Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim 1-20 are rejected under 101 as being directed towards an abstract idea without adding significantly more
Regarding Claim 1
Step 1: “A method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: determining, based on the one or more model parameters from the plurality of communication devices, one or more groups of the plurality of communication devices and assigning at least one group specific machine learning model, among a plurality of group specific machine learning models, to the one or more groups, wherein the plurality of group specific machine learning models are associated with the global machine learning model is an abstract idea that can be done with the aid of pen and paper, a person can determine where a model should go based on parameters.
Step 2A Prong 2: The additional limitations providing a global machine learning model, generated based on federated learning, to a plurality of communication devices is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g) – examiners note: providing a model for federated learning amounts to nothing more than insignificant application
receiving one or more model parameters from the plurality of communication devices based in part on the plurality of communication devices determining local training data generated by the plurality of communication devices implementing the global machine learning model; is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g) – examiners note: receiving parameters amounts to nothing more than mere data gathering
and providing respective group specific machine learning models, among the plurality of group specific machine learning models, to subsets of communication devices of the one or more groups of the plurality of communication devices. is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g) – examiners note: providing models to communication devices amounts to nothing more than insignificant application
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitations providing a global machine learning model, generated based on federated learning, to a plurality of communication devices is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g) - Furthermore the additional element is directed to performing repetitive calculations which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II). – examiners note: providing a model for federated learning amounts to nothing more than insignificant application
receiving one or more model parameters from the plurality of communication devices based in part on the plurality of communication devices determining local training data generated by the plurality of communication devices implementing the global machine learning model; is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g) – furthermore the additional element is directed to storing and retrieving information in memory which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II). – examiners note: receiving parameters amounts to nothing more than mere data gathering
and providing respective group specific machine learning models, among the plurality of group specific machine learning models, to subsets of communication devices of the one or more groups of the plurality of communication devices. is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g) – furthermore the additional element is directed to performing repetitive calculations which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II). – examiners note: providing models to communication devices amounts to nothing more than insignificant application
Regarding Claim 2
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 1
Step 2A Prong 2: The additional limitations receiving updated model parameters, from the subsets of the communication devices of the one or more groups, in response to the subsets of the communication devices determining items of local training content generated by the subsets of the communication devices implementing a respective group specific machine learning model assigned to at least one group of the one or more groups. . is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g) – examiners note: receiving updated parameters amounts to nothing more than mere data gathering
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitations receiving updated model parameters, from the subsets of the communication devices of the one or more groups, in response to the subsets of the communication devices determining items of local training content generated by the subsets of the communication devices implementing a respective group specific machine learning model assigned to at least one group of the one or more groups. . is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g) ) – furthermore the additional element is directed to storing and retrieving information in memory which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II). – examiners note: receiving updated parameters amounts to nothing more than mere data gathering
Regarding Claim 3
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 2
Step 2A Prong 2: The additional limitations generating other machine learning models, wherein at least one of the other machine learning models are specifically tailored to the communication devices of the one or more groups in response to receiving the updated model parameters from the subsets of the communication devices of the one or more groups. are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of creating a model tailored towards a user’s needs
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitations generating other machine learning models, wherein at least one of the other machine learning models are specifically tailored to the communication devices of the one or more groups in response to receiving the updated model parameters from the subsets of the communication devices of the one or more groups. are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of creating a model tailored towards a user’s needs
Regarding Claim 4
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 3
Step 2A Prong 2: The additional limitations wherein: the other machine learning models are different machine learning models personalized for at least one user of a communication device of the subsets of the communication devices. are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of personalizing a model to a user
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitations wherein: the other machine learning models are different machine learning models personalized for at least one user of a communication device of the subsets of the communication devices. are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of personalizing a model to a user
Regarding Claim 5
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 3
Step 2A Prong 2: The additional limitations wherein: the other machine learning models are associated with the global machine learning model. is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitations wherein: the other machine learning models are associated with the global machine learning model. is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
Regarding Claim 6
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: determining at least one perplexity value indicating an improvement of group personalized federated learning associated with the one or more groups in relation to one or more other types of federated learning in response to implementing the at least one of the other machine learning models is a mathematical concept – see MPEP § 2106.04(a)(2)
Step 2A Prong 2 and Step 2B: There are no additional elements recited so the claim does not
provide a practical application and is not considered to be significantly more. As such, the claim is patent
ineligible.
