DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to amendments filed September 9th, 2025, in which claims 1, 8 and 15 have been amended. No claims have been cancelled nor added. The amendments have been entered, and claims 1-20 are currently pending in the case. Claims 1, 8 and 15 are independent claims.
Specification
The use of the term “BLUETOOTH” ¶12, “WI-FI” ¶12 which are a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The following sections follow the 2019 PEG guidelines for analyzing subject matter eligibility.
Regarding claim 1:
Step 1: Claim 1 is directed to a system, therefore it falls under the statuary category of machine.
Step 2A Prong 1: The claim recites, in part:
“determine a loss reduction for each received data set of the plurality of data sets, representing a loss reduction since a previous local loss value included in a previous received data set corresponding to the given vehicle;” This encompasses the mental determination of a loss reduction value by comparing previously observed local loss values.
“determine whether the loss reduction for each received data set of the plurality of data sets exceeds a predefined threshold cutoff value that varies over successive rounds of training and which results in exclusion of at least a plurality of data sets having a positive loss reduction but which positive loss reduction is insufficient to exceed the predefined threshold cutoff value” This encompasses the mental determination of whether an observed loss value for observed data exceeds a predefined threshold value that varies, such that some observed values fail to exceed the threshold, despite a positive observed loss. Further, this limitation is a mathematical concept. Further, this limitation is a mathematical concept.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows:
“a processor configured to:” the limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2). “receive a plurality of data sets relating to differently trained versions of a global machine learning model, from a plurality of vehicles, the data sets including at least a present local loss value experienced by a current version of the global model executing on a given vehicle for which a data set of the plurality of data sets was received;”, “train the global model using federated learning and based on the data sets of the plurality of data sets for which the loss reduction exceeds the predefined cutoff value.” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The limitations
“a processor configured to:” the 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. See MPEP § 2106.05(f)(1). Further, “receive a plurality of data sets relating to differently trained versions of a global machine learning model, from a plurality of vehicles, the data sets including at least a present local loss value experienced by a current version of the global model executing on a given vehicle for which a data set of the plurality of data sets was received;” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d. See MPEP § 2106.05(d)(II). Further, “train the global model using federated learning and based on the data sets of the plurality of data sets for which the loss reduction exceeds the predefined cutoff value.” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is well‐understood, routine, and conventional as taught by Vijaya et al. (WO 2021213626 A1), page 2, lines 8-11 “A typical structure of a federated learning framework is as shown in the Fig.1. Thus, Fig. 1 shows a typical federated learning system, wherein a top node is the global model, which is trained from using client models such as UEs, loT capable devices, etc.”. See MPEP § 2106.05(d).
Regarding claim 2, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part: “wherein the loss reduction is an average loss reduction over a plurality of reporting cycles, wherein a reporting cycle represents an iteration of the data set, including the present local loss value, received for the given vehicle.” This encompasses the mental process of averaging observed data over time.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
Step 2B: The claim does not contain significantly more than the judicial exception.
Regarding claim 3, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“set a mask vector for each vehicle of the plurality of vehicles for which the loss reduction does not exceed the predefined cutoff value;” This encompasses the mental setting of a mask vector for data that does not exceed the threshold.
“modified by the mask vector, such that data sets from each vehicle for which the loss reduction does not exceed the predefined cutoff value are masked out when training the global model.” This encompasses the mental process of excluding certain data from further steps, when it does not meet certain criteria.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows:
“wherein the training is based on the received plurality of data sets” The limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The limitation
“wherein the training is based on the received plurality of data sets” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is well‐understood, routine, and conventional as taught by the background section of specification of the application ¶3 “Machine learning models often utilize large data sets gathered from thousands of sources.”.
Regarding claim 4, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“wait for all of the plurality of vehicles to complete at least one reporting cycle since a prior reporting cycle before training the global model, wherein a reporting cycle represents an iteration of the data set, including the present local loss value, received for the given vehicle.” This encompasses the mental process of waiting for all data to be reported before going on to a next step.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “the processor is configured to” The limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
Step 2B: The claim does not contain significantly more than the judicial exception. The limitation “the processor is configured to” 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. See MPEP § 2106.05(f)(1).
Regarding claim 5, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim is a continuation of the abstract idea identified in the parent claim.
“wait for at least one of a predefined total number or percentage of the plurality of vehicles to complete at least one reporting cycle since a prior reporting cycle before training the global model, wherein a reporting cycle represents an iteration of the data set, including the present local loss value, received for the given vehicle.” This encompasses the mental process of waiting for some predetermined amount of data to be reported before going on to a next step.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “the processor is configured to” The limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
Step 2B: The claim does not contain significantly more than the judicial exception. The limitation “the processor is configured to” 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. See MPEP § 2106.05(f)(1).
Regarding claim 6, the rejection of claim 5 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“determine whether at least one of a total number or percentage of vehicles for which the loss reduction exceeds the predefined threshold cutoff value exceeds a predefined value representing sufficient training data;” This encompasses the mental determination of whether observed loss values exceed a threshold of sufficient training data.
“responsive to the at least one of the total number or percentage not exceeding the predefined value, wait for data sets to be received from an additional number or additional percentage of the plurality of vehicles.” The encompasses the mental process of waiting for data to observe from further sources.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
Step 2B: The claim does not contain significantly more than the judicial exception.
Regarding claim 7, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“determine whether at least one of a total number or percentage of vehicles for which the loss reduction exceeds the predefined threshold cutoff value exceeds a predefined value representing sufficient training data;” This encompasses the mental determination of whether observed loss values exceed a threshold of sufficient training data.
“responsive to the at least one of the total number or percentage not exceeding the predefined value, decrement the threshold cutoff value to include, in the training, data sets of additional vehicles for which the loss reduction did not exceed the threshold cutoff value prior to decrementing the cutoff value.” This encompasses the mental process of decrementing the threshold value to include further datasets to observe.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
Step 2B: The claim does not contain significantly more than the judicial exception.
Regarding claim 8:
Step 1: Claim 1 is directed to a method, therefore it falls under the statuary category of a process.
