DETAILED ACTION
This action is responsive to the amendment filed on 04/21/2026. Claims 1-17 and 30 are pending in the case. Claims 1, 7-8, and 30 are currently amended. Claims 1 and 30 are independent claims.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/21/2026 has been entered.
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 .
Priority
Acknowledgment is made of applicant's claim for domestic priority based on PCT application number PCT/EP2020/066235 filed on 06/11/2020.
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.
Claims 1-17 and 30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1 Statutory Category: Claim 1 is directed to a method, which falls under one of the four statutory categories.
Step 2A Prong 1 Judicial exception: Claim 1 recites, in part, “…encoding the representative dataset …, thereby producing encoded data”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mathematical calculation, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP §2106.04(a)(2)(I)(C). Further, the claim recites: “…clustering the encoded data into a number of clusters, thereby producing cluster centroids”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mathematical concept, see MPEP §2106.04(a)(2)(I). Further, the claim recites: “…grouping each worker node of the plurality of worker nodes into a group of a plurality of groups based on the information from each worker node of the plurality of worker nodes, wherein, for each worker node, the information received from the worker node indicates characteristics of a data distribution of the worker node”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion), in this case evaluation. See MPEP § 2106.04(a)(2)(III). Further, the claim recites: “…subgrouping worker nodes within a first group of the plurality of groups into subgroups based on characteristics of a worker neural network model of each worker node from the first group of the plurality of groups”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion), in this case evaluation. See MPEP § 2106.04(a)(2)(III). Further, the claim recites: “…averaging the worker neural network models of worker nodes within one of the subgroups to generate a subgroup average model”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mathematical calculation, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP §2106.04(a)(2)(I)(C).
Step 2A Prong 2 Integration into a Practical Application: This judicial exception is not integrated into a practical application. In particular the claim recites: “a machine learning system comprising a master node and a plurality of worker nodes, wherein each of the plurality of worker nodes has a local dataset”. This limitation 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). Further, the claim recites: “the master node obtaining a reference dataset”. This limitation is an additional element that amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception. Therefore, this limitation is insignificant extra-solution activity to the judicial exception, see MPEP §2106.05(g). Further, the claim recites: “the master node training an autoencoder (AE) model using the representative dataset, wherein the AE comprises an encoder part”. This limitation is an additional element that amounts to insignificant extra-solution activity to the judicial exception, see MPEP §2106.05(g). Further, the claim recites: “…using the encoder part of the AE…”. This limitation is an additional element that amounts to generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Further, the claim recites several of the steps being performed by “the master node”. These limitations are additional elements that amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). Further, the claim recites: “the master node providing to the worker nodes the encoder part of the AE and an encrypted version of the cluster centroids”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites: “the master node receiving information from each worker node of the plurality of worker nodes, the information from each worker node comprising a result of the worker node encoding its local dataset using the encoder part of the AE to generate encoded local data and clustering its encoded local data using the cluster centroids”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites: “the master node distributing the subgroup average model”. This limitation is an additional element that amounts to a post-solution step for transmitting data output – a nominal addition to the claim that does not meaningfully limit the claim, thus this is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g).
