DETAILED ACTION
Response to Amendment
The amendment filed on 8 June 2026 has been entered.
Claims 1-9, 11-12, 47-54, 56 are pending.
Claims 1, 12, 47, 56 are amended.
Applicant’s amendments to the Claims have overcome each and every objection previously set forth in the Non-Final Office Action, mailed 6 March 2026.
Response to Arguments
Applicant’s remarks, regarding the rejections of claims under 35 USC 101, have been fully considered.
Applicant submits that the amended independent claims do not merely recite collecting information, making a judgment, or applying an abstract idea using generic computer components. Rather, the claims recite specific operations for configuring and operating a federated learning system.
Applicant submits limitations are directed to selecting and implementing a training architecture for a distributed machine-learning system. Thus, the claims are not merely directed to an abstract selection or evaluation, but to a practical application that configures how neural-network training is performed in a federated learning system.
Similarly, amended independent Claims 47 and 56 recite specific control logic performed by a worker entity or federated learning entity in a federated learning system. In response to determining that model performance has degraded relative to a previous performance, the worker entity or federated learning entity determines whether that same entity is to participate in future rounds of training in the federated learning system. This is not merely an abstract evaluation of model performance. Rather, the evaluation is used to control future participation of the entity in the federated training process.
Accordingly, when the claims are considered as a whole, the independent claims integrate any alleged abstract idea into a practical application in the field of distributed machine learning and training. Notably, the claims recite specific operations for selecting training topologies and controlling participation in federated learning, rather than merely reciting an abstract idea on a generic computer.
Examiner notes Applicant’s arguments, as outlined above, are directed to newly amended claim limitations for which Examiner has not yet made a prima facie case for, rendering Applicant’s arguments moot.
Examiner further notes, the judicial exception alone cannot provide the improvement, MPEP 2106.05(a), and further examination of claim limitations of Claims 1, 12, 47, 56 did not identify additional elements recited in the claim beyond the judicial exception, which individually or in combination integrated the judicial exception into a practical application, MPEP 2106.04(d)II., as outlined in the present Office Action.
Applicant’s remarks, regarding the rejections of claims under 35 USC 103, have been fully considered.
Applicant notes Claims 47 and 56 have been amended to recite that, in response to determining that the performance of the model has degraded relative to the previous performance, the determination of whether to participate in future rounds is made by the worker entity (in Claim 47) or federated learning entity (in Claim 56), respectively. The claims also recite that the determination concerns whether that same entity is to participate in future rounds of training in the federated learning system.
Applicant submits McMahan, Szeto, and Duan are considered together, the cited references do not teach or suggest the amended limitation requiring the worker entity or federated learning entity itself to determine, based on degraded model performance, whether that same entity is to participate in future rounds of training.
Applicant’s arguments 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.
Allowable Subject Matter
Claim 1 and analogous claim 12 would be allowable if rewritten or amended to overcome the formal objections, rejections under 35 USC 112(b), and rejections under 35 USC 101, set forth in this Office Action. Claims 2-9, 11, which depend directly or indirectly on Claim 12, would be considered allowable if rewritten or amended to overcome the formal objections, rejections under 35 USC 112(b), and rejections under 35 USC 101, set forth in this Office Action.
Claim 47 would be allowable if rewritten or amended to overcome the formal objections and rejections under 35 USC 101, set forth in this Office Action.
The following is a statement of reasons for the indication of allowable subject matter.
The prior art does not anticipate, or render obvious as a whole, the claim limitations of Claim 1 and analogous claim 12, as disclosed in Applicant’s claims.
Presented with the additional limitations of Claim 1 and analogous claim 12, the prior art, made of record (see Non-Final Office Action, mailed 6 March 2026), does not teach, make obvious, or suggest the claim limitations of Claim 1 and analogous claim 12 required elements, as disclosed in Applicant’s claims.
Specifically, the limitations directed to
“generating a plurality of different training topologies for training the neural network using a federated learning system, wherein the federated learning system includes a plurality of worker entities, wherein the plurality of different training topologies comprises at least one centralized training topology and at least one federated training topology”,
“for respective ones of the plurality of training topologies, estimating a cost of operating the federated learning system using the training topology”,
“selecting a subset of the training topologies based on the estimated cost”,
“determining whether the training topologies in the selected subset can be instantiated”,
“selecting one of the training topologies in the selected subset for implementation based on the determination of whether the training topologies in the selected subset can be instantiated”, of amended Claim 1 and analogous claim 12.
The closest prior arts, listed below, discloses:
Zhao et al. (U.S. Pre-Grant Publication No. 20190312772) teaches techniques are provided for topology-aware provisioning of computing resources in a distributed heterogeneous environment, (see Non-Final Office Action, pg. 21-22, mailed 6 March 2026).
Zeng et al. (NPL: "Resource Management at the Network Edge: A Deep Reinforcement Learning Approach") teaches DRL approach to efficiently manage the resources at the network edge, (see Non-Final Office Action, pg. 22-24, mailed 6 March 2026).
He et al. (NPL: "Central Server Free Federated Learning over Single-sided Trust Social Networks") teaches a central server free federated learning algorithm, (see Non-Final Office Action, pg. 24-25, mailed 6 March 2026).
However, Zhao, as modified by Zeng and He, fails to disclose the required claimed technical features for generating a federated learning topology, involving the “training topologies”, of amended Claim 1 and analogous claim 12, as a whole.
Claim 1 and analogous claim 12, definite and enabled by the Specification, would be allowable if rewritten or amended to overcome the formal objections, rejections under 35 USC 112(b), and rejections under 35 USC 101, set forth in this Office Action. Claims 2-9, 11, which depend directly or indirectly on Claim 12, would be considered allowable if rewritten or amended to overcome the formal objections, rejections under 35 USC 112(b), and rejections under 35 USC 101, set forth in this Office Action.
