DETAILED ACTION
This action is written in response to the RCE filed 3/5/26. 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 Arguments
The Applicants argue that the previous art of record does not anticipate or render obvious the claims as currently amended. The Examiner is not persuaded, and addresses the Applicant’s specific arguments below.
§101 – The Applicant argues: “In this case, the claims are patent eligible for at least the reasons that the claims do not recite a judicial exception, and that the claims recite features that integrate any alleged judicial exception into a practical application. …. the specific features of "transmitting an indication of a training configuration for training of the predictive model to the one or more UEs, the training configuration comprising a set of training parameters based at least in part on one or more constraints of the one or more UEs" and "transmitting a second message indicating whether the UE has implemented the training configuration prior to the training procedure based at least in part on one or more constraints of the UE" provide specific advantages. For example, these features "may reduce processing, reduce power consumption, and provide more efficient utilization of communication resources." Id. [0178]. Thus, the claimed communication features of independent claims 1, 9, 13, 22, and 26 are part of the solution, and are accordingly not "insignificant pre-solution activity" and "post-solution activity," as alleged by the Office Action.” (Remarks, p. 10.)
The Examiner has not alleged that the recited ‘transmitting’ step is a mental process, but rather an insignificant extra-solution activity (ie transmitting the results of a previous mental process step, or perhaps transmitting information that pre-existed the other method step). See §101 rejection infra. The Examiner notes that generating, choosing, configuring or optimizing the recited training configuration are not recited in the claim. Likewise, actually performing the prescribed training is not recited in the claim.
§102 – The Applicant argues: “For example, the initial ML configuration of Wang is not shown to be any "training configuration for training of the predictive model." As described by Wang, the initial ML configuration is "[a neural network (NN)] formation configuration used to form a [deep NN (DNN)], ... such as node connections, coefficients, active layers, weights, biases, pooling, etc." Id. [0026] (emphasis added). That is, the ML configuration is a configuration for forming a NN, rather than a "configuration for training of the predictive model," as recited in amended independent claim 1. Thus, Wang does not disclose at least "training configuration for training of the predictive model," as recited in amended independent claim 1.”
The Examiner it not persuaded, and cites the passage below for this limitation:
[0056] “the base station receives a UE capability information message (not illustrated) from each UE and selects the initial ML configuration based on a common UE capability between the UEs 111, 112, and 113."
For the foregoing reasons, the Examiner maintains the outstanding rejection of claim 1 under §102, which is reproduced infra.
Claim Rejections - 35 USC § 101
Claims 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines.1
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—claim 1 recites a method, which is a process.
Step 2A, prong one: Does the claim recite an abstract idea, law of nature or natural phenomenon? Yes—the claim recites one or more limitations which—under their broadest reasonable interpretation—covers performance of the limitation in the mind (see table below).
Claim limitation
Examiner analysis
1. A method for wireless communications at a network node, comprising:
selecting one or more user equipment (UEs) for a training procedure for a predictive model based at least in part on a trigger to activate the training procedure; and
This is a mental process akin to a human evaluation/judgment/observation.
Because the claim recites limitations which can practically be implemented as mental processes, the claim recites a mental process.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the claim does not recited even generic computer hardware, and does not seem to be clearly linked to any particular real-world technological problem.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No—the additional limitations are addressed below:
transmitting an indication of a training configuration for training of the predictive model to the one or more UEs, the training configuration comprising a set of training parameters based at least in part on one or more constraints of the one or more UEs.
This is insignificant extra-solution activity: transmitting results from a preceding step, or transmitting information that pre-existed the preceding step.
The Examiner notes that generating, choosing, configuring or optimizing the recited training configuration are not recited in the claim. Likewise, actually performing the prescribed training is not recited in the claim.
As noted above, this method claim does not recite even generic computing hardware.
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claims 9, 13, 22 and 26, which each recite analogous methods, as well as to all pending dependent claims. The additional limitations of the dependent claims are addressed briefly below. Taken alone, the additional elements of the dependent claims above do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim limitation
Examiner analysis
2. The method of claim 1, further comprising:
transmitting, to the one or more UEs, an indication of a data radio bearer configured for downloading a model structure and a baseline parameter set associated with the predictive model.
This is insignificant post-solution activity: transmitting results from a preceding step.
3. The method of claim 1, further comprising:
selecting the set of training parameters for the training configuration based at least in part on an estimated link capacity associated with the one or more UEs, a computational capability associated with the one or more UEs, or a combination thereof.
This is a mental process akin to a human judgment/observation.
4. The method of claim 3, wherein the set of training parameters comprises a minimum quantity of epochs for the training procedure to be performed at a UE of the one or more UEs.
This is merely additional information about one or more previously identified mental processes.
5. The method of claim 1, wherein the set of training parameters comprises a model structure identifier associated with the predictive model, a baseline parameter set identifier associated with the predictive model, a training validity area, a maximum quantity of epochs for the training procedure, a minimum quantity of epochs for the training procedure, a training deadline, a set of weights, a periodicity, a server address, or a combination thereof.
This is merely additional information about one or more previously identified mental processes.
6. The method of claim 1, further comprising:
receiving, from at least one UE of the one or more UEs, a message indicating that the at least one UE has implemented the training configuration or indicating that the at least one UE has refrained from implementing the training configuration.
This is insignificant pre-solution activity: gathering data to be processed in subsequent steps.
7. The method of claim 1, further comprising:
receiving, from at least one UE of the one or more UEs, a message indicating that the predictive model is ready for activation at the at least one UE.
This is insignificant pre-solution activity: gathering data to be processed in subsequent steps.
8. The method of claim 1, further comprising:
transmitting, to the one or more UEs, a message comprising an indication to activate the training procedure at the one or more UEs.
This is insignificant post-solution activity: transmitting results from a preceding step.
9. A method for wireless communications at a server, comprising:selecting one or more user equipment (UEs) for a training procedure for a predictive model based at least in part on a trigger to activate the training procedure; andtransmitting an indication of a training configuration for training of the predictive model to the one or more UEs, the training configuration comprising a set of training parameters based at least in part on one or more constraints of the one or more UEs.
‘Selecting’ is a mental process akin to a human evaluation/judgment/observation.‘Transmitting’ is insignificant post-solution activity: transmitting information based on the results of a preceding step.
13. A method for wireless communications at a user equipment (UE), comprising:receiving a first message indicating a training configuration for a training procedure for training of a predictive model, the training configuration comprising a set of training parameters; and transmitting a second message indicating whether the UE has implemented the training configuration prior to the training procedure based at least in part on one or more constraints of the UE and on the set of training parameters.
‘Selecting’ is a mental process akin to a human evaluation/judgment/observation.‘Transmitting’ is insignificant post-solution activity: transmitting information based on the results of a preceding step.
