above does not explicitly teach:
The user equipment according to claim 1, wherein the allocated uplink resourcehe base station and the lurality of user equipment when the base station receives the quantity information coming from thelurality of user equipment.
However, Pezeshki further teaches indicating training dataset size which includes:
The user equipment according to claim 1, wherein the allocated uplink resourcehe base station and the lurality of user equipment when the base station receives the quantity information coming from thelurality of user equipment. (“Some UEs may be considered machine-type communication (MTC) or evolved or enhanced machine-type communication (eMTC) UEs. MTC and eMTC UEs include, for example, robots, drones, remote devices, sensors, meters, monitors, location tags, and/or the like, that may communicate with a base station, another device (e.g., remote device), or some other entity. A wireless node may provide, for example, connectivity for or to a network (e.g., a wide area network such as Internet or a cellular network) via a wired or wireless communication link. Some UEs may be considered Internet-of-Things (IoT) devices, and/or may be implemented as may be implemented as NB-IoT (narrowband internet of things) devices. Some UEs may be considered a Customer Premises Equipment (CPE). UE 120 may be included inside a housing that houses components of UE 120, such as processor components, memory components, and/or the like. In some aspects, the processor components and the memory components may be coupled together. For example, the processor components (e.g., one or more processors) and the memory components (e.g., a memory) may be operatively coupled, communicatively coupled, electronically coupled, electrically coupled, and/or the like.”, Pezeshki [0045]) (Examiner’s Note: examples include MTC types that are non-latency sensitive) (“As shown by reference number 430, the base station 410 may transmit, and the UE 405 may receive a resource allocation for transmitting the update to the base station 410. The resource allocation may include one or more of a time resource, a frequency resource, or a spatial resource. For example, in some aspects, as indicated above, the federated learning configuration may indicate an uplink resource grant for reporting the update. In some aspects, the UE 405 may receive the uplink resource grant based at least in part on an occurrence of the deadline. In some aspects, the resource allocation may be based at least in part on the completion indication.”, Pezeshki [0088])
Chen and Pezeshki are analogous because they pertain to training a global machine learning model.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include indicating training dataset size as described in Pezeshki into Chen. By modifying the method to include indicating training dataset size as taught by Pezeshki, the benefits of improved machine learning techniques (Pezeshki [0005] and Chen [FIG. 1]) are achieved.
As to claim 6:
Chen discloses:
The user equipment according to claim 1, wherein the base station and the lurality of user equipment collectively implement federated learning. (FIG. 1, Chen)
As to claim 11:
Claim 11 is rejected on the same grounds of rejection set forth in claim 1 from the perspective of the network node.
As to claim 13:
Claim 13 is rejected on the same grounds of rejection set forth in claim 4 from the perspective of the network node.
As to claim 16:
Claim 16 is rejected on the same grounds of rejection set forth in claim 6 from the perspective of the network node.
Claim(s) 3, 5, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Pezeshki and Wang, as applied to claim 1 above, and further in view of Li et al. US 20230231690 (hereinafter “Li”)
As to claim 3:
The combination of Chen, Pezeshki, and Wang as described above does not explicitly teach:
The user equipment according to claim 1, wherein the allocated uplink resource
However, Li further teaches uplink resource for URLLC service which includes:
The user equipment according to claim 1, wherein the allocated uplink resource(“The wireless communications system 100 may be configured to support ultra-reliable communications or low-latency communications, or various combinations thereof. For example, the wireless communications system 100 may be configured to support ultra-reliable low-latency communications (URLLC) or mission critical communications.”, Li [0077])
Wang, Chen, Li, and Pezeshki are analogous because they pertain to training a global machine learning model.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include uplink resource for URLLC service as described in Li into Chen as modified by Pezeshki and Wang. By modifying the method to include uplink resource for URLLC service as taught by Li, the benefits of improved machine learning techniques (Pezeshki [0005], Li [0005], Wang [0010], and Chen [FIG. 1]) are achieved.
As to claim 5:
The combination of Chen, Pezeshki, and Wang as described above does not explicitly teach:
The user equipment according to claim 4, wherein the non-latency-sensitive service is an eMBB service.