The prior art does not anticipate, or render obvious as a whole, the claim limitations of Claim 47, as disclosed in Applicant’s claims.
Presented with the additional limitations of Claim 47, the prior art, made of record (see Non-Final Office Action, mailed 6 March 2026), does not teach, make obvious, or suggest the claim limitations of Claim 47 required elements, as disclosed in Applicant’s claims.
Specifically, the limitations directed to
“in response to determining that the performance of the model has degraded relative to the previous performance, determining, by the worker entity, whether the worker entity is to participate in future rounds of training in the federated learning system”, of amended Claim 47.
The closest prior arts, listed below, discloses:
McMahan et al. (U.S. Pre-Grant Publication No. 20190312772) teaches systems and methods for learning differentially private machine-learned models, (see Non-Final Office Action, pg. 38-40, mailed 6 March 2026).
Szeto et al. (NPL: "Resource Management at the Network Edge: A Deep Reinforcement Learning Approach") teaches distributed, online machine learning systems including private data servers, each having local private data, (see Non-Final Office Action, pg. 40-41, mailed 6 March 2026).
Duan et al. (NPL: "Central Server Free Federated Learning over Single-sided Trust Social Networks") teaches a self-balancing federated learning framework, (see Non-Final Office Action, pg. 41-42, mailed 6 March 2026).
However, McMahan, as modified by Szeto and Duan, fails to disclose the required claimed technical features for training a neural network at a worker entity in a federated learning system of amended Claim 47, as a whole.
Claim 47, definite and enabled by the Specification, would be allowable if rewritten or amended to overcome the formal objections and rejections under 35 USC 101, set forth in this Office 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 .
Claim Objections
Claim 1 and analogous claim 12 are objected to because of the following informalities: “the plurality of training topologies” in line 7 should be “the plurality of different training topologies”. Appropriate correction is required.
Claim 1 and analogous claim 12 are objected to because of the following informalities: “the training topologies” in line 9 should be “the plurality of different training topologies”. Appropriate correction is required.
Claim 1 and analogous claim 12 are objected to because of the following informalities: “the training topologies” in line 10 should be “the plurality of different training topologies”. Appropriate correction is required.
Claim 1 and analogous claim 12 are objected to because of the following informalities: “the training topologies” in line 12 should be “the plurality of different training topologies”. Appropriate correction is required.
Claim 47 is objected to because of the following informalities: “the previous performance” in line 14-15 should be “the previous performance of the neural network model”. Appropriate correction is required.
Claim 47 is objected to because of the following informalities: “the model” in lines 6, 7, 8, 10, 11, 14 should be “the neural network model”. Appropriate correction is required.
Claim 48 is objected to because of the following informalities: “the model” in line 2 should be “the neural network model”. Appropriate correction is required.
Claim 49 is objected to because of the following informalities: “the model” in lines 3, 4 should be “the neural network model”. Appropriate correction is required.
Claim 50 is objected to because of the following informalities: "the level of performance degradation" in line 3 should be “a level of performance degradation”. Appropriate correction is required.
Claim 51 is objected to because of the following informalities: “the model” in line 3 should be “the neural network model”. Appropriate correction is required.
Claim 51 is objected to because of the following informalities: "the level of performance has degraded" in line 2 should be “a level of performance has degraded”. Appropriate correction is required.
Claim 52 is objected to because of the following informalities: "the performance of the neural network" in line 4 should be “the performance of the model”. Appropriate correction is required.
Claim 52 is objected to because of the following informalities: "the final model weights" in lines 4, 5, 8 should be “the set of final model weights”. Appropriate correction is required.
Claim 52 is objected to because of the following informalities: "the final neural network weights" in line 10 should be “the set of final model weights”. Appropriate correction is required.
Claim 53 is objected to because of the following informalities: "the final neural network weights" in line 6 should be “the final model weights”. Appropriate correction is required.
Claim 56 is objected to because of the following informalities: “the previous performance” in line 17-18 should be “the previous performance of the model”. Appropriate correction is required.
Claim 56 is objected to because of the following informalities: “the neural network” in line 16 should be “the neural networks”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 2-9, 11-12, 48-54, 56 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 and analogous claim 12 recites the limitation "the training topology" in line 8. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the training topology" has been construed to be “a training topology”. Claims 2-9, 11, which are dependent on claim 12, are similarly rejected.
Claim 1 and analogous claim 12 recites the limitation "the training topologies in the selected subset" in lines 13-14. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the training topologies in the selected subset" has been construed to be “the one of the plurality of different training topologies”. Claims 2-9, 11, which are dependent on claim 12, are similarly rejected.
Claim 6 recites the limitation "the topology" in lines 3, 5. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the topology" has been construed to be “the selected topologies”.
Claim 8 recites the limitation "the topology" in line 1. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the topology" has been construed to be “a topology”.
Claim 9 recites the limitation "the selected topology" in line 3. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the selected topology" has been construed to be “a selected topology”.
Claim 11 recites the limitation "the selected topology" in lines 1, 2. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the selected topology" has been construed to be “the one of the plurality of different training topologies” in line 15 of claim 12.
Claim 56 recites the limitation "the federated learning entity" in line 18. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the federated learning entity" has been construed to be “a federated learning entity”. Claims 48-54, which are dependent on claim 56, are similarly rejected.
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 1-9, 11-12, 47-54, 56 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, abstract idea, without significantly more.
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory
category. MPEP 2106.03:
According to the first part of the Alice analysis, in the instant case, the claims were determined
to be directed to one of the four statutory categories: an article of manufacture, a method/process (Claims 1, 47), a machine/system/product (Claims 2-9, 11-12, 48-54, 56), and a composition of matter. Based on the claims being determined to be within of the four categories (i.e., process, machine, manufacture, or composition of matter), (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea).