22. A method for wireless communications at a server, comprising:transmitting, to a set of user equipments (UEs), a training configuration for a training procedure associated with training of a predictive model, the training configuration comprising a first set of model parameters associated with a first parameter set identifier;
receiving, from one or more UEs of the set of UEs, one or more reports indicating one or more subsets of model parameters output from the training procedure for the predictive model at the UE;
aggregating the subsets of model parameters into a second set of model parameters;
assigning a second parameter set identifier to the second set of model parameters, the second parameter set identifier different from the first parameter set identifier; andtransmitting an indication of the second parameter set identifier.
‘Transmitting’ is insignificant pre-solution activity: transmitting information which appears to preexist the recited method.
‘Receiving’ is insignificant pre-solution activity: gathering information which appears to preexist the recited method; alternately, information which is to be used in subsequent steps.‘Aggregating’ is a mental process, akin to a human evaluation/judgment.‘Assigning is a mental process, akin to a human evaluation/judgment.‘Transmitting’ is insignificant post-solution activity: transmitting information based on the results of a preceding step.
26. A method for wireless communications at a user equipment (UE), comprising:receiving a training configuration for a training procedure associated with training of a predictive model, the training configuration comprising a first set of model parameters associated with a first parameter set identifier, the first set of model parameters based at least in part on one or more constraints of the UE;transmitting a report indicating a subset of model parameters output from the training procedure for the predictive model at the UE; and
receiving an indication of a second parameter set identifier associated with a second set of model parameters based at least in part on transmitting the report, the second parameter set identifier different from the first parameter set identifier.
‘Receiving’ is insignificant pre-solution activity: gathering information to be used in subsequent steps.‘Transmitting’ is insignificant post-solution activity: transmitting the results of a processing step which is not explicitly recited in the claim.‘Receiving’ is insignificant post-solution activity: gathering information to be used in a subsequent iteration of the same mental process identified above.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3, 5-6, 8-9 and 12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang (WO 2022/010685 A1, cited by Applicant on IDS dated 11/29/23).
Regarding claim 1, Wang discloses a method for wireless communications at a network node, comprising:
[0002] "This document describes techniques and apparatuses for federated learning for deep neural networks (DNNs) in a wireless communication system"),
selecting one or more user equipment (UEs) for a training procedure for a predictive model based at least in part on a trigger to activate the training procedure; and
[0057] “In some aspects, the base station 120 selects the set of UEs based on any combination of UE characteristics (e.g., UE capabilities, UE ML capabilities, estimated UE-location) and/or channel conditions (e.g., indicated by signal or link quality parameters).”
[0063] “As another example update condition, the base station 120 requests each UE in the set of UEs to transmit the updated ML information (and/or to perform the training procedure) in response to detecting a trigger event, such as trigger events that correspond to changes in a DNN at a UE. “ (Emphasis added.)
transmitting an indication of a training configuration for training of the predictive model to the one or more UEs, the training configuration comprising a set of training parameters based at least in part on one or more constraints of the one or more UEs.
[0058] "the core network server 302 (not illustrated) selects the initial ML configuration and communicates the ML configuration UEs through base station 120.”
[0056] “the base station receives a UE capability information message (not illustrated) from each UE and selects the initial ML configuration based on a common UE capability between the UEs 111, 112, and 113."
Regarding claim 3, Wang discloses the following further limitation comprising:
selecting the set of training parameters for the training configuration based at least in part on an estimated link capacity associated with the one or more UEs, a computational capability associated with the one or more UEs, or a combination thereof.
[0057] “As another example, the base station 120 receives signal and/or link quality measurements through RRC messages and/or Media Access Control (MAC) layer messages and selects the set of DEs based on the DEs having commensurate (e.g., within a threshold value or range to one another) signal and/or link quality parameters.” (Emphasis added.)
Regarding claim 5, Wang discloses the further limitation wherein the set of training parameters comprises a model structure identifier associated with the predictive model, a baseline parameter set identifier associated with the predictive model, a training validity area, a maximum quantity of epochs for the training procedure, a minimum quantity of epochs for the training procedure, a training deadline, a set of weights, a periodicity, a server address, or a combination thereof.
[The Examiner notes that this is a Markush group.]
‘baseline parameter set’ :: [0009] “baseline ML configuration”
‘set of weights’ :: [0007] “Dynamic reconfiguration of a DNN, such as by modifying various architecture configurations ( e.g., number of layers, layer processing algorithms, down-sampling configurations) and parameter configurations (e.g., coefficients or weights, layer connections, kernel sizes), also provides an ability to adapt how the DNNs process the wireless communications based on changing operating conditions.” (Emphasis added.)
Regarding claim 6, Wang discloses the following further limitation comprising:
receiving, from at least one UE of the one or more UEs, a message indicating that the at least one UE has implemented the training configuration or indicating that the at least one UE has refrained from implementing the training configuration.
Fig. 6 “Request each UE to report updated ML information using a training procedure 620” [Wingdings font/0xE0] “Transmit updated ML information 635”.
Regarding claims 8, Wang discloses the further limitation comprising:
transmitting, to the one or more UEs, a message comprising an indication to activate the training procedure at the one or more UEs.
Fig. 6, “Direct each UE to form a DNN using the initial ML configuration”.
Regarding claim 9, Wang discloses a method for wireless communications at a server, comprising:
[0002] "This document describes techniques and apparatuses for federated learning for deep neural networks (DNNs) in a wireless communication system")
[0032] “In FIG. 3, the core network server 302 may provide all or part of a function, entity, service, and/or gateway in the core network 150”.
selecting one or more user equipment (UEs) for a training procedure for a predictive model based at least in part on a trigger to activate the training procedure; and
[0057] “In some aspects, the base station 120 selects the set of UEs based on any combination of UE characteristics (e.g., UE capabilities, UE ML capabilities, estimated UE-location) and/or channel conditions (e.g., indicated by signal or link quality parameters).”
[0063] “As another example update condition, the base station 120 requests each UE in the set of UEs to transmit the updated ML information (and/or to perform the training procedure) in response to detecting a trigger event, such as trigger events that correspond to changes in a DNN at a UE. “ (Emphasis added.)
transmitting an indication of a training configuration for training of the predictive model to the one or more UEs, the training configuration comprising a set of training parameters based at least in part on one or more constraints of the one or more UEs.
[0058] "the core network server 302 (not illustrated) selects the initial ML configuration and communicates the ML configuration UEs through base station 120.”
[0056] “the base station receives a UE capability information message (not illustrated) from each UE and selects the initial ML configuration based on a common UE capability between the UEs 111, 112, and 113."
