Detailed 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 .
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 - 13 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 – 13 of U.S. Patent No. 12,574,050.
Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the issued patents make obvious the claims of the pending application in that the limitations claimed in the pending application are found in the issued patents even though they are not necessarily presented in the same order as those in the issued patents. “A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim. In re Longi, 759 F.2d at 896, 225 USPQ at 651 (affirming a holding of obviousness-type double patenting because the claims at issue were obvious over claims in four prior art patents); In re Berg, 140 F.3d at 1437, 46 USPQ2d at 1233 (Fed. Cir. 1998) (affirming a holding of obviousness-type double patenting where a patent application claim to a genus is anticipated by a patent claim to a species within that genus). “ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001).
Also, although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the issued patent make obvious the claims of the pending application in that the limitations claimed in the pending application are similarly claimed in the issued patents and the claims are directed to substantially the same subject matter as the parent case though not necessarily presented in the same sequential order or the same claim numbering as shown in the table below.
US Application Number 19/054,454
US Patent Number 12,574,050
(Claim 1)
A computer system comprising: a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that: receive as input a plurality of encrypted codewords from a plurality of client devices; process the encrypted codewords using a deep learning core without decrypting the codewords; and generate as output an encrypted codeword response to the input using the deep learning core; wherein the trained machine learning core was initially trained, using training inputs comprising encrypted codewords, to predict a plurality of probable future encrypted codewords that extend the input sequence.
(Claim 1)
A computer system comprising a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that, when executed by at least one processor, cause the computer system to: orchestrate federated deep learning with a plurality of client devices by receiving a plurality of encrypted codewords from the plurality of client devices; process the encrypted codewords using a deep learning core without decrypting the codewords; and generate as output an encrypted codeword response to the input using the deep learning core; wherein the deep learning core comprises a latent transformer core that was initially trained on encrypted codeword inputs to predict a plurality of probable future encrypted codewords that extend the input sequence.
(Claim 2)
The system of claim 1, wherein the deep learning core comprises a latent transformer architecture.
(Claim 2)
The system of claim 1, wherein the deep learning core comprises a latent transformer architecture.
(Claim 3)
The system of claim 2, wherein each client device further comprises a variational autoencoder encoder that generates latent space vectors from the plurality of codewords.
(Claim 3)
The system of claim 2, wherein each client device further comprises a variational autoencoder encoder that generates latent space vectors from the plurality of codewords.
(Claim 4)
The system of claim 3, wherein the latent transformer architecture processes the latent space vectors.
(Claim 4)
The system of claim 3, wherein the latent transformer architecture processes the latent space vectors.
(Claim 5)
The system of claim 1, wherein the plurality of programming instructions further cause the computing device to: aggregate encrypted model updates from the plurality of client devices; update the deep learning core based on the aggregated encrypted model updates; and facilitate federated learning by iteratively updating the deep learning core based on encrypted updates from the client devices.
(Claim 5)
The system of claim 1, wherein the plurality of programming instructions further cause the computing device to: aggregate encrypted model updates from the plurality of client devices; update the deep learning core based on the aggregated encrypted model updates; and facilitate federated learning by iteratively updating the deep learning core based on encrypted updates from the client devices.
(Claim 6)
The system of claim 5, wherein the plurality of programming instructions further cause the computing device to: implement differential privacy by: adding calibrated noise to the encrypted model updates before aggregation; enforcing a privacy budget across multiple rounds of federated learning; and dynamically adjusting the level of noise based on the privacy budget consumption; thereby enhancing privacy guarantees for individual client datasets while maintaining model utility.
(Claim 6)
The system of claim 5, wherein the plurality of programming instructions further cause the computing device to: implement differential privacy by: adding calibrated noise to the encrypted model updates before aggregation; enforcing a privacy budget across multiple rounds of federated learning; and dynamically adjusting the level of noise based on the privacy budget consumption; thereby enhancing privacy guarantees for individual client datasets while maintaining model utility.
(Claim 7)
A computer-implemented method for federated deep learning using homomorphically- compressed and encrypted data, comprising the steps of: receiving as input a plurality of encrypted codewords from a plurality of client devices; processing the encrypted codewords using a deep learning core without decrypting the codewords; and generating as output an encrypted codeword response to the input using the deep learning core; wherein the trained machine learning core was initially trained, using training inputs comprising encrypted codewords, to predict a plurality of probable future encrypted codewords that extend the input sequence.
(Claim 7)
A method for federated deep learning using homomorphically-compressed and encrypted data, comprising the steps of: orchestrating federated deep learning with a plurality of client devices by receiving a plurality of encrypted codewords from the plurality of client devices; processing the encrypted codewords using a deep learning core without decrypting the codewords; and generating as output an encrypted codeword response to the input using the deep learning core; wherein the deep learning core comprises a latent transformer core that was initially trained on encrypted codeword inputs to predict a plurality of probable future encrypted codewords that extend the input sequence.
(Claim 8)
The method of claim 7, wherein the deep learning core comprises a latent transformer architecture.
(Claim 8)
The method of claim 7, wherein the deep learning core comprises a latent transformer architecture.
(Claim 9)
The method of claim 8, wherein each client device further comprises a variational autoencoder encoder that generates latent space vectors from the plurality of codewords.
(Claim 9)
The method of claim 8, wherein each client device further comprises a variational autoencoder encoder that generates latent space vectors from the plurality of codewords.
(Claim 10)
The method of claim 9, wherein the latent transformer architecture processes the latent space vectors.
(Claim 10)
The method of claim 9, wherein the latent transformer architecture processes the latent space vectors.
(Claim 11)
The method of claim 10, further comprising the step of generating output vectors from processed latent space vectors using a variational autoencoder decoder.
(Claim 11)
The method of claim 10, further comprising the step of generating output vectors from processed latent space vectors using a variational autoencoder decoder.
(Claim 12)
The method of claim 7, further comprising the steps of: aggregating encrypted model updates from the plurality of client devices; updating the deep learning core based on the aggregated encrypted model updates; and facilitating federated learning by iteratively updating the deep learning core based on encrypted updates from the client devices.
(Claim 12)
The method of claim 7, further comprising the steps of: aggregating encrypted model updates from the plurality of client devices; updating the deep learning core based on the aggregated encrypted model updates; and facilitating federated learning by iteratively updating the deep learning core based on encrypted updates from the client devices.
(Claim 13)
The method of claim 12, further comprising the steps of: implementing differential privacy by :adding calibrated noise to the encrypted model updates before aggregation; enforcing a privacy budget across multiple rounds of federated learning; and dynamically adjusting the level of noise based on the privacy budget consumption; thereby enhancing privacy guarantees for individual client datasets while maintaining model utility.
(Claim 13)
The method of claim 12, further comprising the steps of: implementing differential privacy by: adding calibrated noise to the encrypted model updates before aggregation; enforcing a privacy budget across multiple rounds of federated learning; and dynamically adjusting the level of noise based on the privacy budget consumption; thereby enhancing privacy guarantees for individual client datasets while maintaining model utility.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEAN BRUNER JEANGLAUDE whose telephone number is (571)272-1804. The examiner can normally be reached Monday-Thursday 7:00 AM-5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Dameon Levi can be reached at 571-272-2105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JEAN B JEANGLAUDE/Primary Examiner, Art Unit 2845
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