Step 2A Prong One: This part of the eligibility analysis evaluates whether the claim(s) recites a
judicial exception.
Regarding independent claims 1, 12, 47, 56, the claims recite a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG) without significantly more (Step-2A: Prong One). The applicant's claim limitations under broadest reasonable interpretation covers activities classified under mental processes - concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection Ill) and the 2019 PEG. As evaluated below:
Claims 1, 12:
“for respective ones of the plurality of training topologies, estimating a cost of operating the federated learning system using the training topology” (mental process of evaluation)
“selecting a subset of the training topologies based on the estimated cost” (mental process of judgement)
“determining whether the training topologies in the selected subset can be instantiated” (mental process of judgement)
“selecting one of the training topologies in the selected subset for implementation based on the determination of whether the training topologies in the selected subset can be instantiated” (mental process of judgement)
If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is
reasonable to conclude that the claim(s) recites an abstract idea in Step 2A Prong One.
Step 2A Prong Two: This part of the eligibility analysis evaluates whether the claim(s) as a whole integrates the recited judicial exception into a practical application of the exception. As evaluated below:
“generating a plurality of different training topologies”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“for training the neural network using a federated learning system”
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“wherein the federated learning system includes a plurality of worker entities”
“wherein the plurality of different training topologies comprises at least one centralized training topology and at least one federated training topology”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B: This part of the eligibility analysis evaluates whether the claim, as a whole, amounts to
significantly more than the recited exception, i.e., whether any additional element, or combination of
additional elements, adds an inventive concept to the claim. MPEP 2106.05.
First, the additional elements considered as part of the preamble and the additional elements
directed to the use of computer technology are deemed insufficient to transform the judicial exception
to a patentable invention to a patentable invention because they generally link the judicial exception to
the technology environment, see MPEP 2106.05(h).
Second, the additional elements directed to mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Third, the claims are directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception. The courts have found these types of limitations insufficient to transform the judicial exception to a patentable invention, see MPEP 2106.05(g).
Lastly, the claims directed to data gathering activity as noted above, are deemed directed to an insignificant extra-solution activity. The courts have found these types of limitations insufficient to
qualify as "significantly more", see MPEP 2106.05(g).
Furthermore, when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018). Examiner notes Berkheimer: Option 2 - A citation to one or more of the court decisions discussed in MPEP § 2106.05(d}(II} as noting the well understood, routine, conventional nature of the additional element (s) (e.g., limitations directed to mere data gathering):
The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d).
The additional limitations, as analyzed, failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above. Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible. Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole, claims 1, 12 do not recite what the courts have identified as "significantly more".
Claim 47:
“evaluating performance of the model” (mental process of evaluation)
“determining whether the performance of the model has improved or degraded relative to a previous performance of the model following a previous training round of training neural networks in the federated learning system” (mental process of judgement)
“in response to determining that the performance of the model has degraded relative to the previous performance, determining, by the worker entity, whether the worker entity is to participate in future rounds of training in the federated learning system” (mental process of judgement by apply it)
If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is
reasonable to conclude that the claim(s) recites an abstract idea in Step 2A Prong One.
Step 2A Prong Two: This part of the eligibility analysis evaluates whether the claim(s) as a whole integrates the recited judicial exception into a practical application of the exception. As evaluated below:
“in response to determining that the performance of the model has improved relative to the previous performance of the model following the previous training round, transmitting the modified model weights to the master entity for federation by the master entity”
“selecting the modified model weights for use in operating the neural network”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“receiving a set of model weights from a master entity”
“training the neural network model using the set of model weights to obtain a set of modified model weights”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B: This part of the eligibility analysis evaluates whether the claim, as a whole, amounts to
significantly more than the recited exception, i.e., whether any additional element, or combination of
additional elements, adds an inventive concept to the claim. MPEP 2106.05.
First, the additional elements considered as part of the preamble and the additional elements
directed to the use of computer technology are deemed insufficient to transform the judicial exception
to a patentable invention to a patentable invention because they generally link the judicial exception to
the technology environment, see MPEP 2106.05(h).
Second, the additional elements directed to mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Third, the claims are directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception. The courts have found these types of limitations insufficient to transform the judicial exception to a patentable invention, see MPEP 2106.05(g).
Lastly, the claims directed to data gathering activity as noted above, are deemed directed to an insignificant extra-solution activity. The courts have found these types of limitations insufficient to
qualify as "significantly more", see MPEP 2106.05(g).
Furthermore, when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018). Examiner notes Berkheimer: Option 2 - A citation to one or more of the court decisions discussed in MPEP § 2106.05(d}(II} as noting the well understood, routine, conventional nature of the additional element (s) (e.g., limitations directed to mere data gathering):
The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d).
The additional limitations, as analyzed, failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above. Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible. Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole, claim 47 does not recite what the courts have identified as "significantly more".
Claim 56:
“evaluating performance of the model” (mental process of evaluation)
“determining whether the performance of the model has improved or degraded relative to a previous performance of the model following a previous training round of training neural networks in a federated learning system” (mental process of judgement)
“in response to determining that the performance of the model has degraded relative to the previous performance, determining, by the federated learning entity, whether the federated learning entity is to participate in future rounds of training in the federated learning system” (mental process of judgement by apply it)
If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is
reasonable to conclude that the claim(s) recites an abstract idea in Step 2A Prong One.
Step 2A Prong Two: This part of the eligibility analysis evaluates whether the claim(s) as a whole integrates the recited judicial exception into a practical application of the exception. As evaluated below:
“in response to determining that the performance of the model has improved relative to the previous performance of the model following the previous training round, transmitting the modified model weights to the master entity for federation by the master entity”
“selecting the modified model weights for use in operating the neural network”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“receiving a set of model weights from a master entity”
“training a neural network model using the set of model weights to obtain a set of modified model weights”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B: This part of the eligibility analysis evaluates whether the claim, as a whole, amounts to
significantly more than the recited exception, i.e., whether any additional element, or combination of
additional elements, adds an inventive concept to the claim. MPEP 2106.05.