Regarding claim 12, Wang discloses the further limitation wherein the set of training parameters comprises a model structure identifier associated with the predictive model, a baseline parameter set identifier associated with the predictive model, a training validity area, a maximum quantity of epochs for the training procedure, a minimum quantity of epochs for the training procedure, a training deadline, a set of weights, a periodicity, a server address, or a combination thereof.
The Examiner notes that this is a Markush group.
‘baseline parameter set’ :: [0009] “baseline ML configuration”
‘set of weights’ :: [0007] “Dynamic reconfiguration of a DNN, such as by modifying various architecture configurations ( e.g., number of layers, layer processing algorithms, down-sampling configurations) and parameter configurations (e.g., coefficients or weights, layer connections, kernel sizes), also provides an ability to adapt how the DNNs process the wireless communications based on changing operating conditions.” (Emphasis added.)
Claims 1, 3, 5-6 and 8-9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nishio (Nishio, Takayuki, and Ryo Yonetani. "Client selection for federated learning with heterogeneous resources in mobile edge." In ICC 2019-2019 IEEE international conference on communications (ICC), pp. 1-7. IEEE, 2019.).
Regarding claim 1, (alternate rejection) Nishio discloses a method for wireless communications at a network node, comprising:
selecting one or more user equipment (UEs) for a training procedure for a predictive model based at least in part on a trigger to activate the training procedure; and
P. 2, first col., “Our main contribution is a new protocol referred to as FedCS, which can run FL efficiently while an operator of MEC frameworks actively manages the resources of heterogeneous clients. Specifically, FedCS sets a certain deadline for clients to download, update, and upload ML models in the FL protocol. Then, the MEC operator selects clients such that the server can aggregate as many client updates as possible in limited time frames, which makes the overall training process efficient and reduces a required time for training ML models. This is technically formulated by a c1ient selection problem that determines which clients participate in the training process and when each client has to complete the process while considering the computation and communication resource constraints imposed by the client, which we can solve in a greedy fashion.” (Emphasis added.)
P. 3, protocol 2 (reproduced below).
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‘trigger’ :: P. 3, protocol 2, step 1: “Initialization in Protocol I.”
‘selecting one or more user equipment (UEs) :: Protocol 2, step 3: “Client Selection: Using the information, the MEC operator determines which of the clients go to the subsequent steps to complete the steps within a certain deadline.”
‘for a training procedure’ :: Protocol 2, step 5: “Scheduled Update and Upload: The clients update global models and upload the new parameters using the RBs allocated by the MEC operator.”
transmitting an indication of a training configuration for training of the predictive model to the one or more UEs, the training configuration comprising a set of training parameters based at least in part on one or more constraints of the one or more UEs.
P. 3, “First, the new Resource Request step asks random clients to inform the MEC operator of their resource information such as wireless channel states, computational capacities (e.g., if they can spare CPUs or GPUs for updating models), and the size of data resources relevant to the current training task (e.g., if the server is going to train a 'dog vs-cat' classifier, the number of images containing dogs or cats). Then, the operator refers to this information in the subsequent Client Selection step to estimate the time required for the Distribution and Scheduled Update and Upload steps and to determine which clients go to these steps (the specific algorithms for scheduling clients are explained later). In the Distribution step, a global model is distributed to the selected clients via multicast from the BS because it is bandwidth effective for transmitting the same content (i.e., the global model) to client populations.”
Protocol 2, step 3: “Client Selection: Using the information, the MEC operator determines which of the clients go to the subsequent steps to complete the steps within a certain deadline. “.
Protocol 2, step 4: “Distribution: The server distributes the parameters of the global model to the selected clients.”
‘training parameters’ :: ‘parameters’ from step 4 (reproduced above).
Regarding claim 3, Nishio discloses the following further limitation comprising:
selecting the set of training parameters for the training configuration based at least in part on an estimated link capacity associated with the one or more UEs, a computational capability associated with the one or more UEs, or a combination thereof.
The Examiner notes that this is a Markush group.
‘estimated link capacity’ and ‘computational capability’ :: P. 2, first col., “This is technically formulated by a c1ient selection problem that determines which clients participate in the training process and when each client has to complete the process while considering the computation and communication resource constraints imposed by the client, which we can solve in a greedy fashion.” (Emphasis added.)
Regarding claim 5, Nishio discloses the further limitation wherein the set of training parameters comprises a model structure identifier associated with the predictive model, a baseline parameter set identifier associated with the predictive model, a training validity area, a maximum quantity of epochs for the training procedure, a minimum quantity of epochs for the training procedure, a training deadline, a set of weights, a periodicity, a server address, or a combination thereof.
[The Examiner notes that this is a Markush group.]
‘a training deadline’ :: Protocol 2, step 3: “Client Selection: Using the information, the MEC operator determines which of the clients go to the subsequent steps to complete the steps within a certain deadline.”
‘set of weights’ :: P. 5, sec. C: “We implemented a standard convolutional neural network as a global mode for both tasks.”
The Examiner notes that all neural networks inherently comprise a set of weights.
Regarding claim 6, Nishio discloses the following further limitation comprising:
receiving, from at least one UE of the one or more UEs, a message indicating that the at least one UE has implemented the training configuration or indicating that the at least one UE has refrained from implementing the training configuration.
P. 3, protocol 2, (reproduced supra), steps 5-6.
Regarding claims 8, Nishio discloses the further limitation comprising:
transmitting, to the one or more UEs, a message comprising an indication to activate the training procedure at the one or more UEs.
P. 3, protocol 2, (reproduced supra), step4: “The server distributes the parameters of the global model to the selected clients”.
Regarding claim 9, Nishio discloses a method for wireless communications at a server, comprising:
selecting one or more user equipment (UEs) for a training procedure for a predictive model based at least in part on a trigger to activate the training procedure; and
P. 3, protocol 2, (reproduced supra).
‘trigger’ :: P. 3, protocol 2, step 1: “Initialization in Protocol I.”
‘selecting one or more user equipment (UEs) :: Protocol 2, step 3: “Client Selection: Using the information, the MEC operator determines which of the clients go to the subsequent steps to complete the steps within a certain deadline.”
‘for a training procedure’ :: Protocol 2, step 5: “Scheduled Update and Upload: The clients update global models and upload the new parameters using the RBs allocated by the MEC operator.”
transmitting an indication of a training configuration for training of the predictive model to the one or more UEs, the training configuration comprising a set of training parameters based at least in part on one or more constraints of the one or more UEs.
P. 3, “First, the new Resource Request step asks random clients to inform the MEC operator of their resource information such as wireless channel states, computational capacities (e.g., if they can spare CPUs or GPUs for updating models), and the size of data resources relevant to the current training task (e.g., if the server is going to train a 'dog vs-cat' classifier, the number of images containing dogs or cats). Then, the operator refers to this information in the subsequent Client Selection step to estimate the time required for the Distribution and Scheduled Update and Upload steps and to determine which clients go to these steps (the specific algorithms for scheduling clients are explained later). In the Distribution step, a global model is distributed to the selected clients via multicast from the BS because it is bandwidth effective for transmitting the same content (i.e., the global model) to client populations.”