Step 2B Significantly More: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element: “a machine learning system comprising a master node and a plurality of worker nodes, wherein each of the plurality of worker nodes has a local dataset” generally links the use of the judicial exception to a particular technological environment or field of use. Elements that merely generally link the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Further, the additional element: “the master node obtaining a reference dataset” is insignificant extra-solution activity to the judicial exception and is directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). Further, the additional element: “the master node training an autoencoder (AE) model using the representative dataset, wherein the AE comprises an encoder part” is insignificant extra-solution activity to the judicial exception, and is well‐understood, routine, and conventional as taught by activity is supported under Berkheimer Option 2, Challita et al., U.S. Patent Application Publication No. 20240107429, Paragraph 0020, Lines 1-3, “As is apparent for a person skilled in the art, training of a neural network and/or an autoencoder can be done according to various, commonly known methods”. Further, the additional element: “…using the encoder part of the AE…” amounts to generally linking the use of the judicial exception to a particular technological environment or field of use. Elements that merely amount to generally linking the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Further, the additional element of several of the steps being performed by “the master node” amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. Elements that merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process cannot provide an inventive concept. Further, the additional elements: “the master node providing to the worker nodes the encoder part of the AE and an encrypted version of the cluster centroids” and “the master node receiving information from each worker node of the plurality of worker nodes, the information from each worker node comprising a result of the worker node encoding its local dataset using the encoder part of the AE to generate encoded local data and clustering its encoded local data using the cluster centroids” are insignificant extra-solution activity to the judicial exception and are directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). Further, the claim recites the additional element: “the master node distributing the subgroup average model” that amounts to adding insignificant extra-solution activity to the judicial exception. Further, this element is directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is incorporated, and further, the claim recites: “after the grouping of the worker nodes, first determining if there is a substantial change in any local dataset of a worker node from among the plurality of worker nodes; wherein if there is no substantial change in any of the local datasets, the method proceeds to the subgrouping; or if there is a substantial change in any of the local datasets, the grouping is repeated”. This limitation recites mental processes in addition to those identified in the rejection of the parent claim, and thus the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 3, the rejection of claim 1 is incorporated, and further, the claim recites: “after the subgrouping of the worker nodes, second determining if there is a substantial change in any local data sets of the plurality of worker nodes; wherein if there is no substantial change in any of the local datasets, the subgrouping is repeated; or if there is a substantial change in any of the local datasets, the method is repeated from the grouping”. This limitation recites mental processes in addition to those identified in the rejection of the parent claim, and thus the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 4, the rejection of claim 1 is incorporated, and further, the claim recites: “updating the worker neural network model of each worker node of the subgroup with the subgroup average model”. This limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). Elements that amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 5, the rejection of claim 1 is incorporated, and further, the claim recites: “after the grouping, averaging the worker neural network model of each worker node of a group of the plurality of groups to generate a group average model”. This limitation recites mathematical concepts in addition to those identified in the rejection of the parent claim. Thus, the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 6, the rejection of claim 5 is incorporated, and further, the claim recites: “updating the worker neural network model of each worker node of the group with the corresponding group average model”. This limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). Elements that amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 1 is incorporated, and further, the claim recites: “wherein the worker nodes of the first group comprise data distributions with similar characteristics”. This limitation is a continuation of the “…grouping each worker node of the plurality of worker nodes into a group of a plurality of groups based on the information from each worker node of the plurality of worker nodes, wherein, for each worker node, the information received from the worker node indicates characteristics of a data distribution of the worker node” limitation identified as an abstract idea in the rejection of the parent claim. Thus, the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 8, the rejection of claim 1 is incorporated, and further, the claim recites: “wherein the worker nodes of the subgroup used for generating the subgroup average model comprise neural network models with similar characteristics”. This limitation is a continuation of the “…subgrouping worker nodes within a first group of the plurality of groups into subgroups based on characteristics of a worker neural network model of each worker node from the first group of the plurality of groups” limitation identified as an abstract idea in the rejection of the parent claim. Thus, the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 9, the rejection of claim 1 is incorporated, and further, the claim recites: “wherein the grouping and/or the subgrouping is performed using a clustering algorithm”. This limitation is an additional element that amounts to generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Elements that merely amount to generally linking the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 10, the rejection of claim 1 is incorporated, and further, the claim recites: “wherein a representative data set is used to perform the grouping”. This limitation is a continuation of the “…grouping each worker node of the plurality of worker nodes into a group of a plurality of groups based on the information from each worker node of the plurality of worker nodes, wherein, for each worker node, the information received from the worker node indicates characteristics of a data distribution of the worker node” limitation identified as an abstract idea in the rejection of the parent claim, thus the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 11, the rejection of claim 10 is incorporated, and further, the claim recites: “the representative dataset is encoded … to generate encoded data”. This limitation recites mathematical concepts in addition to those identified in the rejection of the parent claim. Thus, the claim recites a judicial exception.