First, the additional elements considered as part of the preamble and the additional elements
directed to the use of computer technology are deemed insufficient to transform the judicial exception
to a patentable invention to a patentable invention because they generally link the judicial exception to
the technology environment, see MPEP 2106.05(h).
Second, the additional elements directed to mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Third, the claims are directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception. The courts have found these types of limitations insufficient to transform the judicial exception to a patentable invention, see MPEP 2106.05(g).
Lastly, the claims directed to data gathering activity as noted above, are deemed directed to an insignificant extra-solution activity. The courts have found these types of limitations insufficient to
qualify as "significantly more", see MPEP 2106.05(g).
Furthermore, when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018). Examiner notes Berkheimer: Option 2 - A citation to one or more of the court decisions discussed in MPEP § 2106.05(d}(II} as noting the well understood, routine, conventional nature of the additional element (s) (e.g., limitations directed to mere data gathering):
The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d).
The additional limitations, as analyzed, failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above. Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible. Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole, claim 56 does not recite what the courts have identified as "significantly more".
Furthermore, regarding dependent claims 2-9, 11 which depend from claim 12, claims 48-54 which depend from claim 56, the claims are directed to a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under the Step2A and 2B:
Claim 2:
Incorporates the rejection of claim 12.
“estimating the cost of operating the federated learning system using a selected topology comprises estimating a cost of data transfer associated with the selected topology” (mental process of evaluation)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 3:
Incorporates the rejection of claim 12.
“estimating the cost of operating the federated learning system using a selected topology comprises estimating a computer performance metric associated with the selected topology” (mental process of evaluation)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 4:
Incorporates the rejection of claim 3.
“estimating the computer performance metric comprises estimating, for a layer in a neural network, a number of floating point operations per second, FLOPS, needed based on a scalar product operation on the layer” (mental process of evaluation)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 5:
Incorporates the rejection of claim 4.
“estimating the number of FLOPS associated with the selected topology comprises estimating the number of FLOPS according to the following formula for a layer in the neural network: 2*m*N(l+1)*[Nl+1] FLOPS per layer where m is a number of samples in a data set processed by the neural network and N(1) is a layer size of the neural network” (mental process of evaluation using abstract idea of mathematical function)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 6:
Incorporates the rejection of claim 12.
“estimating an availability of resources needed to implement the network footprint associated with the topology” (mental process of evaluation)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“for selected topologies, generating a network footprint associated with the topology”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 7:
Incorporates the rejection of claim 3.
“estimating an availability of resources needed to obtain the computer performance metric associated with the selected topology” (mental process of evaluation)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 8:
Incorporates the rejection of claim 12.
“wherein the topology comprises one of a centralized training topology, an isolated training topology, a federated training topology, a round robin topology and a hierarchical topology”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 9:
Incorporates the rejection of claim 12.
“determining that the selected topology is no longer sustainable” (mental process of judgement)
“selecting a different topology for instantiation” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“instantiating the different topology; wherein the different topology is selected based on a cost of the different topology and an ability of the different topology to be instantiated”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions to implement an abstract idea on a computer or mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 11:
Incorporates the rejection of claim 12.
“determining that the selected topology is no longer sustainable comprises determining that the selected topology is no longer sustainable based on cost and/or available resources” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 48:
Incorporates the rejection of claim 56.
“evaluating the performance of the model comprises evaluating the performance of the model against local validation data that is local to a worker entity” (mental process of evaluation)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 49:
Incorporates the rejection of claim 56.
“in response to determining that the performance of the model has degraded relative to the previous performance of the model following the previous training round, determining whether a level of performance degradation is less than a threshold” (mental process of judgement)
“selecting the modified model weights for use in operating the neural network” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“in response to determining that the level of performance degradation is less than the threshold, transmitting the modified model weights to the master entity for federation by the master entity”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 50:
Incorporates the rejection of claim 56.
“in response to determining that the level of performance degradation is greater than a threshold” (mental process of judgement)
“selecting the previous set of model weights for use in operating the neural network” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“transmitting a previous set of model weights generated in connection with the previous training round to the master entity for federation by the master entity”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 51:
Incorporates the rejection of claim 56.
“in response determining that the level of performance has degraded relative to the previous performance of the model following the previous training round, determining whether or not to participate in future rounds of training neural networks in the federated learning system” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 52:
Incorporates the rejection of claim 56.
“evaluating the performance of the neural network using the final model weights” (mental process of evaluation)
“evaluating the performance of the neural network using the modified final model weights” (mental process of evaluation)
“comparing performance of the neural network using the final model weights to performance of the neural network using the modified final neural network weights” (mental process of evaluation)
“selecting a set of neural network weights from among the final neural network weights and the modified final neural network weights in response to the comparison” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“receiving a set of final model weights from the master entity”
“re-training the neural network starting with the final model weights to obtain modified final model weights”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions to implement an abstract idea on a computer or mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 53:
Incorporates the rejection of claim 56.
“comparing performance of the neural network using final model weights to performance of the neural network using modified final neural network weights and intermediate neural network weights generated during a federated learning round” (mental process of evaluation)
“selecting a set of neural network weights from among the final neural network weights, the modified final neural network weights and the intermediate neural network weights in response to the comparison” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 54:
Incorporates the rejection of claim 56.