Step 2A Prong 1: The claim recites, in part:
“determining a loss reduction for each received data set of the plurality of data sets, representing a loss reduction since a previous local loss value included in a previous received data set corresponding to the given vehicle;” This encompasses the mental determination of a loss reduction value by comparing previously observed local loss values.
“determine whether the loss reduction for each received data set of the plurality of data sets exceeds a predefined threshold cutoff value that varies over successive rounds of training and which results in exclusion of at least a plurality of data sets having a positive loss reduction but which positive loss reduction is insufficient to exceed the predefined threshold cutoff value” This encompasses the mental determination of whether an observed loss value for observed data exceeds a predefined threshold value that varies, such that some observed values fail to exceed the threshold, despite a positive observed loss. Further, this limitation is a mathematical concept. Further, this limitation is a mathematical concept.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows:
“receiving a plurality of data sets relating to differently trained versions of a global machine learning model, from a plurality of vehicles, the data sets including at least a present local loss value experienced by a current version of the global model executing on a given vehicle for which a data set of the plurality of data sets was received;”, “training the global model using federated learning and based on the data sets of the plurality of data sets for which the loss reduction exceeds the predefined cutoff value.” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The limitations
Further, “receiving a plurality of data sets relating to differently trained versions of a global machine learning model, from a plurality of vehicles, the data sets including at least a present local loss value experienced by a current version of the global model executing on a given vehicle for which a data set of the plurality of data sets was received;” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d. See MPEP § 2106.05(d)(II). Further, “training the global model using federated learning and based on the data sets of the plurality of data sets for which the loss reduction exceeds the predefined cutoff value.” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is well‐understood, routine, and conventional as taught by Vijaya et al. (WO 2021213626 A1), page 2, lines 8-11 “A typical structure of a federated learning framework is as shown in the Fig.1. Thus, Fig. 1 shows a typical federated learning system, wherein a top node is the global model, which is trained from using client models such as UEs, loT capable devices, etc.”. See MPEP § 2106.05(d).
Regarding claims 9-14:
The rejection of claim 8 is further incorporated, the rejection of claims 2-7 are equally applicable to claims 9-14, respectively.
Regarding claim 15:
Step 1: Claim 15 is directed to “A non-transitory storage medium”, therefore it falls under the statuary category of a manufacture.
Step 2A Prong 1: The claim recites, in part:
“determining a loss reduction for each received data set of the plurality of data sets, representing a loss reduction since a previous local loss value included in a previous received data set corresponding to the given vehicle;” This encompasses the mental determination of a loss reduction value by comparing previously observed local loss values.
“determine whether the loss reduction for each received data set of the plurality of data sets exceeds a predefined threshold cutoff value that varies over successive rounds of training and which results in exclusion of at least a plurality of data sets having a positive loss reduction but which positive loss reduction is insufficient to exceed the predefined threshold cutoff value” This encompasses the mental determination of whether an observed loss value for observed data exceeds a predefined threshold value that varies, such that some observed values fail to exceed the threshold, despite a positive observed loss. Further, this limitation is a mathematical concept. Further, this limitation is a mathematical concept.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows:
“receiving a plurality of data sets relating to differently trained versions of a global machine learning model, from a plurality of vehicles, the data sets including at least a present local loss value experienced by a current version of the global model executing on a given vehicle for which a data set of the plurality of data sets was received;”, “training the global model using federated learning and based on the data sets of the plurality of data sets for which the loss reduction exceeds the predefined cutoff value.” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The limitations
Further, “receiving a plurality of data sets relating to differently trained versions of a global machine learning model, from a plurality of vehicles, the data sets including at least a present local loss value experienced by a current version of the global model executing on a given vehicle for which a data set of the plurality of data sets was received;” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d. See MPEP § 2106.05(d)(II). Further, “training the global model using federated learning and based on the data sets of the plurality of data sets for which the loss reduction exceeds the predefined cutoff value.” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is well‐understood, routine, and conventional as taught by Vijaya et al. (WO 2021213626 A1), page 2, lines 8-11 “A typical structure of a federated learning framework is as shown in the Fig.1. Thus, Fig. 1 shows a typical federated learning system, wherein a top node is the global model, which is trained from using client models such as UEs, loT capable devices, etc.”. See MPEP § 2106.05(d).
Regarding claims 16-20:
The rejection of claim 15 is further incorporated, the rejection of claims 2-3, 5-7 are equally applicable to claims 16-20, respectively.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-8, 10-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Tuor et al. (US20210158099A1, cited in IDS filed on 07 December 2021) hereinafter Tuor in view of Xu et al. ("Accelerating Federated Learning for IoT in Big Data Analytics With Pruning, Quantization and Selective Updating", Xu et al.) hereinafter Xu in further view of Deng ("FAIR: Quality-Aware Federated Learning with Precise User Incentive and Model Aggregation", Deng et al., July 2021) hereinafter Deng.
Regarding claim 1, Tuor teaches A system comprising:
a processor configured to (Tuor, Claim 15 “execution by at least one of the one or more processors capable of performing a method, the method comprising:”):
receive a plurality of data sets relating to differently trained versions of a global machine learning model, from a plurality of vehicles, (Tuor, ¶34 “The federated exchange client 112 may be configured to prepare datasets that are to be exchanged for purposes of generating a global model. The federated exchange client 112 may receive the datasets from the further clients and/or programs that train the local models on the local data samples and generate packets of the datasets that are to be transmitted to a further processing component that generates the global model.”, here the “further clients” can be vehicles as shown in Tuor, Fig 4, 54N “automobile computer system”)
train the global model using federated learning and based on the data sets of the plurality of data sets (Tuor, ¶3 “The federated learning may be used to train a machine learning algorithm such as a global model (e.g., deep neural network) on a plurality of localized datasets”)
Tuor does not teach “the data sets including at least a present local loss value experienced by a current version of the global model executing on a given vehicle for which a data set of the plurality of data sets was received;
determine a loss reduction for each received data set of the plurality of data sets, representing a loss reduction since a previous local loss value included in a previous received data set corresponding to the given vehicle;
determine whether the loss reduction for each received data set of the plurality of data sets exceeds a predefined threshold cutoff value; and
for which the loss reduction exceeds the predefined cutoff value.”