Protocol 2, step 3: “Client Selection: Using the information, the MEC operator determines which of the clients go to the subsequent steps to complete the steps within a certain deadline. “.
Protocol 2, step 4: “Distribution: The server distributes the parameters of the global model to the selected clients.”
‘training parameters’ :: ‘parameters’ from step 4 (reproduced above).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
The following are the references relied upon in the rejections below:
Wang (WO 2022/010685 A1, cited by Applicant on IDS dated 11/29/23)
Ahmadi (Sassan Ahmadi (ed.), Chapter 5 - Radio Resource Control Functions, LTE-Advanced, Academic Press, 2014. PP. 227-287.)
Jiang (Jiang, Zhifeng, Wei Wang, Bo Li, and Qiang Yang. "Towards efficient synchronous federated training: A survey on system optimization strategies." IEEE Transactions on Big Data 9, no. 2 (2022): 437-454. Published 23 May 2022.)
Lian (Lian, Zhuotao, and Chunhua Su. "Decentralized federated learning for internet of things anomaly detection." In Proceedings of the 2022 ACM on Asia conference on computer and communications security, pp. 1249-1251. Published 30 May 2022.)
Liu (Liu J, Huang J, Zhou Y, Li X, Ji S, Xiong H, Dou D. From distributed machine learning to federated learning: A survey. Knowledge and information systems. 2022 Apr; 64(4):885-917.)
Postel (Postel J. Rfc0821: Simple mail transfer protocol. August 1982. 72 pages.)
Yu (Yu, Fuxun, Weishan Zhang, Zhuwei Qin, Zirui Xu, Di Wang, Chenchen Liu, Zhi Tian, and Xiang Chen. "Heterogeneous federated learning." arXiv preprint arXiv:2008.06767. 2020.)
Claims 2 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Wang and Ahmadi.
Regarding claims 2 and 10, Wang discloses the following further limitation comprising:
transmitting, to the one or more UEs, an indication of … configured for downloading a model structure and a baseline parameter set associated with the predictive model.
[0058] "the core network server 302 (not illustrated) selects the initial ML configuration and communicates the ML configuration UEs through base station 120."
Ahmadi discloses the following further limitation which Wang does not disclose comprising an indication of a data radio bearer configured for …
P. 227, “signaling radio bearer (SRB)”.
P. 233, “The RRC connection establishment involves SRB1 establishment. The procedure is also used to transfer the initial NAS dedicated information/message from the UE to the E-UTRAN. The UE initiates the RRC connection establishment procedure when the upper layers request establishment of an RRC connection while the UE is in the RRC_IDLE state.”
See also p. 235-36.
At the time of filing, it would have been obvious to a person of ordinary skill to apply radio bearer services (as taught by Ahmadi) to the federated learning system of Wang because the former a standard component of 5g wireless communication protocols.
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Wang and Yu.
Regarding claim 3, Yu discloses the following further limitation which Wang does not disclose comprising:
selecting the set of training parameters for the training configuration based at least in part on an estimated link capacity associated with the one or more UEs, a computational capability associated with the one or more UEs, or a combination thereof.
P. 7, sec. 5.2: “Communication Efficiency with Different Local Epochs” and “Computational Efficiency with Local Training Budgets.”
At the time of filing, it would have been obvious to a person of ordinary skill to apply the technique disclosed by Yu for setting different local epoch values in combination with the Wang system because this can provide for improved performance with reduced computing resources and/or communication resources.
Regarding claim 4, Yu discloses the further limitation wherein the set of training parameters comprises a minimum quantity of epochs for the training procedure to be performed at a UE of the one or more UEs.
P. 7, sec. 5.2, “Fig. 4 (a) shows the convergence accuracy performance comparison under local epoch settings {10, 20, 50, 70, 100, 150}. All models are trained with 54 averaging rounds as per settings in [13] for fair comparison.”
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Wang and Postel.
Regarding claim 7, Postel discloses the following further limitation which Wang does not disclose comprising:
receiving, from at least one UE of the one or more UEs, a message indicating that the [node] is ready for activation at the at least one UE.
P. 41, sec. 4.3, “The communication between the sender and receiver is intended to be an alternating dialogue, controlled by the sender. As such, the sender issues a command and the receiver responds with a reply. The sender must wait for this response before sending further commands. One important reply is the connection greeting. Normally, a receiver will send a 220 "Service ready" reply when the connection is completed. The sender should wait for this greeting message before sending any commands.”
At the time of filing, it would have been obvious to a person of ordinary skill to apply the message transfer protocol “service ready” (as taught by Postel) to the Wang system because this will ensure that model data or training data is not sent before the receiving node is ready to receive it.
Claims 13 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Wang and Liu.
Regarding claim 13, Wang discloses a method for wireless communications at a user equipment (UE), comprising:
receiving a first message indicating a training configuration for a training procedure for training of predictive model, the training configuration comprising a set of training parameters; and
[0056] “In aspects, the base station receives a UE capability information message (not illustrated) from each UE and selects the initial ML configuration based on a common UE capability between the UEs 111, 112, and 113.”
[0058] “Alternatively, or additionally, the core network server 302 (not illustrated) selects the initial ML configuration and communicates the ML configuration UEs through base station 120.”
transmitting a second message indicating whether the UE has implemented the training configuration …. based at least in part on one or more constraints of the UE and on the set of training parameters.
[0067] “In response to detecting the condition and/or in response to performing a training procedure, at 635, 636, and/or 637, the UEs 111, 112, and 113 transmit a message that indicates updated ML information to the base station 120.” (Emphasis added.)
The Examiner notes that neural networks are trained iteratively. See eg discussion at Wang [0072]:
“This allows the base station 120 to analyze updated ML information and adapt the common DNN to optimize (and re-optimize and/or iteratively optimize) the processing as the operating environment changes …”. (Emphasis added.)
See also fig. 6, items 635-640.
[0114] “For example, the network entity 105-a or the server 203 may select the set of training parameters based on an estimated link capacity associated with one or more of the UEs 115,”
Liu discloses the following further limitation which Wang does not disclose:
transmitting a second message indicating whether the UE has implemented the training configuration prior to the training procedure based at least in part on the set of training parameters.
P. 891, “The monitoring enables the users to get the real-time status of the distributed training process. As the training process of FL models can be very long, e.g., from several hours to days [79], it is of much importance to track the execution status, which allows the user to verify whether the training proceeds normally. The log service is generally supported by major FL systems, which can be used to analyze the training process. In addition, the log generated during the training process can be used to debug the system or adjust the FL model.” (Emphasis added.)