Further, the claim recites: “in the grouping, an encoder model is trained using the representative data set”. This limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). Elements that merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process cannot provide an inventive concept. Further, the claim recites: “using the encoder model”. This limitation is an additional element that amounts to generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Elements that merely amount to generally linking the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 12, the rejection of claim 11 is incorporated, and further, the claim recites: “determine clusters” and “a cluster representative for each cluster is identified, wherein each cluster representative corresponds to a group of the plurality of groups”. These limitations recite mental processes in addition to those identified in the rejection of the parent claim, thus the claim recites a judicial exception.
Further, the claim recites: “in the grouping, a clustering algorithm is run on the encoded data”. This limitation is an additional element that amounts to generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Elements that merely amount to generally linking the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 13, the rejection of claim 12 is incorporated, and further, the claim recites: “wherein, in the grouping, the method further comprises determining to which group a worker node belongs by encoding the local data set of a worker node using the encoder model and using the cluster representative for each cluster”. This limitation is a continuation of the “…grouping each worker node of the plurality of worker nodes into a group of a plurality of groups based on the information from each worker node of the plurality of worker nodes, wherein, for each worker node, the information received from the worker node indicates characteristics of a data distribution of the worker node” limitation identified as an abstract idea in the rejection of the parent claim, thus the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 14, the rejection of claim 1 is incorporated, and further, the claim recites: “wherein the subgrouping further comprises: computing an inverse of a neural network of each of the worker nodes to generate a backward neural network”. This limitation recites mathematical concepts in addition to those identified in the parent claim. Further, the claim recites: “generate a set of representations”. This limitation recites mathematical concepts in addition to those identified in the rejection of the parent claim. Further, the claim recites “generate a set of predicted responses”. This limitation recites mathematical concepts in addition to those identified in the rejection of the parent claim. Further, the claim recites: “determining a loss value between the set of responses and the set of predicted responses”. This limitation recites mathematical concepts in addition to those identified in the rejection of the parent claim. Further, the claim recites: “group the worker nodes into subgroups”. This limitation recites mental processes in addition to those identified in the rejection of the parent claim.
Further, the claim recites: “obtaining a set of responses using the representative dataset”, “feeding the set of responses into the backward neural network”, and “feeding the set of representations into the neural network”. These limitations are additional elements that amount to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, these limitations are directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). Further, the claim recites: “running a clustering algorithm on the loss values”. This limitation 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). Elements that amount to generally linking the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 15, the rejection of claim 1 is incorporated, and further, the claim recites: “wherein each of the worker nodes comprise the same neural network architecture for at least a portion of the neural network of each worker node”. This limitation is an additional element that amounts to generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Elements that merely generally link use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 16, the rejection of claim 1 is incorporated, and further, the claim recites: “wherein the dataset of the worker node is at least one of: time series data generated from network performance measurements, counters, sensor data from IoT devices, temperature, vibration, data from computer/cloud deployments, CPU usage, memory usage”. This limitation 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). Elements that amount to generally linking the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 17, the rejection of claim 1 is incorporated, and further, the claim recites: “wherein at least one worker node of the plurality of worker nodes is grouped into multiple groups of the plurality of groups”. This limitation is a continuation of the “…grouping each worker node of the plurality of worker nodes into a group of a plurality of groups based on the information from each worker node of the plurality of worker nodes, wherein, for each worker node, the information received from the worker node indicates characteristics of a data distribution of the worker node” limitation identified as an abstract idea in the rejection of the parent claim. Thus, the claim recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 30:
Step 1 Statutory Category: Claim 30 is directed to a machine, which falls under one of the four statutory categories.