“combining final neural network weights with modified final neural network weights and/or one or more sets of intermediate neural network weights to obtain a combined set of neural network weights”
“operating the neural network using the combined set of neural network weights”
“wherein combining first neural network weights with the modified final neural network weights and/or one or more sets of intermediate neural network weights comprises generating a weighted average of the first neural network weights and the modified final neural network weights and/or one or more sets of intermediate neural network weights”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The dependent claims as analyzed above, do not recite limitations that integrated the judicial exception into a practical application. In addition, the claim limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step-2B). Therefore, the claims do not recite any limitations, when considered individually or as a whole, that recite what have the courts have identified as "significantly more", see MPEP 2106.05; and therefore, as a whole the claims are not patent eligible. As shown above, the dependent claims do not provide any additional elements that when considered individually or as an ordered combination, amount to significantly more than the abstract idea identified. Therefore, as a whole, the dependent claims do not recite what have the courts have identified as "significantly more" than the recited judicial exception. Therefore, claims 2-9, 11, 48-54 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception and does not recite, when claim elements are examined individually and as a whole, elements that the courts have identified as "significantly more" than the recited judicial exception.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 48-53, 56 are rejected under 35 U.S.C. 103 as being unpatentable over McMahan et al. (U.S. Pre-Grant Publication No. 20190227980, hereinafter ‘McMahan'), in view of Szeto et al. (U.S. Pre-Grant Publication No. 20180018590, hereinafter ‘Szeto'), and further in view of Goetz et al. (NPL: "Active Federated Learning", hereinafter 'Goetz').
Regarding claim 56, McMahan teaches A federated learning entity, comprising: a processing circuit; and a memory coupled to the processing circuit and comprising non-transitory computer readable program instructions that, when executed by the processing circuit, cause the federated learning entity to perform operations of ([0004] One example aspect of the present disclosure is directed to a computing system. The computing system can include one or more server computing devices. The one or more server computing devices can include one or more processors and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors cause the one or more server computing devices to perform operations.):
receiving a set of model weights from a master entity ([0023] A machine-learned model can then be provided to the selected client computing devices. For example, one or more server computing devices can receiving a set of model weights from a master entity provide a global set of parameters for a machine-learned model to each selected client computing device.);
training a neural network model using the set of model weights to obtain a set of modified model weights ([0024] In some implementations, after receiving the machine-learned model, each selected client computing device can then determine a local update based at least in part on a local dataset stored locally on the selected client computing device and provide the local update to the one or more server computing devices.; [0025] For example, in some implementations, determining the local update can include training the machine-learned model based at least in part on the local dataset to generate a locally-trained model. In some implementations, the machine-learned model can be trained via stochastic gradient descent. For example, each selected client computing device can perform some number of mini-batch stochastic gradient descent steps to generate the training the neural network model using the set of model weights to obtain a set of modified model weights locally-trained model, such as updated local values for the global set of parameters for the machine-learned model.);
evaluating performance of the model; determining whether the performance of the model has improved or degraded relative to a previous performance of the model following a previous training round of training neural networks in a federated learning system ([0026] In some implementations, determining the local update can include evaluating performance of the model determining a difference between the locally-trained model and the machine-learned model. For example, in some implementations, the difference between the locally-trained model and the machine-learned model can be determined by determining whether the performance of the model has improved or degraded relative to a previous performance of the model following a previous training round of training neural networks in a federated learning system determining a difference between the global set of parameters for the machine-learned model provided by the one or more server computing devices and the updated local values for the global set of parameters determined by training the machine-learned model with the local dataset.);
in response to determining that the performance of the model has improved relative to the previous performance of the model following the previous training round, transmitting the modified model weights to the master entity for federation by the master entity ([0030] Each client computing device can then in response to determining that the performance of the model has improved relative to the previous performance of the model following the previous training round provide the local update to the one or more server computing devices. For example, in some implementations, the local update can be clipped before transmitting the modified model weights to the master entity for federation by the master entity being provided to the one or more server computing devices, as described herein. In various implementations, the local update can be provided as one or more vectors, matrices, parameters, or other formats, and may be encoded before being provided to the one or more server computing devices.) and
McMahan fails to teach selecting the modified model weights for use in operating the neural network; and in response to determining that the performance of the model has degraded relative to the previous performance, determining, by the federated learning entity, whether the federated learning entity is to participate in future rounds of training in the federated learning system.
Szeto teaches selecting the modified model weights for use in operating the neural network ([0104] At operation 560, the modeling engine calculates a model similarity score as a function of the proxy model parameters and actual model parameters. As discussed above, the selecting the modified model weights parameters can be compared pairwise considering that each model is built from the same implementation of the machine learning algorithm and considering that the proxy data has similar features as the private data. In addition to using the proxy and actual model parameters, the modeling engine can also use other factors available in calculating the similarity score. Example additional factors can include accuracies of the model, cross fold validation, accuracy gain, sensitivities, specificities, distributions of the pairwise comparisons (e.g., average value, distributions about zero, etc.). In some embodiments, the actual private data training set can be used to cross-validate the proxy model. If the accuracy of the predictions from the trained proxy model on the actual private data training set is sufficiently high (e.g., within 10%, 5%, 1%, or closer), then the trained proxy model could be considered similar to the trained actual model. Further, if the similarity score fails to satisfy similarity criteria (e.g., falls below a threshold, etc.), then the modeling engine can repeat operations 540 through 560.; [0107] In other embodiments, the global modeling engine also transmits the trained global model back to one or more of the private data servers. The private data servers can then for use in operating the neural network leverage the global trained model to conduct local prediction studies in support of local clinical decision making workflows. In addition, the private data servers can also use the global model as a foundation for continued online learning. Thus, the global model becomes a basis for continued machine learning as new private data becomes available. As new data becomes available, method 500 can be repeated to improve the global modeling engine.); and
McMahan and Szeto are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of McMahan, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Szeto to McMahan before the effective filing date of the claimed invention in order to construct communication channels among computing devices over a network to exchange machine learning data while respecting data privacy of the underlying raw data (cf. Szeto, [0029] One should appreciate that the disclosed techniques provide many advantageous technical effects including construction of communication channels among computing devices over a network to exchange machine learning data while respecting data privacy of the underlying raw data. The computing devices are able to exchange “learned” information or knowledge among each other without comprising privacy. More specifically, rather than transmitting private or secured data to remote computing devices, the disclosed private data servers attempt to “learn” information automatically about the local private data via computer-based implementations of one or more machine learning algorithms. The learned information is then exchanged with other computers lacking authorization to access the private data. Further, it should be appreciated that the technical effects include computationally building trained proxy models from distributed, private data and their corresponding data distributions.).