At the time of filing, it would have been obvious to a person of ordinary skill to transmit a confirmation message from the UE to the global server (as taught by Liu) in combination with the Wang system. There are only three possibilities for when such a message could be transmitted: prior to training, during training, or after training. All three are valid options, but prior to training confers the advantage of the earliest notice to the global server, so that system engineers could be immediately informed as to the status of the UE.
Regarding claim 21, Wang discloses the further limitation wherein the set of training parameters comprises a model structure identifier associated with the predictive model, a baseline parameter set identifier associated with the predictive model, a training validity area, a maximum quantity of epochs for the training procedure, a minimum quantity of epochs for the training procedure, a training deadline, a set of weights, a periodicity, a server address, or a combination thereof.
The Examiner notes that this is a Markush group.
‘baseline parameter set’ :: [0009] “baseline ML configuration”
‘set of weights’ :: [0007] “Dynamic reconfiguration of a DNN, such as by modifying various architecture configurations ( e.g., number of layers, layer processing algorithms, down-sampling configurations) and parameter configurations (e.g., coefficients or weights, layer connections, kernel sizes), also provides an ability to adapt how the DNNs process the wireless communications based on changing operating conditions.” (Emphasis added.)
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Wang, Liu and Ahmadi.
Regarding claim 14, Wang discloses the further limitation comprising: …
downloading the model structure and the baseline parameter set ….
Fig. 6, “Direct each UE to form a DNN using the initial ML configuration”.
Ahmadi discloses the following further limitation which Wang/Liu does not disclose:
receiving an indication of a data radio bearer configured for downloading a model structure and a baseline parameter set associated with the predictive model; and
P. 227, “signaling radio bearer (SRB)”.
P. 233, “The RRC connection establishment involves SRB1 establishment. The procedure is also used to transfer the initial NAS dedicated information/message from the UE to the E-UTRAN. The UE initiates the RRC connection establishment procedure when the upper layers request establishment of an RRC connection while the UE is in the RRC_IDLE state.”
See also p. 235-36.
The obviousness analysis of claims 2/10 applies equally here.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Wang, Liu and Postel.
Regarding claim 20, Postel discloses the following further limitation which Wang/Liu do not disclose comprising:
configuring the training procedure in accordance with the set of training parameters, wherein the second message further comprises an indication that configuration of the training procedure is complete.
P. 36 “250 Requested mail action okay, completed”.
The obviousness analysis of claims 7/23 applies equally here.
Claims 22, 24, 26 and 28-29 are rejected under 35 U.S.C. 103 as being unpatentable over Wang and Lian.
Regarding claim 22, (alternate rejection) Wang discloses a method for wireless communications at a server, comprising:
[0002] "This document describes techniques and apparatuses for federated learning for deep neural networks (DNNs) in a wireless communication system")
transmitting, to a set of user equipments (UEs), a training configuration for a training procedure associated with training of a predictive model, the training configuration comprising a first set of model parameters … based at least in part on one or more constraints of the set of UEs;
[0056] “the base station receives a UE capability information message (not illustrated) from each UE and selects the initial ML configuration based on a common UE capability between the UEs 111, 112, and 113."
[0058] "the core network server 302 (not illustrated) selects the initial ML configuration and communicates the ML configuration UEs through base station 120.”
[0114] “For example, the network entity 105-a or the server 203 may select the set of training parameters based on an estimated link capacity associated with one or more of the UEs 115,”
receiving, from one or more UEs of the set of UEs, one or more reports indicating one or more subsets of model parameters output from the training procedure for the predictive model at the UE;
Id. See also fig. 6, items 635-640.
aggregating the subsets of model parameters into a second set of model parameters; …
[0070] "At 650, the base station 120 determines a common ML configuration for the subset of UEs. In determining the common ML configuration, the base station 120 applies federated learning techniques that aggregate the updated ML information received from multiple UEs”.
Lian discloses the following further limitations which Wang does not disclose:
… a first set of model parameters associated with a first parameter set identifier …
P. 1250, second col. “Each client will get the initial model and other participant in formation. And get the initial version number, where the version starts from 0.”
assigning a second parameter set identifier to the second set of model parameters, the second parameter set identifier different from the first parameter set identifier; and
P. 1250, second col., “After the model is obtained, the customers in customer 𝑖 and𝑈𝑖 are weighted averaged. where |𝐷𝑗| represents the local data volume of user 𝑗. After completion, the client 𝑖 updates the local model via stochastic gradient descent, and updates the version number, initializing the set 𝑈𝑖. The training ends after 𝑅 rounds.”
transmitting an indication of the second parameter set identifier.
Id.
At the time of filing, it would have been obvious to a person of ordinary skill to apply version control in a FL system (as taught by Lian) in combination with the FL system of Wang because this documenting version information lets system operators know the status of the training process. This is particularly important in heterogeneous device environments where clients may not perform computing tasks at the same rate.
Regarding claim 24, Wang discloses the further limitation comprising:
transmitting, to the set of UEs, a message indicating the second set of model parameters for the predictive model, the second set of model parameters comprising an updated set of model parameters for the predictive model.
Fig. 6, “Direct each UE to form a DNN using the initial ML configuration”.
Regarding claim 26, (alternate rejection) Wang discloses a method for wireless communications at a user equipment (UE), comprising:
[0002] "This document describes techniques and apparatuses for federated learning for deep neural networks (DNNs) in a wireless communication system")
receiving a training configuration for a training procedure associated with a predictive model, the training configuration comprising a first set of model parameters … the first set of model parameters based at least in part on one or more constraints of the UE;
[0056] “the base station receives a UE capability information message (not illustrated) from each UE and selects the initial ML configuration based on a common UE capability between the UEs 111, 112, and 113."
[0058] "the core network server 302 (not illustrated) selects the initial ML configuration and communicates the ML configuration UEs through base station 120.”
[0114] “For example, the network entity 105-a or the server 203 may select the set of training parameters based on an estimated link capacity associated with one or more of the UEs 115,”
transmitting a report indicating a subset of model parameters output from the training procedure for the predictive model at the UE; and
Fig. 6, items 635-640.
receiving an indication of a second parameter set … associated with a second set of model parameters based at least in part on transmitting the report, the second parameter set identifier different from the first parameter set identifier.
[0068] “Accordingly, at 640, the base station 120 receives updated ML information from at least some of the UEs, where the updated ML information can indicate any combination of ML parameters, ML architectures, and/or ML gradients. As one example, the UEs send an indication of an index into a neural network table or transmit an indication of ML parameters, ML architectures, and/or ML gradients.”
