DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-10 and 12-18 of U.S. Patent No. 12169584. Although the claims at issue are not identical, they are not patentably distinct from each other because conflicting claims are in a patent by the same inventive entity. Furthermore, where claims in the instant application are broader than the claims of the ‘ 584 patent, it would have been obvious to one of ordinary skill in the art at the time the invention was made to omit elements when the remaining elements perform as before. A person of ordinary skill could have arrived at the present claims by omitting the details of the ‘584 patent claims. See In re Karlson (CCPA) 136 USPQ 184, decided January 16, 1963 ("Omission of element and its function in combination is obvious expedient if remaining elements perform same function as before").
Instant application
‘584 patent
Claims 1, 8, 15 (claim 1 exemplary)
At least one non-transitory computer-readable storage medium comprising instructions to cause at least one processor circuit to at least:
distribute a central model to a first endpoint, a second endpoint, and a third endpoint;
access a first locally trained model, the first locally trained model created by training the central model at the first endpoint using first local data, the first local data local to the first endpoint;
access a second locally trained model, the second locally trained model created by training the central model at the second endpoint using second local data, the second local data different from the first local data, the second local data local to the second endpoint;
access a third model from the third endpoint;
analyze the first endpoint, the second endpoint, and the third endpoint to select the first locally trained model and the second locally trained model for aggregation;
aggregate the first locally trained model and the second locally trained model to produce a new central model;
provide the new central model to the first endpoint; and
provide the new central model to the second endpoint.
Claims 1, 8, 16 (claim 1 exemplary)
At least one non-transitory computer readable storage medium comprising instructions to cause at least one processor circuit to at least:
distribute a central model to a first endpoint, a second endpoint, and a third endpoint;
access a first locally trained model, the first locally trained model created by training the central model at the first endpoint using first local data, the first local data local to the first endpoint;
access a second locally trained model, the second locally trained model created by training the central model at the second endpoint using second local data, the second local data different from the first local data, the second local data local to the second endpoint;
access a third model from the third endpoint;
aggregate the first locally trained model and the second locally trained model to produce a new central model, the third model excluded from the new central model as a result of the third endpoint being a non-trusted device;
provide the new central model to the first endpoint; and
provide the new central model to the second endpoint.
Claims 2, 9, 16 (claim 2 exemplary)
The at least one non-transitory computer-readable storage medium of claim 1, wherein the instructions cause one or more of the at least one processor circuit to access the first locally trained model without having access to the first local data and the instructions cause one or more of the at least one processor circuit to access the second locally trained model without having access to the second local data.
Claims 2, 9, 17 (claim 2 exemplary)
The at least one non-transitory computer readable
storage medium of claim 1, wherein the instructions cause one or more of the at least one processor circuit to access the first locally trained model without having access to the first local data and the instructions cause one or more of the at least one processor circuit to access the second locally trained model without having access to the second local data.
Claims 3, 10, 17 (claim 3 exemplary)
The at least one non-transitory computer-readable storage medium of claim 1, wherein the first endpoint is implemented using a first hardware configuration and the second endpoint is implemented using a second hardware configuration different from the first hardware configuration.
Claims 3, 10, 18 (claim 3 exemplary)
The at least one non-transitory computer readable
storage medium of claim 1, wherein the first endpoint is implemented using a first hardware configuration and the second endpoint is implemented using a second hardware configuration different from the first hardware configuration.
Claims 4, 11, 18 (claim 4 exemplary)
The at least one non-transitory computer-readable storage medium of claim 1, wherein the second locally trained model is formatted in an encrypted format.
Claims 4, 12 (claim 4 exemplary)
The at least one non-transitory computer readable
storage medium of claim 1, wherein the second locally trained model is formatted in an encrypted format..
Claims 5, 12, 19 (claim 5 exemplary)
The at least one non-transitory computer-readable storage medium of claim 4, wherein the at least one processor circuit cannot associate the second local data with the second endpoint.
Claims 5, 13 (claim 5 exemplary)
The at least one non-transitory computer readable storage medium of claim 4, wherein the at least one processor circuit cannot associate the second local data with the second endpoint.
