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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/06/2026 has been entered.
Response to Arguments
Applicant's arguments filed on 07/06/2026 have been fully considered:
Regarding applicant's arguments filed with respect to the prior art rejections have been fully considered but they are moot. Applicant has amended the claims to recite new combinations of limitations. Please see below for new grounds of rejection, necessitated by Amendment.
Claim Rejections - 35 USC § 103
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) 1-19 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Briggs et al. (“A Review of Privacy Preserving Federated Learning for Private IoT Analytics”, Submitted on 24 Apr 2020, Machine Learning (cs.LG); Cryptography and Security (cs.CR); Machine Learning (stat.ML)) in view of Chen et al. (“A Joint Learning and Communications Framework for Federated Learning over Wireless Networks”, arXiv:1909.07972v1 [cs.NI] 17 Sep 2019).
Regarding claim 1.
Briggs teaches a method in a first node of a communications network for training a machine learning model, see page 7, figure 1, “send global model to clients”, also see page 6, algorithm 1 line 12, “procedure CLIENTUPDATE(k;w)”, also see page 5, “Once a round of training is completed on the device, the model parameters are communicated to an aggregating server, such as a parameter server provided by a third party. Although the training data itself is never disclosed to the third-party,” i.e. corresponding to a first message comprising instructions for training the machine learning model, also see page 5, “Once a round of training is completed on the device, the model parameters are communicated to an aggregating server, such as a parameter server provided by a third party.”, also see page 4, “The parameter server performs some form of aggregation on the updates to synchronise a global model and the parameters (for example, weights in a neural network) are then shared with the individual compute nodes for the next iteration/round of training.”);
responsive to receiving the first message, acting as an aggregator in the distributed learning process for a subset of other nodes selected by the first node from a plurality of nodes that have an established radio channel allocation with the first node (see page 7, figure 1, “Average all client model parameters" and also see page 6, algorithm 1 line 12, discloses "procedure FederatedAveraging", also see page 6, “The FederatedAveraging algorithm (shown in algorithm 1) randomly selects a fraction of the clients to participate in each round of training. Each client k computes the gradients on the current state of the global model wt and updates the parameters wk t+1 in the standard fashion…All clients communicate their updates to the aggregating server, which then calculates a weighted average of the contributions from each client to update the global model”, i.e. corresponding to acting as an aggregator in the distributed learning process for a subset of other nodes), see page 7, figure 1, "Clients train local models on their own data", also see page 6, “Each client k computes the gradients on the current state of the global model wt and updates the parameters wk t+1 in the standard fashion…All clients communicate their updates to the aggregating server, which then calculates a weighted average of the contributions from each client to update the global model:”, also see page 6, algorithm 1 line 19, discloses "return w to server", i.e. corresponding to causing the subset of other nodes to perform training on local copies of the machine learning model).
Briggs do not specifically teach wherein the first node comprises a radio base station; wherein the plurality of nodes comprise user equipments, UEs, within a radio coverage area of the radio base station.
Chen teaches wherein the first node comprises a radio base station; wherein the plurality of nodes comprise user equipments, UEs, within a radio coverage area of the radio base station (see abstract, “wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users… due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm… under a given user selection and uplink resource block (RB) allocation 2 scheme.”, also see figure 2 on page 6 and under the figure, “The BS is used to integrate the local FL models and generate a shared FL model.”).
Both Briggs and Chen pertain to the problem of wireless communication and federated learning, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Briggs and Chen to teach the above limitations. The motivation for doing so would be “wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm... Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation and 2) a standard FL algorithm with random user selection and resource allocation.” (see Chen abstract).
Regarding claim 2.
Briggs and Chen teaches the method as in claim 1,
Briggs further teaches wherein the step of acting as an aggregator in the distributed learning process comprises: initiating the distributed learning process in the subset of other nodes by forwarding the first message to the subset of other nodes (see page 6, algorithm 1 lines 2-7, discloses server initializes w0...for each round t=1,2…for set st of clients runs CLIENTUPDATE(k;wt), also see page 7, figure 1, "send global model to client").
Regarding claim 3.