However, Xu teaches the data sets including at least a present local loss value experienced by a current version of the global model executing on a given client for which a data set of the plurality of data sets was received (Xu, page 38459-38460, col 2, section III/A “• Step 2 (Training and updating local model): In round t, based on the global model
M
G
t
, client c updates the local model parameters
M
c
t
using its local data. Specifically, it aims to obtain optimal parameters
M
c
t
that minimize the loss function
L
(
M
c
t
)
. Then, client c uploads the updated local parameters to S.
PNG
media_image1.png
101
400
media_image1.png
Greyscale
• Step 3 (Aggregating and updating global model): In round t, S aggregates all the received local models, with the objective to minimize the global loss function [3]:”
Since S is the central server which compiles the local loss model, partly by summing
L
(
M
c
t
)
, it is shown that the local loss value experienced by a current version of the global model is sent to the server.)
determine a loss reduction for each received data set of the plurality of data sets, representing a loss reduction since a previous local loss value included in a previous received data set corresponding to the given client (Xu, page 38462, col 1, section E ,¶2 “Intuitively, the loss function is anticipated to be converged in each round, i.e., the loss value should be gradually reduced and closer to the optimal value. However, the loss value often fluctuates erratically in the training process. In our design, each client records the loss value llast in the last upload, and compares it with the current value lcurrent .”);
determine whether the loss reduction for each received data set of the plurality of data sets exceeds a predefined threshold cutoff value (Xu, page 38462, col 1, section E ,¶2 “If llast > lcurrent , this indicates that the current update is beneficial to the loss function and should be uploaded to the FL server. Otherwise, this indicates that in current round lcurrent is undesirable” here, by comparing the loss value to the previous round and finding those where it was higher than the previous round are undesirable, the loss reduction threshold is effectively set to 0 (i.e. only those with a positive loss reduction are desirable, “exceeds a predefined threshold value”.); and
for which the loss reduction exceeds the predefined cutoff value (Xu, page 38462, col 1, section E ,¶2 “If llast > lcurrent , this indicates that the current update is beneficial to the loss function and should be uploaded to the FL server. Otherwise, this indicates that in current round lcurrent is undesirable. Accordingly, the current update are prevented by the client to be uploaded, and the FL server will have to use the latest update of this client instead.”).
Tuor and Xu are analogous art because both references concern methods for federated learning. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Tuor’s federated learning algorithm on vehicles to incorporate the loss reduction taught by Xu. The motivation for doing so would have been to only incorporate those local models in the federated learning that have shown an accuracy improvement from the last iteration, and are thus beneficial. Xu, page 38462, Section E, ¶2 “If llast > lcurrent , this indicates that the current update is beneficial to the loss function and should be uploaded to the FL server. Otherwise, this indicates that in current round lcurrent is undesirable.”
Tuor in view of Xu does not teach “that varies over successive rounds of training and which results in exclusion of at least a plurality of data sets having a positive loss reduction but which positive loss reduction is insufficient to exceed the predefined threshold cutoff value”
However Deng teaches that varies over successive rounds of training and which results in exclusion of at least a plurality of data sets having a positive loss reduction but which positive loss reduction is insufficient to exceed the predefined threshold cutoff value (Deng, page 4, col 2, ¶1 “Suppose the average test loss value of task
l
j
t
’s global model at time ts is lossj(ts) and the average training loss value of node i’s local model at time te is lossi,j(te). We define the training data quality of node i in iteration t as
m
ⅈ
,
j
t
=
l
o
s
s
j
t
s
-
l
o
s
s
i
,
j
t
e
. (7)
Combining the amount of data (denoted by
D
ⅈ
,
j
t
) used for training, the learning quality of node i in iteration t is defined as follows
PNG
media_image2.png
209
1281
media_image2.png
Greyscale
” here, it can be seen that the threshold defined in equation 8 varies with successive rounds of training, and further can exclude (set to 0) those nodes which do not meet the loss reduction threshold, because the threshold is set from an average of loss reductions, it can exclude nodes even if the loss reduction is positive.)
Tuor in view of Xu and Deng are analogous art because both references concern methods for federated learning. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Tuor/Xu’s federated learning algorithm on vehicles to incorporate the varying loss reduction threshold taught by Deng. The motivation for doing so would have been to effectively aggregate the model updates and generate a superior globe learning model as stated in Deng, page 2, col 1, ¶6 “Extensive experiments are carried out to demonstrate the efficacy of FAIR, where the incentive mechanism can stimulate more high-quality model updates, and the devised aggregation algorithm can effectively aggregate the model updates, collectively contributing to a superior globe learning model.”
Regarding claim 3, Tuor in view of Xu in further view of Deng teaches The system of claim 1, wherein the processor is further configured to:
set a mask vector for each client of the plurality of clients for which the loss reduction does not exceed the predefined cutoff value (Xu, page 38462, col 1, section E, ¶2 “If llast > lcurrent , this indicates that the current update is beneficial to the loss function and should be uploaded to the FL server. Otherwise, this indicates that in current round lcurrent is undesirable.” Here, by not uploading the current update to the server, it can be considered masked from training.); and
wherein the training is based on the received plurality of data sets modified by the mask vector, such that data sets from each client for which the loss reduction does not exceed the predefined cutoff value are masked out when training the global model (Xu, page 38462, col 1, section E ,¶2 “If llast > lcurrent , this indicates that the current update is beneficial to the loss function and should be uploaded to the FL server. Otherwise, this indicates that in current round lcurrent is undesirable.” Here, only those that exceeded a cutoff value are used for training, the equivalent of being masked out. Further, Xu, page 38460, col 1, section B, ¶1 “Finally, we propose selective updating to avoid unnecessary local model updates from some clients, so as to reduce the total number of updates in training”)
Regarding claim 4, Tuor in view of Xu in further view of Deng teaches The system of claim 1, wherein the processor is configured to wait for all of the plurality of clients to complete at least one reporting cycle since a prior reporting cycle before training the global model, wherein a reporting cycle represents an iteration of the data set, including the present local loss value, received for the given client (Xu, Page 38459, col 2, section III/A, ¶1 “Each client c uses its local dataset dc to train a local model mc, and then sends the local parameters as an update to S. The FL server S collects all the local models
m
=
∪
c
∈
C
m
c
, and finally obtains the global model MG according to some aggregation rule [2].” Here, it is shown that that the server collects all local models. Further, as shown above, the server collecting the models includes receiving the local loss value, Xu, page 38459, col 2, section A “S aggregates all the received local models, with the objective to minimize the global loss function [3]”).