Goetz teaches in response to determining that the performance of the model has degraded relative to the previous performance, determining, by the federated learning entity, whether the federated learning entity is to participate in future rounds of training in the federated learning system ([1 Introduction, pg. 1] In this paper we introduce Active Federated Learning (AFL) to preferentially train on users which are more beneficial to the model during that training iteration. Motivated by ideas from Active Learning, we propose using a value function which can be evaluated on the user’s device and returns a valuation to the server indicating the likely utility of training on that user. The determining, by the federated learning entity server collects these valuations and whether the federated learning entity is to participate in future rounds of training in the federated learning system converts them to probabilities with which the next cohort of users is selected for training. By in response to determining that the performance of the model has degraded relative to the previous performance using simple a value function related to the loss the user’s data suffers under the current model, we can reduce the number of training rounds required for the model to achieve a specified level of accuracy by 20-70%.; [4.1 Loss valuation, pg. 3] One very natural value function is to use the loss of the users data vk = 1 √ nk l(xk,yk;w). It is already calculated during model training and is increasing with how poorly the model performs on the clients data. Additionally it mimics common resampling techniques when the required structure is present in the data. If there is extreme class imbalance and weak separation of the classes, data points of the minority class will have significantly higher loss than majority class data points. Therefore we will prefer users with more minority data, mimicking resampling the minority class data. Similarly if the noise depends on the distance from the classification boundary such as in [3], using the loss replicates margin based resampling techniques. Finally if all data points are equally valuable then users with more data will be given higher valuations. Most importantly these adaptations to the data do not require the practitioner to know the specific structure being exploited. This is particularly important in the Federated setting, where information about the data is limited.).
McMahan, Szeto, and Goetz are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of McMahan and Szeto, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Goetz to McMahan before the effective filing date of the claimed invention in order to reduce the number of required training iterations by 20-70% while maintaining the same model accuracy (cf. Goetz, [Abstract, pg. 1] Federated Learning allows for population level models to be trained without centralizing client data by transmitting the global model to clients, calculating gradients locally, then averaging the gradients. Downloading models and uploading gradients uses the client’s bandwidth, so minimizing these transmission costs is important. The data on each client is highly variable, so the benefit of training on different clients may differ dramatically. To exploit this we propose Active Federated Learning, where in each round clients are selected not uniformly at random, but with a probability conditioned on the current model and the data on the client to maximize efficiency. We propose a cheap, simple and intuitive sampling scheme which reduces the number of required training iterations by 20-70% while maintaining the same model accuracy, and which mimics well known resampling techniques under certain conditions.).
Regarding claim 48, McMahan, as modified by Szeto and Goetz, teaches The federated learning entity of claim 56.
Szeto teaches wherein evaluating the performance of the model comprises evaluating the performance of the model against local validation data that is local to a worker entity ([0121] The disclosed approach of distributed, online machine learning can leverage numerous techniques for validating trained models. One approach includes a first private data server sending its trained actual model to other private data servers. The other private data servers can then wherein evaluating the performance of the model comprises evaluating the performance of the model against local validation data that is local to the worker entity validate the trained actual model on their own local data and send the results back to the first private data server. Additionally, a global modeling engine could also execute one or more cross-fold validation steps on the trained actual models using the global collection of aggregated proxy data. The reverse is also true. The global modeling engine can send the global mode to one or more private data servers to have the global model validated on each private data server's local data. One should appreciate that the validation of the various models is to be performed on data sets selected according to the same data selection requirements to ensure a proper analysis.).
McMahan, Szeto, and Goetz are combinable for the same rationale as set forth above with respect to claim 56.
Regarding claim 49, McMahan, as modified by Szeto and Goetz, teaches The federated learning entity of claim 56.
Szeto teaches the operations further comprising: in response to determining that the performance of the model has degraded relative to the previous performance of the model following the previous training round, determining whether a level of performance degradation is less than a threshold ([0104] In addition to using the proxy and actual model parameters, the modeling engine can also use other factors available in calculating the similarity score. Example additional factors can include accuracies of the model, cross fold validation, accuracy gain, sensitivities, specificities, distributions of the pairwise comparisons (e.g., average value, distributions about zero, etc.). In some embodiments, the actual private data training set can be used to cross-validate the proxy model. If the accuracy of the predictions from the trained proxy model on the actual private data training set is sufficiently high (e.g., within 10%, 5%, 1%, or closer), then the trained proxy model could be considered similar to the trained actual model. Further, if the in response to determining that the performance of the model has degraded relative to the previous performance of the model following the previous training round, determining whether a level of performance degradation is less than a threshold similarity score fails to satisfy similarity criteria (e.g., falls below a threshold, etc.), then the modeling engine can repeat operations 540 through 560.); and
in response to determining that the level of performance degradation is less than the threshold, transmitting the modified model weights to the master entity for federation by the master entity and selecting the modified model weights for use in operating the neural network ([0105] in response to determining that the level of performance degradation is less than the threshold Under the condition that the similarity score satisfies similarity criteria, the modeling engine can proceed to operation 570. Operation 570 includes transmitting the modified model weights to the master entity for federation by the master entity transmitting the set of proxy data, possibly along with other information, over the network to at least one non-private computing device. The non-private computing device could be a centralized hub that selecting the modified model weights for use in operating the neural network aggregates proxy data from private servers or peer hubs or a combination of both. The proxy data can be transmitted over the network as a file (e.g., HDF5), serialized in a mark-up language (e.g., XML, YAML, JSON, etc.), a zip archive, or other format. Additional information beyond the proxy data can also be sent to the remote computing device, a global modeling engine or peer machine for example, including the actual model parameters, proxy model parameters, data distributions, similarity score(s), or other information. Providing the model parameters enables the remote computing device to re-instantiate the trained models and conduct localized validation of the work performed by the private data server's modeling engine. One should note that the actual private data is not transmitted thereby respecting privacy.).
