DETAILED ACTION
This action is responsive to the Claims filed on 04/02/2024. Claims 44-63 are pending in the case. Claims 44, 54 and 63 are independent claims.
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 44, 54 and 63 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1, 21 and 41 of U.S. Patent No. 11972408, respectively. Although the claims at issue are not identical, they are not patentably distinct from each other because:with respect to claim 44:
The following limitations of claim 44 correspond to the italicized limitations of claim 1 of 11972408, the differences in word choice have been underlined.
A system, comprising: at least one processor; and at least one memory including program instructions (A system, comprising: at least one processor; and at least one memory including program code)
which when executed by the at least one processor causes operations comprising: identifying, based at least on a first activation map associated with a first hidden layer of a first machine learning model, a first plurality of input samples altering one or more low probabilistic regions of the first activation map, the first hidden layer including a plurality of neurons, each of the plurality of neurons applying an activation function to generate an output, and the one or more low probabilistic regions of the first activation map being occupied by values having below a threshold probability of being output by the plurality of neurons; and embedding, in the first hidden layer of the first machine learning model, a first digital watermark corresponding to the first plurality of input samples, the embedding of the first digital watermark includes training, based at least on training data including the first plurality of input samples, the first machine learning model (which when executed by the at least one processor provides operations comprising: identifying, based at least on a first activation map associated with a first hidden layer of a first machine learning model, a first plurality of input samples altering one or more low probabilistic regions of the first activation map, the first hidden layer including a plurality of neurons, each of the plurality of neurons applying an activation function to generate an output, and the one or more low probabilistic regions of the first activation map being occupied by values having below a threshold probability of being output by the plurality of neurons; embedding, in the first hidden layer of the first machine learning model, a first digital watermark corresponding to the first plurality of input samples, the embedding of the first digital watermark includes training, based at least on training data including the first plurality of input samples, the first machine learning model)
Examiner notes the underlined words are understood to have the same scope.
Finally, claim 44 of the instant application recites, wherein the first digital watermark enables an owner of the first machine learning model to detect unauthorized deployment of the first machine learning model.
This limitation is an intended result and therefore is not given patentable weight. Further it is clear from the disclosure of 11972408 (pp. 0038 “The presence of the digital watermark may enable the owner of the machine learning model to determine whether the machine learning model and/or the proprietary training data used to generate the machine learning model are being misused by a third party.”). The presence of a watermark has the result of enabling the owner to detect unauthorized use or deployment, as such the reference claim anticipates the instant claim because the reference claim recites embedding a digital watermark therefore having the same intended result.
with respect to claim 54
The following limitations of claim 54 correspond to the italicized limitations of claim 21 of 11972408, the differences in word choice have been underlined.
A computer-implemented method, comprising: identifying, based at least on a first activation map associated with a first hidden layer of a first machine learning model, a first plurality of input samples altering one or more low probabilistic regions of the first activation map, the first hidden layer including a plurality of neurons, each of the plurality of neurons applying an activation function to generate an output, and the one or more low probabilistic regions of the first activation map being occupied by values having below a threshold probability of being output by the plurality of neurons; and embedding, in the first hidden layer of the first machine learning model, a first digital watermark corresponding to the first plurality of input samples, the embedding of the first digital watermark includes training, based at least on training data including the first plurality of input samples, the first machine learning model (A computer-implemented method, comprising: identifying, based at least on a first activation map associated with a first hidden layer of a first machine learning model, a first plurality of input samples altering one or more low probabilistic regions of the first activation map, the first hidden layer including a plurality of neurons, each of the plurality of neurons applying an activation function to generate an output, and the one or more low probabilistic regions of the first activation map being occupied by values having below a threshold probability of being output by the plurality of neurons; embedding, in the first hidden layer of the first machine learning model, a first digital watermark corresponding to the first plurality of input samples, the embedding of the first digital watermark includes training, based at least on training data including the first plurality of input samples, the first machine learning model)
Finally claim 54 of the instant application recites, wherein the first digital watermark enables an owner of the first machine learning model to detect unauthorized deployment of the first machine learning model.