Regarding claim 5, Tuor in view of Xu in further view of Deng teaches The system of claim 1, wherein the processor is configured to wait for at least one of a predefined total number or percentage of the plurality of clients to complete at least one reporting cycle since a prior reporting cycle before training the global model, wherein a reporting cycle represents an iteration of the data set, including the present local loss value, received for the given client (Xu, Page 38459, col 2, section III/A, ¶1 “Each client c uses its local dataset dc to train a local model mc, and then sends the local parameters as an update to S. The FL server S collects all the local models
m
=
∪
c
∈
C
m
c
, and finally obtains the global model MG according to some aggregation rule [2].” Here, it is shown that that the server collects all local models, which under the broadest reasonable interpretation means waiting for “at least one of a predefined total number or percentage”. Further, as shown above, the server collecting the models includes receiving the local loss value, Xu, page 38459, col 2, section A “S aggregates all the received local models, with the objective to minimize the global loss function [3]” ).
Regarding claim 6, Tuor in view of Xu in further view of Deng teaches The system of claim 5, wherein the processor is further configured to:
determine whether at least one of a total number or percentage of vehicles for which the loss reduction exceeds the predefined threshold cutoff value exceeds a predefined value representing sufficient training data (Tuor, ¶45 “When the contribution program 132 determines that the currently available contributors have a sufficient usefulness to perform the federated learning process, the contribution program 132 may determine that the modelling program 122 should perform the federated learning process based on the datasets of the currently available contributors.” here the “contributors” can be vehicles as shown in Tuor, Fig 4, 54N “automobile computer system”); and
responsive to the at least one of the total number or percentage not exceeding the predefined value, wait for data sets to be received from an additional number or additional percentage of the plurality of vehicles (Tuor, ¶45 “However, when the contribution program 132 determines that the currently available contributors have an insufficient usefulness to perform the federated learning process, the contribution program 132 may determine that the modelling program 122 should hold on performing the federated learning process and wait for more and/or different contributors to become available.” here the “contributors” can be vehicles as shown in Tuor, Fig 4, 54N “automobile computer system”).
Regarding claim 7, Tuor in view of Xu in further view of Deng teaches The system of claim 1, wherein the processor is further configured to:
determine whether at least one of a total number or percentage of vehicles for which the loss reduction exceeds the predefined threshold cutoff value exceeds a predefined value representing sufficient training data (Tuor, ¶45 “When the contribution program 132 determines that the currently available contributors have a sufficient usefulness to perform the federated learning process, the contribution program 132 may determine that the modelling program 122 should perform the federated learning process based on the datasets of the currently available contributors.” here the “contributors” can be vehicles as shown in Tuor, Fig 4, 54N “automobile computer system”); and
responsive to the at least one of the total number or percentage not exceeding the predefined value, decrement the threshold cutoff value to include, in the training, data sets of additional vehicles for which the loss reduction did not exceed the threshold cutoff value prior to decrementing the cutoff value (Tuor, ¶68 “The exemplary embodiments are also described with regard to the usefulness threshold (e.g., local and/or global) being set to a predetermined value. However, the exemplary embodiments may modify the usefulness threshold to be dynamic and/or adaptable according to the conditions being experienced by the federated learning system 100. For example, the contribution program 132 may have determined that the global usefulness metric has been below the global usefulness threshold for a predetermined duration of time. As a result of such a condition, the contribution program 132 may select to modify the global usefulness threshold (e.g., lower the usefulness threshold).” Here, the “usefulness threshold” can be a loss reduction as shown from the fact the usefulness score may be determined by the datasets Tuor, ¶60 “The execution server 130 may determine a usefulness of the datasets that are provided by the currently available contributors (step 204). For example, the execution server 130 may utilize a local and/or a global usefulness metric relative to a local and/or global usefulness threshold, respectively.” and that the datasets can contain loss information, over iterations Tuor, ¶48 “For example, the distance of features of the dataset from the contributor and the validation dataset may not be too large or too small. The features of the dataset may include, for example, an output from lower layers of a cellular neural network (CNN) or a deep neural network, a gradient of a model parameter, a gradient computed on a minibatch of training data during a model training process, etc.”).
Regarding claim 8, Tour teaches A method comprising:
receiving a plurality of data sets relating to differently trained versions of a global machine learning model, from a plurality of vehicles, (Tuor, ¶34 “The federated exchange client 112 may be configured to prepare datasets that are to be exchanged for purposes of generating a global model. The federated exchange client 112 may receive the datasets from the further clients and/or programs that train the local models on the local data samples and generate packets of the datasets that are to be transmitted to a further processing component that generates the global model.”, here the “further clients” can be vehicles as shown in Tuor, Fig 4, 54N “automobile computer system”)
training the global model using federated learning and based on the data sets of the plurality of data sets (Tuor, ¶3 “The federated learning may be used to train a machine learning algorithm such as a global model (e.g., deep neural network) on a plurality of localized datasets”)
Tuor does not teach “the data sets including at least a present local loss value experienced by a current version of the global model executing on a given vehicle for which a data set of the plurality of data sets was received;
determining a loss reduction for each received data set of the plurality of data sets, representing a loss reduction since a previous local loss value included in a previous received data set corresponding to the given vehicle;
determining whether the loss reduction for each received data set of the plurality of data sets exceeds a predefined threshold cutoff value; and
for which the loss reduction exceeds the predefined cutoff value.”