Regarding Claim 7
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: the receiving the one or more model parameters further comprises determining that the local training data satisfies a predetermined threshold of training data is a mental process that can be done with the aid of pen and paper, a person can observe data and determine if it satisfies their threshold
Step 2A Prong 2 and Step 2B: There are no additional elements recited so the claim does not
provide a practical application and is not considered to be significantly more. As such, the claim is patent
ineligible.
Regarding Claim 8
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: wherein: the determining the one or more groups of the plurality of communication devices and the assigning of the at least one group specific machine learning model to the one or more groups comprises determining one or more shared characteristics of users of the communication devices or determining that items of the one or more model parameters are similar among the communication devices of the one or more groups is an abstract idea that can be done with the aid of pen and paper, a person can determine shared characteristics between a person to assign a model
Step 2A Prong 2 and Step 2B: There are no additional elements recited so the claim does not
provide a practical application and is not considered to be significantly more. As such, the claim is patent
ineligible.
Regarding Claim 9
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: wherein the shared characteristics of the users comprise a plurality of determined items of demographic information that the users have in common is a mental process that can be done with the aid of pen and paper, a person can assign demographics based off what things users have in common
Step 2A Prong 2 and Step 2B: There are no additional elements recited so the claim does not
provide a practical application and is not considered to be significantly more. As such, the claim is patent
ineligible.
Regarding Claim 10
Step 1: “An apparatus” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 1
Step 2A Prong 2: See the analysis of Claim 1
Step 2B: See the analysis of Claim 1
Regarding Claim 11
Step 1: “The apparatus” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 2
Step 2A Prong 2: See the analysis of Claim 2
Step 2B: See the analysis of Claim 2
Regarding Claim 12
Step 1: “The apparatus” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 3
Step 2A Prong 2: See the analysis of Claim 3
Step 2B: See the analysis of Claim 3
Regarding Claim 13
Step 1: “The apparatus” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 4
Step 2A Prong 2: See the analysis of Claim 4
Step 2B: See the analysis of Claim 4
Regarding Claim 14
Step 1: “The apparatus” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 5
Step 2A Prong 2: See the analysis of Claim 5
Step 2B: See the analysis of Claim 5
Regarding Claim 15
Step 1: “The apparatus” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 7
Step 2A Prong 2: See the analysis of Claim 7
Step 2B: See the analysis of Claim 7
Regarding Claim 16
Step 1: “The apparatus” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 8
Step 2A Prong 2: See the analysis of Claim 8
Step 2B: See the analysis of Claim 8
Regarding Claim 17
Step 1: “The apparatus” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 9
Step 2A Prong 2: See the analysis of Claim 9
Step 2B: See the analysis of Claim 9
Regarding Claim 18
Step 1: “A non-transitory computer-readable medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 1
Step 2A Prong 2: See the analysis of Claim 1
Step 2B: See the analysis of Claim 1
Regarding Claim 19
Step 1: “The computer-readable medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 2
Step 2A Prong 2: See the analysis of Claim 2
Step 2B: See the analysis of Claim 2
Regarding Claim 20
Step 1: “A non-transitory computer-readable medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 3
Step 2A Prong 2: See the analysis of Claim 3
Step 2B: See the analysis of Claim 3
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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.