Lian discloses the following further limitations which Wang does not disclose:
… a first set of model parameters associated with a first parameter set identifier …
P. 1250, second col. “Each client will get the initial model and other participant in formation. And get the initial version number, where the version starts from 0.”
receiving an indication of a second parameter set identifier associated with a second set of model parameters based at least in part on transmitting the report, the second parameter set identifier different from the first parameter set identifier.
P. 1250, second col., “After the model is obtained, the customers in customer 𝑖 and𝑈𝑖 are weighted averaged. where |𝐷𝑗| represents the local data volume of user 𝑗. After completion, the client 𝑖 updates the local model via stochastic gradient descent, and updates the version number, initializing the set 𝑈𝑖. The training ends after 𝑅 rounds.”
The obviousness analysis of claim 22 applies equally here.
Regarding claim 28, Wang discloses the following further limitation comprising:
receiving a message indicating the second set of model parameters for the predictive model, the second set of model parameters comprising an updated set of model parameters for the predictive model.
Fig. 6 “Request each UE to report updated ML information using a training procedure 620” [Wingdings font/0xE0] “Transmit updated ML information 635”. The examiner notes that this is an iterative process.
Regarding claim 29, Wang discloses the following further limitation comprising:
performing the training procedure for the predictive model using the second set of model parameters and based at least in part on the second parameter set identifier.
Fig. 6 “Request each UE to report updated ML information using a training procedure 620” [Wingdings font/0xE0] “Perform training 630” [Wingdings font/0xE0] “Transmit updated ML information 635”. The examiner notes that this is an iterative process.
Claims 23 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Lian and Postel.
Regarding claim 23, Postel discloses the following further limitation which Wang/Lian do not disclose comprising:
receiving, from at least one UE of the one or more UEs, a message indicating that the [node] is ready for activation at the at least one UE.
P. 41, sec. 4.3, “The communication between the sender and receiver is intended to be an alternating dialogue, controlled by the sender. As such, the sender issues a command and the receiver responds with a reply. The sender must wait for this response before sending further commands. One important reply is the connection greeting. Normally, a receiver will send a 220 "Service ready" reply when the connection is completed. The sender should wait for this greeting message before sending any commands.”
At the time of filing, it would have been obvious to a person of ordinary skill to apply the message transfer protocol “service ready” (as taught by Postel) to the Wang/Lian system because this will ensure that model data or training data is not sent before the receiving node is ready to receive it.
Regarding claim 27, Postel discloses the following further limitation which Wang/Lian do not disclose comprising:
transmitting a message indicating that the [node] is ready … based at least in part on receiving the indication.
P. 41, sec. 4.3, “The communication between the sender and receiver is intended to be an alternating dialogue, controlled by the sender. As such, the sender issues a command and the receiver responds with a reply. The sender must wait for this response before sending further commands. One important reply is the connection greeting. Normally, a receiver will send a 220 "Service ready" reply when the connection is completed. The sender should wait for this greeting message before sending any commands.”
The obviousness analysis of claim 23 applies equally here.
Claim 22, 25-26 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Nishio and Lian.
Regarding claim 22, Nishio discloses a method for wireless communications at a server, comprising:
transmitting, to a set of user equipments (UEs), a training configuration for a training procedure associated with training of a predictive model, the training configuration comprising a first set of model parameters … based at least in part on one or more constraints of the set of UEs;
P. 3, “First, the new Resource Request step asks random clients to inform the MEC operator of their resource information such as wireless channel states, computational capacities (e.g., if they can spare CPUs or GPUs for updating models), and the size of data resources relevant to the current training task (e.g., if the server is going to train a 'dog vs-cat' classifier, the number of images containing dogs or cats). Then, the operator refers to this information in the subsequent Client Selection step to estimate the time required for the Distribution and Scheduled Update and Upload steps and to determine which clients go to these steps (the specific algorithms for scheduling clients are explained later). In the Distribution step, a global model is distributed to the selected clients via multicast from the BS because it is bandwidth effective for transmitting the same content (i.e., the global model) to client populations.”
Protocol 2, step 3: “Client Selection: Using the information, the MEC operator determines which of the clients go to the subsequent steps to complete the steps within a certain deadline. “.
Protocol 2, step 4: “Distribution: The server distributes the parameters of the global model to the selected clients.”
‘training parameters’ :: ‘parameters’ from step 4 (reproduced above).
receiving, from one or more UEs of the set of UEs, one or more reports indicating one or more subsets of model parameters output from the training procedure for the predictive model at the one or more UE;
P. 3, protocol 2, step 5: “Scheduled Update and Upload: The clients update global models and upload the new parameters using the RBs allocated by the MEC operator.”
aggregating the subsets of model parameters into a second set of model parameters;
Id.
Lian discloses the following further limitations which Nishio does not disclose:
… a first set of model parameters associated with a first parameter set identifier …
P. 1250, second col. “Each client will get the initial model and other participant in formation. And get the initial version number, where the version starts from 0.”
assigning a second parameter set identifier to the second set of model parameters, the second parameter set identifier different from the first parameter set identifier; and
P. 1250, second col., “After the model is obtained, the customers in customer 𝑖 and𝑈𝑖 are weighted averaged. where |𝐷𝑗| represents the local data volume of user 𝑗. After completion, the client 𝑖 updates the local model via stochastic gradient descent, and updates the version number, initializing the set 𝑈𝑖. The training ends after 𝑅 rounds.”
transmitting an indication of the second parameter set identifier.
Id.
At the time of filing, it would have been obvious to a person of ordinary skill to apply version control in a FL system (as taught by Lian) in combination with the FL system of Nishio because this documenting version information lets system operators know the status of the training process. This is particularly important in heterogeneous device environments where clients may not perform computing tasks at the same rate.
Regarding claim 25, Nishio discloses the further limitation wherein the second parameter set identifier comprises a temporary parameter set … ;
The Examiner notes that this is a Markush group.
P. 3, protocol 2 (reproduced supra), step 4: “Distribution: The server distributes the parameters of the global model to the selected clients.”
The Examiner interprets “temporary parameter set identifier” according to its broadest reasonable interpretation as encompassing the parameters of the global model which are transmitted to the clients. These parameters are temporary because they are subsequently updated by the clients.
Lian discloses the following further limitation which Nishio does not disclose:
wherein the second parameter set identifier comprises a temporary parameter set identifier or a combination of the first parameter set identifier and a version tag.
P. 1250, second col., “After the model is obtained, the customers in customer 𝑖 and 𝑈𝑖 are weighted averaged. where |𝐷𝑗| represents the local data volume of user 𝑗. After completion, the client 𝑖 updates the local model via stochastic gradient descent, and updates the version number, initializing the set 𝑈𝑖. The training ends after 𝑅 rounds.”