McMahan, Szeto, and Goetz are combinable for the same rationale as set forth above with respect to claim 56.
Regarding claim 50, McMahan, as modified by Szeto and Goetz, teaches The federated learning entity of claim 56.
Szeto teaches the operations further comprising: in response to determining that the level of performance degradation is greater than a threshold, transmitting a previous set of model weights generated in connection with the previous training round to the master entity for federation by the master entity, and selecting the previous set of model weights for use in operating the neural network ([0050] As proxy data 260 is generated and relayed to the global model server 130, the global model server aggregates the data and generates an updated global model. Once the global model is updated, it can be determined whether the updated global model is an improvement over the previous version of the global model. If the updated global model is an improvement (e.g., the predictive accuracy is improved), new parameters may be provided to the private data servers via the updated model instructions 230. At the private data server 124, the in response to determining that the level of performance degradation is greater than the threshold performance of the trained actual model (e.g., whether the model improves or worsens) can be evaluated to determine whether the models instructions provided by the updated global model result in an improved trained actual model. transmitting a previous set of model weights generated in connection with the previous training round to the master entity for federation by the master entity, and selecting the previous set of model weights for use in operating the neural network Parameters associated with various machine learning model versions may be stored so that earlier machine learning models may be later retrieved, if needed.).
McMahan, Szeto, and Goetz are combinable for the same rationale as set forth above with respect to claim 56.
Regarding claim 51, McMahan, as modified by Szeto and Goetz, teaches The federated learning entity of claim 56.
Szeto teaches the operations further comprising, in response determining that the level of performance has degraded relative to the previous performance of the model following the previous training round, determining whether or not to participate in future rounds of training neural networks in the federated learning system ([0068] For example, the response determining that the level of performance has degraded relative to the previous performance of the model following the previous training round, determining whether or not to participate in future rounds of training neural networks in the federated learning system conditions could include a number of iterations or epochs to execute on the training data, learning rates, convergence requirements, time limits for training, initial conditions, sensitivity, specificity or other types of conditions that are required or optional. Convergence requirements can include first order derivatives such as “rates of change”, second order derivatives such as “acceleration”, or higher order time derivatives or even higher order derivatives of other dimensions in the attribute space of the data, etc.).
McMahan, Szeto, and Goetz are combinable for the same rationale as set forth above with respect to claim 56.
Regarding claim 52, McMahan, as modified by Szeto and Goetz, teaches The federated learning entity of claim 56.
Szeto teaches the operations further comprising: receiving a set of final model weights from the master entity; evaluating the performance of the neural network using the final model weights; re-training the neural network starting with the final model weights to obtain modified final model weights; evaluating the performance of the neural network using the modified final model weights; comparing performance of the neural network using the final model weights to performance of the neural network using the modified final neural network weights; and selecting a set of neural network weights from among the final neural network weights and the modified final neural network weights in response to the comparison ([0050] As proxy data 260 is generated and relayed to the global model server 130, the global model server aggregates the data and generates an updated global model. Once the global model is updated, it can be determined whether the updated global model is an improvement over the previous version of the global model. If the updated global model is an improvement (e.g., the predictive accuracy is improved), receiving a set of final model weights from the master entity new parameters may be provided to the private data servers via the updated model instructions 230. At the private data server 124, the evaluating the performance of the neural network using the final model weights performance of the trained actual model (e.g., comparing performance of the neural network using the final model weights to performance of the neural network using the modified final neural network weights; and whether the model improves or worsens) can be evaluating the performance of the neural network using the modified final model weights evaluated to determine whether the models instructions provided by the updated global model result in an re-training the neural network starting with the final model weights to obtain modified final model weights improved trained actual model. selecting a set of neural network weights from among the final neural network weights and the modified final neural network weights in response to the comparison Parameters associated with various machine learning model versions may be stored so that earlier machine learning models may be later retrieved, if needed.).
McMahan, Szeto, and Goetz are combinable for the same rationale as set forth above with respect to claim 56.
Regarding claim 53, McMahan, as modified by Szeto and Goetz, teaches The federated learning entity of claim 56.