Claim’s 1, 8-9, 10, and 16-18 are rejected under U.S.C. 102(a)(1) as being anticipated by Yang et al (NPL: Personalized Federated Learning on Non-IID Data via Group-based Meta-learning) (“Yang”)
Regarding Claim 1, Yang teaches A method comprising: providing a global machine learning model, generated based on federated learning to a plurality of communication devices ([Page 3: 1-7] teaches generating a set of global models and assigning them to clients with this framework used to demonstrate the limitations of a single model approach for federated meta-learning which teaches this limitation)
Receiving one or more model parameters from the plurality of communication devices based in part on the plurality of communication devices determining local training data generated by the plurality of communication devices implementing the global machine learning model; ([Section 4.1-The federated optimization stage] teaches client downloading the corresponding group model to optimize that model with its own personalized data and then sending the model back to the server which teaches this limitation)
determining, based on the one or more model parameters from the plurality of communication devices, one or more groups of the plurality of communication devices and assigning at least one group specific machine learning model, among a plurality of group specific machine learning models, to the one or more groups, wherein the plurality of group specific machine learning models are associated with the global machine learning model ([Section 4.1-The personalization stage] teaches providing clients with a personalized model based off of the group wise meta model. With each client being assigned to different groups based off of their task representation [Section 4.1-The initialization stage] and this task representation grouping is being interpreted as parameters for the model which teaches this limitation)
and providing respective group specific machine learning models, among the plurality of group specific machine learning models, to subsets of communication devices of the one or more groups of the plurality of communication devices. ([Section 4.1-The personalization stage] teaches providing clients with a personalized model based off of the group wise meta model which teaches this limitation)
Regarding Claim 8, Yang also teaches wherein: the determining the one or more groups of the plurality of communication devices and the assigning of the at least one group specific machine learning model to the one or more groups comprises determining one or more shared characteristics of users of the communication devices or determining that items of the one or more model parameters are similar among the communication devices of the one or more groups. ([Section 4.1-initizlaizantion stage] teaches assigning groups based off of the task representations for each client which can be interpreted as characteristics in [Section 4.1-the personalization stage] personalized models are assigned to each client which teaches this limitation)
Regarding Claim 9, Yang also teaches wherein the shared characteristics of the users comprise a plurality of determined items of demographic information that the users have in common. ([Section 4.1-The initialization stage] teaches grouping different clients into groups based off of their task representation, which teaches this limitation)
Regarding Claim 10, See the analysis of Claim 1
Regarding Claim 16, See the analysis of Claim 8
Regarding Claim 17, See the analysis of Claim 9
Regarding Claim 18, See the analysis of Claim 1
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim’s 2-5, 11-14, and 19-20 are rejected under U.S.C. 103 as being unpatentable over Yang et al (NPL: Personalized Federated Learning on Non-IID Data via Group-based Meta-learning) (“Yang”), in view of Xie ex al (CN 115840900) (“Xie”)
Regarding Claim 2, Yang teaches all the limitations of Claim 2
Yang does not teach further comprising: receiving updated model parameters, from the subsets of the communication devices of the one or more groups, in response to the subsets of the communication devices determining items of local training content generated by the subsets of the communication devices implementing a respective group specific machine learning model assigned to at least one group of the one or more groups.
However, Xie does teach further comprising: receiving updated model parameters, from the subsets of the communication devices of the one or more groups, in response to the subsets of the communication devices determining items of local training content generated by the subsets of the communication devices implementing a respective group specific machine learning model assigned to at least one group of the one or more groups. ([0013] teaches a parameter sever receiving a gradient uploaded by all clients that is used to generate a weight vector for each group, that is being interpreted as receiving updated model parameters from communication devices of different groups, and [0014-0015] teaches sending those parameters to different group servers to execute the federal learning training and that teaches the rest of the limitation.)