Regarding claim 26, Nishio discloses a method for wireless communications at a user equipment (UE), comprising:
receiving a training configuration for a training procedure associated with a predictive model, the training configuration comprising a first set of model parameters … the first set of model parameters based at least in part on one or more constraints of the UE;
P. 3, “First, the new Resource Request step asks random clients to inform the MEC operator of their resource information such as wireless channel states, computational capacities (e.g., if they can spare CPUs or GPUs for updating models), and the size of data resources relevant to the current training task (e.g., if the server is going to train a 'dog vs-cat' classifier, the number of images containing dogs or cats). Then, the operator refers to this information in the subsequent Client Selection step to estimate the time required for the Distribution and Scheduled Update and Upload steps and to determine which clients go to these steps (the specific algorithms for scheduling clients are explained later). In the Distribution step, a global model is distributed to the selected clients via multicast from the BS because it is bandwidth effective for transmitting the same content (i.e., the global model) to client populations.”
Protocol 2, step 3: “Client Selection: Using the information, the MEC operator determines which of the clients go to the subsequent steps to complete the steps within a certain deadline. “.
Protocol 2, step 4: “Distribution: The server distributes the parameters of the global model to the selected clients.”
‘training parameters’ :: ‘parameters’ from step 4 (reproduced above).
transmitting a report indicating a subset of model parameters output from the training procedure for the predictive model at the UE; and
P. 3, protocol 2, step 5: “Scheduled Update and Upload: The clients update global models and upload the new parameters using the RBs allocated by the MEC operator.”
receiving an indication of a second parameter set … associated with a second set of model parameters based at least in part on transmitting the report, the second parameter set identifier different from the first parameter set identifier.
The Examiner notes that the training procedure described throughout Nishio is in iterative process performed over many epochs (training cycles).
Lian discloses the following further limitations which Nishio does not disclose:
… a first set of model parameters associated with a first parameter set identifier …
P. 1250, second col. “Each client will get the initial model and other participant in formation. And get the initial version number, where the version starts from 0.”
receiving an indication of a second parameter set identifier associated with a second set of model parameters based at least in part on transmitting the report, the second parameter set identifier different from the first parameter set identifier.
P. 1250, second col., “After the model is obtained, the customers in customer 𝑖 and𝑈𝑖 are weighted averaged. where |𝐷𝑗| represents the local data volume of user 𝑗. After completion, the client 𝑖 updates the local model via stochastic gradient descent, and updates the version number, initializing the set 𝑈𝑖. The training ends after 𝑅 rounds.”
The obviousness analysis of claim 22 applies equally here.
Regarding claim 30, Nishio discloses the further limitation wherein the second parameter set identifier comprises a temporary parameter set …
The Examiner notes that this is a Markush group.
P. 3, protocol 2 (reproduced supra), step 4: “Distribution: The server distributes the parameters of the global model to the selected clients.”
The Examiner interprets “temporary parameter set identifier” according to its broadest reasonable interpretation as encompassing the parameters of the global model which are transmitted to the clients. These parameters are temporary because they are subsequently updated by the clients.
Lian discloses the following further limitation which Nishio does not disclose:
wherein the second parameter set identifier comprises a temporary parameter set identifier or a combination of the first parameter set identifier and a version tag.
P. 1250, second col., “After the model is obtained, the customers in customer 𝑖 and 𝑈𝑖 are weighted averaged. where |𝐷𝑗| represents the local data volume of user 𝑗. After completion, the client 𝑖 updates the local model via stochastic gradient descent, and updates the version number, initializing the set 𝑈𝑖. The training ends after 𝑅 rounds.”
Claims 1, 9, 11, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Jiang.
Regarding claim 1, (alternate rejection) Jiang discloses a method for wireless communications at a network node, comprising:
selecting one or more user equipment (UEs) for a training procedure for a predictive model based at least in part on a trigger to activate the training procedure; and
P. 438, fig. 1 (reproduced below).
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The Examiner interprets ‘trigger’ according to its broadest reasonable interpretation as encompassing the initiation of the ‘selection’ phase, as illustrated above.
P. 441, second col., “Besides heuristic methods, some researchers use reinforcement learning (RL) algorithms to learn which clients to select in the presence of data heterogeneity. For example, FAVOR [19] seeks to reduce the number of rounds to reach a target accuracy with a deep Q-learning network (DQN) [105]. To capture each client’s statistical characteristics, it takes the low-dimension representations of local models as the RL states. Compared to random selection, FAVOR can reduce the communication rounds by up to 49% in three image classification tasks.”
transmitting an indication of a training configuration for training of the predictive model to the one or more UEs, the training configuration comprising a set of training parameters based at least in part on one or more constraints of the one or more UEs.
P. 440, first col., “Resource Heterogeneity: due to the variability in hardware specifications and system-level constraints, clients in federated training typically possess different capabilities in computation (CPU/GPU/NPU, memory, and storage), communication (connectivity and bandwidth) and power (battery level and lifespan) [103].”
P. 443, second col., “Load Balancing. Given the variations in computing power and data volume, clients may not finish the training process at the same time. To mitigate the resulting straggler effects, [42], [43] suggest balancing the amount of training data across clients. Specifically, they turn to RL techniques for determining the optimal number of data units used in a communication round for each participant, intending to minimize the time and energy consumption and maximize the volume of involved data. …. HeteroFL [46] assigns sub-models with different widths of hidden channels to clients so that clients with fewer capabilities can train smaller sub-models.” (Emphasis added.)
P. 444, first col., “Apart from the computational load, it is sometimes also beneficial to balance the communication load across clients, especially when the network conditions are complicated as in wireless connections. For example, targeting a mobile edge computing (MEC) scenario where Time Division Multiple Access (TDMA) is implemented, [47] optimizes both the data batch size and uplink/downlink frame time slots for each client to achieve the maximum learning efficiency. In addition to coping with CPU computing, the authors further extend the optimization problem to the scenario where devices are equipped with GPUs for training.” (Emphasis added.)
Although Jiang discloses each of the limitations of claim 1, as illustrated above, it does so in the context of a survey paper. At the time of filing, it would have been obvious to a person of ordinary skill to combine the client selection techniques taught by Jiang with the training load configuration techniques taught by Jiang because each of these approaches can help ensure an efficient use of processing or communication resources to accomplish a particular computing task.
Regarding claim 9, (alternate rejection) Jiang discloses a method for wireless communications at a server, comprising:
selecting one or more user equipment (UEs) for a training procedure for a predictive model based at least in part on a trigger to activate the training procedure; and
P. 438, fig. 1 (reproduced below).
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The Examiner interprets ‘trigger’ according to its broadest reasonable interpretation as encompassing the initiation of the ‘selection’ phase, as illustrated above.