Claims 6, 13, 20 (claim 6 exemplary)
The at least one non-transitory computer-readable storage medium of claim 1, wherein the instructions cause one or more of the at least one processor circuit to update the central model using at least a first portion of the first locally trained model and a second portion of the second locally trained model.
Claims 6, 14 (claim 6 exemplary)
The at least one non-transitory computer readable
storage medium of claim 1, wherein the instructions cause one or more of the at least one processor circuit to update the central model using at least a first portion of the first locally trained model and a second portion of the second locally
trained model.
Claims 7, 14 (claim 7 exemplary)
The at least one non-transitory computer-readable storage medium of claim 1, wherein the instructions to aggregate the first locally trained model and the second locally trained model are executed using a trusted execution environment of the at least one processor circuit.
Claims 7, 15 (claim 7 exemplary)
The at least one non-transitory computer readable
storage medium of claim 1, wherein the instructions to aggregate the first locally trained model and the second locally trained model are executed using a trusted execution environment of the at least one processor circuit.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6, 8-13 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2019/0012592 to Beser et al. (“Beser”) in view of U.S. Patent Publication No. 2018/0240011 to Tan et al. (“Tan”) and further in view of U.S. Patent No. 2020/0380340 to Blanchard et al. (“Blanchard”).
As to claim 1, Beser discloses at least one non-transitory computer-readable storage medium comprising instructions to cause at least one processor circuit (Beser: fig 1-3, [0008; 40]: non-transitory computer-readable medium 38 [0008; 40]) to at least:
distribute a central model to a first endpoint, a second endpoint, and a third endpoint (Beser: fig 1-3, [0011-41]: fig 3 … step 50 a central model is downloaded (distribute a central model to …) from a central server to first plurality of artificial neural networks (ANNs) (first endpoint(s)) and to a second plurality of artificial neural networks (ANNs) (second endpoint(s)) [0041] … ANN1 ANN2 ANN3 (third endpoint(s)) comprise one or more networks of related computers, cellphones, watches, mobile devices … may be located within single installation, multiple installations, single location, multiple locations etc [0030]);
access a first locally trained model, the first locally trained model created by training the central model at the first endpoint using first local data, the first local data local to the first endpoint (Beser: fig 1-3, [0011-41]: fig 3 … step 52 a first local model within first federation is computed based on the first local data as applied to central model [0041]);
access a second locally trained model, the second locally trained model created by training the central model at the second endpoint using second local data, the second local data different from the first local data, the second local data local to the second endpoint (Beser: fig 1-3, [0011-41]: fig 3 … as is a second local model within the second federation based on the second local data as applied to the central model at step 54 [0041] … securing communications between a central server and clusters of ANNs to update a central model of the central server from local datasets obtained at the edges of the ANNs (first endpoint(s) and second endpoint(s)) without directly exposing the central server to the local datasets … whereby unconnected federations receive benefit from external dataset trainings at other federations (using second local data, the second local data different from the first local data) [0042]);
access a third model from the third endpoint (Beser: fig 1-3, [0011-41]: … central server maintains a first second and third download connection D1 D2 D3 with the first second third ANN1 ANN2 ANN3 and in like fashion the first second third ANN1 ANN2 ANN3 maintain a first second and third upload connection D1 D2 D3 with central server [0031]).
Beser did not explicitly disclose analyze the first endpoint, the second endpoint, and the third endpoint to select the first locally trained model and the second locally trained model for aggregation.
Tan discloses analyze the first endpoint, the second endpoint, and the third endpoint to select the first locally trained model and the second locally trained model for aggregation (Tan: fig 1- 13; abstract: in model merging techniques, distributed local training occurs in each local site (analyze the first endpoint, the second endpoint, and the third endpoint ...) and copies of the local machine learning models are sent to a central site for aggregation of learning by merging the models (... to select the first locally trained model and the second locally trained model for aggregation)).
Beser and Tan are analogous art because they are from the same field of endeavor with respect to models.
Before the effective filing date, for AIA , it would have been obvious to a person of ordinary skill in the art to incorporate the strategies by Tan into the medium by Beser. The suggestion/motivation would have been to provide a central machine learning model that may be trained on various representations/transformations of data seen at local machine learning models, including sampled selections of data-label pairs etc (Tan: [0017]).
