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 .
Applicant’s amendment received on 10/31/25 has been received and entered, claims 1-15, and 17-20 are pending.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-15, and 17-20 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.
Amendment support request
The claims have been amended to recite “emulating”. The term is not found in the specification nor any equivalent thereof. It is requested that the support section in the specification be provided in the response to this action.
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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Claim 1 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/178,576. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Claim 1 of instant application recites:
nstructing a second neural network emulating the first neural network, the second neural network being constructed by inserting a convolutional neural network approximating an identity function into the first neural network at an input of a target layer within the first neural network; and
using the second neural network that emulates the first neural network on said input datum.
While claim 1 of ‘576 recites the following, similar subject matter as above:
(a) constructing a second neural network corresponding to the first neural network, into which is inserted, at the input of a target layer of the first neural network, at least one auto-encoder neural network trained to add a parasitic noise to its input; (b) using the second neural network on said input datum.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
The remaining claims (2-15, and 17-20) of instant application follow same rational and therefore also rejected.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 6-10, 14-15, and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Weinberger (US Patent No. 11227187 B1).
As to claim 1, Weinberger discloses A method for secure application of a first neural network on an input datum, the method comprising, by data processing circuitry of a terminal:
constructing a second neural network emulating the first neural network, the second neural network being constructed by inserting a convolutional neural network approximating an identity function into the first neural network at an input of a target layer within the first neural network (col. 35, lines 7-65); and
using the second neural network that emulates the first neural network on said input datum (col. 35, lines 7-65).
As to claim 2, Weinberger discloses The method as claimed in claim 1, wherein said convolutional neural network has an output size smaller than an input size of said target layer to approximate only certain input channels of this target layer (vol. 23, lines 50-61, size can be selectable).
As to claim 3, Weinberger discloses The method as claimed in claim 1, wherein the constructing further comprises selecting said target layer of the first neural network from among the layers of said first neural network (col. 24, lines 40-32).
As to claim 4, Weinberger discloses The method as claimed in claim 1, wherein the constructing further comprises selecting input channels of said target layer to be approximated from among all of the input channels of the target layer (col. 24, lines 40-32).
As to claim 6, Weinberger discloses The method as claimed in claim 1, further comprising obtaining parameters of the first neural network and of the convolutional neural network approximating the identity function (col. 8, lines 1-19).
As to claim 7, Weinberger discloses The method as claimed in claim 6, wherein the obtaining further comprises obtaining parameters of a set of convolutional neural networks approximating the identity function, and
the constructing further comprises selecting, from said set, at least one convolutional neural network approximating the identity function to be used in the second neural network (col. 36, lines 5-25).
As to claim 8, Weinberger discloses The method as claimed in claim 7, wherein the constructing further comprises, for each selected convolutional neural network approximating the identity function, selecting said target layer of the first neural network from among the layers of said first neural network and or selecting the input channels of said target layer to be approximated from among all of the input channels of the target layer (col. 8, lines 1-25).
As to claim 9, Weinberger discloses The method as claimed in claim 7, wherein the constructing further comprises selecting, beforehand, a number of convolutional neural networks approximating the identity function of said set to be selected (col. 24, lines 40-32).
As to claim 10, Weinberger discloses The method as claimed in claim 6, wherein the obtaining further comprises, implemented by data processing circuitry of a server, learning the parameters of the first neural network and of the convolutional neural network approximating the identity function from at least one learning database (col. 24, lines 40-32).
As to claim 14, Weinberger discloses A method for learning parameters of a second neural network, the method comprising, by data processing circuitry of a server:
constructing the second neural network emulating a first neural network, the second neural network being constructed by inserting a convolutional neural network approximating an identity function into the first neural network at an input of a target layer within the first neural network; and
learning the parameters of the second neural network that emulates the first neural network from a learning database.
As to claim 15, Weinberger discloses A method for secure application of a first neural network on an input datum, the method comprising;
learning parameters of the second neural network as claimed in claim 14; and
using, by data processing circuitry of a terminal, the second neural network on said input datum (col. 36, lines 5-25).
As to claim Weinberger discloses A non-transitory computer readable storage medium readable by a computer on which is stored code instructions for executing a method as claimed in claim 1.
Note: Claim 17 is a dependent claim and follows claim 1’s rejection.
As to claim 18, Weinberger discloses The method as claimed in claim 2, wherein the constructing further comprises selecting said target layer of the first neural network from among the layers of said first neural network (col. 24, lines 40-55).
As to claim 19, Weinberger discloses The method as claimed in claim 2, wherein the constructing further comprises selecting input channels of said target layer to be approximated from among all of the input channels of the target layer (col. 8, 1-24).
As to claim 20, Weinberger discloses The method as claimed in claim 3, wherein the constructing further comprises selecting input channels of said target layer to be approximated from among all of the input channels of the target layer (col. 8, 1-24).
Claims 5, and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Weinberger (US Patent No. 11227187 B1) in view of Zuyev et al. (US Pub. No. 20180350066).
As to claim 5, Weinberger does not disclose The method as claimed in claim 1, wherein the convolutional neural network approximating the identity function has an output size equal to a product of two integers.
Zuyev et al. teaches wherein the convolutional neural network approximating the identity function has an output size equal to a product of two integers (Zuyev et al. paragraph 0045, The filter size is design choice and can be any integer).
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention, to combine the teaching of the cited references and modify Weinberger’s CNN
system to include specific filter size of two integers because it specifies exact result sought and limits the outcome.
As to claim 11, Weinberger does not disclose The method as claimed in claim 1, wherein the first neural network and the convolutional neural network approximating the identity function further includes an alternation of linear layers and of non-linear layers with an activation function such as a ReLU function.
Zuyev et al. teaches wherein wherein the first neural network and the convolutional neural network approximating the identity function further includes an alternation of linear layers and of non-linear layers with an activation function such as a ReLU function (paragraph 0025).
It would have been obvious to one of ordinary skill in the art before the effective filing date of
the claimed invention, to combine the teaching of the cited references and modify Weinberger’s CNN
system to include ReLU to provide complex pattern learning in neural network.
As to claim 12, Weinberger as modified discloses The method as claimed in claim 11, wherein said target layer is a linear layer (Zuyev et al. paragraph 0028).
As to claim 13, Weinberger as modified discloses The method as claimed in claim 11, wherein the convolutional neural network approximating the identity function includes two or three linear layers, which are filter convolutional layers, of a size 5x5 (Zuyev et al. paragraph 0045, The filter size is design choice and can be any integer).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO 892 for list of completed relevant cited prior art.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/NEVEEN ABEL JALIL/Supervisory Patent Examiner, Art Unit 2152 January 14, 2026