Briggs and Chen teaches the method as in claim 2,
Briggs further teaches wherein the subset of other nodes comprise all other nodes having an established radio channel allocation with the first node and wherein the step of acting as an aggregator in the distributed learning process comprises: initiating the distributed learning process by forwarding the first message to all other nodes having an established radio channel allocation with the first node (see page 7, figure 1, "send global model to client", also see page 6, algorithm 4, discloses “m<- max (C – K, 1)”, ).
Regarding claim 4.
Briggs and Chen teaches the method as in claim 1,
Briggs further teaches wherein the subset of other nodes are selected by the first node from the plurality of nodes based on one or more of: a criteria related to traffic sent between the first node and each of the other nodes and - a criteria related to a user of each of the other nodes (see figure 1, “send global model to clients”, also see page 8, "by selecting clients based on client resource constraints in a mobile edge computing environment [...] federated learning can be sped up considerably", and discloses to "train a CNN to detect a 'wake word’ ", corresponding to criteria related to the traffic sent and related to a user of each of the other nodes).
Regarding claim 5.
Briggs and Chen teaches the method as in claim 1,
Briggs further teaches wherein the step of acting as an aggregator in the distributed learning process further comprises: receiving a third message from each of the other nodes in the subset of other nodes, each third message comprising a result of training performed on the machine learning model by the respective other node; and - aggregating the results of the training performed by the subset of other nodes (see figure 1: "Send model parameters back to server", "Average all client model parameters", also see page 8, “updates from all clients are averaged in the global shared model. ”, i.e. corresponding to a message from each node comprising a result of training performed on the machine learning model by the respective node and aggregating the results).
Regarding claim 6.
Briggs and Chen teaches the method as in claim 5,
Briggs further teaches further comprising sending a fifth message to each of the other nodes in the subset of other nodes, each fifth message comprising a first parameter that may be used to mask information sent between the first node and the respective other node; and wherein the result of the training in each third message is masked using the first parameter (see page 9, section "IV.A Privacy preserving methods", discloses "1) Anonymisation: Anonymisation or de-identification is achieved by removing any information that might identify an individual"… "2) Encryption:", also see table 2 in page 12, i.e. corresponding to masking the result of the training and of aggregation).
Regarding claim 7.
Briggs and Chen teaches the method as in claim 1,
Briggs further teaches wherein the first message is received from a second node and wherein the method further comprises: sending a fourth message comprising the aggregated results of the training to the second node for the second node to combine with aggregated results of training from a third node in the communications network (see figure 1: "Send global model to clients" and section "III.B. Communication-efficient federated learning", discloses "using FederatedAveraging, the authors showed that increasing computation on the client between communication rounds significantly reduced the number of communication rounds required to converge to a threshold test accuracy compared to a single epoch trained on all available data on the client”, also see page 13, “Fog computing nodes could feasibly be leveraged as aggregating servers to remove the round-trip communication between clients to cloud servers in the averaging step of federated learning. Fog computing could also bring other benefits, such as sharing the computational burden by hierarchically averaging many large client models.”, i.e. corresponding to sending messages comprising the aggregated results of the training to a node).
Regarding claim 8.
Briggs and Chen teaches the method as in claim 7,
Briggs further teaches further comprising receiving from the second node a sixth message comprising a second parameter that may be used to mask information sent between the second node and the first node; and - masking the aggregated results of the training in the fourth message, using the second parameter (see page 9, section "IV.A Privacy preserving methods", discloses "1) Anonymisation: Anonymisation or de-identification is achieved by removing any information that might identify an individual"… "2) Encryption:", also see table 2 in page 12, i.e. corresponding to masking the result of the training and of aggregation).
Regarding claim 9.
Briggs and Chen teaches the method as in claim 7,
Briggs further teaches wherein the second node comprises a packet gateway, PG (see page 7, figure 1: "Server", and section "III.B. Communication- efficient federated learning", discloses an "aggregating server", corresponding to a packet gateway).
Regarding claim 10.
Briggs and Chen teaches the method as in claim 1,
Briggs further teaches wherein the first node comprises a first radio base station, RBS, evolved NodeB, eNB, or New Radio NodeB, gNB (see page 7, figure 1, a radio base station; phone).