This limitation is an intended result and therefore is not given patentable weight. Further it is clear from the disclosure of 11972408 (pp. 0038 “The presence of the digital watermark may enable the owner of the machine learning model to determine whether the machine learning model and/or the proprietary training data used to generate the machine learning model are being misused by a third party.”). The presence of a watermark has the result of enabling the owner to detect unauthorized use or deployment, as such the reference claim anticipates the instant claim because the reference claim recites embedding a digital watermark therefore having the same intended result.
with respect to claim 63
The following limitations of claim 63 correspond to the italicized limitations of claim 41 of 11972408, the differences in word choice have been underlined.
non-transitory computer readable medium storing instructions, which when executed by at least one data processor, result in operations comprising: identifying, based at least on a first activation map associated with a first hidden layer of a first machine learning model, a first plurality of input samples altering one or more low probabilistic regions of the first activation map, the first hidden layer including a plurality of neurons, each of the plurality of neurons applying an activation function to generate an output, and the one or more low probabilistic regions of the first activation map being occupied by values having below a threshold probability of being output by the plurality of neurons; and embedding, in the first hidden layer of the first machine learning model, a first digital watermark corresponding to the first plurality of input samples, the embedding of the first digital watermark includes training, based at least on training data including the first plurality of input samples, the first machine learning model, wherein the first digital watermark enables an owner of the first machine learning model to detect unauthorized deployment of the first machine learning model (A non-transitory computer readable medium storing instructions, which when executed by at least one data processor, result in operations comprising: identifying, based at least on a first activation map associated with a first hidden layer of a first machine learning model, a first plurality of input samples altering one or more low probabilistic regions of the first activation map, the first hidden layer including a plurality of neurons, each of the plurality of neurons applying an activation function to generate an output, and the one or more low probabilistic regions of the first activation map being occupied by values having below a threshold probability of being output by the plurality of neurons; embedding, in the first hidden layer of the first machine learning model, a first digital watermark corresponding to the first plurality of input samples, the embedding of the first digital watermark includes training, based at least on training data including the first plurality of input samples, the first machine learning model)
Finally claim 63 of the instant application recites, wherein the first digital watermark enables an owner of the first machine learning model to detect unauthorized deployment of the first machine learning model.
This limitation is an intended result and therefore is not given patentable weight. Further it is clear from the disclosure of 11972408 (pp. 0038 “The presence of the digital watermark may enable the owner of the machine learning model to determine whether the machine learning model and/or the proprietary training data used to generate the machine learning model are being misused by a third party.”). The presence of a watermark has the result of enabling the owner to detect unauthorized use or deployment, as such the reference claim anticipates the instant claim because the reference claim recites embedding a digital watermark therefore having the same intended result.
Allowable Subject Matter
Independent claims 44, 54 and 63 recite allowable subject matter and would be considered allowable but for the intervening rejection(s).
Specifically, none of the reference of record either alone or in combination fairly disclose or suggest the limitations of at least claim 44:
From at least claim 44:
identifying, based at least on a first activation map associated with a first hidden layer of a first machine learning model… , a first plurality of input samples … the one or more low probabilistic regions of the first activation map being occupied by values having below a threshold probability of being output by the plurality of neurons… and embedding, in the first hidden layer of the first machine learning model, a first digital watermark corresponding to the first plurality of input samples, the embedding of the first digital watermark includes training…
The closest prior art of record Uchida et al. “Embedding Watermarks into Deep Neural Networks” discloses embedding watermark bits into a neural network via training by projecting a vector X onto weights of a neural network layer. This is done with and/or without training the machine learning model. However, Uchida does not disclose identification of neurons with low probabilistic regions of an activation map according to a threshold probability. Further, Nagai et al “Digital watermarking for deep neural networks” discloses again embedding a binary T-bit vector into the parameters of the layers of the neural network, however this embedding is not according to the identified input samples whose activation values being below a threshold probability. Further, Merrer et al “adversarial frontier stitching for remote neural network watermarking” discloses a watermarking approach built upon model boundary modification and the use of random adversarial samples that lie near decision boundaries rather than embedding the watermark according to the values of a feature map.
Conclusion
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/J.R.G./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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