However, Xu teaches the data sets including at least a present local loss value experienced by a current version of the global model executing on a given client for which a data set of the plurality of data sets was received (Xu, page 38459-38460, col 2, section III/A “• Step 2 (Training and updating local model): In round t, based on the global model
M
G
t
, client c updates the local model parameters
M
c
t
using its local data. Specifically, it aims to obtain optimal parameters
M
c
t
that minimize the loss function
L
(
M
c
t
)
. Then, client c uploads the updated local parameters to S.
PNG
media_image1.png
101
400
media_image1.png
Greyscale
• Step 3 (Aggregating and updating global model): In round t, S aggregates all the received local models, with the objective to minimize the global loss function [3]:”
Since S is the central server which compiles the local loss model, partly by summing
L
(
M
c
t
)
, it is shown that the local loss value experienced by a current version of the global model is sent to the server.)
determining a loss reduction for each received data set of the plurality of data sets, representing a loss reduction since a previous local loss value included in a previous received data set corresponding to the given client (Xu, page 38462, col 1, section E ,¶2 “Intuitively, the loss function is anticipated to be converged in each round, i.e., the loss value should be gradually reduced and closer to the optimal value. However, the loss value often fluctuates erratically in the training process. In our design, each client records the loss value llast in the last upload, and compares it with the current value lcurrent .”);
determining whether the loss reduction for each received data set of the plurality of data sets exceeds a predefined threshold cutoff value (Xu, page 38462, col 1, section E ,¶2 “If llast > lcurrent , this indicates that the current update is beneficial to the loss function and should be uploaded to the FL server. Otherwise, this indicates that in current round lcurrent is undesirable” here, by comparing the loss value to the previous round and finding those where it was higher than the previous round are undesirable, the loss reduction threshold is effectively set to 0 (i.e. only those with a positive loss reduction are desirable, “exceeds a predefined threshold value”.); and
for which the loss reduction exceeds the predefined cutoff value (Xu, page 38462, col 1, section E ,¶2 “If llast > lcurrent , this indicates that the current update is beneficial to the loss function and should be uploaded to the FL server. Otherwise, this indicates that in current round lcurrent is undesirable. Accordingly, the current update are prevented by the client to be uploaded, and the FL server will have to use the latest update of this client instead.”).
Tuor and Xu are analogous art because both references concern methods for federated learning. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Tuor’s federated learning algorithm on vehicles to incorporate the loss reduction taught by Xu. The motivation for doing so would have been to only incorporate those local models in the federated learning that have shown an accuracy improvement from the last iteration, and are thus beneficial. Xu, page 38462, Section E, ¶2 “If llast > lcurrent , this indicates that the current update is beneficial to the loss function and should be uploaded to the FL server. Otherwise, this indicates that in current round lcurrent is undesirable.”
Tuor in view of Xu does not teach “that varies over successive rounds of training and which results in exclusion of at least a plurality of data sets having a positive loss reduction but which positive loss reduction is insufficient to exceed the predefined threshold cutoff value”
However Deng teaches that varies over successive rounds of training and which results in exclusion of at least a plurality of data sets having a positive loss reduction but which positive loss reduction is insufficient to exceed the predefined threshold cutoff value (Deng, page 4, col 2, ¶1 “Suppose the average test loss value of task
l
j
t
’s global model at time ts is lossj(ts) and the average training loss value of node i’s local model at time te is lossi,j(te). We define the training data quality of node i in iteration t as
m
ⅈ
,
j
t
=
l
o
s
s
j
t
s
-
l
o
s
s
i
,
j
t
e
. (7)
Combining the amount of data (denoted by
D
ⅈ
,
j
t
) used for training, the learning quality of node i in iteration t is defined as follows
PNG
media_image2.png
209
1281
media_image2.png
Greyscale
” here, it can be seen that the threshold defined in equation 8 varies with successive rounds of training, and further can exclude (set to 0) those nodes which do not meet the loss reduction threshold, because the threshold is set from an average of loss reductions, it can exclude nodes even if the loss reduction is positive.)
Tuor in view of Xu and Deng are analogous art because both references concern methods for federated learning. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Tuor/Xu’s federated learning algorithm on vehicles to incorporate the varying loss reduction threshold taught by Deng. The motivation for doing so would have been to effectively aggregate the model updates and generate a superior globe learning model as stated in Deng, page 2, col 1, ¶6 “Extensive experiments are carried out to demonstrate the efficacy of FAIR, where the incentive mechanism can stimulate more high-quality model updates, and the devised aggregation algorithm can effectively aggregate the model updates, collectively contributing to a superior globe learning model.”
Regarding claims 10-14, they are rejected under the same rationale as claims 3-7 respectively.
Regarding claim 15 , Tour teaches A non-transitory storage medium, storing instructions that, when executed by one or more processors, cause the one or more processors to perform a method comprising (Tuor, claim 15 “one or more computer-readable storage media, and program instructions collectively stored on the one or more of the computer-readable storage media for execution by at least one of the one or more processors capable of performing a method, the method comprising:”):
receiving a plurality of data sets relating to differently trained versions of a global machine learning model, from a plurality of vehicles, (Tuor, ¶34 “The federated exchange client 112 may be configured to prepare datasets that are to be exchanged for purposes of generating a global model. The federated exchange client 112 may receive the datasets from the further clients and/or programs that train the local models on the local data samples and generate packets of the datasets that are to be transmitted to a further processing component that generates the global model.”, here the “further clients” can be vehicles as shown in Tuor, Fig 4, 54N “automobile computer system”)
training the global model using federated learning and based on the data sets of the plurality of data sets (Tuor, ¶3 “The federated learning may be used to train a machine learning algorithm such as a global model (e.g., deep neural network) on a plurality of localized datasets”)
Tuor does not teach “the data sets including at least a present local loss value experienced by a current version of the global model executing on a given vehicle for which a data set of the plurality of data sets was received;
determining a loss reduction for each received data set of the plurality of data sets, representing a loss reduction since a previous local loss value included in a previous received data set corresponding to the given vehicle;
determining whether the loss reduction for each received data set of the plurality of data sets exceeds a predefined threshold cutoff value; and
for which the loss reduction exceeds the predefined cutoff value.”