Regarding claim 11.
Briggs and Chen teaches the method as in claim 1,
Briggs further teaches wherein the subset of other nodes comprise user equipments, UEs (see figure 1, a user equipment; phone).
Regarding claim 12.
Briggs and Chen teaches the method as in claim 11,
Briggs further teaches further comprising: initiating a handover procedure to establish a radio channel allocation with a new UE; and - as part of the handover procedure, receiving from the new UE, a third parameter that is used to mask information sent between the first node and the new UE, such that the first node acts as an aggregator with respect to training performed on the machine learning model by the new UE (see page 9, section "IV.A Privacy preserving methods", page 9 discloses "2) Encryption: 2) Encryption: Anonymisation presents several difficult challenges in order to provide statistics about data without disclosing sensitive information. Encrypting data provides better privacy protection but the ability to perform useful statistical analysis on encrypted data requires specialist methods. Homomorphic encryption [72] allows for processing of data in its encrypted form. Earlier efforts (termed “Somewhat Homomorphic Encryption”) allowed for simple addition and multiplication operations on encrypted data [73], but were shortly followed by Fully Homomorphic Encryption allowing for any arbitrary function to be applied to data in ciphertext form to yield an encrypted result [72].", corresponding to transmitting a parameter for masking at handover).
Regarding claim 13.
Briggs teaches a method in a second node of a communications network for training a machine learning model, the method comprising: sending a first message to a plurality of first nodes, see page 7, figure 1, “send global model to clients”, also see page 6, algorithm 1 line 12, “procedure CLIENTUPDATE(k;w)”, also see page 5, “Once a round of training is completed on the device, the model parameters are communicated to an aggregating server, such as a parameter server provided by a third party. Although the training data itself is never disclosed to the third-party,” i.e. corresponding to a first message comprising instructions for training the machine learning model),
wherein the first message causes each first node in the plurality of first nodes to act as an aggregator in the distributed learning process for a subset of other nodes selected by the respective first node from a plurality of nodes that have an established radio channel allocation with the respective first node (see page 7, figure 1, "Clients train local models on their own data" and “Average all client model parameters" and also see page 6, algorithm 1 line 12, discloses "procedure FederatedAveraging", also see page 6, “The FederatedAveraging algorithm (shown in algorithm 1) randomly selects a fraction of the clients to participate in each round of training. Each client k computes the gradients on the current state of the global model wt and updates the parameters wk t+1 in the standard fashion…All clients communicate their updates to the aggregating server, which then calculates a weighted average of the contributions from each client to update the global model”),
Briggs do not specifically teach wherein each first node in the plurality of first nodes comprises a respective radio base station; wherein, for each first node in the plurality of first nodes, the plurality of nodes comprise user equipments, UEs, within a radio coverage area of the respective radio base station.
Chen teaches wherein each first node in the plurality of first nodes comprises a respective radio base station; wherein, for each first node in the plurality of first nodes, the plurality of nodes comprise user equipments, UEs, within a radio coverage area of the respective radio base station (see abstract, “wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users… due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm… under a given user selection and uplink resource block (RB) allocation 2 scheme.”, also see figure 2 on page 6 and under the figure, “The BS is used to integrate the local FL models and generate a shared FL model.”).
Both Briggs and Chen pertain to the problem of wireless communication and federated learning, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Briggs and Chen to teach the above limitations. The motivation for doing so would be “wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm... Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation and 2) a standard FL algorithm with random user selection and resource allocation.” (see Chen abstract).
Regarding claim 14.
Briggs and Chen teaches the method as in claim 13,
Briggs further teaches further comprising: receiving, from each of the plurality of first nodes, a fourth message comprising aggregated results of training performed by the subset of other nodes with an established radio channel allocation with the respective first node; and - acting as an aggregator in the distributed learning process for the plurality of first nodes by aggregating the results of the training as reported in each fourth message (see figure 1: "Send model parameters back to server", "Average all client model parameters", also see page 8, “updates from all clients are averaged in the global shared model. ”, i.e. corresponding to a message from each node comprising a result of training performed on the machine learning model by the respective node and aggregating the results).