Beser did not explicitly disclose aggregate the first locally trained model and the second locally trained model to produce a new central model.
Blanchard discloses aggregate the first locally trained model and the second locally trained model to produce a new central model (Blanchard: fig 1-10, [0015-132]: fig 1 … a first computer “parameter server” and n worker computers similar to a general distributed system model … a portion f of the workers are possible “Byzantine” i.e. they may deliver erroneous and/or arbitrary results [0049] … deep learning solution deployed and trained over several computers and parameter vector is a vector comprising all the synaptic weights and the internal parameters of the deep learning model … cost function is any measure of deviation between what deep learning model predicts and what the computers actually observe [0058] … the present method operates only on a subset of the received estimate vectors (aggregate the first locally trained model and the second locally trained model to produce a new central model) [0017]).
Beser, Tan and Blanchard are analogous art because they are from the same field of endeavor with respect to models.
Before the effective filing date, for AIA , it would have been obvious to a person of ordinary skill in the art to incorporate the strategies by Blanchard into the medium by Beser and Tan. The suggestion/motivation would have been to provide a method different from an averaging approach that takes into account all vectors, even the erroneous ones (Blanchard: [0017]) and provide a distributed machine learning implementation that is both fault tolerant i.e. delivers correct results even in the presence of arbitrarily erroneous workers (see with [0049] – Byzantine) and efficient i.e. less computationally intensive (Blanchard: [0014]).
Beser, Tan and Blanchard further disclose provide the new central model to the first endpoint; and provide the new central model to the second endpoint (Beser: fig 1-3, [0011-41]: fig 3 … step 68 an updated central model from the central server is downloaded to (provide the new central model ...) the first plurality of artificial neural networks (ANNs) (first endpoint(s)) and to a second plurality of artificial neural networks (ANNs) (second endpoint(s)) [0041]).
Same motivation applies as mentioned above to make the proposed modification.
As to claim 2, Beser, Tan and Blanchard disclose wherein the instructions cause one or more of the at least one processor circuit to access the first locally trained model without having access to the first local data and the instructions cause one or more of the at least one processor circuit to access the second locally trained model without having access to the second local data (Beser: fig 1-3, [0011-41]: fig 3 … securing communications between a central server and clusters of ANNs to update a central model of the central server from local datasets obtained at the edges of the ANNs (first endpoint(s) and second endpoint(s)) without directly exposing the central server to the local datasets … whereby unconnected federations receive benefit from external dataset trainings at other federations [0042]).
For motivation, see rejection of claim 1.
As to claim 3, Beser, Tan and Blanchard disclose wherein the first endpoint is implemented using a first hardware configuration and the second endpoint is implemented using a second hardware configuration different from the first hardware configuration (Beser: fig 1-3, [0011-41]: ANNs and/or central server comprise one or more controllers comprising processors … configured to implement one or more ASIC, DSP, FPGA or various combinations for performing various functions [0039] … federated ANNs control their interaction with the central server and vice versa using an export schema to specify what information flows from an ANN and/or central server as well as import schema to specify what information flows into an ANN and/or the central server [0019]).
For motivation, see rejection of claim 1.
As to claim 4, Beser, Tan and Blanchard disclose wherein the second locally trained model is formatted in an encrypted format (Beser: fig 1-3, [0011-41]: fig 1-2 … when update from first local model is available, first ANN uploads the first update to the central server using authentication and encryption … when update from second local model is available, second ANN uploads the second update to the central server using authentication and encryption [0036] … messages sent between central server and ANNs are authenticated and encrypted [0025]).
For motivation, see rejection of claim 1.
As to claim 5, Beser, Tan and Blanchard disclose wherein the at least one processor circuit cannot associate the second local data with the second endpoint (Tan: fig 6-13, [0057-94]: … model merging techniques only transmit model parameters and not the actual data or gradient values from each local node to the central site for performing global learning … this enhances privacy since the raw data does not leave the local sites and model parameters sent provide very limited information about the ensemble of data at the local sites [0069; 86]).
For motivation, see rejection of claim 1.