Szeto teaches the operations further comprising: comparing performance of the neural network using final model weights to performance of the neural network using modified final neural network weights and intermediate neural network weights generated during a federated learning round; and selecting a set of neural network weights from among the final neural network weights, the modified final neural network weights and the intermediate neural network weights in response to the comparison ([0099] Upon reception of the package, the modeling engine can, if configured to do so, execute training in a secured container. In other embodiments, the model instructions provide a pointer to a locally stored implementation of the machine learning algorithm. Further, the model instructions can include additional information that permit the modeling engine to complete its local training tasks including similarity criteria, similarity score definition, query conversion instructions for selecting private data from a local database, a pre-trained model as a base-line, or other information.; [0100] Operation 520 includes the modeling engine creating the trained actual model according to the model instructions and as a function of at least some of the local private data by training the implementation of the machine learning algorithm on the local private data.; [0104] At operation 560, the modeling engine calculates a model similarity score as a function of the proxy model parameters and actual model parameters. As discussed above, the parameters can be comparing performance of the neural network using the final model weights to performance of the neural network using the modified final neural network weights and intermediate sets of neural network weights generated compared pairwise considering that each model is built from the same implementation of the machine learning algorithm and considering that the proxy data has similar features as the private data. In addition to using the proxy and actual model parameters, the modeling engine can also use other factors available in calculating the similarity score. Example additional factors can include accuracies of the model, cross fold validation, accuracy gain, sensitivities, specificities, distributions of the pairwise comparisons (e.g., average value, distributions about zero, etc.). In some embodiments, the actual private data training set can be used to cross-validate the proxy model. If the accuracy of the predictions from the trained proxy model on the actual private data training set is sufficiently high (e.g., within 10%, 5%, 1%, or closer), then the trained proxy model could be considered similar to the trained actual model. Further, if the similarity score fails to satisfy similarity criteria (e.g., falls below a threshold, etc.), then the modeling engine can repeat operations 540 through 560.; [0107] In other embodiments, the global modeling engine also transmits the trained global model back to one or more of the private data servers. The private data servers can then selecting a set of neural network weights from among the final neural network weights, the modified final neural network weights and the intermediate neural network weights in response to the comparison leverage the global trained model to conduct local prediction studies in support of local clinical decision making workflows. In addition, the private data servers can also use the global model as a foundation for continued online learning. Thus, the global model becomes a basis for continued machine learning as new private data becomes available. during a federated learning round As new data becomes available, method 500 can be repeated to improve the global modeling engine.).
McMahan, Szeto, and Goetz are combinable for the same rationale as set forth above with respect to claim 56.
Claim 54 is rejected under 35 U.S.C. 103 as being unpatentable over McMahan, in view of Szeto, Goetz, and further in view of Samek et al. (U.S. Pre-Grant Publication No. 20210065002, hereinafter ‘Samek').
Regarding claim 54, McMahan, as modified by Szeto and Goetz, teaches The federated learning entity of claim 56.
Szeto teaches the operations further comprising: combining final neural network weights with modified final neural network weights and/or one or more sets of intermediate neural network weights to obtain a combined set of neural network weights ([0050] Once the global model is updated, it can be determined whether the updated global model is an improvement over the previous version of the global model. If the updated global model is an improvement (e.g., the predictive accuracy is improved), new parameters may be provided to the private data servers via the updated model instructions 230. At the private data server 124, the performance of the trained actual model (e.g., whether the model improves or worsens) can be evaluated to determine whether the models instructions provided by the updated global model result in an improved trained actual model. Parameters associated with various machine learning model versions may be stored so that earlier machine learning models may be later retrieved, if needed.; [0081] The non-private computing device that receives the knowledge can then aggregate the knowledge with knowledge gained from other private data servers 224. One should appreciate that the non-private computing device (see FIG. 1, non-private computing device 130) could also be a different private data server in the ecosystem, a centralized machine learning hub or service, a global modeling engine, a cloud-based service, or other type of computing device suitably configured to receive the data. From the perspective of a central modeling service operating as the non-private computing device, the central modeling service can aggregate all the proxy data sets as a new aggregated training data set to create a trained global aggregated model. The aggregated model can then be transmitted back to interested stakeholders, private data server 224 for example, for use as a classifier or predictor of patient treatments and outcomes.; [0108] However, by combining the final neural network weights with the modified final neural network weights and/or one or more sets of intermediate neural network weights to obtain a combined set of neural network weights identifying which parameters are most predictive using the machine learning systems as described herein, data sets having in common these key predictive parameters may be combined. In other embodiments, model instructions may be modified, e.g., limited to include key predictive features, and used to regenerate proxy data, proxy data distributions, and other types of learned information. This regenerated information can then be sent to the global model server, where it is aggregated.); and
McMahan, as modified by Szeto and Goetz, fails to teach operating the neural network using the combined set of neural network weights; wherein combining first neural network weights with the modified final neural network weights and/or one or more sets of intermediate neural network weights comprises generating a weighted average of the first neural network weights and the modified final neural network weights and/or one or more sets of intermediate neural network weights.
Samek teaches operating the neural network using the combined set of neural network weights; wherein combining first neural network weights with the modified final neural network weights and/or one or more sets of intermediate neural network weights comprises generating a weighted average of the first neural network weights and the modified final neural network weights and/or one or more sets of intermediate neural network weights ([0044] In step 38, the server 12 then operating the neural network using the combined set of neural network weights merges all the parameterization updates received from the clients 14, the combining first neural network weights with the modified final neural network weights and/or one or more sets of intermediate neural network weights comprises generating a weighted average of the first neural network weights and the modified final neural network weights and/or one or more sets of intermediate neural network weights merging representing a kind of averaging such as by use of a weighted average with the weights considering, for instance, the amount of training data using which the parameterization update of a respective client has been obtained in step 34. The parameterization update thus obtained at step 38 at this end of cycle i indicates the parameterization setting for the download 32 at the beginning of the subsequent cycle i+1.).
McMahan, Szeto, Goetz, and Samek are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of McMahan, Szeto, and Goetz, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Samek to McMahan before the effective filing date of the claimed invention in order to perform the download of information on parameterization settings to the individual clients/nodes by downloading merged parameterization updates resulting from merging the parameterization updates of the clients in each cycle to improve efficiency of distributed learning scenarios (cf. Samek, [0018] In accordance with a further aspect of the present application, distributed learning scenarios, irrespective of being of the federated or data-parallel learning type, are made more efficient by performing the download of information on parameterization settings to the individual clients/nodes by downloading merged parameterization updates resulting from merging the parameterization updates of the clients in each cycle and, additionally, performing this download of merged parameterization updates using lossy coding of an accumulated merge parameterization update.).
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 extension fee 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 date of this final action.
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/MM/Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