Step 2A Prong 1 Judicial exception: Claim 30 recites, in part, “encoding the representative dataset …, thereby producing encoded data”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mathematical calculation, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP §2106.04(a)(2)(I)(C). Further, the claim recites: “clustering the encoded data into a number of clusters, thereby producing cluster centroids”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mathematical concept, see MPEP §2106.04(a)(2)(I). Further, the claim recites: “grouping each worker node of the plurality of worker nodes into a group of a plurality of groups based on the information from each worker node of the plurality of worker nodes, wherein, for each worker node, the information received from the worker node indicates characteristics of a data distribution of the worker node”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion), in this case evaluation. See MPEP § 2106.04(a)(2)(III). Further, the claim recites: “subgrouping worker nodes within a first group of the plurality of groups into subgroups based on characteristics of a worker neural network model of each worker node from the first group of the plurality of groups”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion), in this case evaluation. See MPEP § 2106.04(a)(2)(III). Further, the claim recites: “averaging the worker neural network models of worker nodes within one of the subgroups to generate a subgroup average model”. This limitation, under the broadest reasonable interpretation, covers the recitation of a mathematical calculation, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP §2106.04(a)(2)(I)(C).
Step 2A Prong 2 Integration into a Practical Application: This judicial exception is not integrated into a practical application. In particular the claim recites: “a master node configured to communicate with a plurality of worker nodes in a machine learning system, wherein each of the plurality of worker nodes has a local dataset”. This limitation 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). Further, the claim recites: “processing circuitry and a non-transitory machine-readable medium storing instructions”. This limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). Further, the claim recites: “obtaining a reference dataset”. This limitation is an additional element that amounts to mere data gathering. It is necessary to acquire the data in order to use the recited judicial exception. Therefore, this limitation is insignificant extra-solution activity to the judicial exception, see MPEP §2106.05(g). Further, the claim recites: “training an autoencoder (AE) model using the representative dataset, wherein the AE comprises an encoder part”. This limitation is an additional element that amounts to insignificant extra-solution activity to the judicial exception, see MPEP §2106.05(g). Further, the claim recites: “…using the encoder part of the AE…”. This limitation is an additional element that amounts to generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). Further, the claim recites the method being performed by “the master node”. This limitation is an additional element that amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §2106.05(f). Further, the claim recites: “providing to the worker nodes the encoder part of the AE and an encrypted version of the cluster centroids”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites: “receiving information from each worker node of the plurality of worker nodes, the information from each worker node comprising a result of the worker node encoding its local dataset using the encoder part of the AE to generate encoded local data and clustering its encoded local data using the cluster centroids”. This limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g). Further, the claim recites: “distributing the subgroup average model”. This limitation is an additional element that amounts to a post-solution step for transmitting data output – a nominal addition to the claim that does not meaningfully limit the claim, thus this is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §2106.05(g).
Step 2B Significantly More: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element: “a master node configured to communicate with a plurality of worker nodes in a machine learning system, wherein each of the plurality of workers nodes has a local dataset” generally links the use of the judicial exception to a particular technological environment or field of use. Elements that merely generally link the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Further, the claim recites the additional element: “processing circuitry and a non-transitory machine-readable medium storing instructions” that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. Elements that merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process cannot provide an inventive concept. Further, the additional element: “obtaining a reference dataset” is insignificant extra-solution activity to the judicial exception and is directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). Further, the additional element: “training an autoencoder (AE) model using the representative dataset, wherein the AE comprises an encoder part” is insignificant extra-solution activity to the judicial exception, and is well‐understood, routine, and conventional as taught by activity is supported under Berkheimer Option 2, Challita et al., U.S. Patent Application Publication No. 20240107429, Paragraph 0020, Lines 1-3, “As is apparent for a person skilled in the art, training of a neural network and/or an autoencoder can be done according to various, commonly known methods”. Further, the additional element: “…using the encoder part of the AE…” amounts to generally linking the use of the judicial exception to a particular technological environment or field of use. Elements that merely amount to generally linking the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. Further, the additional element of the method being performed by “the master node” amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. Elements that merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer in its ordinary capacity as a tool to perform an existing process cannot provide an inventive concept. Further, the additional elements: “providing to the worker nodes the encoder part of the AE and an encrypted version of the cluster centroids” and “receiving information from each worker node of the plurality of worker nodes, the information from each worker node comprising a result of the worker node encoding its local dataset using the encoder part of the AE to generate encoded local data and clustering its encoded local data using the cluster centroids” are insignificant extra-solution activity to the judicial exception and are directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). Further, the claim recites the additional element: “distributing the subgroup average model” that amounts to adding insignificant extra-solution activity to the judicial exception. Further, this element is directed to receiving or transmitting data over a network which courts have recognized as well-understood, routine, and conventional when they are claimed in a generic manner, see MPEP §2106.05(d)(II). The claim is not patent eligible.