However, Xu teaches the data sets including at least a present local loss value experienced by a current version of the global model executing on a given client for which a data set of the plurality of data sets was received (Xu, page 38459-38460, col 2, section III/A “• Step 2 (Training and updating local model): In round t, based on the global model
M
G
t
, client c updates the local model parameters
M
c
t
using its local data. Specifically, it aims to obtain optimal parameters
M
c
t
that minimize the loss function
L
(
M
c
t
)
. Then, client c uploads the updated local parameters to S.
PNG
media_image1.png
101
400
media_image1.png
Greyscale
• Step 3 (Aggregating and updating global model): In round t, S aggregates all the received local models, with the objective to minimize the global loss function [3]:”
Since S is the central server which compiles the local loss model, partly by summing
L
(
M
c
t
)
, it is shown that the local loss value experienced by a current version of the global model is sent to the server.)
determining a loss reduction for each received data set of the plurality of data sets, representing a loss reduction since a previous local loss value included in a previous received data set corresponding to the given client (Xu, page 38462, col 1, section E ,¶2 “Intuitively, the loss function is anticipated to be converged in each round, i.e., the loss value should be gradually reduced and closer to the optimal value. However, the loss value often fluctuates erratically in the training process. In our design, each client records the loss value llast in the last upload, and compares it with the current value lcurrent .”);
determining whether the loss reduction for each received data set of the plurality of data sets exceeds a predefined threshold cutoff value (Xu, page 38462, col 1, section E ,¶2 “If llast > lcurrent , this indicates that the current update is beneficial to the loss function and should be uploaded to the FL server. Otherwise, this indicates that in current round lcurrent is undesirable” here, by comparing the loss value to the previous round and finding those where it was higher than the previous round are undesirable, the loss reduction threshold is effectively set to 0 (i.e. only those with a positive loss reduction are desirable, “exceeds a predefined threshold value”.); and
for which the loss reduction exceeds the predefined cutoff value (Xu, page 38462, col 1, section E ,¶2 “If llast > lcurrent , this indicates that the current update is beneficial to the loss function and should be uploaded to the FL server. Otherwise, this indicates that in current round lcurrent is undesirable. Accordingly, the current update are prevented by the client to be uploaded, and the FL server will have to use the latest update of this client instead.”).
Tuor and Xu are analogous art because both references concern methods for federated learning. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Tuor’s federated learning algorithm on vehicles to incorporate the loss reduction taught by Xu. The motivation for doing so would have been to only incorporate those local models in the federated learning that have shown an accuracy improvement from the last iteration, and are thus beneficial. Xu, page 38462, Section E, ¶2 “If llast > lcurrent , this indicates that the current update is beneficial to the loss function and should be uploaded to the FL server. Otherwise, this indicates that in current round lcurrent is undesirable.”
Tuor in view of Xu does not teach “that varies over successive rounds of training and which results in exclusion of at least a plurality of data sets having a positive loss reduction but which positive loss reduction is insufficient to exceed the predefined threshold cutoff value”
However Deng teaches that varies over successive rounds of training and which results in exclusion of at least a plurality of data sets having a positive loss reduction but which positive loss reduction is insufficient to exceed the predefined threshold cutoff value (Deng, page 4, col 2, ¶1 “Suppose the average test loss value of task
l
j
t
’s global model at time ts is lossj(ts) and the average training loss value of node i’s local model at time te is lossi,j(te). We define the training data quality of node i in iteration t as
m
ⅈ
,
j
t
=
l
o
s
s
j
t
s
-
l
o
s
s
i
,
j
t
e
. (7)
Combining the amount of data (denoted by
D
ⅈ
,
j
t
) used for training, the learning quality of node i in iteration t is defined as follows
PNG
media_image2.png
209
1281
media_image2.png
Greyscale
” here, it can be seen that the threshold defined in equation 8 varies with successive rounds of training, and further can exclude (set to 0) those nodes which do not meet the loss reduction threshold, because the threshold is set from an average of loss reductions, it can exclude nodes even if the loss reduction is positive.)
Tuor in view of Xu and Deng are analogous art because both references concern methods for federated learning. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Tuor/Xu’s federated learning algorithm on vehicles to incorporate the varying loss reduction threshold taught by Deng. The motivation for doing so would have been to effectively aggregate the model updates and generate a superior globe learning model as stated in Deng, page 2, col 1, ¶6 “Extensive experiments are carried out to demonstrate the efficacy of FAIR, where the incentive mechanism can stimulate more high-quality model updates, and the devised aggregation algorithm can effectively aggregate the model updates, collectively contributing to a superior globe learning model.”
Regarding claims 17-20, they are rejected under the same rationale as claims 3 and 5-7 respectively.
Claims 2, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Tuor in view of Xu in view of Deng in further view of Reddi et al. (“Adaptive Federated Optimization”, Reddi et al.) hereinafter Reddi.
Regarding claim 2, Tuor in view of Xu in further view of Deng teaches received for the given vehicle (Tuor, Fig 4, 54N “automobile computer system”).
Tuor in view of Xu in further view of Deng does not teach “wherein the loss reduction is an average loss reduction over a plurality of reporting cycles, wherein a reporting cycle represents an iteration of the data set, including the present local loss value”.
However Reddi teaches wherein the loss reduction is an average loss reduction over a plurality of reporting cycles, wherein a reporting cycle represents an iteration of the data set, including the present local loss value (Reddi, page 7, ¶1 “Second, to account for varying numbers of gradient steps per client, we weight the average of the client outputs
Δ
i
t
by each client’s number of training samples.” and further Reddi, page 7, ¶3 “Therefore, we tune by selecting the parameters that minimize the average training loss over the last 100 rounds of training.” Here it is show ).