Xie and Yang are analogous art because they both are centered around federated learning
It would have been obvious to a person skilled in the art before the effective filling date of the
claimed invention to combine Yang with the updated parameters technique of Xie. Doing so would improve the performance of local client models ([Xie-0069] “ Thirdly, the personalized federal learning method based on the self-adaptive clustering layering solves the problem of data statistics heterogeneity in federal learning and the problem of unbalanced performance of a global model and a local model in personalized federal learning, and achieves the technical effect of greatly improving the personalized performance of the local model of the client on the premise of not damaging global generalization capability.”)
Regarding Claim 3, Yang and Xie teaches all the limitations of Claim 2.
Xie also teaches further comprising: generating other machine learning models, wherein at least one of the other machine learning models are specifically tailored to the communication devices of the one or more groups in response to receiving the updated model parameters from the subsets of the communication devices of the one or more groups. ([0014] teaches receiving parameters and executing them on personalized federal learning training in the different groups which teaches this limitation. It is being interpreted. The generated personalized weight vector layer in [0013] that is sent out to the different client groups as model parameters is being interpreted as generating other machine learning models which teaches this limitation)
Regarding Claim 4, Yang and Xie teaches all the limitations of Claim 3.
Xie also teaches wherein: the other machine learning models are different machine learning models personalized for at least one user of a communication device of the subsets of the communication devices. ([0014] teaches personalized federal learning for different group servers, with those group servers containing different client groups, it can be reasonably interpreted that the machine learning models are personalized for at least one user of a communication device in that group which teaches this limitation
Regarding Claim 5 Yang and Xie teaches all the limitations of Claim 3.
Xie also teaches wherein: the other machine learning models are different machine learning models personalized for at least one user of a communication device of the subsets of the communication devices. ([0014] The group model parameters are being sent to different group servers which teaches this limitation)
Regarding Claim 11, See the analysis of Claim 2
Regarding Claim 12, See the analysis of Claim 3
Regarding Claim 13, See the analysis of Claim 4
Regarding Claim 14, See the analysis of Claim 5
Regarding Claim 19, See the analysis of Claim 2
Regarding Claim 20, See the analysis of Claim 3
Claim 6 is rejected under U.S.C. 103 as being unpatentable over Yang et al (NPL: Personalized Federated Learning on Non-IID Data via Group-based Meta-learning) (“Yang”), in view of Xie et al (CN 115840900) (“Xie”), and Ji et al (NPL: Learning Private Neural Language Modeling with Attentive Aggregation) (“Ji”)
Regarding Claim 6, Yang and Xie teaches all the limitations of Claim 3
Yang does not teach further comprising: determining at least one perplexity value indicating an improvement of group personalized federated learning associated with the one or more groups in relation to one or more other types of federated learning in response to implementing the at least one of the other machine learning models.
However, Ji does teach further comprising: determining at least one perplexity value indicating an improvement of group personalized federated learning associated with the one or more groups in relation to one or more other types of federated learning in response to implementing the at least one of the other machine learning models. ([Fig 2] teaches testing perplexity throughout different rounds of communications on a different number of clients selected for federated aggregation. The visual of the perplexity values in the graph teaches this limitation)
Yang, Xie, and Ji are analogous art because they all deal with federated learning
It would have been obvious to a person skilled in the art before the effective filling date of the
claimed invention to combine Yang with the updated parameters technique of Xie and the perplexity visualization of Ji. Doing so would improve performance of federated learning by minimizing distance between the server and client models ([Ji-Abstract] “Abstract—Mobile keyboard suggestion is typically regarded as a word-level language modeling problem. Centralized machine learning techniques require the collection of massive user data for training purposes, which may raise privacy concerns in relation to users’ sensitive data. Federated learning (FL) provides a promising approach to learning private language modeling for intelligent personalized keyboard suggestions by training models on distributed clients rather than training them on a central server. To obtain a global model for prediction, existing FL algorithms simply average the client models and ignore the importance of each client during model aggregation. Furthermore, there is no optimization for learning a well-generalized global model on the central server. To solve these problems, we propose a novel model aggregation with an attention mechanism considering the contribution of client models to the global model, together with an optimization technique during server aggregation. Our proposed attentive aggregation method minimizes the weighted distance between the server model and client models by iteratively updating parameters while attending to the distance between the server model and client models. Experiments on two popular language modeling datasets and a social media dataset show that our proposed method outperforms its counterparts in terms of perplexity and communication cost in most settings of comparison.”)