P. 441, second col., “Besides heuristic methods, some researchers use reinforcement learning (RL) algorithms to learn which clients to select in the presence of data heterogeneity. For example, FAVOR [19] seeks to reduce the number of rounds to reach a target accuracy with a deep Q-learning network (DQN) [105]. To capture each client’s statistical characteristics, it takes the low-dimension representations of local models as the RL states. Compared to random selection, FAVOR can reduce the communication rounds by up to 49% in three image classification tasks.”
transmitting an indication of a training configuration for training of the predictive model to the one or more UEs, the training configuration comprising a set of training parameters based at least in part on one or more constraints of the one or more UEs.
P. 440, first col., “Resource Heterogeneity: due to the variability in hardware specifications and system-level constraints, clients in federated training typically possess different capabilities in computation (CPU/GPU/NPU, memory, and storage), communication (connectivity and bandwidth) and power (battery level and lifespan) [103].”
P. 443, second col., “Load Balancing. Given the variations in computing power and data volume, clients may not finish the training process at the same time. To mitigate the resulting straggler effects, [42], [43] suggest balancing the amount of training data across clients. Specifically, they turn to RL techniques for determining the optimal number of data units used in a communication round for each participant, intending to minimize the time and energy consumption and maximize the volume of involved data. …. HeteroFL [46] assigns sub-models with different widths of hidden channels to clients so that clients with fewer capabilities can train smaller sub-models.” (Emphasis added.)
P. 444, first col., “Apart from the computational load, it is sometimes also beneficial to balance the communication load across clients, especially when the network conditions are complicated as in wireless connections. For example, targeting a mobile edge computing (MEC) scenario where Time Division Multiple Access (TDMA) is implemented, [47] optimizes both the data batch size and uplink/downlink frame time slots for each client to achieve the maximum learning efficiency. In addition to coping with CPU computing, the authors further extend the optimization problem to the scenario where devices are equipped with GPUs for training.” (Emphasis added.)
The obviousness analysis of claim 1 applies equally here.
Regarding claim 11, Jiang discloses the further limitation comprising:
selecting the set of training parameters for the training configuration based at least in part on an estimated link capacity associated with the one or more UEs, a computational capability associated with the one or more UEs, or a combination thereof,
The Examiner notes that this is a Markush group.
P. 440, first col., “Resource Heterogeneity: due to the variability in hardware specifications and system-level constraints, clients in federated training typically possess different capabilities in computation (CPU/GPU/NPU, memory, and storage), communication (connectivity and bandwidth) and power (battery level and lifespan) [103].”
P. 443, second col., “Load Balancing. Given the variations in computing power and data volume, clients may not finish the training process at the same time. To mitigate the resulting straggler effects, [42], [43] suggest balancing the amount of training data across clients. Specifically, they turn to RL techniques for determining the optimal number of data units used in a communication round for each participant, intending to minimize the time and energy consumption and maximize the volume of involved data. …. HeteroFL [46] assigns sub-models with different widths of hidden channels to clients so that clients with fewer capabilities can train smaller sub-models.” (Emphasis added.)
P. 444, first col., “Apart from the computational load, it is sometimes also beneficial to balance the communication load across clients, especially when the network conditions are complicated as in wireless connections. For example, targeting a mobile edge computing (MEC) scenario where Time Division Multiple Access (TDMA) is implemented, [47] optimizes both the data batch size and uplink/downlink frame time slots for each client to achieve the maximum learning efficiency. In addition to coping with CPU computing, the authors further extend the optimization problem to the scenario where devices are equipped with GPUs for training.” (Emphasis added.)
wherein the set of training parameters comprises a minimum quantity of epochs for the training procedure to be performed at a UE of the one or more UEs.
P. 437, second col., “Configuration. The server next sends the global model status and configuration profiles (e.g., the number of local epochs or the reporting deadline) to each of the selected clients.” (Emphasis added.)
P. 448, second col., “Aligned with the observations in the literature [68], most prior arts consistently configure different clients. Although there exist some heterogeneity-aware efforts like load balancing where the number of batches, batch size, and number of local epochs can vary across clients (Section 3.2.3)”. (Emphasis added.)
Regarding claim 13, (alternate rejection) Jiang discloses a method for wireless communications at a user equipment (UE), comprising:
receiving a first message indicating a training configuration for a training procedure for training of a predictive model, the training configuration comprising a set of training parameters; and
P. 438, fig. 1 (reproduced below).
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P. 441, second col., “Besides heuristic methods, some researchers use reinforcement learning (RL) algorithms to learn which clients to select in the presence of data heterogeneity. For example, FAVOR [19] seeks to reduce the number of rounds to reach a target accuracy with a deep Q-learning network (DQN) [105]. To capture each client’s statistical characteristics, it takes the low-dimension representations of local models as the RL states. Compared to random selection, FAVOR can reduce the communication rounds by up to 49% in three image classification tasks.”
transmitting a second message indicating whether the UE has implemented the training configuration prior to the training procedure based at least in part on one or more constraints of the UE and on the set of training parameters.
P. 440, first col., “Resource Heterogeneity: due to the variability in hardware specifications and system-level constraints, clients in federated training typically possess different capabilities in computation (CPU/GPU/NPU, memory, and storage), communication (connectivity and bandwidth) and power (battery level and lifespan) [103].”
P. 443, second col., “Load Balancing. Given the variations in computing power and data volume, clients may not finish the training process at the same time. To mitigate the resulting straggler effects, [42], [43] suggest balancing the amount of training data across clients. Specifically, they turn to RL techniques for determining the optimal number of data units used in a communication round for each participant, intending to minimize the time and energy consumption and maximize the volume of involved data. …. HeteroFL [46] assigns sub-models with different widths of hidden channels to clients so that clients with fewer capabilities can train smaller sub-models.” (Emphasis added.)
P. 444, first col., “Apart from the computational load, it is sometimes also beneficial to balance the communication load across clients, especially when the network conditions are complicated as in wireless connections. For example, targeting a mobile edge computing (MEC) scenario where Time Division Multiple Access (TDMA) is implemented, [47] optimizes both the data batch size and uplink/downlink frame time slots for each client to achieve the maximum learning efficiency. In addition to coping with CPU computing, the authors further extend the optimization problem to the scenario where devices are equipped with GPUs for training.” (Emphasis added.)
The obviousness analysis of claim 1 applies equally here.
Allowable Subject Matter
Claims 15-19 are allowable over the prior art, but are rejected under §101.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Vincent Gonzales whose telephone number is (571) 270-3837. The examiner can normally be reached on Monday-Friday 7 a.m. to 4 p.m. MT. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Miranda Huang, can be reached at (571) 270-7092.
Information regarding the status of an application may be obtained from the USPTO Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov./Vincent Gonzales/Primary Examiner, Art Unit 2124
1 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.