Regarding claim 15.
Briggs and Chen teaches the method as in claim 14,
Briggs further teaches further comprising sending a sixth message to each first node in the plurality of first nodes, each sixth message comprising a second parameter that may be used to mask information sent between the second node and the respective first node; and wherein the result of the training in each fourth message is masked using the second parameter (see page 9, section "IV.A Privacy preserving methods", discloses "1) Anonymisation: Anonymisation or de-identification is achieved by removing any information that might identify an individual"… "2) Encryption:", also see table 2 in page 12, i.e. corresponding to masking the result of the training and of aggregation).
Regarding claim 16.
Briggs and Chen teaches the method as in claim 13,
Briggs further teaches wherein the second node comprises a packet gateway, PG (see page 7, figure 1: "Server", and section "III.B. Communication- efficient federated learning", discloses an "aggregating server", corresponding to a packet gateway).
Regarding claim 17.
Briggs teaches a method in a user equipment (UE) of a communications network for training a machine learning model, the method comprising: receiving a second message from a first node in the communications network with which the UE has an established radio channel allocation, see page 7, figure 1, “send global model to clients”, also see page 6, algorithm 1 line 12, “procedure CLIENTUPDATE(k;w)”, also see page 5, “Once a round of training is completed on the device, the model parameters are communicated to an aggregating server, such as a parameter server provided by a third party. Although the training data itself is never disclosed to the third-party,” i.e. corresponding to a first message comprising instructions for training the machine learning model);
- training a local copy of the machine learning model, according to the instructions (see page 7, figure 1, "Clients train local models on their own data", also see page 6, “Each client k computes the gradients on the current state of the global model wt and updates the parameters wk t+1 in the standard fashion…All clients communicate their updates to the aggregating server, which then calculates a weighted average of the contributions from each client to update the global model:”, also see page 6, algorithm 1 line 19, discloses "return w to server", i.e. corresponding to causing the subset of other nodes to perform training on local copies of the machine learning model);
and - sending a third message comprising a result of the training to the first node for aggregation by the first node with results of training performed by other UEs (see page 7, figure 1, “Average all client model parameters" and also see page 6, algorithm 1 line 12, discloses "procedure FederatedAveraging", also see page 6, “The FederatedAveraging algorithm (shown in algorithm 1) randomly selects a fraction of the clients to participate in each round of training. Each client k computes the gradients on the current state of the global model wt and updates the parameters wk t+1 in the standard fashion…All clients communicate their updates to the aggregating server, which then calculates a weighted average of the contributions from each client to update the global model”, i.e. corresponding to acting as an aggregator in the distributed learning process for a subset of other nodes).
Briggs do not specifically teach wherein the first node comprises a radio base station, wherein the UE is within a radio coverage area of the radio base station.
Chen teaches wherein the first node comprises a radio base station, wherein the UE is within a radio coverage area of the radio base station (see abstract, “wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users… due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm… under a given user selection and uplink resource block (RB) allocation 2 scheme.”, also see figure 2 on page 6 and under the figure, “The BS is used to integrate the local FL models and generate a shared FL model.”).
Both Briggs and Chen pertain to the problem of wireless communication and federated learning, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Briggs and Chen to teach the above limitations. The motivation for doing so would be “wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm... Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation and 2) a standard FL algorithm with random user selection and resource allocation.” (see Chen abstract).
Regarding claim 18.
Briggs and Chen teaches the method as in claim 17,
Briggs further teaches further comprising receiving a fifth message comprising a first parameter that may be used to mask information sent to the first node; and wherein the step of sending a third message comprising a result of the training comprises masking the result of the training using the first parameter (see page 9, section "IV.A Privacy preserving methods", discloses "1) Anonymisation: Anonymisation or de-identification is achieved by removing any information that might identify an individual"… "2) Encryption:", also see table 2 in page 12, i.e. corresponding to masking the result of the training and of aggregation).
Regarding claim 19.