Tuor/Xu/Deng and Reddi are analogous art because both references concern methods for federated learning. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the combination of Tuor / Xu/Deng’s federated learning algorithm on vehicles to incorporate the average loss reduction taught by Reddi. The motivation for doing so would have been to outperform uniform weighting, Reddi, page 7, ¶1 “This follows the approach of (McMahan et al., 2017), and can often outperform uniform weighting”.
Regarding claims 9 and 16, they are rejected under the same rationale as claim 2.
Response to Arguments
Applicant's arguments filed September 9th, 2025 have been fully considered but they are not persuasive.
Applicant’s amendments have overcome the prior art rejections of the previous office action.
Regarding the objections to the drawings and undefined acronyms in the specification, Applicant’s amended specification has overcome the objections, which are withdrawn.
Applicant’s arguments with respect to the 35 U.S.C. § 103 rejections have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding the U.S.C. 35 § 101 rejections, Applicant first argues “With all due respect to the Office's PEG, the law makes it fairly clear that the inquiry under Prong 1 of Step 2A is the Step 1 Alice analysis - which requires a determination of what the claim is "directed to.'' The fact that a claim includes an abstract idea is immaterial, unless the claim is also directed to the abstract idea.”. However, the MPEP states “Examiners should determine whether a claim recites an abstract idea by (1) identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and (2) determining whether the identified limitations(s) fall within at least one of the groupings of abstract ideas listed above. The groupings of abstract ideas, and their relationship to the body of judicial precedent, are further discussed in MPEP § 2106.04(a)(2). If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is reasonable to conclude that the claim recites an abstract idea in Step 2A Prong One. The claim then requires further analysis in Step 2A Prong Two, to determine whether any additional elements in the claim integrate the abstract idea into a practical application, see MPEP § 2106.04(d).” See MPEP § 2106.04(a). As identified in the 35 U.S.C. § 101 rejection above, the limitation “determine a loss reduction for each received data set of the plurality of data sets, representing a loss reduction since a previous local loss value included in a previous received data set corresponding to the given vehicle;” was identified as encompassing the mental determination of a loss reduction value by comparing previously observed local loss values. The limitation “determine whether the loss reduction for each received data set of the plurality of data sets exceeds a predefined threshold cutoff value that varies over successive rounds of training and which results in exclusion of at least a plurality of data sets having a positive loss reduction but which positive loss reduction is insufficient to exceed the predefined threshold cutoff value” was identified as encompassing the mental determination of whether an observed loss value for observed data exceeds a predefined threshold value that varies, such that some observed values fail to exceed the threshold, despite a positive observed loss. Therefore, the claims have been identified as falling within an abstract grouping in Step 2A Prong 1 as set forth in the MPEP.
Applicant next argues “the result of applying the solution is an improvement to the functioning of machine learning -- faster convergence on an appropriate central model and reduced computational and communication overhead, essentially by applying those models that appear to have ''learned'' more than a threshold since their last provision to the central model”. However, the MPEP states “Likewise, eligibility should not be evaluated based on whether the claim recites a "useful, concrete, and tangible result," State Street Bank, 149 F.3d 1368, 1374, 47 USPQ2d 1596, 1602 (Fed. Cir. 1998) (quoting In re Alappat, 33 F.3d 1526, 1544, 31 USPQ2d 1545, 1557 (Fed. Cir. 1994)), as this test has been superseded. In re Bilski, 545 F.3d 943, 959-60, 88 USPQ2d 1385, 1394-95 (Fed. Cir. 2008) (en banc), aff'd by Bilski v. Kappos, 561 U.S. 593, 95 USPQ2d 1001 (2010).” See MPEP § 2106/I. An allegation that the abstract idea has a faster convergence and reduced computational and communication overhead does not integrate it into a practical application.
Applicant next argues “The last remaining factor in the '"directed to" analysis is whether the way of achieving the improvement constitutes a technological solution as opposed to, for example, a conventional methodology. This typically focuses on whether the claims focus on specific asserted improvements in computer capabilities or instead on a process or system that qualifies as an abstract idea for which computers are used merely as a tool,'' ld at 1293. This, presumably is where the Examiner's conclusion about "mental process" comes into play - the allegation is that the computers are simply a tool to facilitate a mental process.”. The conclusion regarding the claims being directed to a mental process was outlined in Step 2A Prong 1 of the Alice/Mayo analysis. The MPEP states “The Alice/Mayo two-part test is the only test that should be used to evaluate the eligibility of claims under examination. While the machine-or-transformation test is an important clue to eligibility, it should not be used as a separate test for eligibility. Instead it should be considered as part of the "integration" determination or "significantly more" determination articulated in the Alice/Mayo test. Bilski v. Kappos, 561 U.S. 593, 605, 95 USPQ2d 1001, 1007 (2010). See MPEP § 2106.04(d) for more information about evaluating whether a claim reciting a judicial exception is integrated into a practical application and MPEP § 2106.05(b) and MPEP § 2106.05(c) for more information about how the machine-or-transformation test fits into the Alice/Mayo two-part framework.” See MPEP § 2106/I. Regarding the recitation of computer components, in Step 2A Prong 2 and Step 2B it was determined that certain limitations are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). The argument is unpersuasive, the claim is directed to a mental process, and the claim as a whole does not include additional limitations amounting to significantly more than the exception.
Applicant next argues “Per TecSec: "software can make patent-eligible improvements to computer technology, and related claims are eligible as long as they are directed to non-abstract improvements to the functionality of a computer." Id.” However, this argument is unpersuasive — the applicant merely uses a computer to perform processes which can be performed by a mental process. An improvement to selecting models for faster convergence may be an improvement in an abstract idea, but not an improvement in the functioning of a computer, as a computer.