Response to Arguments
Applicant’s amendments to claim 7 with respect to the 35 U.S.C. 112(b) indefiniteness rejections to the claims have been fully considered, and overcome the rejections set forth in the nonfinal office action dated 03/03/2026. Consequently, the 35 U.S.C. 112(b) indefiniteness rejections to the claims have been withdrawn.
Applicant’s arguments regarding the 35 U.S.C. 101 rejections of the claims have been fully considered but are unpersuasive.
Argument 1:
Applicant argues, on page 8, final two paragraphs – page 9, paragraphs 1-2 of the response, that claim 1 does not recite a mental process because the steps cannot be practically performed in the human mind.
Examiner Response:
Examiner respectfully disagrees. Applicant specifically points to “the master node obtaining a reference dataset”, “the master node training an [AE] model using the representative dataset”, “the master node providing to the worker nodes the encoder part of the AE and an encrypted version of the cluster centroids”, “the master node receiving information from each worker node of the plurality of worker nodes”, and “the master node distributing the subgroup average model”. These limitations were not identified as abstract ideas during the 35 U.S.C. 101 analysis above, for a more in depth analysis of these limitations please see the updated 35 U.S.C. 101 rejection above. Further, applicant points to “the master node encoding the representative dataset using the encoder part of the AE”, “the master node clustering the encoded data into a number of clusters”, and “the master node averaging the worker neural network models of worker nodes within one of the subgroups to generate a subgroup average model”. These limitations were not identified as a mental process in the 35 U.S.C. 101 rejection above, but rather mathematical concepts; for a more in depth analysis of these limitations please see the updated 35 U.S.C. 101 rejection above. Further, applicant points to “the master node grouping each worker node of the plurality of worker nodes into a group of a plurality of groups based on the result from each worker node of the plurality of worker nodes”. With regard to this limitation, a person could, with the use of pen and paper, group nodes based on information regarding those items; a person could reasonably review results and exercise judgment in their head using pattern recognition skills to group the nodes, and using a pen and paper could visualize the nodes and circle them to place them into groups. Further, applicant points to “the master node subgrouping worker nodes within a first group of the plurality of groups into subgroups based on characteristics of a worker neural network model of each worker node from the first group of the plurality of groups”. With regard to this limitation, a person could, with the use of pen and paper, identify characteristics of worker neural network models, by using their judgement in their head, and subgroup worker nodes within a first group ingo a subgroup, and using pen and paper, could visualize the nodes and circle them to place them into subgroups.
Argument 2:
Applicant next argues, on page 10 – page 11, paragraph 1 of the response, that the additional elements integrate the abstract ideas into a practical application.
Examiner Response:
Examiner respectfully disagrees. Applicant specifically points to the “grouping”, “subgrouping”, and “averaging” steps; however, an inventive concept cannot be furnished by the unpatentable abstract idea itself, see MPEP 2106.05(I). An improvement to the “grouping”, “subgrouping”, and “averaging” steps may be an improvement in an abstract idea, but not an improvement in the functioning of a computer, as a computer, see MPEP 2106.05(a)(II).