However, Li further teaches eMBB service which includes:
The user equipment according to claim 4, wherein the non-latency-sensitive service is an eMBB service. (“In some examples, a carrier may support multiple cells, and different cells may be configured according to different protocol types (e.g., MTC, narrowband IoT (NB-IoT), enhanced mobile broadband (eMBB)) that may provide access for different types of devices.”, Li [0072])
Wang, Chen, Li, and Pezeshki are analogous because they pertain to training a global machine learning model.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include eMBB service as described in Li into Chen as modified by Pezeshki and Wang. By modifying the method to include eMBB service as taught by Li, the benefits of improved machine learning techniques (Pezeshki [0005], Li [0005], Wang [0010], and Chen [FIG. 1]) are achieved.
As to claim 15:
Claim 15 is rejected on the same grounds of rejection set forth in claim 5 from the perspective of the network node.
Claim(s) 7-9 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Pezeshki and Wang, as applied to claim 1 above, and further in view of Chen et al. US 20230354087 (hereinafter “Chen2”)
As to claim 7:
The combination of Chen, Pezeshki, and Wang as described above does not explicitly teach:
The user equipment according to claim 1, wherein uploading of the parameters of the local model satisfies a prescribed QoS requirement, the QoS requirement being specified by a prescribed 5QI value.
However, Chen2 further teaches 5QI QoS requirement which includes:
The user equipment according to claim 1, wherein uploading of the parameters of the local model satisfies a prescribed QoS requirement, the QoS requirement being specified by a prescribed 5QI value. (“The QoS parameter mainly includes a 5QI, an ARP, a Reflective QoS Attribute (RQA), a Guaranteed Flow Bit Rate (GFBR), a Maximum Flow Bit Rate (MFBR), notification control, an Aggregate Maximum Bit Rate (AMBR), etc. Each parameter will be explained below.”, Chen2 [0045]) (FIG. 2 and FIG. 3, Chen2)
Chen, Pezeshki, Wang, and Chen2 are analogous because they pertain to managing uplink resource for multiple UEs.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include 5QI QoS requirement as described in Chen2 into Chen as modified by Pezeshki and Wang. By modifying the method to include 5QI QoS requirement as taught by Chen2, the benefits of improved machine learning techniques (Pezeshki [0005], Wang [0010], and Chen [FIG. 1]) and improved resource management of UEs (Chen2 [0045, FIG. 2-3]) are achieved.
As to claim 8:
The combination of Chen, Pezeshki, and Wang as described above does not explicitly teach:
The user equipment according to claim 1, wherein the processing circuitry is further configured to: receive parameters of the next global model from the ase station to update the parameters of the local model, wherein transmission of the parameters of the next global model satisfies a prescribed QoS requirement, the QoS requirement being specified by a prescribed 5QI value.
However, Chen2 further teaches 5QI QoS requirement which includes:
The user equipment according to claim 1, wherein the processing circuitry is further configured to: receive parameters of the next global model from the ase station to update the parameters of the local model, wherein transmission of the parameters of the next global model satisfies a prescribed QoS requirement, the QoS requirement being specified by a prescribed 5QI value. (“The QoS parameter mainly includes a 5QI, an ARP, a Reflective QoS Attribute (RQA), a Guaranteed Flow Bit Rate (GFBR), a Maximum Flow Bit Rate (MFBR), notification control, an Aggregate Maximum Bit Rate (AMBR), etc. Each parameter will be explained below.”, Chen2 [0045]) (FIG. 2 and FIG. 3, Chen2)
Chen, Pezeshki, Wang, and Chen2 are analogous because they pertain to managing uplink resource for multiple UEs.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include 5QI QoS requirement as described in Chen2 into Chen as modified by Pezeshki and Wang. By modifying the method to include 5QI QoS requirement as taught by Chen2, the benefits of improved machine learning techniques (Pezeshki [0005], Wang [0010], and Chen [FIG. 1]) and improved resource management of UEs (Chen2 [0045, FIG. 2-3]) are achieved.
As to claim 9:
The combination of Chen, Pezeshki, and Wang as described above does not explicitly teach:
The user equipment according to claim 7, wherein the prescribed 5QI value defines at least one of the following: a resource type being guaranteed bit rate GBR, a default priority level being 70, a packet delay budget being 1Oms, a packet error rate being 10-6, and a default averaging window being 2000ms.
