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 .
Here applicant attorney James Proscia was contacted by phone on 12/1/25 and an election without traverse was made to claims 1-12, group 1.
Election/Restriction
Restriction to one of the following inventions is required under 35 U.S.C. 121:
I. Claims 1-12 are, drawn to a federated market place, for data collaboration classified in G06N3/098
II. Claim 13-19 are drawn to a market place for revenue sharing, , classified in G06q/2015.
The inventions are independent or distinct, each from the other because:
Inventions I and II are related as subcombinations disclosed as usable together in a single combination. The subcombinations are distinct if they do not overlap in scope and are not obvious variants, and if it is shown that at least one subcombination is separately usable. In the instant case, subcombination II has separate utility such as generic revenue sharing model. See MPEP § 806.05(d).
The examiner has required restriction between subcombinations usable together. Where applicant elects a subcombination and claims thereto are subsequently found allowable, any claim(s) depending from or otherwise requiring all the limitations of the allowable subcombination will be examined for patentability in accordance with 37 CFR 1.104. See MPEP § 821.04(a). Applicant is advised that if any claim presented in a divisional application is anticipated by, or includes all the limitations of, a claim that is allowable in the present application, such claim may be subject to provisional statutory and/or nonstatutory double patenting rejections over the claims of the instant application.
Restriction for examination purposes as indicated is proper because all the inventions listed in this action are independent or distinct for the reasons given above and there would be a serious search and/or examination burden if restriction were not required because one or more of the following reasons apply:
Claims 12-19 are withdrawn. However, it was agreed that the applicant may create parallel sets of claims for the same invention (group 1) and introduce them, ie method, system, medium etc.
Applicant is advised that the reply to this requirement to be complete must include (i) an election of an invention to be examined even though the requirement may be traversed (37 CFR 1.143) and (ii) identification of the claims encompassing the elected invention.
The election of an invention may be made with or without traverse. To reserve a right to petition, the election must be made with traverse. If the reply does not distinctly and specifically point out supposed errors in the restriction requirement, the election shall be treated as an election without traverse. Traversal must be presented at the time of election in order to be considered timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are added after the election, applicant must indicate which of these claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.
During a telephone conversation with James Proscia on 12/1/25 a provisional election was made without traverse to prosecute the invention of I, claims 1-12. Affirmation of this election must be made by applicant in replying to this Office action. Claims 13-19 are withdrawn from further consideration by the examiner, 37 CFR 1.142(b), as being drawn to a non-elected invention.
Claim Objections
Claims 1-12 are objected to because of the following informalities: Elected claims 1-12 are directed to a “federated learning marketplace comprising”. What is the statutory class for this invention. Applicant could claim a system with hardware, however the hardware type elements “clients”, “central coordinator”, “model consumers” could be broadly humans. Appropriate correction is required.
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 1-12 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more
Claim 1, and dependents 2-12 are directed to a “learning marketplace” which as per the above objection is taken to be a system claim. However, applicant should consider a system with computers rather than “clients” or “coordinators”. Step 1 (yes)
These limitations under their broadest reasonable interpretation cover performance of the limitation as certain methods of organizing human activity, such as a commercial or legal interaction such as business relations which are a fundamental economic practice.
The abstract elements include;
a central coordinator that is responsible for orchestrating the execution of the federated learning environment; a plurality of (clients) that jointly train machine learning and deep learning models on (client computing devices) without sharing their local private datasets, the clients only sharing their locally trained model parameters with the central coordinator wherein the central coordinator aggregates local models and computes a new global model and wherein this process repeats for a number of synchronization periods or asynchronously until specific convergence criteria are met; and a plurality of model consumers that are provided licenses to use trained machine learning and deep learning models, wherein the central coordinator is configured to receive a first revenue stream from the plurality of (clients) and a second revenue stream from the plurality of model consumers.
If the claim limitations under their broadest reasonable interpretation covers performance of the limitation as a fundamental economic practice (or business activity), then it falls within the certain methods of organizing human activity grouping of abstract ideas. Accordingly the claim recites an abstract idea.
The elements that might not be abstract are as follows;
Here it is assumed that clients and client computing devices are computer type elements. “consumers” could be humans or potentially consumer computers. “central coordinator” is taken to be a human or entity.
Claim 1 is merely applying generic computing components to recited abstract limitations. The recitation of generic computer components to a claim does not necessarily preclude the claim from reciting an abstract idea. Claims 2-12 are rejected by virtue of dependency.
Step 2a prong 1 yes, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular the claims recite the additional elements,
Clients and client computing devices are computer type elements. “consumers” could be humans or potentially consumer computers. “central coordinator” is taken to be a human or entity.
The computer hardware/software is recited at a high level of generality such that it amounts to no more than “apply it”. The elements when considered separately or as an ordered combination do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are a high level of generality.
Therefore claims 1-12 are directed to an abstract idea without a practical application . Step 2A prong 2.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and as an ordered combination they do not add significantly more or inventive concept to the exception discussed above. The additional elements of using computer hardware amounts to no more than mere instructions to apply to the exception. The application of an abstract idea with generic computing elements does not create a practical application as currently claimed.
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept when considered separately or as an ordered combination. Thus claims 1-12 are not patent eligible. Step 2B No.