Claim 7 and 15 are rejected under U.S.C. 103 as being unpatentable over Yang et al (NPL: Personalized Federated Learning on Non-IID Data via Group-based Meta-learning) (“Yang”), in view of Albaseer et al (NPL: Threshold-Based Data Exclusion Approach for Energy-Efficient Federated Edge Learning) (“Albaseer”)
Regarding Claim 7, Yang teaches all the limitations of Claim 1.
Yang does not teach the receiving the one or more model parameters further comprises determining that the local training data satisfies a predetermined threshold of training data.
However, Albaseer does teach the receiving the one or more model parameters further comprises determining that the local training data satisfies a predetermined threshold of training data. ([Abstract] teaches selecting samples based on a predetermined threshold that they think would enhance the model quality and using that as a training algorithm. The sample selection here is being interpreted as model parameters which teaches this limitation)
Yang and Albaseer are analogous are because they both deal with federated learning
It would have been obvious to a person skilled in the art before the effective filling date of the claimed invention to combine Yang with the threshold selection process of Albaseer. Doing so would allow for more efficient training ([Albaseer-Abstract] “Abstract—Federated edge learning (FEEL) is a promising distributed learning technique for next-generation wireless networks. FEEL preserves the user’s privacy, reduces the communication costs, and exploits the unprecedented capabilities of edge devices to train a shared global model by leveraging a massive amount of data generated at the network edge. However, FEEL might significantly shorten energy-constrained participating devices’ lifetime due to the power consumed during the model training round. This paper proposes a novel approach that endeavors to minimize computation and communication energy consumption during FEEL rounds to address this issue. First, we introduce a modified local training algorithm that intelligently selects only the samples that enhance the model’s quality based on a predetermined threshold probability. Then, the problem is formulated as joint energy minimization and resource allocation optimization problem to obtain the optimal local computation time and the optimal transmission time that minimize the total energy consumption considering the worker’s energy budget, available bandwidth, channel states, beamforming, and local CPU speed. After that, we introduce a tractable solution to the formulated problem that ensures the robustness of FEEL. Our simulation results show that our solution substantially outperforms the baseline FEEL algorithm as it reduces the local consumed energy by up to 79%.”)
Regarding Claim 15, See the analysis of Claim 7
Conclusion
The prior art made of record and relied upon is considered to applicant’s disclosure
Xiaoyan Duan US 20240396811 A1 (2022-09-15) ([Abstract] “A federated learning group processing, a device and a functional entity are provided, where the federated learning group processing method includes: obtaining the characteristic information of the federated learning (FL) group; determining the second functional entity according to the characteristic information of the FL group; adding the second functional entity to the FL group”)
Samek et al US 20220108177 A1 (2022-04-07) ([Abstract] “A concept for Federated Learning which is more efficient and/or robust is presented. Beyond this, concepts for specifying clients and/or measuring training data similarities in a manner more suitable for being applied in Federated Learning environments, are described.”)
Hyukjae Jang US 20210390152 A1 (2021-12-16) ([Abstract] “Disclosed is a method, system, and non-transitory computer-readable record medium for providing a multi-model through federated learning using personalization. The method includes classifying users into a plurality of groups; and generating a prediction model for a service as a multi-model through federated learning for each of the plurality of groups”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to URIAH V MOORE whose telephone number is (571)384-8341. The examiner can normally be reached Monday-Friday 8am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mariela Reyes can be reached at (571)270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from 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. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/URIAH VENDELL MOORE/Examiner, Art Unit 2142 /Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
Prosecution Insights