However, Chen2 further teaches 5QI QoS requirement which includes:
The user equipment according to claim 7, wherein the prescribed 5QI value defines at least one of the following: a resource type being guaranteed bit rate GBR, a default priority level being 70, a packet delay budget being 1Oms, a packet error rate being 10-6, and a default averaging window being 2000ms. (“The QoS parameter mainly includes a 5QI, an ARP, a Reflective QoS Attribute (RQA), a Guaranteed Flow Bit Rate (GFBR), a Maximum Flow Bit Rate (MFBR), notification control, an Aggregate Maximum Bit Rate (AMBR), etc. Each parameter will be explained below.”, Chen2 [0045]) (FIG. 2 and FIG. 3, Chen2)
Chen, Pezeshki, Wang, and Chen2 are analogous because they pertain to managing uplink resource for multiple UEs.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include 5QI QoS requirement as described in Chen2 into Chen as modified by Pezeshki and Wang. By modifying the method to include 5QI QoS requirement as taught by Chen2, the benefits of improved machine learning techniques (Pezeshki [0005], Wang [0010], and Chen [FIG. 1]) and improved resource management of UEs (Chen2 [0045, FIG. 2-3]) are achieved.
As to claim 17:
Claim 17 is rejected on the same grounds of rejection set forth in claim 7 from the perspective of the network node.
As to claim 18:
Claim 18 is rejected on the same grounds of rejection set forth in claim 8 from the perspective of the network node.
As to claim 19:
Claim 19 is rejected on the same grounds of rejection set forth in claim 9 from the perspective of the network node.
Claim(s) 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Pezeshki, Wang, and Chen2, as applied to claim 9 above, and further in view of 3GPP TS 23.501 V17.1.1 (hereinafter “TS23501”)
As to claim 10:
The combination of Chen, Pezeshki, Wang, and Chen2 as described above does not explicitly teach:
The user equipment according to claim 9, wherein the 5QI value is equal to 87.
However, TS23501 further teaches 5QI mapping which includes:
The user equipment according to claim 9, wherein the 5QI value is equal to 87. (Table 5.7.4-1: Standardized 5QI to QoS characteristics mapping, TS23501)
Chen, Pezeshki, Wang, Chen2, and TS23501 are analogous because they pertain to managing uplink resource for multiple UEs.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include 5QI mapping as described in TS23501 into Chen as modified by Pezeshki, Wang, and Chen2. By modifying the method to include 5QI mapping as taught by TS23501, the benefits of improved machine learning techniques (Pezeshki [0005], Wang [0010], and Chen [FIG. 1]) and improved resource management of UEs (Chen2 [0045, FIG. 2-3] and TS23501 [Table 5.7.4-1]) are achieved.
As to claim 20:
Claim 20 is rejected on the same grounds of rejection set forth in claim 10 from the perspective of the network node.
Allowable Subject Matter
Claim 41 is 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.
As per claim 41 the cited prior art either alone or in combination fails to teach the combined features of:
The base station according to claim 13, wherein minimizing the objective function comprises solving for A(n) and L(n) to minimize E(F(g(n)) -F(g*)) +1I|L(n)||o + p1dll{(AL(n) +(n)) >0};g (n) indicates the next global model obtained after an nth iteration; g* indicates an optimal global model; F(g) indicates an error function; U indicates a total number of the plurality of user equipment; L; (n) indicates an uplink resource allocation for ultra-reliable low-latency communications services for a user equipment i; A; (n) indicates an uplink resource allocation for enhanced mobile broadband services for the user equipment i;d; indicates the distance between the user equipment i and the base station;11 and p indicate scaling factors;E(-) indicates mathematical expectation;||11-o indicates a 0 norm; andll{} indicates an indicative function.
Conclusion
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 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 ANDREW C KIM whose telephone number is (703)756-5607. The examiner can normally be reached M-F 9AM - 5PM (PST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sujoy K Kundu can be reached at (571) 272-8586. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.C.K./
Examiner
Art Unit 2471
/SUJOY K KUNDU/Supervisory Patent Examiner, Art Unit 2471
Prosecution Insights