Argument 3:
Applicant next argues, in page 11, paragraphs 2-3 of the response, that the claimed invention enables “dynamically (re-)grouping workers in a system”.
Examiner Response:
Examiner respectfully disagrees. While claim 1 does recite steps of grouping and subgrouping workers in a system, there are no limitations to suggest “dynamically (re-)grouping” the workers. Further, an improvement in grouping the workers may be an improvement in an abstract idea, but not an improvement in the functioning of a computer, as a computer, see MPEP 2106.05(a)(II). Further, applicant argues the advantage of the claimed invention is that it enables “performance of the models [to] be improved by grouping data samples […] which have similar characteristics”. An inventive concept cannot be furnished by the unpatentable abstract idea (e.g. “grouping”) itself, see MPEP 2106.05(I).
Argument 4:
Applicant next argues, in page 12, paragraph 3 – page 13 of the response, that the office has not provided evidence proving that the features recited in amended claim 1 are well-understood, routine, or conventional.
Examiner Response:
Examiner respectfully disagrees. For each additional element identified in the 35 U.S.C. 101 rejection above, examiner has provided citations to the MPEP as to how each element is considered, please see MPEP 2106.05(I)(A) – “Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:”. Further, for elements considered “Adding insignificant extra-solution activity to the judicial exception” additional evidence for each limitation has been provided including citations to MPEP 2106.05(II) and Berkheimer Option 2 evidence. For a more in depth analysis of each limitation, please see the updated 35 U.S.C. 101 rejection above.
Applicant's arguments regarding the remainder of the claims rely upon the arguments asserted with respect to the independent claims, and are thus unpersuasive.
Conclusion
Claims 1-17 and 30 have been rejected under 35 U.S.C. 101 only. A complete prior art search was performed for these claims; however, no prior art was uncovered the disclose or fairly suggest the following claimed features:
After detailed search, the cited arts, neither alone nor in combination, teach the claimed subject matter of claims 1 and 30:
…[the master node] encoding the representative dataset using the encoder part of the AE, thereby producing encoded data;
[the master node] clustering the encoded data into a number of clusters, thereby producing cluster centroids;
[the master node] providing to the worker nodes the encoder part of the AE and an encrypted version of the cluster centroids;
[the master node] receiving information from each worker node of the plurality of worker nodes, the information from each worker node comprising a result of the worker node encoding its local dataset using the encoder part of the AE to generate encoded local data and clustering its encoded local data using the cluster centroids;
[the master node] grouping each worker node of the plurality of worker nodes into a group of a plurality of groups based on the information from each worker node of the plurality of worker nodes, wherein, for each worker node, the information received from the worker node indicates characteristics of a data distribution of the worker node…
The closest prior art of record includes:
Sattler et al., Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints, 10/4/2019, https://arxiv.org/pdf/1910.01991 discloses clustered federated learning to group the client population into clusters with jointly trainable data distributions. However, Sattler does not teach providing to the worker nodes the encoder part of the AE and an encrypted version of the cluster centroids; the master node receiving information from each worker node of the plurality of worker nodes, the information from each worker node comprising a result of the worker node encoding its local dataset using the encoder part of the AE to generate encoded local data and clustering its encoded local data using the cluster centroids; and the master node grouping each worker node of the plurality of worker nodes into a group of a plurality of groups based on that information as required by the claims.
Huang et al., Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records, Journal of Biomedical Informatics, Volume 99, November 2019, https://doi.org/10.1016/i,jbi.2019.103291 discloses a community-based federated machine learning method to cluster distributed data into clinically meaningful communities that captured similar diagnoses and geological locations, and learnt one model for each community. However, Huang does not teach the master node generating encoded data and generating clusters using the encoded data as required by the claims.
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/M.C.S./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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