The dependent claims 2-12 do not add further elements and are thus rejected by virtue of dependency on claim 1.
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.
Claim(s) 1-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication 11853891 to De Brouwer
As per claim 1 De Brouwer discloses 1. A federated learning marketplace comprising: a central coordinator that is responsible for orchestrating the execution of the federated learning environment; a plurality of clients that jointly train machine learning and deep learning models on client computing devices without sharing their local private datasets, De Brouwer (col. 2 lines 55-60, purpose of federated learning is to protect private data of the clients)
the clients only sharing their locally trained model parameters with the central coordinator wherein the central coordinator aggregates local models
De Brouwer (col. 3 lines 30-40, send out tensors and then aggregate the local models)
and computes a new global model and wherein this process repeats for a number of synchronization periods or asynchronously until specific convergence criteria are met;
De Brouwer (“or” is a choice, specific, is a general term as the criteria are not listed, col. 15 lines 20-40 discloses convergence for a federated model)
and a plurality of model consumers that are provided licenses to use trained machine learning and deep learning models, wherein the central coordinator is configured to receive a first revenue stream from the plurality of clients and a second revenue stream from the plurality of model consumers.
Arnon (0068, revenue)
It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the federated model disclosure of De Brouwer with the multiparty solving teachings of Arnon for the motivation of allowing multiparty computation” …. And protect privacy at the same time. (0002)
As per claim 2 De Brouwer discloses; The federated learning marketplace of claim 1, wherein at least a portion of collected fees from model consumers are distributed to clients that contributed to training of a machine learning and deep learning model that is used by model consumers.
De Brouwer, (col. 21 lines 5-15, license there models out as new revenue)
Arnon(0068, 0078, generate revenue by sharing data)
As per claim 3, De Brouwer discloses; The federated learning marketplace of claim 1, wherein the first revenue stream includes an annual license fee that enables clients to form or join coalitions/federations and collaboratively train the machine learning and deep learning models on their own private datasets.
De Brouwer, (col. 21 lines 5-15, license there models out as new revenue, enables is a capability and “or” is a choice so only one side of the choice is required)
Arnon(0068, 0078, generate revenue by sharing data)
As per claim 4 De Brouwer discloses; The federated learning marketplace of claim 1, wherein clients that have contributed to training of any federated model have free access to this specific model for lifetime.
De Brouwer, (col. 17 and 18, it appears that the contributors are sharing freely)
As per claim 5 De Brouwer discloses;
the federated learning marketplace of claim 1, wherein the central coordinator orchestrates the execution of the federated learning marketplace with a coordinator computing device.
De Brouwer (Col. 8 lines 35-40 coordinating server)
As per claim 6 De Brouwer discloses; The federated learning marketplace of claim 5, wherein the coordinator computing device is
configured to aggregate the local models and compute the new global model.
De Brouwer (fig. 9 model aggregation)
As per claim 7 De Brouwer discloses; The federated learning marketplace of claim 2, wherein the machine learning and deep learning models are selected from the group consisting of convolutional neural networks, recurrent neural networks, generative adversarial networks, linear regression, decision trees, support vector machines, random forests, and other machine learning algorithms.
D(fig. 16 convolutional, col 20 bottom adversarial, regression, col. 12 lines 45-50, trees col. 2 lines 55-60, vector, col. 13, lines 20-25, fig. 7 randomized trial etc.)
As per claim 8 De Brouwer, does not explicitly disclose what Arnon teaches;
The federated learning marketplace of claim 1 configured to distribute training of machine learning and the deep learning models by allowing geographically distributed institutions to establish federated coalitions, the federated learning marketplace providing a synergy between model owners, model provider and model. (0035, geographically distributed, different locations, allowing is more of a possibility or feature, providing synergy is an outcome of sharing)
It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the federated model disclosure of De Brouwer with the multiparty solving teachings of Arnon for the motivation of allowing multiparty computation” …. And protect privacy at the same time. (0002)
As per claim 9 De Brouwer, discloses;
The federated learning marketplace of claim 8, wherein the federated learning marketplace is established for biomedical and healthcare domains.
De Brouwer (col. 6 lines 60-65)
As per claim 10 De Brouwer discloses;
The federated learning marketplace of claim 8, wherein once a federated model has been trained, it is stored in a model repository for versioning, bookkeeping and serving. (col. 3 lines 15-25)
In regards to claim 11, similar to claim 1, federated model with revenue sharing. “needs to pay” is an outcome of revenue sharing.
As per claim 12 De Brouwer and Arnon in combination disclose; The federated learning marketplace of claim 8, wherein a revenue sharing model is established so that sites that contributed data and resources to train a federated model receive a share of revenues obtained from users of that model as further incentive to participate in a federation, creating an expanding, virtuous cycle of participation.
See claim 1, the “incentive to participate and creating an expanding virtuous cycle” are basically the outcome of revenue sharing and collaboration.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. From IP.com
Federated Learning: The Pioneering Distributed Machine Learning and Privacy-Preserving Data Technology, IP.com 2022
Blockchain Based Global Financial Service Platform, IEEE 2021
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUCE I EBERSMAN whose telephone number is (571)270-3442. The examiner can normally be reached 8:00 am - 5:00 pm Monday-Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael W Anderson can be reached at 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRUCE I EBERSMAN/Primary Examiner, Art Unit 3693