Briggs and Chen teaches the method as in claim 18,
Briggs further teaches wherein the method further comprises: performing a handover procedure from the first node to a new node; and as part of the handover procedure, sending the first parameter to the new node (see page 9, section "IV.A Privacy preserving methods", page 9 discloses "2) Encryption: 2) Encryption: Anonymisation presents several difficult challenges in order to provide statistics about data without disclosing sensitive information. Encrypting data provides better privacy protection but the ability to perform useful statistical analysis on encrypted data requires specialist methods. Homomorphic encryption [72] allows for processing of data in its encrypted form. Earlier efforts (termed “Somewhat Homomorphic Encryption”) allowed for simple addition and multiplication operations on encrypted data [73], but were shortly followed by Fully Homomorphic Encryption allowing for any arbitrary function to be applied to data in ciphertext form to yield an encrypted result [72].", corresponding to transmitting a parameter for masking at handover).
Claim 21 recites a first node in a communications network for training a machine learning model, the first node comprising: a memory comprising instruction data representing a set of instructions; and a processor configured to communicate with the memory and to execute the set of instructions to perform the method recited in claim 1. Therefore the rejection of claim 1 above applies equally here. Briggs also teaches the addition elements of claim 21 not recited in claim 1 comprising a memory comprising instruction data representing a set of instructions; and a processor configured to communicate with the memory and to execute the set of instructions (see page 3, “The Gated Recurrent Unit (GRU) [21] and the more sophisticated Long/Short Term Memory unit (LSTM) [22] make use of memory cells that act as gates choosing what information to accumulate (remember) and which to forget…Both of these factors require large sums of memory and compute capabilities. To scale complex deep networks trained on lots of data requires concurrency across multiple CPUs or more commonly GPUs (most often in a local cluster). GPUs are optimized to perform matrix calculations and are well suited for the operations required to compute activations across a DNN. Concurrency can be achieved in a variety of ways as discussed below.”).
Related arts:
SAMEK et al. (US 20220108177 A1) teaches in ¶ 6, Federated Learning resolves this issue as it allows multiple parties to jointly train a deep learning model on their combined data, without any of the participants having to reveal their data to a centralized server [10]. This form of privacy-preserving collaborative learning is achieved by following a simple three step protocol illustrated in FIG. 1. In the first step 32 shown left, all participating clients 14 download the latest master model custom-character from the server 12.
Kadav et al. (US 20160125316 A1) teaches providing distributed learning over a plurality of parallel machine network nodes by allocating a per-sender receive queue at every machine network node and performing distributed in-memory training.
Feng et al. (US 20170220949 A1) teaches Each sub-set of data is allocated to a corresponding node for estimating values of the one or more parameters based on the sub-set of data. Estimated values of the one or more parameters obtained based on a corresponding sub-set of data allocated to the node, are received from each of the plurality of nodes. The one or more parameters of the machine learning model are estimated based on the estimated values of the one or more parameters generated by at least some of the plurality of nodes.
VANDIKAS et al. (US 20240256973 A1) teaches herein the training is distributed across a plurality of computing nodes and updates to the machine learning model, as determined by the plurality of computing nodes, are aggregated using secure multi-party computation. The method includes: i) obtaining an aggregated characteristic of updates to the machine learning model provided by a first subset of the plurality of computing nodes; ii) comparing the aggregated characteristic to an equivalent reference; and iii) identifying whether the first subset of nodes are contributing updates that are corrupting the machine learning model, based on the comparison.
Lee et al. (US 20230107221 A1) teaches machine learning workload by configuring the groups of training nodes, the set of intermediate aggregator nodes, and a global aggregator node for the set of intermediate aggregator nodes and by configuring channels between training nodes in a group, between the groups of training nodes and the set of intermediate aggregator nodes, and between the set of intermediate aggregator nodes and the global aggregator node.
Barton et al. (US 20200293942 A1) teaches training allows the fog nodes to be continually trained within the fog layer without the need for the cloud. Furthermore, the metered training allows the fog node to operate normally as the training is performed only when spare resources are available at the fog node. The disclosed technology also relates to a process of sharing better trained machine learning models of a fog node with other similar fog nodes thereby speeding up the training process for other fog nodes within the fog layer.
Conclusion
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/IMAD KASSIM/Primary Examiner, Art Unit 2129
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