Applicant next argues “Absent a conclusion as to what the claims are "directed to," it is impossible to complete the steps required by Alice…Merely noting elements of the claims that might qualify as mental steps does not actually resolve what the claims are directed to.” However, the MPEP states “Examiners should determine whether a claim recites an abstract idea by (1) identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and (2) determining whether the identified limitations(s) fall within at least one of the groupings of abstract ideas listed above. The groupings of abstract ideas, and their relationship to the body of judicial precedent, are further discussed in MPEP § 2106.04(a)(2). If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is reasonable to conclude that the claim recites an abstract idea in Step 2A Prong One. The claim then requires further analysis in Step 2A Prong Two, to determine whether any additional elements in the claim integrate the abstract idea into a practical application, see MPEP § 2106.04(d).” See MPEP § 2106.04(a). As established above, and following MPEP guidance, specific limitations were identified as falling into at least one of the groupings of abstract ideas listed (mental process) and therefore it is reasonable to conclude the claim recites an abstract idea in Step 2A Prong One.
Applicant next argues “Applicant submits that the claims are directed to: "pruning individually modified learning models based on a threshold loss reduction from a prior iteration of a given model to create a dataset for improving convergence of a central model while reducing computational overhead by excluding models having threshold loss reduction below the cutoff"”. The MPEP states “Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015).” See MPEP § 2106.04(a)(2)/III. The method as described above is a mental process, and could be performed in the human mind, or by a human using a pen and paper. A person could use observed loss values to determine whether a model should or should not be used when updating a central model, the removing of these models from the converging process thus speeding up convergence and reducing computational overhead. The use of a computer in the claimed process is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). Therefore, the claims are directed to the abstract idea of a mental process, and the claim as a whole does not include additional limitations amounting to significantly more than the exception.
Applicant next argues “If the decision to prune based on a change in loss reduction is a specific departure from earlier approaches that improves computer technology, then the claims should pass muster at Step 1 of the Alice inquiry (What the PEG calls Prong 1 of Step 2.)”. However, the MPEP states “Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application? In Prong Two, examiners evaluate whether the claim as a whole integrates the exception into a practical application of that exception.” See MPEP § 2106.04(a)(2). However, the claims do not recite additional elements that integrate the judicial exception into a practical application. The pruning decision is a mental process as identified under Step 2A prong 1, and further elements do not integrate it into a practical application. Using a computer to perform the judicial exception may be an improvement in an abstract idea, but not an improvement in the functioning of a computer, as a computer.
Applicant next argues “Applicant submits that the specificity requirement is fulfilled by the particular nature of the pruning employed, specifically reliant on a change in loss reduction, and in a manner that does not simply use all models which experience loss reduction, but which is selective about which it uses and excludes models having insufficient, albeit positive, loss reduction.” However this is still a amental process which can be performed in the human mind. The selection of models based on observed criteria and according to rules can be performed in the human mind, or by a human using a pen and paper. The alleged improvement of being selective based on criteria may be an improvement to the abstract idea of model selection, but it does not integrate the judicial exception into a practical application.
Applicant next argues “Moreover, at a minimum the claims constitute a specific method that results in a practical application through the use of the resulting pruned sets, pruned through an inventive approach… - Again, because the claims do not pre-empt the broader notion of “using pruned data” but rather rely on data pruned in a particular manner, which the specification identifies as inventive, and because the claims put this pruned data to a technological use (training the central model) and because the specification notes that this accelerates model-convergence as well as reduces computational overhead -- these claims minimally constitute the specific technological improvement necessary for the practical application exception.”. However, this argument is unpersuasive — the applicant merely uses a computer to perform processes which can be performed by a human mind. An improvement to pruning datasets may be an improvement in an abstract idea, but not an improvement in the functioning of a computer, as a computer.
Applicant next argues “The only way to conclude that the claims pre-empt a broad abstract idea and are directed to a broad abstract idea is to exempt the particular nature of the pruning decisions (reliant on change in loss value) and contend that the claims are more broadly directed to pruning data to improve federated learning (or something similar), this would then render the "loss-reduction over a variable threshold" notion eligible for consideration under inventive step. This test generally hinges on whether that which is claimed to constitute the inventive step can be said to be merely conventional activity. Again, this step would only be reached were the claims found to be directed to a broader idea divorced from the use of loss reduction in conjunction with a variable threshold over iterations of a model, and in that instance, the specific determination would then be eligible for consideration as part of the inventive step. Barring a showing that this is conventional activity, the claims pass the step 2 test for inventive step, relying on an unconventional approach for pruning trained models received from remote entities.”. However, the MPEP states “At Step 2A Prong Two or Step 2B, there is no requirement for evidence to support a finding that the exception is not integrated into a practical application or that the additional elements do not amount to significantly more than the exception unless the examiner asserts that additional limitations are well-understood, routine, conventional activities in Step 2B.” See MPEP § 2106.07(a)/III. “A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination.”. See MPEP § 2106.05(d)/I As noted in the 35 U.S.C. § 101 rejection above, those elements that were identified as being well‐understood, routine, conventional activities were supported by what the courts have recognized, or with a citation showing them as well‐understood, routine, and conventional, e.g. receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d. See MPEP § 2106.05(d)(II).
Applicant next argues “For at least these reasons, the claims are directed to patent-eligible subject matter. If the Examiner disagrees, it is respectfully requested that the Examiner formally indicate that which the claims are directed to be "directed to," as required by the legal analysis of a 101 2-step analysis. It is very difficult to address a 101 rejection, or to understand whether a 101 rejection was properly formulated, in the absence of such identification”. As noted in the 35 U.S.C. § 101 rejection under Step 2A Prong 1 of the prior Office Action and above, the claims are directed to a mental process. As required by the MPEP, certain limitations were identified as falling within at least one of the groupings of abstract ideas. “Examiners should determine whether a claim recites an abstract idea by (1) identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and (2) determining whether the identified limitations(s) fall within at least one of the groupings of abstract ideas listed above…If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is reasonable to conclude that the claim recites an abstract idea in Step 2A Prong One.” See MPEP § 2106.04(a).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACOB Z SUSSMAN MOSS whose telephone number is (571) 272-1579. The examiner can normally be reached Monday - Friday, 9 a.m. - 5 p.m. ET.
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, Kakali Chaki can be reached on (571) 272-3719. 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.
/J.S.M./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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