As to claim 6, see similar rejection to claim 5 where the medium is taught by the medium.
As to claims 8-13, see similar rejection to claims 1-6, respectively where the server is taught by the medium.
As to claims 15-20, see similar rejection to claims 1-6, respectively where the server is taught by the medium.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2019/0012592 to Beser et al. (“Beser”) in view of U.S. Patent Publication No. 2018/0240011 to Tan et al. (“Tan”), U.S. Patent No. 2020/0380340 to Blanchard et al. (“Blanchard”) and further in view of U.S. Patent Publication No. 2021/0192360 A1 to Bitauld et al. (“Bitauld”).
As to claim 7, Beser, Tan and Blanchard disclose the medium of claim 1.
For motivation, see rejection of claim 1.
Beser did not explicitly disclose wherein the instructions to aggregate the first locally trained model and the second locally trained model are executed using a trusted execution environment of the at least one processor circuit.
Bitauld discloses wherein the instructions to aggregate the first locally trained model and the second locally trained model are executed using a trusted execution environment of the at least one processor circuit (Bitauld: fig 1-5 [0029-74]: fig 5 block 530 running, in trusted execution environment, training process configured to obtain parameters of neural network, using training data … fig 1-2 … process of training neural network takes place in TEE (trusted execution environment)108 [0051] … a so-called ‘student-teacher’ approach to train more private network(s) (first second … n locally trained model(s)) and these systems work by training ensembles of ‘teachers’ on subsets of the private data, after the ensemble is trained a ‘student’ is trained to predict the aggregate output of the ‘teachers’ on publicly available and potentially unlabeled data and, in this way, the ‘student’ network can never be reverse engineered to reveal original private data [0055] … the trained neural network remains in the TEE or another TEE [0056]).
Beser, Tan, Blanchard and Bitauld are analogous art because they are from the same field of endeavor with respect to neural networks.
Before the effective filing date, for AIA , it would have been obvious to a person of ordinary skill in the art to incorporate the strategies by Bitauld into the medium by Beser, Tan and Blanchard. The suggestion/motivation would have been to provide training neural network(s) in a TEE to provide the advantage of training data is concealed from outside parties during transmission, storage and processing necessary during neural network training processes (Bitauld: [0044]).
As to claim 14, see similar rejection to claim 7 where the server is taught by the medium.
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
A) US 12081412 – Li
An apparatus and system to provide a federated learning scheme between a RAN and connected UEs are described. A gNB-DU, gNB-CU, or LMF acts as a central server that selects an AI/ML model, trains the AI/ML model, and transmits the AI/ML model to UEs. The UEs act as local nodes that each send a model request to the central server, receive the AI/ML model in response to the request, trains the AI/ML model locally with data, and report updated parameters to the central server. The central server aggregates parameters from the local nodes and updates the AI/ML model.
B) US 20250272575 – Rami
The invention concerns a method that includes a computation loop including a transmission step for computing, by a central node, an output of a current aggregated model for each image of a central dataset. The method also includes transferring, to each of n local nodes, data representative of the aggregated model; and a set of prototypes. The method also includes a training step including for each local node, training a respective local computer vision model to obtain a respective trained local model; and performing supervised training of the aggregated model, based on the central dataset, thereby obtaining a trained central model. The method also includes an aggregation step including for each local node, transferring, to the central node, corresponding local model data; and updating the aggregated model based on the local model data; and data representative of the trained central model.
C) US 20250061376 – Yun
There is provided a federated learning system. The federated learning system comprises: a central server including a central learning model; and a plurality of client devices, each including a local learning model trained by performing federated learning with the central learning model, wherein the central server is configured to transmit status information of the central learning model to each client device, receive status information of the trained local learning model from each client device, and update the central learning model based on the status information of the trained local learning model, wherein each client device is configured to update the status information of the central learning model to the local learning model, train the local learning model by using individual training data, determine the status information of the trained local learning model, and transmit status information of the trained local learning model to the central server.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUNE SISON whose telephone number is (571)270-5693. The examiner can normally be reached 9:00 am - 5:00 pm.
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/JUNE SISON/Primary Examiner, Art Unit 2455